From eb335ddb7719cbb204077c311f0b55a916573955 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 2 Aug 2022 11:23:51 +0200 Subject: [PATCH 01/77] refactor the segmentation steps to be more functional --- evaluation/lmc_eval.py | 4 +- plantseg/segmentation/dtws.py | 46 +--- plantseg/segmentation/functional/__init__.py | 5 + .../segmentation/functional/segmentation.py | 216 ++++++++++++++++++ plantseg/segmentation/functional/utils.py | 43 ++++ plantseg/segmentation/gasp.py | 48 +--- plantseg/segmentation/lmc.py | 97 ++------ plantseg/segmentation/multicut.py | 41 +--- plantseg/segmentation/simpleitkws.py | 25 +- plantseg/segmentation/utils.py | 28 --- 10 files changed, 312 insertions(+), 241 deletions(-) create mode 100644 plantseg/segmentation/functional/__init__.py create mode 100644 plantseg/segmentation/functional/segmentation.py create mode 100644 plantseg/segmentation/functional/utils.py diff --git a/evaluation/lmc_eval.py b/evaluation/lmc_eval.py index 8a728601..6ef98281 100644 --- a/evaluation/lmc_eval.py +++ b/evaluation/lmc_eval.py @@ -8,7 +8,7 @@ from elf.segmentation.multicut import multicut_kernighan_lin, transform_probabilities_to_costs from elf.segmentation.watershed import distance_transform_watershed -from plantseg.segmentation.lmc import segment_volume_lmc +from plantseg.segmentation.functional.segmentation import lifted_multicut_from_nuclei_pmaps N_THREADS = 16 @@ -44,7 +44,7 @@ def read_segment_write(boundary_pmap_files, nuclei_pmap_files=None): with h5py.File(nuclei_pmap_files, 'r') as f: nuclei_pmaps = f['predictions'][0] - mc = segment_volume_lmc(boundary_pmaps, nuclei_pmaps) + mc = lifted_multicut_from_nuclei_pmaps(boundary_pmaps, nuclei_pmaps) output_file = os.path.splitext(boundary_pmap_files)[0] + '_mc.h5' with h5py.File(output_file, 'w') as f: diff --git a/plantseg/segmentation/dtws.py b/plantseg/segmentation/dtws.py index 81901c7c..ab534dcd 100644 --- a/plantseg/segmentation/dtws.py +++ b/plantseg/segmentation/dtws.py @@ -1,50 +1,9 @@ import time from functools import partial -import numpy as np -from elf.segmentation.watershed import distance_transform_watershed, stacked_watershed - from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import AbstractSegmentationStep - - -def compute_distance_transfrom_watershed(pmaps, threshold, sigma_seeds, - stacked=False, sigma_weights=2., - min_size=100, alpha=1.0, - pixel_pitch=None, apply_nonmax_suppression=False, - n_threads=None): - """ Wrapper around elf.distance_transform_watershed - - Args: - pmaps (np.ndarray): input height map. - threshold (float): value for the threshold applied before distance transform. - sigma_seeds (float): smoothing factor for the watershed seed map. - stacked (bool): if true the ws will be executed in 2D slice by slice, otherwise in 3D. - sigma_weights (float): smoothing factor for the watershed weight map (default: 2). - min_size (int): minimal size of watershed segments (default: 100) - alpha (float): alpha used to blend input_ and distance_transform in order to obtain the - watershed weight map (default: .9) - pixel_pitch (list-like[int]): anisotropy factor used to compute the distance transform (default: None) - apply_nonmax_suppression (bool): whether to apply non-maximum suppression to filter out seeds. - Needs nifty. (default: False) - n_threads (int): if not None, parallelize the 2D stacked ws. (default: None) - - Returns: - np.ndarray: watershed segmentation - """ - gui_logger.info('Computing segmentation with dtWS...') - ws_kwargs = dict(threshold=threshold, sigma_seeds=sigma_seeds, - sigma_weights=sigma_weights, - min_size=min_size, alpha=alpha, - pixel_pitch=pixel_pitch, apply_nonmax_suppression=apply_nonmax_suppression) - if stacked: - # WS in 2D - ws, _ = stacked_watershed(pmaps, ws_function=distance_transform_watershed, n_threads=n_threads, **ws_kwargs) - else: - # WS in 3D - ws, _ = distance_transform_watershed(pmaps, **ws_kwargs) - - return ws +from plantseg.segmentation.functional.segmentation import dt_watershed class DistanceTransformWatershed(AbstractSegmentationStep): @@ -64,7 +23,7 @@ def __init__(self, file_suffix='_dtws', state=state) - self.dt_watershed = partial(compute_distance_transfrom_watershed, + self.dt_watershed = partial(dt_watershed, threshold=ws_threshold, sigma_seeds=ws_sigma, stacked=ws_2D, sigma_weights=ws_w_sigma, min_size=ws_minsize, n_threads=n_threads) @@ -72,6 +31,7 @@ def __init__(self, def process(self, pmaps): runtime = time.time() + gui_logger.info('Computing segmentation with dtWS...') segmentation = self.dt_watershed(pmaps) # stop real world clock timer diff --git a/plantseg/segmentation/functional/__init__.py b/plantseg/segmentation/functional/__init__.py new file mode 100644 index 00000000..2e1861dc --- /dev/null +++ b/plantseg/segmentation/functional/__init__.py @@ -0,0 +1,5 @@ +from plantseg.segmentation.functional.segmentation import dt_watershed +from plantseg.segmentation.functional.segmentation import gasp +from plantseg.segmentation.functional.segmentation import multicut +from plantseg.segmentation.functional.segmentation import lifted_multicut_from_nuclei_segmentation +from plantseg.segmentation.functional.segmentation import lifted_multicut_from_nuclei_pmaps diff --git a/plantseg/segmentation/functional/segmentation.py b/plantseg/segmentation/functional/segmentation.py new file mode 100644 index 00000000..fd5d5f07 --- /dev/null +++ b/plantseg/segmentation/functional/segmentation.py @@ -0,0 +1,216 @@ +from typing import List + +import nifty +import nifty.graph.rag as nrag +import numpy as np +from elf.segmentation import GaspFromAffinities +from elf.segmentation import stacked_watershed, lifted_multicut as lmc, \ + project_node_labels_to_pixels +from elf.segmentation.features import compute_rag, lifted_problem_from_probabilities, lifted_problem_from_segmentation +from elf.segmentation.multicut import multicut_kernighan_lin +from elf.segmentation.watershed import distance_transform_watershed, apply_size_filter +from numpy.typing import ArrayLike + +from plantseg.segmentation.functional.utils import shift_affinities, compute_mc_costs + + +def dt_watershed(boundary_pmaps: ArrayLike, + threshold: float, + sigma_seeds: float, + stacked: bool = False, + sigma_weights: float = 2., + min_size: int = 100, + alpha: float = 1.0, + pixel_pitch: List[int] = None, + apply_nonmax_suppression: bool = False, + n_threads: int = None) -> ArrayLike: + """ Wrapper around elf.distance_transform_watershed + + Args: + boundary_pmaps (np.ndarray): input height map. + threshold (float): value for the threshold applied before distance transform. + sigma_seeds (float): smoothing factor for the watershed seed map. + stacked (bool): if true the ws will be executed in 2D slice by slice, otherwise in 3D. + sigma_weights (float): smoothing factor for the watershed weight map (default: 2). + min_size (int): minimal size of watershed segments (default: 100) + alpha (float): alpha used to blend input_ and distance_transform in order to obtain the + watershed weight map (default: .9) + pixel_pitch (list-like[int]): anisotropy factor used to compute the distance transform (default: None) + apply_nonmax_suppression (bool): whether to apply non-maximum suppression to filter out seeds. + Needs nifty. (default: False) + n_threads (int): if not None, parallelize the 2D stacked ws. (default: None) + + Returns: + np.ndarray: watershed segmentation + """ + ws_kwargs = dict(threshold=threshold, sigma_seeds=sigma_seeds, + sigma_weights=sigma_weights, + min_size=min_size, alpha=alpha, + pixel_pitch=pixel_pitch, apply_nonmax_suppression=apply_nonmax_suppression) + if stacked: + # WS in 2D + ws, _ = stacked_watershed(boundary_pmaps, + ws_function=distance_transform_watershed, + n_threads=n_threads, + **ws_kwargs) + else: + # WS in 3D + ws, _ = distance_transform_watershed(boundary_pmaps, **ws_kwargs) + + return ws + + +def gasp(boundary_pmaps: ArrayLike, + superpixels: ArrayLike = None, + gasp_linkage_criteria: str = 'average', + beta: float = 0.5, + post_minsize: int = 100, + n_threads: int = 6) -> ArrayLike: + + if superpixels is not None: + def superpixel_gen(*args, **kwargs): + return superpixels + else: + superpixel_gen = None + + run_GASP_kwargs = {'linkage_criteria': gasp_linkage_criteria, + 'add_cannot_link_constraints': False, + 'use_efficient_implementations': False} + + # pmaps are interpreted as affinities + affinities = np.stack([boundary_pmaps, boundary_pmaps, boundary_pmaps], axis=0) + + offsets = [[0, 0, 1], [0, 1, 0], [1, 0, 0]] + # Shift is required to correct aligned affinities + affinities = shift_affinities(affinities, offsets=offsets) + + # invert affinities + affinities = 1 - affinities + + # Init and run Gasp + gasp_instance = GaspFromAffinities(offsets, + superpixel_generator=superpixel_gen, + run_GASP_kwargs=run_GASP_kwargs, + n_threads=n_threads, + beta_bias=beta) + # running gasp + segmentation, _ = gasp_instance(affinities) + + # init and run size threshold + if post_minsize > 0: + segmentation, _ = apply_size_filter(segmentation.astype('uint32'), boundary_pmaps, post_minsize) + return segmentation + + +def multicut(boundary_pmaps: ArrayLike, + superpixels: ArrayLike, + beta: float = 0.5, + post_minsize: int = 50) -> ArrayLike: + + rag = compute_rag(superpixels) + + # Prob -> edge costs + boundary_pmaps = boundary_pmaps.astype('float32') + costs = compute_mc_costs(boundary_pmaps, rag, beta=beta) + + # Creating graph + graph = nifty.graph.undirectedGraph(rag.numberOfNodes) + graph.insertEdges(rag.uvIds()) + + # Solving Multicut + node_labels = multicut_kernighan_lin(graph, costs) + segmentation = nifty.tools.take(node_labels, superpixels) + + # run size threshold + if post_minsize > 0: + segmentation, _ = apply_size_filter(segmentation, boundary_pmaps, post_minsize) + return segmentation + + +def lifted_multicut_from_nuclei_pmaps(boundary_pmaps: ArrayLike, + nuclei_pmaps: ArrayLike, + superpixels: ArrayLike, + beta: float = 0.5, + post_minsize: int = 50) -> ArrayLike: + # compute the region adjacency graph + rag = compute_rag(superpixels) + + # compute multi cut edges costs + boundary_pmaps = boundary_pmaps.astype('float32') + costs = compute_mc_costs(boundary_pmaps, rag, beta) + + # assert nuclei pmaps are floats + nuclei_pmaps = nuclei_pmaps.astype('float32') + input_maps = [nuclei_pmaps] + assignment_threshold = .9 + + # compute lifted multicut features from boundary pmaps + lifted_uvs, lifted_costs = lifted_problem_from_probabilities(rag, superpixels, + input_maps, assignment_threshold, + graph_depth=4) + + # solve the full lifted problem using the kernighan lin approximation introduced in + # http://openaccess.thecvf.com/content_iccv_2015/html/Keuper_Efficient_Decomposition_of_ICCV_2015_paper.html + node_labels = lmc.lifted_multicut_kernighan_lin(rag, costs, lifted_uvs, lifted_costs) + segmentation = project_node_labels_to_pixels(rag, node_labels) + + # run size threshold + if post_minsize > 0: + segmentation, _ = apply_size_filter(segmentation.astype('uint32'), boundary_pmaps, post_minsize) + return segmentation + + +def lifted_multicut_from_nuclei_segmentation(boundary_pmaps: ArrayLike, + nuclei_seg: ArrayLike, + superpixels: ArrayLike, + beta: float = 0.5, + post_minsize: int = 50) -> ArrayLike: + # compute the region adjacency graph + rag = compute_rag(superpixels) + + # compute multi cut edges costs + boundary_pmaps = boundary_pmaps.astype('float32') + costs = compute_mc_costs(boundary_pmaps, rag, beta) + max_cost = np.abs(np.max(costs)) + lifted_uvs, lifted_costs = lifted_problem_from_segmentation(rag, superpixels, nuclei_seg, + overlap_threshold=0.2, + graph_depth=4, + same_segment_cost=5 * max_cost, + different_segment_cost=-5 * max_cost) + + # solve the full lifted problem using the kernighan lin approximation introduced in + # http://openaccess.thecvf.com/content_iccv_2015/html/Keuper_Efficient_Decomposition_of_ICCV_2015_paper.html + lifted_costs = lifted_costs.astype('float64') + lifted_costs = lifted_costs[:, 0] + node_labels = lmc.lifted_multicut_kernighan_lin(rag, costs, lifted_uvs, lifted_costs) + segmentation = project_node_labels_to_pixels(rag, node_labels) + + # run size threshold + if post_minsize > 0: + segmentation, _ = apply_size_filter(segmentation.astype('uint32'), boundary_pmaps, post_minsize) + return segmentation + + +def simple_itk_watershed(boundary_pmaps: ArrayLike, + threshold: float, + sigma: float, + minsize: int): + import SimpleITK as sitk + from vigra.filters import gaussianSmoothing + + if sigma > 0: + # fix ws sigma length + # ws sigma cannot be shorter than pmaps dims + max_sigma = (np.array(boundary_pmaps.shape) - 1) / 3 + ws_sigma = np.minimum(max_sigma, np.ones(max_sigma.ndim) * sigma) + boundary_pmaps = gaussianSmoothing(boundary_pmaps, ws_sigma) + + # Itk watershed + size filtering + itk_pmaps = sitk.GetImageFromArray(boundary_pmaps) + itk_segmentation = sitk.MorphologicalWatershed(itk_pmaps, + threshold, + markWatershedLine=False, + fullyConnected=False) + itk_segmentation = sitk.RelabelComponent(itk_segmentation, minsize) + segmentation = sitk.GetArrayFromImage(itk_segmentation).astype(np.uint16) + return segmentation diff --git a/plantseg/segmentation/functional/utils.py b/plantseg/segmentation/functional/utils.py new file mode 100644 index 00000000..5ca309ad --- /dev/null +++ b/plantseg/segmentation/functional/utils.py @@ -0,0 +1,43 @@ +import numpy as np +from elf.segmentation import compute_boundary_mean_and_length +from elf.segmentation.multicut import transform_probabilities_to_costs + + +def shift_affinities(affinities, offsets): + rolled_affs = [] + for i, _ in enumerate(offsets): + offset = offsets[i] + shifts = tuple([int(off / 2) for off in offset]) + + padding = [[0, 0] for _ in range(len(shifts))] + for ax, shf in enumerate(shifts): + if shf < 0: + padding[ax][1] = -shf + elif shf > 0: + padding[ax][0] = shf + + padded_inverted_affs = np.pad(affinities, pad_width=((0, 0),) + tuple(padding), mode='constant') + + crop_slices = tuple( + slice(padding[ax][0], padded_inverted_affs.shape[ax + 1] - padding[ax][1]) for ax in range(3)) + + padded_inverted_affs = np.roll(padded_inverted_affs[i], shifts, axis=(0, 1, 2))[crop_slices] + rolled_affs.append(padded_inverted_affs) + del padded_inverted_affs + + rolled_affs = np.stack(rolled_affs) + return rolled_affs + + +def compute_mc_costs(boundary_pmaps, rag, beta): + # compute the edge costs + features = compute_boundary_mean_and_length(rag, boundary_pmaps) + costs, sizes = features[:, 0], features[:, 1] + + # transform the edge costs from [0, 1] to [-inf, inf], which is + # necessary for the multicut. This is done by interpreting the values + # as probabilities for an edge being 'true' and then taking the negative log-likelihood. + # in addition, we weight the costs by the size of the corresponding edge + + costs = transform_probabilities_to_costs(costs, edge_sizes=sizes, beta=beta) + return costs diff --git a/plantseg/segmentation/gasp.py b/plantseg/segmentation/gasp.py index 7d1c1727..a6c7813b 100644 --- a/plantseg/segmentation/gasp.py +++ b/plantseg/segmentation/gasp.py @@ -1,14 +1,9 @@ import time from functools import partial -import numpy as np -from elf.segmentation import GaspFromAffinities -from elf.segmentation.watershed import apply_size_filter - from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import AbstractSegmentationStep -from plantseg.segmentation.dtws import compute_distance_transfrom_watershed -from plantseg.segmentation.utils import shift_affinities +from plantseg.segmentation.functional.segmentation import dt_watershed, gasp class WSSegmentationFeeder: @@ -55,12 +50,12 @@ def __init__(self, self.ws_minsize = ws_minsize self.ws_sigma = ws_sigma - # Post processing size threshold + # Postprocessing size threshold self.post_minsize = post_minsize self.n_threads = n_threads - self.dt_watershed = partial(compute_distance_transfrom_watershed, + self.dt_watershed = partial(dt_watershed, threshold=ws_threshold, sigma_seeds=ws_sigma, stacked=ws_2D, sigma_weights=ws_w_sigma, min_size=ws_minsize, n_threads=n_threads) @@ -73,42 +68,19 @@ def process(self, pmaps): # In this case the agglomeration is initialized with superpixels: # use additional option 'intersect_with_boundary_pixels' to break the SP along the boundaries # (see CREMI-experiments script for an example) + gui_logger.info('Computing segmentation with dtWS...') ws = self.dt_watershed(pmaps) - def superpixel_gen(*args, **kwargs): - return ws - else: - superpixel_gen = None + ws = None gui_logger.info('Clustering with GASP...') # Run GASP - run_GASP_kwargs = {'linkage_criteria': self.gasp_linkage_criteria, - 'add_cannot_link_constraints': False, - 'use_efficient_implementations': False} - - # pmaps are interpreted as affinities - affinities = np.stack([pmaps, pmaps, pmaps], axis=0) - - offsets = [[0, 0, 1], [0, 1, 0], [1, 0, 0]] - # Shift is required to correct aligned affinities - affinities = shift_affinities(affinities, offsets=offsets) - - # invert affinities - affinities = 1 - affinities - - # Init and run Gasp - gasp_instance = GaspFromAffinities(offsets, - superpixel_generator=superpixel_gen, - run_GASP_kwargs=run_GASP_kwargs, - n_threads=self.n_threads, - beta_bias=self.beta) - # running gasp - segmentation, _ = gasp_instance(affinities) - - # init and run size threshold - if self.post_minsize > self.ws_minsize: - segmentation, _ = apply_size_filter(segmentation.astype('uint32'), pmaps, self.post_minsize) + segmentation = gasp(pmaps, ws, + gasp_linkage_criteria=self.gasp_linkage_criteria, + beta=self.beta, + post_minsize=self.post_minsize, + n_threads=self.n_threads) # stop real world clock timer runtime = time.time() - runtime diff --git a/plantseg/segmentation/lmc.py b/plantseg/segmentation/lmc.py index 18650221..653b1e97 100644 --- a/plantseg/segmentation/lmc.py +++ b/plantseg/segmentation/lmc.py @@ -2,18 +2,11 @@ import time from functools import partial -import elf.segmentation.lifted_multicut as lmc -import numpy as np -from elf.segmentation.features import compute_rag, compute_boundary_mean_and_length -from elf.segmentation.features import lifted_problem_from_probabilities -from elf.segmentation.features import project_node_labels_to_pixels, lifted_problem_from_segmentation -from elf.segmentation.multicut import transform_probabilities_to_costs -from elf.segmentation.watershed import apply_size_filter - from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import AbstractSegmentationStep from plantseg.pipeline.utils import load_paths -from plantseg.segmentation.dtws import compute_distance_transfrom_watershed +from plantseg.segmentation.functional.segmentation import dt_watershed, lifted_multicut_from_nuclei_pmaps, \ + lifted_multicut_from_nuclei_segmentation class LiftedMulticut(AbstractSegmentationStep): @@ -51,13 +44,13 @@ def __init__(self, self.ws_sigma = ws_sigma self.ws_w_sigma = ws_w_sigma - # Post processing size threshold + # Postprocessing size threshold self.post_minsize = post_minsize # Multithread self.n_threads = n_threads - self.dt_watershed = partial(compute_distance_transfrom_watershed, + self.dt_watershed = partial(dt_watershed, threshold=ws_threshold, sigma_seeds=ws_sigma, stacked=ws_2D, sigma_weights=ws_w_sigma, min_size=ws_minsize, n_threads=n_threads) @@ -66,25 +59,22 @@ def process(self, pmaps): boundary_pmaps, nuclei_pmaps = pmaps runtime = time.time() + gui_logger.info('Computing segmentation with dtWS...') ws = self.dt_watershed(boundary_pmaps) gui_logger.info('Clustering with LiftedMulticut...') if self.is_segmentation: - segmentation = segment_volume_lmc_from_seg(boundary_pmaps, - nuclei_pmaps, - watershed_segmentation=ws, - beta=self.beta) + segmentation = lifted_multicut_from_nuclei_segmentation(boundary_pmaps, + nuclei_pmaps, + superpixels=ws, + beta=self.beta, + post_minsize=self.post_minsize) else: - segmentation = segment_volume_lmc(boundary_pmaps, - nuclei_pmaps, - watershed_segmentation=ws, - beta=self.beta) - - if self.post_minsize > self.ws_minsize: - segmentation = segmentation.astype('uint32') - boundary_pmaps = boundary_pmaps.astype('float32') - segmentation, _ = apply_size_filter(segmentation, boundary_pmaps, self.post_minsize) - + segmentation = lifted_multicut_from_nuclei_pmaps(boundary_pmaps, + nuclei_pmaps, + superpixels=ws, + beta=self.beta, + post_minsize=self.post_minsize) # stop real world clock timer runtime = time.time() - runtime gui_logger.info(f"Clustering took {runtime:.2f} s") @@ -127,60 +117,3 @@ def _find_nuclei_pmaps_path(self, input_path): return None -def compute_mc_costs(boundary_pmaps, rag, beta): - # compute the edge costs - features = compute_boundary_mean_and_length(rag, boundary_pmaps) - costs, sizes = features[:, 0], features[:, 1] - - # transform the edge costs from [0, 1] to [-inf, inf], which is - # necessary for the multicut. This is done by interpreting the values - # as probabilities for an edge being 'true' and then taking the negative log-likelihood. - # in addition, we weight the costs by the size of the corresponding edge - - costs = transform_probabilities_to_costs(costs, edge_sizes=sizes, beta=beta) - return costs - - -def segment_volume_lmc(boundary_pmaps, nuclei_pmaps, watershed_segmentation, beta): - # compute the region adjacency graph - rag = compute_rag(watershed_segmentation) - - # compute multi cut edges costs - costs = compute_mc_costs(boundary_pmaps, rag, beta) - - # assert nuclei pmaps are floats - nuclei_pmaps = nuclei_pmaps.astype('float32') - input_maps = [nuclei_pmaps] - assignment_threshold = .9 - # compute lifted multicut features from boundary pmaps - lifted_uvs, lifted_costs = lifted_problem_from_probabilities(rag, watershed_segmentation, - input_maps, assignment_threshold, - graph_depth=4) - - # solve the full lifted problem using the kernighan lin approximation introduced in - # http://openaccess.thecvf.com/content_iccv_2015/html/Keuper_Efficient_Decomposition_of_ICCV_2015_paper.html - node_labels = lmc.lifted_multicut_kernighan_lin(rag, costs, lifted_uvs, lifted_costs) - lifted_segmentation = project_node_labels_to_pixels(rag, node_labels) - return lifted_segmentation - - -def segment_volume_lmc_from_seg(boundary_pmaps, nuclei_seg, watershed_segmentation, beta): - # compute the region adjacency graph - rag = compute_rag(watershed_segmentation) - - # compute multi cut edges costs - costs = compute_mc_costs(boundary_pmaps, rag, beta) - max_cost = np.abs(np.max(costs)) - lifted_uvs, lifted_costs = lifted_problem_from_segmentation(rag, watershed_segmentation, nuclei_seg, - overlap_threshold=0.2, - graph_depth=4, - same_segment_cost=5 * max_cost, - different_segment_cost=-5 * max_cost) - - # solve the full lifted problem using the kernighan lin approximation introduced in - # http://openaccess.thecvf.com/content_iccv_2015/html/Keuper_Efficient_Decomposition_of_ICCV_2015_paper.html - lifted_costs = lifted_costs.astype('float64') - lifted_costs = lifted_costs[:, 0] - node_labels = lmc.lifted_multicut_kernighan_lin(rag, costs, lifted_uvs, lifted_costs) - lifted_segmentation = project_node_labels_to_pixels(rag, node_labels) - return lifted_segmentation diff --git a/plantseg/segmentation/multicut.py b/plantseg/segmentation/multicut.py index 04b3b6ee..fd6d22c8 100644 --- a/plantseg/segmentation/multicut.py +++ b/plantseg/segmentation/multicut.py @@ -1,16 +1,11 @@ import time from functools import partial -import nifty -import nifty.graph.rag as nrag -import numpy as np -from elf.segmentation.features import compute_rag -from elf.segmentation.multicut import multicut_kernighan_lin, transform_probabilities_to_costs -from elf.segmentation.watershed import distance_transform_watershed, apply_size_filter +from elf.segmentation.watershed import apply_size_filter from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import AbstractSegmentationStep -from plantseg.segmentation.dtws import compute_distance_transfrom_watershed +from plantseg.segmentation.functional.segmentation import dt_watershed, multicut class MulticutFromPmaps(AbstractSegmentationStep): @@ -44,20 +39,27 @@ def __init__(self, self.ws_sigma = ws_sigma self.ws_w_sigma = ws_w_sigma - # Post processing size threshold + # Postprocessing size threshold self.post_minsize = post_minsize # Multithread self.n_threads = n_threads - self.dt_watershed = partial(compute_distance_transfrom_watershed, + self.dt_watershed = partial(dt_watershed, threshold=ws_threshold, sigma_seeds=ws_sigma, stacked=ws_2D, sigma_weights=ws_w_sigma, min_size=ws_minsize, n_threads=n_threads) def process(self, pmaps): runtime = time.time() - segmentation = self.segment_volume(pmaps) + gui_logger.info('Computing segmentation with dtWS...') + ws = self.dt_watershed(pmaps) + + gui_logger.info('Clustering with MultiCut...') + segmentation = multicut(pmaps, + superpixels=ws, + beta=self.beta, + post_minsize=self.post_minsize) if self.post_minsize > self.ws_minsize: segmentation, _ = apply_size_filter(segmentation, pmaps, self.post_minsize) @@ -67,22 +69,3 @@ def process(self, pmaps): gui_logger.info(f"Clustering took {runtime:.2f} s") return segmentation - - def segment_volume(self, pmaps): - ws = self.dt_watershed(pmaps) - - gui_logger.info('Clustering with MultiCut...') - rag = compute_rag(ws) - # Computing edge features - features = nrag.accumulateEdgeMeanAndLength(rag, pmaps, numberOfThreads=1) # DO NOT CHANGE numberOfThreads - probs = features[:, 0] # mean edge prob - edge_sizes = features[:, 1] - # Prob -> edge costs - costs = transform_probabilities_to_costs(probs, edge_sizes=edge_sizes, beta=self.beta) - # Creating graph - graph = nifty.graph.undirectedGraph(rag.numberOfNodes) - graph.insertEdges(rag.uvIds()) - # Solving Multicut - - node_labels = multicut_kernighan_lin(graph, costs) - return nifty.tools.take(node_labels, ws) diff --git a/plantseg/segmentation/simpleitkws.py b/plantseg/segmentation/simpleitkws.py index ecef704c..46c6cecd 100644 --- a/plantseg/segmentation/simpleitkws.py +++ b/plantseg/segmentation/simpleitkws.py @@ -1,7 +1,6 @@ import numpy as np -import SimpleITK as sitk -from vigra.filters import gaussianSmoothing +from plantseg.segmentation.functional.segmentation import simple_itk_watershed from plantseg.pipeline.steps import AbstractSegmentationStep @@ -16,7 +15,6 @@ def __init__(self, n_threads=8, state=True, **kwargs): - super().__init__(input_paths=predictions_paths, save_directory=save_directory, file_suffix='_itkws', @@ -27,19 +25,8 @@ def __init__(self, self.ws_sigma = ws_sigma def process(self, pmaps): - if self.ws_sigma > 0: - # fix ws sigma length - # ws sigma cannot be shorter than pmaps dims - max_sigma = (np.array(pmaps.shape) - 1) / 3 - ws_sigma = np.minimum(max_sigma, np.ones(max_sigma.ndim) * self.ws_sigma) - pmaps = gaussianSmoothing(pmaps, ws_sigma) - - # Itk watershed + size filtering - itk_pmaps = sitk.GetImageFromArray(pmaps) - itk_segmentation = sitk.MorphologicalWatershed(itk_pmaps, - self.ws_threshold, - markWatershedLine=False, - fullyConnected=False) - itk_segmentation = sitk.RelabelComponent(itk_segmentation, self.ws_minsize) - - return sitk.GetArrayFromImage(itk_segmentation).astype(np.uint16) + segmentation = simple_itk_watershed(pmaps, + threshold=self.ws_threshold, + sigma=self.ws_sigma, + minsize=self.ws_minsize) + return segmentation diff --git a/plantseg/segmentation/utils.py b/plantseg/segmentation/utils.py index 337a0184..e353806e 100644 --- a/plantseg/segmentation/utils.py +++ b/plantseg/segmentation/utils.py @@ -1,5 +1,3 @@ -import numpy as np - SUPPORTED_ALGORITMS = ["GASP", "MutexWS", "DtWatershed", "MultiCut", "LiftedMulticut", "SimpleITK"] @@ -39,29 +37,3 @@ def configure_segmentation_step(predictions_paths, config): from .lmc import LiftedMulticut assert 'nuclei_predictions_path' in config, "Missing 'nuclei_predictions_path' config attribute for 'LiftedMulticut'" return LiftedMulticut(**config) - - -def shift_affinities(affinities, offsets): - rolled_affs = [] - for i, _ in enumerate(offsets): - offset = offsets[i] - shifts = tuple([int(off / 2) for off in offset]) - - padding = [[0, 0] for _ in range(len(shifts))] - for ax, shf in enumerate(shifts): - if shf < 0: - padding[ax][1] = -shf - elif shf > 0: - padding[ax][0] = shf - - padded_inverted_affs = np.pad(affinities, pad_width=((0, 0),) + tuple(padding), mode='constant') - - crop_slices = tuple( - slice(padding[ax][0], padded_inverted_affs.shape[ax + 1] - padding[ax][1]) for ax in range(3)) - - padded_inverted_affs = np.roll(padded_inverted_affs[i], shifts, axis=(0, 1, 2))[crop_slices] - rolled_affs.append(padded_inverted_affs) - del padded_inverted_affs - - rolled_affs = np.stack(rolled_affs) - return rolled_affs From 44f2ba9e2e39c6644061ea0f2662628b0ad9d0e4 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 2 Aug 2022 19:17:08 +0200 Subject: [PATCH 02/77] new prediction workflow, now it uses the same abstract steps as segmentation --- plantseg/pipeline/raw2seg.py | 10 +- plantseg/pipeline/steps.py | 6 +- plantseg/predictions/array_dataset.py | 92 +++++++++++ plantseg/predictions/array_predictor.py | 168 ++++++++++++++++++++ plantseg/predictions/functional/__init__.py | 0 plantseg/predictions/predict.py | 60 ++++++- plantseg/predictions/utils.py | 112 ++++++++++++- 7 files changed, 443 insertions(+), 5 deletions(-) create mode 100644 plantseg/predictions/array_dataset.py create mode 100644 plantseg/predictions/array_predictor.py create mode 100644 plantseg/predictions/functional/__init__.py diff --git a/plantseg/pipeline/raw2seg.py b/plantseg/pipeline/raw2seg.py index fdbe5251..38dd226e 100644 --- a/plantseg/pipeline/raw2seg.py +++ b/plantseg/pipeline/raw2seg.py @@ -28,7 +28,15 @@ def configure_preprocessing_step(input_paths, config): def configure_cnn_step(input_paths, config): - return UnetPredictions(input_paths, config) + model_name = config['model_name'] + patch = config.get('patch', (80, 160, 160)) + stride = config.get('stride', 'Accurate (slowest)') + device = config.get('device', 'cuda') + version = config.get('version', 'best') + model_update = config.get('model_update', False) + mirror_padding = config.get('mirror_padding', (80, 160, 160)) + return UnetPredictions(input_paths, model_name=model_name, patch=patch, stride=stride, + device=device, version=version, model_update=model_update, mirror_padding=mirror_padding) def configure_cnn_postprocessing_step(input_paths, config): diff --git a/plantseg/pipeline/steps.py b/plantseg/pipeline/steps.py index f4d6cb27..6720700a 100644 --- a/plantseg/pipeline/steps.py +++ b/plantseg/pipeline/steps.py @@ -72,7 +72,11 @@ def read_process_write(self, input_path): output_data = self.process(input_data) # voxel_size may change after pre-/post-processing (i.e. when scaling is used) - scale_factor = np.array(output_data.shape) / np.array(input_data.shape) + if output_data.ndim == 4: + scale_factor = np.array(output_data[0].shape) / np.array(input_data.shape) + else: + scale_factor = np.array(output_data.shape) / np.array(input_data.shape) + voxel_size = np.array(voxel_size) * scale_factor output_path = self._create_output_path(input_path) diff --git a/plantseg/predictions/array_dataset.py b/plantseg/predictions/array_dataset.py new file mode 100644 index 00000000..05c1ec6c --- /dev/null +++ b/plantseg/predictions/array_dataset.py @@ -0,0 +1,92 @@ +import numpy as np +from torch.utils.data import Dataset +import pytorch3dunet.augment.transforms as transforms +from pytorch3dunet.datasets.utils import get_slice_builder, calculate_stats, default_prediction_collate +from pytorch3dunet.unet3d.utils import get_logger + +logger = get_logger('ArrayDataset') + + +class ArrayDataset(Dataset): + """ + Based on pytorch-3dunet AbstractHDF5Dataset + https://github.com/wolny/pytorch-3dunet/blob/master/pytorch3dunet/datasets/hdf5.py + + Inference only implementation of torch.utils.data.Dataset + """ + + def __init__(self, raw, + slice_builder_config, + transformer_config, + mirror_padding=(16, 32, 32), + global_normalization=True, + **kwargs): + """ + :param raw: numpy array containing the raw image + :para'/home/adrian/workspace/ilastik-datasets/VolkerDeconv/train'm slice_builder_config: configuration of the SliceBuilder + :param transformer_config: data augmentation configuration + :param mirror_padding (int or tuple): number of voxels padded to each axis + """ + self.slice_builder_config = slice_builder_config + + if mirror_padding is not None: + if isinstance(mirror_padding, int): + mirror_padding = (mirror_padding,) * 3 + else: + assert len(mirror_padding) == 3, f"Invalid mirror_padding: {mirror_padding}" + + self.mirror_padding = mirror_padding + + self.raw = raw + + if global_normalization: + stats = calculate_stats(self.raw) + else: + stats = {'pmin': None, 'pmax': None, 'mean': None, 'std': None} + + self.transformer = transforms.Transformer(transformer_config, stats) + self.raw_transform = self.transformer.raw_transform() + + # 'test' phase used only for predictions so ignore the label dataset + self.label = None + self.weight_map = None + + # add mirror padding if needed + if self.mirror_padding is not None: + z, y, x = self.mirror_padding + pad_width = ((z, z), (y, y), (x, x)) + if self.raw.ndim == 4: + channels = [np.pad(r, pad_width=pad_width, mode='reflect') for r in self.raw] + self.raw = np.stack(channels) + else: + self.raw = np.pad(self.raw, pad_width=pad_width, mode='reflect') + + # build slice indices for raw and label data sets + slice_builder = get_slice_builder(self.raw, self.label, self.weight_map, slice_builder_config) + self.raw_slices = slice_builder.raw_slices + self.label_slices = slice_builder.label_slices + self.weight_slices = slice_builder.weight_slices + + self.patch_count = len(self.raw_slices) + logger.info(f'Number of patches: {self.patch_count}') + + def __getitem__(self, idx): + if idx >= len(self): + raise StopIteration + + # get the slice for a given index 'idx' + raw_idx = self.raw_slices[idx] + # get the raw data patch for a given slice + raw_patch_transformed = self.raw_transform(self.raw[raw_idx]) + # discard the channel dimension in the slices: predictor requires only the spatial dimensions of the volume + if len(raw_idx) == 4: + raw_idx = raw_idx[1:] + return raw_patch_transformed, raw_idx + + def __len__(self): + return self.patch_count + + @classmethod + def prediction_collate(cls, batch): + """Default collate_fn. Override in child class for non-standard datasets.""" + return default_prediction_collate(batch) diff --git a/plantseg/predictions/array_predictor.py b/plantseg/predictions/array_predictor.py new file mode 100644 index 00000000..5f7ec015 --- /dev/null +++ b/plantseg/predictions/array_predictor.py @@ -0,0 +1,168 @@ +import numpy as np +import torch +from pytorch3dunet.unet3d.utils import get_logger +from pytorch3dunet.unet3d.utils import remove_halo +from plantseg.predictions.array_dataset import ArrayDataset +from torch.utils.data import DataLoader + +logger = get_logger('UNetArrayPredictor') + + +class ArrayPredictor: + """ + Based on pytorch-3dunet StandardPredictor + https://github.com/wolny/pytorch-3dunet/blob/master/pytorch3dunet/unet3d/predictor.py + + Applies the model on the given dataset and returns the results as a list of numpy arrays. + Predictions from the network are kept in memory. If the results from the network don't fit in into RAM + use `LazyPredictor` instead. + Args: + model (Unet3D): trained 3D UNet model used for prediction + config (dict): global config dict + """ + + def __init__(self, model, config, device, **kwargs): + self.model = model + self.config = config + self.device = device + self.predictor_config = kwargs + + def __call__(self, test_dataset): + assert isinstance(test_dataset, ArrayDataset) + + test_loader = DataLoader(test_dataset, batch_size=1, collate_fn=test_dataset.prediction_collate) + + logger.info(f"Processing...") + + out_channels = self.config.get('out_channels') + + # prediction_channel = self.config.get('prediction_channel', None) + prediction_channel = None + if prediction_channel is not None: + logger.info(f"Saving only channel '{prediction_channel}' from the network output") + + output_heads = self.config.get('output_heads', 1) + + logger.info(f'Running prediction on {len(test_loader)} batches...') + + # dimensionality of the output predictions + volume_shape = self.volume_shape(test_dataset) + if prediction_channel is None: + prediction_maps_shape = (out_channels,) + volume_shape + else: + # single channel prediction map + prediction_maps_shape = (1,) + volume_shape + + logger.info(f'The shape of the output prediction maps (CDHW): {prediction_maps_shape}') + + patch_halo = self.predictor_config.get('patch_halo', (4, 8, 8)) + self._validate_halo(patch_halo, test_dataset.slice_builder_config) + logger.info(f'Using patch_halo: {patch_halo}') + + # allocate prediction and normalization arrays + logger.info('Allocating prediction and normalization arrays...') + prediction_maps, normalization_masks = self._allocate_prediction_maps(prediction_maps_shape, + output_heads) + + # Sets the module in evaluation mode explicitly + # It is necessary for batchnorm/dropout layers if present as well as final Sigmoid/Softmax to be applied + self.model.eval() + # Run predictions on the entire input dataset + with torch.no_grad(): + for batch, indices in test_loader: + # send batch to device + batch = batch.to(self.device) + + # forward pass + predictions = self.model(batch) + + # wrap predictions into a list if there is only one output head from the network + if output_heads == 1: + predictions = [predictions] + + # for each output head + for prediction, prediction_map, normalization_mask in zip(predictions, + prediction_maps, + normalization_masks): + + # convert to numpy array + prediction = prediction.cpu().numpy() + + # for each batch sample + for pred, index in zip(prediction, indices): + # save patch index: (C,D,H,W) + if prediction_channel is None: + channel_slice = slice(0, out_channels) + else: + channel_slice = slice(0, 1) + index = (channel_slice,) + index + + if prediction_channel is not None: + # use only the 'prediction_channel' + logger.info(f"Using channel '{prediction_channel}'...") + pred = np.expand_dims(pred[prediction_channel], axis=0) + + logger.info(f'Saving predictions for slice:{index}...') + + # remove halo in order to avoid block artifacts in the output probability maps + u_prediction, u_index = remove_halo(pred, index, volume_shape, patch_halo) + # accumulate probabilities into the output prediction array + prediction_map[u_index] += u_prediction + # count voxel visits for normalization + normalization_mask[u_index] += 1 + + # save results + logger.info(f'Returning predictions') + prediction_maps = self._normalize_results(prediction_maps, + normalization_masks, + test_dataset.mirror_padding) + return prediction_maps + + @staticmethod + def _allocate_prediction_maps(output_shape, output_heads): + # initialize the output prediction arrays + prediction_maps = [np.zeros(output_shape, dtype='float32') for _ in range(output_heads)] + # initialize normalization mask in order to average out probabilities of overlapping patches + normalization_masks = [np.zeros(output_shape, dtype='uint8') for _ in range(output_heads)] + return prediction_maps, normalization_masks + + @staticmethod + def _normalize_results(prediction_maps, normalization_masks, mirror_padding): + def _slice_from_pad(pad): + if pad == 0: + return slice(None, None) + else: + return slice(pad, -pad) + + # save probability maps + out_prediction_maps = [] + for prediction_map, normalization_mask in zip(prediction_maps, normalization_masks): + prediction_map = prediction_map / normalization_mask + + if mirror_padding is not None: + z_s, y_s, x_s = [_slice_from_pad(p) for p in mirror_padding] + + logger.info(f'Dataset loaded with mirror padding: {mirror_padding}. Cropping before saving...') + + prediction_map = prediction_map[:, z_s, y_s, x_s] + + out_prediction_maps.append(prediction_map) + return out_prediction_maps + + @staticmethod + def _validate_halo(patch_halo, slice_builder_config): + patch = slice_builder_config['patch_shape'] + stride = slice_builder_config['stride_shape'] + + patch_overlap = np.subtract(patch, stride) + + assert np.all(patch_overlap - patch_halo >= 0),\ + f"Not enough patch overlap for stride: {stride} and halo: {patch_halo}" + + @staticmethod + def volume_shape(dataset): + raw = dataset.raw + if raw.ndim == 3: + return raw.shape + else: + return raw.shape[1:] diff --git a/plantseg/predictions/functional/__init__.py b/plantseg/predictions/functional/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/plantseg/predictions/predict.py b/plantseg/predictions/predict.py index ffe99aec..c1832c6d 100755 --- a/plantseg/predictions/predict.py +++ b/plantseg/predictions/predict.py @@ -7,8 +7,9 @@ from pytorch3dunet.datasets.utils import get_test_loaders from pytorch3dunet.unet3d import utils from pytorch3dunet.unet3d.model import get_model +from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device from plantseg.pipeline import gui_logger -from plantseg.predictions.utils import create_predict_config +from plantseg.pipeline.steps import GenericPipelineStep def _get_output_file(dataset, model_name, suffix='_predictions'): @@ -58,7 +59,7 @@ def _check_patch_size(paths, config): return valid_paths -class UnetPredictions: +class _UnetPredictions: def __init__(self, paths, cnn_config): assert isinstance(paths, list) self.state = cnn_config.get("state", True) @@ -130,3 +131,58 @@ def _update_voxel_size(input_paths, output_paths): with h5py.File(out_path, 'r+') as f: f['predictions'].attrs['element_size_um'] = voxel_size + + +class UnetPredictions(GenericPipelineStep): + def __init__(self, + input_paths, + model_name, + patch, + stride, + device, + version='best', + model_update=False, + mirror_padding=(80, 160, 160), + input_type="data_float32", + output_type="data_float32", + out_ext=".h5", + state=True): + h5_output_key = "predictions" + + # model config + self.model_name = model_name + self.device = device + self.version = version + self.model_update = model_update + + # loader config + self.patch = patch + self.stride = stride + self.mirror_padding = mirror_padding + + model, model_config, model_path = get_model_config(model_name, model_update=model_update) + utils.load_checkpoint(model_path, model) + + device = set_device(device) + model = model.to(device) + + predictor, predictor_config = get_predictor_config(model_name) + self.predictor = predictor(model=model, config=model_config, device=device, **predictor_config) + + self.loader, self.loader_config = get_loader_config(model_name, + patch=patch, + stride=stride, + mirror_padding=mirror_padding) + + super().__init__(input_paths, + input_type=input_type, + output_type=output_type, + save_directory=model_name, + out_ext=out_ext, + state=state, + h5_output_key=h5_output_key) + + def process(self, raw): + raw_loader = self.loader(raw, **self.loader_config) + pmaps = self.predictor(raw_loader) + return pmaps[0] diff --git a/plantseg/predictions/utils.py b/plantseg/predictions/utils.py index ee50383a..f4b9d405 100644 --- a/plantseg/predictions/utils.py +++ b/plantseg/predictions/utils.py @@ -4,8 +4,11 @@ import requests import yaml -from plantseg import plantseg_global_path, PLANTSEG_MODELS_DIR +from plantseg import plantseg_global_path, PLANTSEG_MODELS_DIR, home_path +from pytorch3dunet.unet3d.model import get_model from plantseg.pipeline import gui_logger +from plantseg.predictions.array_predictor import ArrayPredictor +from plantseg.predictions.array_dataset import ArrayDataset # define constant values CONFIG_TRAIN_YAML = "config_train.yml" @@ -23,6 +26,113 @@ } +def get_train_config(model_name): + # Load train config and add missing info + train_config_path = os.path.join(home_path, + PLANTSEG_MODELS_DIR, + model_name, + CONFIG_TRAIN_YAML) + with open(train_config_path, 'r') as f: + config_train = yaml.full_load(f) + return config_train + + +def get_predict_template(): + predict_template_path = os.path.join(plantseg_global_path, + "resources", + "config_predict_template.yaml") + with open(predict_template_path, 'r') as f: + predict_template = yaml.full_load(f) + return predict_template + + +def get_model_config(model_name, model_update=False, version='best'): + check_models(model_name, update_files=model_update) + config_train = get_train_config(model_name) + model_config = config_train.pop('model') + model = get_model(model_config) + + model_path = os.path.join(home_path, + PLANTSEG_MODELS_DIR, + model_name, + f"{version}_checkpoint.pytorch") + return model, model_config, model_path + + +def set_device(device): + # Add correct device for inference + if device == 'cuda': + device = torch.device("cuda:0") + elif device == 'cpu': + device = torch.device("cpu") + else: + raise RuntimeError(f"Unsupported device type: {device}") + return device + + +def get_loader_config(model_name, patch, stride, mirror_padding, num_workers=8, global_normalization=True): + predict_template = get_predict_template() + loader_config = predict_template.pop('loaders') + + loader_config["num_workers"] = num_workers + loader_config["mirror_padding"] = mirror_padding + + # Add patch and stride to the config + loader_config["test"]["slice_builder"]["patch_shape"] = patch + stride_key, stride_shape = stride, get_stride_shape(patch, "Balanced") + + if type(stride_key) is list: + loader_config["test"]["slice_builder"]["stride_shape"] = stride_key + elif type(stride_key) is str: + stride_shape = get_stride_shape(patch, stride_key) + loader_config["test"]["slice_builder"]["stride_shape"] = stride_shape + else: + raise RuntimeError(f"Unsupported stride type: {type(stride_key)}") + + # Set paths to None + loader_config["test"]["file_paths"] = None + + config_train = get_train_config(model_name) + if config_train["model"]["name"] == "UNet2D": + # make sure that z-pad is 0 for 2d UNet + mirror_padding[0] = 0 + # make sure to skip the patch size validation for 2d unet + loader_config["test"]["slice_builder"]["skip_shape_check"] = True + + # z-dim of patch and stride has to be one + patch_shape = loader_config["test"]["slice_builder"]["patch_shape"] + stride_shape = loader_config["test"]["slice_builder"]["stride_shape"] + + if patch_shape[0] != 1: + gui_logger.warning(f"Incorrect z-dimension in the patch_shape for the 2D UNet prediction. {patch_shape[0]}" + f" was given, but has to be 1. Defaulting default value: 1") + loader_config["test"]["slice_builder"]["patch_shape"][0] = 1 + + if stride_shape[0] != 1: + gui_logger.warning(f"Incorrect z-dimension in the stride_shape for the 2D UNet prediction. " + f"{stride_shape[0]} was given, but has to be 1. Defaulting default value: 1") + loader_config["test"]["slice_builder"]["stride_shape"][0] = 1 + + detaset_config = {'slice_builder_config': loader_config['test']['slice_builder'], + 'transformer_config': loader_config['test']['transformer'], + 'mirror_padding': loader_config['mirror_padding'], + 'global_normalization': global_normalization + } + + return ArrayDataset, detaset_config + + +def get_predictor_config(model_name): + predict_template = get_predict_template() + patch_halo = predict_template['predictor']['patch_halo'] + + config_train = get_train_config(model_name) + if config_train["model"]["name"] == "UNet2D": + patch_halo[0] = 0 + + return ArrayPredictor, {'patch_halo': patch_halo} + + def create_predict_config(paths, cnn_config): """ Creates the configuration file needed for running the neural network inference""" From e6f898a52d28acc6bec059dce3c27441045881ce Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 2 Aug 2022 22:19:43 +0200 Subject: [PATCH 03/77] minor refactor of the generic step --- plantseg/pipeline/steps.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/plantseg/pipeline/steps.py b/plantseg/pipeline/steps.py index 6720700a..5f0e7625 100644 --- a/plantseg/pipeline/steps.py +++ b/plantseg/pipeline/steps.py @@ -72,10 +72,10 @@ def read_process_write(self, input_path): output_data = self.process(input_data) # voxel_size may change after pre-/post-processing (i.e. when scaling is used) - if output_data.ndim == 4: - scale_factor = np.array(output_data[0].shape) / np.array(input_data.shape) - else: - scale_factor = np.array(output_data.shape) / np.array(input_data.shape) + in_shape = input_data.shape + out_shape = output_data[0].shape if output_data.ndim == 4 else output_data.shape + + scale_factor = np.array(out_shape) / np.array(in_shape) voxel_size = np.array(voxel_size) * scale_factor From 429f0e0607f7cda1231328502837805061c1c91b Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 2 Aug 2022 22:29:40 +0200 Subject: [PATCH 04/77] add prediction functional --- .../predictions/functional/predictions.py | 31 +++++++++++++++++++ plantseg/predictions/predict.py | 10 +++--- 2 files changed, 36 insertions(+), 5 deletions(-) create mode 100644 plantseg/predictions/functional/predictions.py diff --git a/plantseg/predictions/functional/predictions.py b/plantseg/predictions/functional/predictions.py new file mode 100644 index 00000000..7c19fb83 --- /dev/null +++ b/plantseg/predictions/functional/predictions.py @@ -0,0 +1,31 @@ +from pytorch3dunet.unet3d import utils +from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device +from numpy.typing import ArrayLike +from typing import Tuple, Union + + +def unet_predictions(raw: ArrayLike, + model_name: str, + patch: Tuple[int, int, int] = (80, 160, 160), + stride: Union[str, Tuple[int, int, int]] = 'Accurate (slowest)', + device: str = 'cuda', + version: str = 'best', + model_update: bool = False, + mirror_padding: Tuple[int, int, int] = (16, 32, 32)): + model, model_config, model_path = get_model_config(model_name, model_update=model_update, version=version) + utils.load_checkpoint(model_path, model) + + device = set_device(device) + model = model.to(device) + + predictor, predictor_config = get_predictor_config(model_name) + predictor = predictor(model=model, config=model_config, device=device, **predictor_config) + + loader, loader_config = get_loader_config(model_name, + patch=patch, + stride=stride, + mirror_padding=mirror_padding) + + raw_loader = loader(raw, **loader_config) + pmaps = predictor(raw_loader) + return pmaps[0] \ No newline at end of file diff --git a/plantseg/predictions/predict.py b/plantseg/predictions/predict.py index c1832c6d..3dd6ab44 100755 --- a/plantseg/predictions/predict.py +++ b/plantseg/predictions/predict.py @@ -136,13 +136,13 @@ def _update_voxel_size(input_paths, output_paths): class UnetPredictions(GenericPipelineStep): def __init__(self, input_paths, - model_name, - patch, - stride, - device, + model_name: str, + patch = (80, 160, 160),, + stride = 'Accurate (slowest)', + device = 'cuda', version='best', model_update=False, - mirror_padding=(80, 160, 160), + mirror_padding=(16, 32, 32), input_type="data_float32", output_type="data_float32", out_ext=".h5", From d4ba3c80763c5af2a29492295fe63db744851df5 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 2 Aug 2022 22:45:06 +0200 Subject: [PATCH 05/77] fix 3dunet version in github CI --- .github/workflows/build-deploy-on-conda.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-deploy-on-conda.yml b/.github/workflows/build-deploy-on-conda.yml index b3045107..4bc0e302 100644 --- a/.github/workflows/build-deploy-on-conda.yml +++ b/.github/workflows/build-deploy-on-conda.yml @@ -27,7 +27,7 @@ jobs: conda build -c conda-forge -c awolny conda-recipe - name: Install dependencies run: | - conda install -c conda-forge -c awolny --use-local plantseg + conda install -c conda-forge -c awolny --use-local pytorch-3dunet=1.3.7 plantseg - name: Run pytest run: | conda install pytest From 5c7bee7ca4cb9fa976dfb61d03ebfadebe4b5cb4 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 3 Aug 2022 09:44:08 +0200 Subject: [PATCH 06/77] rm old implementation of predictor and bug fixes --- plantseg/predictions/predict.py | 106 +------------------------------- plantseg/predictions/utils.py | 16 ++--- 2 files changed, 11 insertions(+), 111 deletions(-) diff --git a/plantseg/predictions/predict.py b/plantseg/predictions/predict.py index 3dd6ab44..424f062e 100755 --- a/plantseg/predictions/predict.py +++ b/plantseg/predictions/predict.py @@ -1,36 +1,10 @@ -import importlib -import os -import time - import h5py -import torch -from pytorch3dunet.datasets.utils import get_test_loaders from pytorch3dunet.unet3d import utils -from pytorch3dunet.unet3d.model import get_model from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import GenericPipelineStep -def _get_output_file(dataset, model_name, suffix='_predictions'): - basepath, basename = os.path.split(dataset.file_path) - basename = f"{os.path.splitext(basename)[0]}{suffix}.h5" - os.makedirs(os.path.join(basepath, model_name), exist_ok=True) - return os.path.join(basepath, model_name, basename) - - -def _get_predictor(model, output_file, config): - predictor_config = config.get('predictor', {}) - class_name = predictor_config.get('name', 'StandardPredictor') - - m = importlib.import_module('pytorch3dunet.unet3d.predictor') - predictor_class = getattr(m, class_name) - - output_dir, _ = os.path.split(output_file) - - return predictor_class(model, output_dir, config, **predictor_config) - - def _check_patch_size(paths, config): axis = ['z', 'x', 'y'] patch_size = config["patch"] @@ -59,87 +33,13 @@ def _check_patch_size(paths, config): return valid_paths -class _UnetPredictions: - def __init__(self, paths, cnn_config): - assert isinstance(paths, list) - self.state = cnn_config.get("state", True) - # check if all file in paths are large enough for the patch size in the config - self.paths = _check_patch_size(paths, cnn_config) if self.state else paths - self.cnn_config = cnn_config - - def __call__(self): - logger = utils.get_logger('UNet3DPredictor') - - if not self.state: - # skip network predictions and return input_paths - gui_logger.info(f"Skipping '{self.__class__.__name__}'. Disabled by the user.") - return self.paths - else: - # create config/download models only when cnn_prediction enabled - config = create_predict_config(self.paths, self.cnn_config) - - # Create the model - model = get_model(config['model']) - - # Load model state - model_path = config['model_path'] - model_name = config["model_name"] - - logger.info(f"Loading model '{model_name}' from {model_path}") - utils.load_checkpoint(model_path, model) - logger.info(f"Sending the model to '{config['device']}'") - model = model.to(config['device']) - - logger.info('Loading HDF5 datasets...') - - # Run prediction - output_paths = [] - for test_loader in get_test_loaders(config): - gui_logger.info(f"Running network prediction on {test_loader.dataset.file_path}...") - runtime = time.time() - - logger.info(f"Processing '{test_loader.dataset.file_path}'...") - - output_file = _get_output_file(test_loader.dataset, model_name) - - predictor = _get_predictor(model, output_file, config) - - # run the model prediction on the entire dataset and save to the 'output_file' H5 - predictor(test_loader) - - # save resulting output path - output_paths.append(output_file) - - runtime = time.time() - runtime - gui_logger.info(f"Network prediction took {runtime:.2f} s") - - self._update_voxel_size(self.paths, output_paths) - - # free GPU memory after the inference is finished - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - return output_paths - - @staticmethod - def _update_voxel_size(input_paths, output_paths): - for in_path, out_path in zip(input_paths, output_paths): - voxel_size = (1., 1., 1.) - with h5py.File(in_path, 'r') as f: - if 'element_size_um' in f['raw'].attrs: - voxel_size = f['raw'].attrs['element_size_um'] - - with h5py.File(out_path, 'r+') as f: - f['predictions'].attrs['element_size_um'] = voxel_size - - class UnetPredictions(GenericPipelineStep): def __init__(self, input_paths, model_name: str, - patch = (80, 160, 160),, - stride = 'Accurate (slowest)', - device = 'cuda', + patch=(80, 160, 160), + stride='Accurate (slowest)', + device='cuda', version='best', model_update=False, mirror_padding=(16, 32, 32), diff --git a/plantseg/predictions/utils.py b/plantseg/predictions/utils.py index f4b9d405..b20770e6 100644 --- a/plantseg/predictions/utils.py +++ b/plantseg/predictions/utils.py @@ -113,13 +113,13 @@ def get_loader_config(model_name, patch, stride, mirror_padding, num_workers=8, f"{stride_shape[0]} was given, but has to be 1. Defaulting default value: 1") loader_config["test"]["slice_builder"]["stride_shape"][0] = 1 - detaset_config = {'slice_builder_config': loader_config['test']['slice_builder'], - 'transformer_config': loader_config['test']['transformer'], - 'mirror_padding': loader_config['mirror_padding'], - 'global_normalization': global_normalization - } + dataset_config = {'slice_builder_config': loader_config['test']['slice_builder'], + 'transformer_config': loader_config['test']['transformer'], + 'mirror_padding': loader_config['mirror_padding'], + 'global_normalization': global_normalization + } - return ArrayDataset, detaset_config + return ArrayDataset, dataset_config def get_predictor_config(model_name): @@ -133,9 +133,9 @@ def get_predictor_config(model_name): return ArrayPredictor, {'patch_halo': patch_halo} -def create_predict_config(paths, cnn_config): +def old_create_predict_config(paths, cnn_config): """ Creates the configuration file needed for running the neural network inference""" - + # TODO safe remove it # Load template config prediction_config = yaml.load( open(os.path.join(plantseg_global_path, "resources", "config_predict_template.yaml"), 'r'), From 3bd292b9f4ba7d1cfd4681f63a89d5be116a8b40 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 3 Aug 2022 11:09:51 +0200 Subject: [PATCH 07/77] major io refactor, now all file io is handled in plantseg.io --- plantseg/dataprocessing/dataprocessing.py | 48 +---- .../dataprocessing/functional/__init__.py | 0 .../functional/dataprocessing.py | 35 ++++ plantseg/gui/gui_tools.py | 3 +- plantseg/io/__init__.py | 0 plantseg/io/io.py | 166 ++++++++++++++++++ plantseg/pipeline/__init__.py | 3 - plantseg/pipeline/raw2seg.py | 3 +- plantseg/pipeline/steps.py | 65 ++----- plantseg/pipeline/utils.py | 92 +--------- plantseg/predictions/predict.py | 21 +-- tests/test_data_processing.py | 3 +- 12 files changed, 235 insertions(+), 204 deletions(-) create mode 100644 plantseg/dataprocessing/functional/__init__.py create mode 100644 plantseg/dataprocessing/functional/dataprocessing.py create mode 100644 plantseg/io/__init__.py create mode 100644 plantseg/io/io.py diff --git a/plantseg/dataprocessing/dataprocessing.py b/plantseg/dataprocessing/dataprocessing.py index 40ba5337..5d7bbaba 100644 --- a/plantseg/dataprocessing/dataprocessing.py +++ b/plantseg/dataprocessing/dataprocessing.py @@ -1,36 +1,11 @@ -import numpy as np -from scipy.ndimage import zoom -from skimage.filters import median -from skimage.morphology import ball, disk from skimage.transform import resize -from vigra.filters import gaussianSmoothing +from plantseg.dataprocessing.functional.dataprocessing import image_rescale, image_median, image_gaussian_smoothing,\ + image_crop from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import GenericPipelineStep -def _rescale(image, factor, order): - if np.array_equal(factor, [1, 1, 1]): - return image - else: - return zoom(image, zoom=factor, order=order) - - -def _median(image, radius): - if image.shape[0] == 1: - shape = image.shape - median_image = median(image[0], disk(radius)) - return median_image.reshape(shape) - else: - return median(image, ball(radius)) - - -def _gaussian(image, sigma): - max_sigma = (np.array(image.shape) - 1) / 3 - sigma = np.minimum(max_sigma, np.ones(max_sigma.ndim) * sigma) - return gaussianSmoothing(image, sigma) - - def _no_filter(image, param): return image @@ -79,19 +54,12 @@ def process(self, image): image = resize(image, output_shape, self.order) else: # use standard rescaling - image = _rescale(image, self.factor, self.order) + image = image_rescale(image, self.factor, self.order) self.img_count += 1 return image -def _parse_crop(crop_str): - crop_str = crop_str.replace('[', '').replace(']', '') - return tuple( - (slice(*(int(i) if i else None for i in part.strip().split(':'))) if ':' in part else int(part.strip())) for - part in crop_str.split(',')) - - class DataPreProcessing3D(GenericPipelineStep): def __init__(self, input_paths, @@ -115,8 +83,6 @@ def __init__(self, if factor is None: factor = [1, 1, 1] - if crop is not None: - crop = _parse_crop(crop) self.crop = crop # rescaling @@ -130,9 +96,9 @@ def __init__(self, assert filter_param is not None if filter_type == "median": - self.filter = _median + self.filter = image_median else: - self.filter = _gaussian + self.filter = image_gaussian_smoothing self.filter_param = filter_param else: @@ -143,9 +109,9 @@ def process(self, image): gui_logger.info(f"Preprocessing files...") if self.crop is not None: gui_logger.info(f"Cropping input image to: {self.crop}") - image = image[self.crop] + image = image_crop(image, self.crop) image = self.filter(image, self.filter_param) - image = _rescale(image, self.factor, self.order) + image = image_rescale(image, self.factor, self.order) return image diff --git a/plantseg/dataprocessing/functional/__init__.py b/plantseg/dataprocessing/functional/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/plantseg/dataprocessing/functional/dataprocessing.py b/plantseg/dataprocessing/functional/dataprocessing.py new file mode 100644 index 00000000..07c4c86c --- /dev/null +++ b/plantseg/dataprocessing/functional/dataprocessing.py @@ -0,0 +1,35 @@ +import numpy as np +from scipy.ndimage import zoom +from skimage.morphology import disk, ball +from skimage.filters import median +from vigra import gaussianSmoothing + + +def image_rescale(image, factor, order): + if np.array_equal(factor, [1, 1, 1]): + return image + else: + return zoom(image, zoom=factor, order=order) + + +def image_median(image, radius): + if image.shape[0] == 1: + shape = image.shape + median_image = median(image[0], disk(radius)) + return median_image.reshape(shape) + else: + return median(image, ball(radius)) + + +def image_gaussian_smoothing(image, sigma): + max_sigma = (np.array(image.shape) - 1) / 3 + sigma = np.minimum(max_sigma, np.ones(max_sigma.ndim) * sigma) + return gaussianSmoothing(image, sigma) + + +def image_crop(image, crop_str): + crop_str = crop_str.replace('[', '').replace(']', '') + slices = tuple((slice(*(int(i) + if i else None for i in part.strip().split(':'))) + if ':' in part else int(part.strip())) for part in crop_str.split(',')) + return image[slices] diff --git a/plantseg/gui/gui_tools.py b/plantseg/gui/gui_tools.py index 18fd1a10..eede8656 100644 --- a/plantseg/gui/gui_tools.py +++ b/plantseg/gui/gui_tools.py @@ -10,7 +10,8 @@ from plantseg.__version__ import __version__ from plantseg.gui import stick_all, stick_ew, var_to_tkinter, convert_rgb, PLANTSEG_GREEN from plantseg.pipeline import gui_logger -from plantseg.pipeline.utils import read_tiff_voxel_size, read_h5_voxel_size, TIFF_EXTENSIONS, H5_EXTENSIONS +from plantseg.io.io import read_tiff_voxel_size, read_h5_voxel_size, TIFF_EXTENSIONS, H5_EXTENSIONS + current_model = None current_segmentation = None diff --git a/plantseg/io/__init__.py b/plantseg/io/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/plantseg/io/io.py b/plantseg/io/io.py new file mode 100644 index 00000000..2de491df --- /dev/null +++ b/plantseg/io/io.py @@ -0,0 +1,166 @@ +import tifffile +import h5py +import warnings +import os + +TIFF_EXTENSIONS = [".tiff", ".tif"] +H5_EXTENSIONS = [".hdf", ".h5", ".hd5", "hdf5"] + +# allowed h5 keys +H5_KEYS = ["raw", "predictions", "segmentation"] +allowed_data_format = TIFF_EXTENSIONS + H5_EXTENSIONS + + +def read_tiff_voxel_size(file_path): + """ + Implemented based on information found in https://pypi.org/project/tifffile + """ + + def _xy_voxel_size(tags, key): + assert key in ['XResolution', 'YResolution'] + if key in tags: + num_pixels, units = tags[key].value + return units / num_pixels + # return default + return 1. + + with tifffile.TiffFile(file_path) as tiff: + image_metadata = tiff.imagej_metadata + if image_metadata is not None: + z = image_metadata.get('spacing', 1.) + else: + # default voxel size + z = 1. + + tags = tiff.pages[0].tags + # parse X, Y resolution + y = _xy_voxel_size(tags, 'YResolution') + x = _xy_voxel_size(tags, 'XResolution') + # return voxel size + return [z, y, x] + + +def read_h5_voxel_size(f, h5key): + ds = f[h5key] + + # parse voxel_size + if 'element_size_um' in ds.attrs: + voxel_size = ds.attrs['element_size_um'] + else: + warnings.warn('Voxel size not found, returning default [1.0, 1.0. 1.0]', RuntimeWarning) + voxel_size = [1.0, 1.0, 1.0] + + return voxel_size + + +def _find_input_key(h5_file): + found_datasets = [] + + def visitor_func(name, node): + if isinstance(node, h5py.Dataset): + found_datasets.append(name) + + h5_file.visititems(visitor_func) + + if not found_datasets: + raise RuntimeError(f"No datasets found in '{h5_file.filename}'") + + if len(found_datasets) == 1: + return found_datasets[0] + else: + for h5_key in H5_KEYS: + if h5_key in found_datasets: + return h5_key + + raise RuntimeError(f"Ambiguous datasets '{found_datasets}' in {h5_file.filename}. " + f"plantseg expects only one dataset to be present in input H5.") + + +def load_h5(path, key, slices=None, info_only=False): + + with h5py.File(path, 'r') as f: + if key is None: + key = _find_input_key(f) + + voxel_size = read_h5_voxel_size(f, key) + file_shape = f[key].shape + + infos = (voxel_size, file_shape, key) + if info_only: + return infos + + file = f[key][...] if slices is None else f[key][slices] + + return file, infos + + +def load_tiff(path, info_only=False): + file = tifffile.imread(path) + try: + voxel_size = read_tiff_voxel_size(path) + except: + # ZeroDivisionError could happen while reading the voxel size + warnings.warn('Voxel size not found, returning default [1.0, 1.0. 1.0]', RuntimeWarning) + voxel_size = [1.0, 1.0, 1.0] + + infos = (voxel_size, file.shape, None) + if info_only: + return infos + else: + return file, infos + + +def smart_load(path, key=None, info_only=False, default=load_tiff): + _, ext = os.path.splitext(path) + if ext in H5_EXTENSIONS: + return load_h5(path, key, info_only=info_only) + + elif ext in H5_EXTENSIONS: + return load_tiff(path, info_only=info_only) + + else: + print(f"No default found for {ext}, reverting to default loader") + return default(path) + + +def load_shape(path, key=None): + _, data_shape, _ = smart_load(path, key=key, info_only=True) + return data_shape + + +def create_h5(path, stack, key, voxel_size=(1.0, 1.0, 1.0), mode='a'): + with h5py.File(path, mode) as f: + f.create_dataset(key, data=stack, compression='gzip') + # save voxel_size + f[key].attrs['element_size_um'] = voxel_size + + +def del_h5_key(path, key, mode='a'): + with h5py.File(path, mode) as f: + if key in f: + del f[key] + f.close() + + +def rename_h5_key(path, old_key, new_key, mode='r+'): + """ Rename the 'old_key' dataset to 'new_key' """ + with h5py.File(path, mode) as f: + if old_key in f: + f[new_key] = f[old_key] + del f[old_key] + f.close() + + +def create_tiff(path, stack, voxel_size): + # taken from: https://pypi.org/project/tifffile docs + z, y, x = stack.shape + stack.shape = 1, z, 1, y, x, 1 # dimensions in TZCYXS order + spacing, y, x = voxel_size + resolution = (1. / x, 1. / y) + # Save output results as tiff + tifffile.imsave(path, + data=stack, + dtype=stack.dtype, + imagej=True, + resolution=resolution, + metadata={'axes': 'TZCYXS', 'spacing': spacing, 'unit': 'um'}) \ No newline at end of file diff --git a/plantseg/pipeline/__init__.py b/plantseg/pipeline/__init__.py index dba3d707..b88b621e 100644 --- a/plantseg/pipeline/__init__.py +++ b/plantseg/pipeline/__init__.py @@ -14,9 +14,6 @@ stream_handler.setFormatter(formatter) gui_logger.addHandler(stream_handler) -# allowed h5 keys -H5_KEYS = ["raw", "predictions", "segmentation"] - # Resources directory RESOURCES_DIR = "resources" raw2seg_config_template = os.path.join(plantseg_global_path, RESOURCES_DIR, "raw2seg_template.yaml") diff --git a/plantseg/pipeline/raw2seg.py b/plantseg/pipeline/raw2seg.py index 38dd226e..7d776563 100644 --- a/plantseg/pipeline/raw2seg.py +++ b/plantseg/pipeline/raw2seg.py @@ -4,7 +4,8 @@ from plantseg.dataprocessing.dataprocessing import DataPreProcessing3D from plantseg.pipeline import gui_logger from plantseg.pipeline.config_validation import config_validation -from plantseg.pipeline.utils import load_paths, load_shape +from plantseg.pipeline.utils import load_paths +from plantseg.io.io import load_shape from plantseg.predictions.predict import UnetPredictions from plantseg.segmentation.utils import configure_segmentation_step diff --git a/plantseg/pipeline/steps.py b/plantseg/pipeline/steps.py index 5f0e7625..9d776436 100644 --- a/plantseg/pipeline/steps.py +++ b/plantseg/pipeline/steps.py @@ -1,13 +1,11 @@ import os -import h5py import numpy as np -import tifffile import yaml from plantseg.pipeline import gui_logger -from plantseg.pipeline.utils import read_tiff_voxel_size, read_h5_voxel_size, find_input_key, SUPPORTED_TYPES, \ - TIFF_EXTENSIONS, H5_EXTENSIONS +from plantseg.pipeline.utils import SUPPORTED_TYPES +from plantseg.io.io import smart_load, create_tiff, create_h5 class GenericPipelineStep: @@ -101,28 +99,9 @@ def load_stack(self, file_path, check_input_type=True): tuple(nd.array, tuple(float)): (numpy array containing stack's data, stack's data voxel size) """ _, ext = os.path.splitext(file_path) - - if ext in TIFF_EXTENSIONS: - # load tiff file - data = tifffile.imread(file_path) - # parse voxel_size - voxel_size = read_tiff_voxel_size(file_path) - elif ext in H5_EXTENSIONS: - # load data from H5 file - with h5py.File(file_path, "r") as f: - h5_input_key = find_input_key(f) - gui_logger.info(f"Found '{h5_input_key}' dataset inside {file_path}") - # set h5_output_key to be the same as h5_input_key if h5_output_key not defined - if self.h5_output_key is None: - self.h5_output_key = h5_input_key - - ds = f[h5_input_key] - data = ds[...] - - # Parse voxel size - voxel_size = read_h5_voxel_size(file_path) - else: - raise RuntimeError(f"Unsupported file extension: {ext}") + data, (voxel_size, data_shape, key) = smart_load(file_path, key=None) + if self.h5_output_key is None: + self.h5_output_key = key # reshape data to 3D always data = np.nan_to_num(data) @@ -139,12 +118,9 @@ def save_raw_dataset(self, input_path, output_path, voxel_size): """ raw_path = self._raw_path(input_path) if os.path.exists(raw_path): - with h5py.File(raw_path, 'r') as f: - raw = f['raw'][...] - with h5py.File(output_path, 'r+') as f: - f.create_dataset('raw', data=raw, compression='gzip') - # save voxel_size - f['raw'].attrs['element_size_um'] = voxel_size + raw, _, _ = smart_load(raw_path, key='raw') + create_h5(output_path, stack=raw, key='raw', voxel_size=voxel_size, mode='a') + else: gui_logger.warning(f'Cannot save raw input: {raw_path} not found') @@ -197,28 +173,11 @@ def save_output(self, data, output_path, voxel_size): assert ext in [".h5", ".tiff"], f"Unsupported file extension {ext}" if ext == ".h5": - # Save output results as h5 - with h5py.File(output_path, "w") as f: - if self.h5_output_key is None: - # this can happen if input file is tiff and h5_output_key was not specified - self.h5_output_key = 'raw' - - f.create_dataset(self.h5_output_key, data=data, compression='gzip') - # save voxel_size - f[self.h5_output_key].attrs['element_size_um'] = voxel_size + key = self.h5_output_key if self.h5_output_key is not None else 'raw' + create_h5(output_path, stack=data, key=key, voxel_size=voxel_size, mode='w') + elif ext == ".tiff": - # taken from: https://pypi.org/project/tifffile docs - z, y, x = data.shape - data.shape = 1, z, 1, y, x, 1 # dimensions in TZCYXS order - spacing, y, x = voxel_size - resolution = (1. / x, 1. / y) - # Save output results as tiff - tifffile.imsave(output_path, - data=data, - dtype=data.dtype, - imagej=True, - resolution=resolution, - metadata={'axes': 'TZCYXS', 'spacing': spacing, 'unit': 'um'}) + create_tiff(output_path, stack=data, voxel_size=voxel_size) self._log_params(output_path) diff --git a/plantseg/pipeline/utils.py b/plantseg/pipeline/utils.py index 86b04fc4..bcd88098 100644 --- a/plantseg/pipeline/utils.py +++ b/plantseg/pipeline/utils.py @@ -2,20 +2,13 @@ import logging import os import warnings - -import h5py -import tifffile - -from plantseg.pipeline import gui_logger, H5_KEYS +from plantseg.io.io import allowed_data_format warnings.simplefilter('once', UserWarning) loggers = {} -ALLOWED_DATA_FORMAT = [".tiff", ".tif", ".hdf", ".hdf5", ".h5", ".hd5"] SUPPORTED_TYPES = ["labels", "data_float32", "data_uint8"] -TIFF_EXTENSIONS = [".tiff", ".tif"] -H5_EXTENSIONS = [".hdf", ".h5", ".hd5", "hdf5"] def load_paths(base_path): @@ -27,12 +20,12 @@ def load_paths(base_path): only_file = [] for path in paths: _, ext = os.path.splitext(path) - if os.path.isfile(path) and ext in ALLOWED_DATA_FORMAT: + if os.path.isfile(path) and ext in allowed_data_format: only_file.append(path) return sorted(only_file) else: path, ext = os.path.splitext(base_path) - if ext in ALLOWED_DATA_FORMAT: + if ext in allowed_data_format: return [base_path] else: raise RuntimeError(f"Unsupported file type: '{ext}'") @@ -51,82 +44,3 @@ def __init__(self, log_queue): def emit(self, record): self.log_queue.put(record) - - -def find_input_key(h5_file): - found_datasets = [] - - def visitor_func(name, node): - if isinstance(node, h5py.Dataset): - found_datasets.append(name) - - h5_file.visititems(visitor_func) - - if not found_datasets: - raise RuntimeError(f"No datasets found in '{h5_file.filename}'") - - if len(found_datasets) == 1: - return found_datasets[0] - else: - for h5_key in H5_KEYS: - if h5_key in found_datasets: - return h5_key - - raise RuntimeError(f"Ambiguous datasets '{found_datasets}' in {h5_file.filename}. " - f"plantseg expects only one dataset to be present in input H5.") - - -def load_shape(input_file): - _, ext = os.path.splitext(input_file) - if ext in TIFF_EXTENSIONS: - data = tifffile.imread(input_file) - return data.shape - elif ext in H5_EXTENSIONS: - with h5py.File(input_file, "r") as f: - h5_input_key = find_input_key(f) - return f[h5_input_key].shape - else: - raise RuntimeError(f"Unsupported file extension: {ext}") - - -def read_tiff_voxel_size(file_path): - """ - Implemented based on information found in https://pypi.org/project/tifffile - """ - - def _xy_voxel_size(tags, key): - assert key in ['XResolution', 'YResolution'] - if key in tags: - num_pixels, units = tags[key].value - return units / num_pixels - # return default - return 1. - - with tifffile.TiffFile(file_path) as tiff: - image_metadata = tiff.imagej_metadata - if image_metadata is not None: - z = image_metadata.get('spacing', 1.) - else: - # default voxel size - z = 1. - - tags = tiff.pages[0].tags - # parse X, Y resolution - y = _xy_voxel_size(tags, 'YResolution') - x = _xy_voxel_size(tags, 'XResolution') - # return voxel size - return [z, y, x] - - -def read_h5_voxel_size(file_path): - with h5py.File(file_path, "r") as f: - h5key = find_input_key(f) - ds = f[h5key] - - # parse voxel_size - if 'element_size_um' in ds.attrs: - return ds.attrs['element_size_um'] - else: - gui_logger.warn(f"Cannot find 'element_size_um' attribute for dataset '{h5key}'. " - f"Using default voxel_size: {[1., 1., 1.]}") - return [1., 1., 1.] \ No newline at end of file diff --git a/plantseg/predictions/predict.py b/plantseg/predictions/predict.py index 424f062e..3830e816 100755 --- a/plantseg/predictions/predict.py +++ b/plantseg/predictions/predict.py @@ -2,18 +2,17 @@ from pytorch3dunet.unet3d import utils from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device from plantseg.pipeline import gui_logger +from plantseg.io.io import load_shape from plantseg.pipeline.steps import GenericPipelineStep -def _check_patch_size(paths, config): +def _check_patch_size(paths, patch_size): axis = ['z', 'x', 'y'] - patch_size = config["patch"] valid_paths = [] for path in paths: incorrect_axis = [] - with h5py.File(path, 'r') as f: - raw_size = f["raw"].shape + raw_size = load_shape(path, key='raw') for _ax, _patch_size, _raw_size in zip(axis, patch_size, raw_size): if _patch_size > _raw_size: @@ -49,16 +48,7 @@ def __init__(self, state=True): h5_output_key = "predictions" - # model config - self.model_name = model_name - self.device = device - self.version = version - self.model_update = model_update - - # loader config - self.patch = patch - self.stride = stride - self.mirror_padding = mirror_padding + valid_paths = _check_patch_size(input_paths, patch_size=patch) model, model_config, model_path = get_model_config(model_name, model_update=model_update) utils.load_checkpoint(model_path, model) @@ -74,12 +64,13 @@ def __init__(self, stride=stride, mirror_padding=mirror_padding) - super().__init__(input_paths, + super().__init__(valid_paths, input_type=input_type, output_type=output_type, save_directory=model_name, out_ext=out_ext, state=state, + file_suffix='_predictions', h5_output_key=h5_output_key) def process(self, raw): diff --git a/tests/test_data_processing.py b/tests/test_data_processing.py index 452ab8b0..ff45739c 100644 --- a/tests/test_data_processing.py +++ b/tests/test_data_processing.py @@ -3,7 +3,8 @@ import h5py import numpy as np -from plantseg.dataprocessing.dataprocessing import DataPostProcessing3D, DataPreProcessing3D, _gaussian +from plantseg.dataprocessing.dataprocessing import DataPostProcessing3D, DataPreProcessing3D +from plantseg.dataprocessing.functional.dataprocessing import image_gaussian_smoothing class TestDataProcessing: From 57791daf966fd37a9d7c52bcf7fb04fa9221d3e6 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 3 Aug 2022 13:30:52 +0200 Subject: [PATCH 08/77] fix errors in test --- plantseg/pipeline/raw2seg.py | 4 +++- plantseg/predictions/predict.py | 2 +- tests/resources/test_config.yaml | 2 +- tests/test_network_predictions.py | 13 ++++++------- 4 files changed, 11 insertions(+), 10 deletions(-) diff --git a/plantseg/pipeline/raw2seg.py b/plantseg/pipeline/raw2seg.py index 7d776563..e7644f97 100644 --- a/plantseg/pipeline/raw2seg.py +++ b/plantseg/pipeline/raw2seg.py @@ -34,10 +34,12 @@ def configure_cnn_step(input_paths, config): stride = config.get('stride', 'Accurate (slowest)') device = config.get('device', 'cuda') version = config.get('version', 'best') + state = config.get('state', True) model_update = config.get('model_update', False) mirror_padding = config.get('mirror_padding', (80, 160, 160)) return UnetPredictions(input_paths, model_name=model_name, patch=patch, stride=stride, - device=device, version=version, model_update=model_update, mirror_padding=mirror_padding) + device=device, version=version, model_update=model_update, + mirror_padding=mirror_padding, state=state) def configure_cnn_postprocessing_step(input_paths, config): diff --git a/plantseg/predictions/predict.py b/plantseg/predictions/predict.py index 3830e816..057d0563 100755 --- a/plantseg/predictions/predict.py +++ b/plantseg/predictions/predict.py @@ -50,7 +50,7 @@ def __init__(self, valid_paths = _check_patch_size(input_paths, patch_size=patch) - model, model_config, model_path = get_model_config(model_name, model_update=model_update) + model, model_config, model_path = get_model_config(model_name, model_update=model_update, version=version) utils.load_checkpoint(model_path, model) device = set_device(device) diff --git a/tests/resources/test_config.yaml b/tests/resources/test_config.yaml index 0e94ef2d..eff996b9 100644 --- a/tests/resources/test_config.yaml +++ b/tests/resources/test_config.yaml @@ -40,7 +40,7 @@ cnn_prediction: # patch size given to the network (adapt to fit in your GPU mem) patch: [32, 128, 128] # stride between patches (make sure the the patches overlap in order to get smoother prediction maps) - stride: "Balanced" + stride: "Accurate (slowest)" # "best" refers to best performing on the val set (recommended), alternatively "last" refers to the last version before interruption version: best # If "True" forces downloading networks from the online repos diff --git a/tests/test_network_predictions.py b/tests/test_network_predictions.py index 461b5678..40ea1508 100644 --- a/tests/test_network_predictions.py +++ b/tests/test_network_predictions.py @@ -3,16 +3,16 @@ import h5py import numpy as np -from plantseg.predictions.predict import UnetPredictions +from plantseg.pipeline.raw2seg import configure_cnn_step class TestUnetPredictions: def test_state_false(self, prediction_config): prediction_config['cnn_prediction']['state'] = False paths = [prediction_config['path']] - preds = UnetPredictions(paths, prediction_config['cnn_prediction']) + step = configure_cnn_step(paths, prediction_config['cnn_prediction']) - assert paths == preds() + assert paths == step() def test_unet_predictions(self, prediction_config): """ @@ -20,12 +20,11 @@ def test_unet_predictions(self, prediction_config): """ paths = [prediction_config['path']] cnn_config = prediction_config['cnn_prediction'] + step = configure_cnn_step(paths, prediction_config['cnn_prediction']) - preds = UnetPredictions(paths, cnn_config) + output_paths = step() - output_paths = preds() - - # assert output_paths correcltly created + # assert output_paths correctly created basepath, basename = os.path.split(paths[0]) expected_paths = [ os.path.join(basepath, cnn_config['model_name'], os.path.splitext(basename)[0] + '_predictions.h5') From 62dc39bc96da81d6e8627eb4aa6dd44de959810a Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 3 Aug 2022 13:44:03 +0200 Subject: [PATCH 09/77] minor refactors and imports optimization --- plantseg/dataprocessing/dataprocessing.py | 2 +- plantseg/dataprocessing/functional/dataprocessing.py | 2 +- plantseg/gui/gui_tools.py | 3 +-- plantseg/io/__init__.py | 3 +++ plantseg/io/io.py | 7 ++++--- plantseg/{predictions/README.md => napari/__init__.py} | 0 plantseg/pipeline/raw2seg.py | 2 +- plantseg/pipeline/steps.py | 2 +- plantseg/pipeline/utils.py | 3 ++- plantseg/predictions/array_dataset.py | 2 +- plantseg/predictions/array_predictor.py | 3 ++- plantseg/predictions/functional/__init__.py | 1 + plantseg/predictions/functional/predictions.py | 6 ++++-- plantseg/predictions/predict.py | 6 +++--- plantseg/predictions/utils.py | 6 +++--- plantseg/run_plantseg.py | 4 +++- plantseg/segmentation/functional/__init__.py | 4 ++-- plantseg/segmentation/simpleitkws.py | 4 +--- 18 files changed, 34 insertions(+), 26 deletions(-) rename plantseg/{predictions/README.md => napari/__init__.py} (100%) diff --git a/plantseg/dataprocessing/dataprocessing.py b/plantseg/dataprocessing/dataprocessing.py index 5d7bbaba..f1907957 100644 --- a/plantseg/dataprocessing/dataprocessing.py +++ b/plantseg/dataprocessing/dataprocessing.py @@ -1,6 +1,6 @@ from skimage.transform import resize -from plantseg.dataprocessing.functional.dataprocessing import image_rescale, image_median, image_gaussian_smoothing,\ +from plantseg.dataprocessing.functional.dataprocessing import image_rescale, image_median, image_gaussian_smoothing, \ image_crop from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import GenericPipelineStep diff --git a/plantseg/dataprocessing/functional/dataprocessing.py b/plantseg/dataprocessing/functional/dataprocessing.py index 07c4c86c..bd626063 100644 --- a/plantseg/dataprocessing/functional/dataprocessing.py +++ b/plantseg/dataprocessing/functional/dataprocessing.py @@ -1,7 +1,7 @@ import numpy as np from scipy.ndimage import zoom -from skimage.morphology import disk, ball from skimage.filters import median +from skimage.morphology import disk, ball from vigra import gaussianSmoothing diff --git a/plantseg/gui/gui_tools.py b/plantseg/gui/gui_tools.py index eede8656..ecdd6dcd 100644 --- a/plantseg/gui/gui_tools.py +++ b/plantseg/gui/gui_tools.py @@ -9,9 +9,8 @@ from plantseg import custom_zoo, home_path, PLANTSEG_MODELS_DIR, model_zoo_path from plantseg.__version__ import __version__ from plantseg.gui import stick_all, stick_ew, var_to_tkinter, convert_rgb, PLANTSEG_GREEN +from plantseg.io import read_tiff_voxel_size, read_h5_voxel_size, TIFF_EXTENSIONS, H5_EXTENSIONS from plantseg.pipeline import gui_logger -from plantseg.io.io import read_tiff_voxel_size, read_h5_voxel_size, TIFF_EXTENSIONS, H5_EXTENSIONS - current_model = None current_segmentation = None diff --git a/plantseg/io/__init__.py b/plantseg/io/__init__.py index e69de29b..5cfe8e7a 100644 --- a/plantseg/io/__init__.py +++ b/plantseg/io/__init__.py @@ -0,0 +1,3 @@ +from plantseg.io.io import smart_load, create_tiff, create_h5, load_shape +from plantseg.io.io import read_tiff_voxel_size, read_h5_voxel_size +from plantseg.io.io import allowed_data_format, H5_EXTENSIONS, TIFF_EXTENSIONS diff --git a/plantseg/io/io.py b/plantseg/io/io.py index 2de491df..d8c3bd4d 100644 --- a/plantseg/io/io.py +++ b/plantseg/io/io.py @@ -1,7 +1,8 @@ -import tifffile -import h5py -import warnings import os +import warnings + +import h5py +import tifffile TIFF_EXTENSIONS = [".tiff", ".tif"] H5_EXTENSIONS = [".hdf", ".h5", ".hd5", "hdf5"] diff --git a/plantseg/predictions/README.md b/plantseg/napari/__init__.py similarity index 100% rename from plantseg/predictions/README.md rename to plantseg/napari/__init__.py diff --git a/plantseg/pipeline/raw2seg.py b/plantseg/pipeline/raw2seg.py index e7644f97..23e8f653 100644 --- a/plantseg/pipeline/raw2seg.py +++ b/plantseg/pipeline/raw2seg.py @@ -2,10 +2,10 @@ from plantseg.dataprocessing.dataprocessing import DataPostProcessing3D from plantseg.dataprocessing.dataprocessing import DataPreProcessing3D +from plantseg.io.io import load_shape from plantseg.pipeline import gui_logger from plantseg.pipeline.config_validation import config_validation from plantseg.pipeline.utils import load_paths -from plantseg.io.io import load_shape from plantseg.predictions.predict import UnetPredictions from plantseg.segmentation.utils import configure_segmentation_step diff --git a/plantseg/pipeline/steps.py b/plantseg/pipeline/steps.py index 9d776436..1699f8fe 100644 --- a/plantseg/pipeline/steps.py +++ b/plantseg/pipeline/steps.py @@ -5,7 +5,7 @@ from plantseg.pipeline import gui_logger from plantseg.pipeline.utils import SUPPORTED_TYPES -from plantseg.io.io import smart_load, create_tiff, create_h5 +from plantseg.io import smart_load, create_tiff, create_h5 class GenericPipelineStep: diff --git a/plantseg/pipeline/utils.py b/plantseg/pipeline/utils.py index bcd88098..0e6d55eb 100644 --- a/plantseg/pipeline/utils.py +++ b/plantseg/pipeline/utils.py @@ -2,7 +2,8 @@ import logging import os import warnings -from plantseg.io.io import allowed_data_format + +from plantseg.io import allowed_data_format warnings.simplefilter('once', UserWarning) diff --git a/plantseg/predictions/array_dataset.py b/plantseg/predictions/array_dataset.py index 05c1ec6c..7b479a6c 100644 --- a/plantseg/predictions/array_dataset.py +++ b/plantseg/predictions/array_dataset.py @@ -1,8 +1,8 @@ import numpy as np -from torch.utils.data import Dataset import pytorch3dunet.augment.transforms as transforms from pytorch3dunet.datasets.utils import get_slice_builder, calculate_stats, default_prediction_collate from pytorch3dunet.unet3d.utils import get_logger +from torch.utils.data import Dataset logger = get_logger('ArrayDataset') diff --git a/plantseg/predictions/array_predictor.py b/plantseg/predictions/array_predictor.py index 5f7ec015..bc04b4d5 100644 --- a/plantseg/predictions/array_predictor.py +++ b/plantseg/predictions/array_predictor.py @@ -2,9 +2,10 @@ import torch from pytorch3dunet.unet3d.utils import get_logger from pytorch3dunet.unet3d.utils import remove_halo -from plantseg.predictions.array_dataset import ArrayDataset from torch.utils.data import DataLoader +from plantseg.predictions.array_dataset import ArrayDataset + logger = get_logger('UNetArrayPredictor') diff --git a/plantseg/predictions/functional/__init__.py b/plantseg/predictions/functional/__init__.py index e69de29b..d23796e9 100644 --- a/plantseg/predictions/functional/__init__.py +++ b/plantseg/predictions/functional/__init__.py @@ -0,0 +1 @@ +from plantseg.predictions.functional.predictions import unet_predictions diff --git a/plantseg/predictions/functional/predictions.py b/plantseg/predictions/functional/predictions.py index 7c19fb83..4c3050ac 100644 --- a/plantseg/predictions/functional/predictions.py +++ b/plantseg/predictions/functional/predictions.py @@ -1,7 +1,9 @@ +from typing import Tuple, Union + +from numpy.typing import ArrayLike from pytorch3dunet.unet3d import utils + from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device -from numpy.typing import ArrayLike -from typing import Tuple, Union def unet_predictions(raw: ArrayLike, diff --git a/plantseg/predictions/predict.py b/plantseg/predictions/predict.py index 057d0563..90c3d0b8 100755 --- a/plantseg/predictions/predict.py +++ b/plantseg/predictions/predict.py @@ -1,9 +1,9 @@ -import h5py from pytorch3dunet.unet3d import utils -from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device -from plantseg.pipeline import gui_logger + from plantseg.io.io import load_shape +from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import GenericPipelineStep +from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device def _check_patch_size(paths, patch_size): diff --git a/plantseg/predictions/utils.py b/plantseg/predictions/utils.py index b20770e6..42cb284c 100644 --- a/plantseg/predictions/utils.py +++ b/plantseg/predictions/utils.py @@ -1,14 +1,14 @@ import os -import torch import requests +import torch import yaml +from pytorch3dunet.unet3d.model import get_model from plantseg import plantseg_global_path, PLANTSEG_MODELS_DIR, home_path -from pytorch3dunet.unet3d.model import get_model from plantseg.pipeline import gui_logger -from plantseg.predictions.array_predictor import ArrayPredictor from plantseg.predictions.array_dataset import ArrayDataset +from plantseg.predictions.array_predictor import ArrayPredictor # define constant values CONFIG_TRAIN_YAML = "config_train.yml" diff --git a/plantseg/run_plantseg.py b/plantseg/run_plantseg.py index d490a6e1..24290051 100644 --- a/plantseg/run_plantseg.py +++ b/plantseg/run_plantseg.py @@ -1,7 +1,9 @@ import argparse + import yaml -from plantseg.pipeline.raw2seg import raw2seg + from plantseg.gui.plantsegapp import PlantSegApp +from plantseg.pipeline.raw2seg import raw2seg def parser(): diff --git a/plantseg/segmentation/functional/__init__.py b/plantseg/segmentation/functional/__init__.py index 2e1861dc..6ba98796 100644 --- a/plantseg/segmentation/functional/__init__.py +++ b/plantseg/segmentation/functional/__init__.py @@ -1,5 +1,5 @@ from plantseg.segmentation.functional.segmentation import dt_watershed from plantseg.segmentation.functional.segmentation import gasp -from plantseg.segmentation.functional.segmentation import multicut -from plantseg.segmentation.functional.segmentation import lifted_multicut_from_nuclei_segmentation from plantseg.segmentation.functional.segmentation import lifted_multicut_from_nuclei_pmaps +from plantseg.segmentation.functional.segmentation import lifted_multicut_from_nuclei_segmentation +from plantseg.segmentation.functional.segmentation import multicut diff --git a/plantseg/segmentation/simpleitkws.py b/plantseg/segmentation/simpleitkws.py index 46c6cecd..47e42604 100644 --- a/plantseg/segmentation/simpleitkws.py +++ b/plantseg/segmentation/simpleitkws.py @@ -1,7 +1,5 @@ -import numpy as np - -from plantseg.segmentation.functional.segmentation import simple_itk_watershed from plantseg.pipeline.steps import AbstractSegmentationStep +from plantseg.segmentation.functional.segmentation import simple_itk_watershed class SimpleITKWatershed(AbstractSegmentationStep): From d16f1faf4c92733afe79ec034c26310e34479969 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 3 Aug 2022 13:45:00 +0200 Subject: [PATCH 10/77] remove readme --- plantseg/{segmentation/README.md => napari/viewer.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename plantseg/{segmentation/README.md => napari/viewer.py} (100%) diff --git a/plantseg/segmentation/README.md b/plantseg/napari/viewer.py similarity index 100% rename from plantseg/segmentation/README.md rename to plantseg/napari/viewer.py From 415bd75784c21c9e250f64282fe9cd12634cbb47 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 3 Aug 2022 14:09:22 +0200 Subject: [PATCH 11/77] fix type error --- plantseg/segmentation/functional/segmentation.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/plantseg/segmentation/functional/segmentation.py b/plantseg/segmentation/functional/segmentation.py index fd5d5f07..893c2dd3 100644 --- a/plantseg/segmentation/functional/segmentation.py +++ b/plantseg/segmentation/functional/segmentation.py @@ -15,8 +15,8 @@ def dt_watershed(boundary_pmaps: ArrayLike, - threshold: float, - sigma_seeds: float, + threshold: float = 0.5, + sigma_seeds: float = 1., stacked: bool = False, sigma_weights: float = 2., min_size: int = 100, @@ -43,6 +43,8 @@ def dt_watershed(boundary_pmaps: ArrayLike, Returns: np.ndarray: watershed segmentation """ + + boundary_pmaps = boundary_pmaps.astype('float32') ws_kwargs = dict(threshold=threshold, sigma_seeds=sigma_seeds, sigma_weights=sigma_weights, min_size=min_size, alpha=alpha, @@ -78,6 +80,7 @@ def superpixel_gen(*args, **kwargs): 'use_efficient_implementations': False} # pmaps are interpreted as affinities + boundary_pmaps = boundary_pmaps.astype('float32') affinities = np.stack([boundary_pmaps, boundary_pmaps, boundary_pmaps], axis=0) offsets = [[0, 0, 1], [0, 1, 0], [1, 0, 0]] From d5125819f3a3f1dd9b1523e6e2cdd01d9bd29381 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 3 Aug 2022 14:34:16 +0200 Subject: [PATCH 12/77] reduce logging from functional predictions --- plantseg/predictions/array_dataset.py | 5 ++- plantseg/predictions/array_predictor.py | 40 ++++++++++++------- .../predictions/functional/predictions.py | 19 +++++---- plantseg/predictions/predict.py | 14 +++---- plantseg/predictions/utils.py | 34 ++++++++-------- 5 files changed, 64 insertions(+), 48 deletions(-) diff --git a/plantseg/predictions/array_dataset.py b/plantseg/predictions/array_dataset.py index 7b479a6c..3ecad939 100644 --- a/plantseg/predictions/array_dataset.py +++ b/plantseg/predictions/array_dataset.py @@ -20,6 +20,7 @@ def __init__(self, raw, transformer_config, mirror_padding=(16, 32, 32), global_normalization=True, + verbose_logging=True, **kwargs): """ :param raw: numpy array containing the raw image @@ -68,7 +69,9 @@ def __init__(self, raw, self.weight_slices = slice_builder.weight_slices self.patch_count = len(self.raw_slices) - logger.info(f'Number of patches: {self.patch_count}') + + if verbose_logging: + logger.info(f'Number of patches: {self.patch_count}') def __getitem__(self, idx): if idx >= len(self): diff --git a/plantseg/predictions/array_predictor.py b/plantseg/predictions/array_predictor.py index bc04b4d5..2e6c9d21 100644 --- a/plantseg/predictions/array_predictor.py +++ b/plantseg/predictions/array_predictor.py @@ -22,29 +22,32 @@ class ArrayPredictor: config (dict): global config dict """ - def __init__(self, model, config, device, **kwargs): + def __init__(self, model, config, device, verbose_logging=True, **kwargs): self.model = model self.config = config self.device = device self.predictor_config = kwargs + self.mute_logging = verbose_logging def __call__(self, test_dataset): assert isinstance(test_dataset, ArrayDataset) test_loader = DataLoader(test_dataset, batch_size=1, collate_fn=test_dataset.prediction_collate) - logger.info(f"Processing...") + if self.mute_logging: + logger.info(f"Processing...") out_channels = self.config.get('out_channels') # prediction_channel = self.config.get('prediction_channel', None) prediction_channel = None - if prediction_channel is not None: + if self.mute_logging and prediction_channel is not None: logger.info(f"Saving only channel '{prediction_channel}' from the network output") output_heads = self.config.get('output_heads', 1) - logger.info(f'Running prediction on {len(test_loader)} batches...') + if self.mute_logging: + logger.info(f'Running prediction on {len(test_loader)} batches...') # dimensionality of the output predictions volume_shape = self.volume_shape(test_dataset) @@ -54,14 +57,18 @@ def __call__(self, test_dataset): # single channel prediction map prediction_maps_shape = (1,) + volume_shape - logger.info(f'The shape of the output prediction maps (CDHW): {prediction_maps_shape}') + if self.mute_logging: + logger.info(f'The shape of the output prediction maps (CDHW): {prediction_maps_shape}') patch_halo = self.predictor_config.get('patch_halo', (4, 8, 8)) self._validate_halo(patch_halo, test_dataset.slice_builder_config) - logger.info(f'Using patch_halo: {patch_halo}') - # allocate prediction and normalization arrays - logger.info('Allocating prediction and normalization arrays...') + if self.mute_logging: + logger.info(f'Using patch_halo: {patch_halo}') + + if self.mute_logging: + # allocate prediction and normalization arrays + logger.info('Allocating prediction and normalization arrays...') prediction_maps, normalization_masks = self._allocate_prediction_maps(prediction_maps_shape, output_heads) @@ -98,12 +105,13 @@ def __call__(self, test_dataset): channel_slice = slice(0, 1) index = (channel_slice,) + index - if prediction_channel is not None: + if self.mute_logging and prediction_channel is not None: # use only the 'prediction_channel' logger.info(f"Using channel '{prediction_channel}'...") pred = np.expand_dims(pred[prediction_channel], axis=0) - logger.info(f'Saving predictions for slice:{index}...') + if self.mute_logging: + logger.info(f'Saving predictions for slice:{index}...') # remove halo in order to avoid block artifacts in the output probability maps u_prediction, u_index = remove_halo(pred, index, volume_shape, patch_halo) @@ -113,10 +121,12 @@ def __call__(self, test_dataset): normalization_mask[u_index] += 1 # save results - logger.info(f'Returning predictions') + if self.mute_logging: + logger.info(f'Returning predictions') prediction_maps = self._normalize_results(prediction_maps, normalization_masks, - test_dataset.mirror_padding) + test_dataset.mirror_padding, + mute_logging=self.mute_logging) return prediction_maps @staticmethod @@ -128,7 +138,7 @@ def _allocate_prediction_maps(output_shape, output_heads): return prediction_maps, normalization_masks @staticmethod - def _normalize_results(prediction_maps, normalization_masks, mirror_padding): + def _normalize_results(prediction_maps, normalization_masks, mirror_padding, mute_logging=False): def _slice_from_pad(pad): if pad == 0: return slice(None, None) @@ -142,8 +152,8 @@ def _slice_from_pad(pad): if mirror_padding is not None: z_s, y_s, x_s = [_slice_from_pad(p) for p in mirror_padding] - - logger.info(f'Dataset loaded with mirror padding: {mirror_padding}. Cropping before saving...') + if mute_logging: + logger.info(f'Dataset loaded with mirror padding: {mirror_padding}. Cropping before saving...') prediction_map = prediction_map[:, z_s, y_s, x_s] diff --git a/plantseg/predictions/functional/predictions.py b/plantseg/predictions/functional/predictions.py index 4c3050ac..6731cd24 100644 --- a/plantseg/predictions/functional/predictions.py +++ b/plantseg/predictions/functional/predictions.py @@ -3,7 +3,7 @@ from numpy.typing import ArrayLike from pytorch3dunet.unet3d import utils -from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device +from plantseg.predictions.utils import get_dataset_config, get_model_config, get_predictor_config, set_device def unet_predictions(raw: ArrayLike, @@ -21,13 +21,16 @@ def unet_predictions(raw: ArrayLike, model = model.to(device) predictor, predictor_config = get_predictor_config(model_name) - predictor = predictor(model=model, config=model_config, device=device, **predictor_config) + predictor = predictor(model=model, + config=model_config, + device=device, + verbose_logging=False, **predictor_config) - loader, loader_config = get_loader_config(model_name, - patch=patch, - stride=stride, - mirror_padding=mirror_padding) + dataset_builder, dataset_config = get_dataset_config(model_name, + patch=patch, + stride=stride, + mirror_padding=mirror_padding) - raw_loader = loader(raw, **loader_config) + raw_loader = dataset_builder(raw, verbose_logging=False, **dataset_config) pmaps = predictor(raw_loader) - return pmaps[0] \ No newline at end of file + return pmaps[0] diff --git a/plantseg/predictions/predict.py b/plantseg/predictions/predict.py index 90c3d0b8..06cf8ab5 100755 --- a/plantseg/predictions/predict.py +++ b/plantseg/predictions/predict.py @@ -3,7 +3,7 @@ from plantseg.io.io import load_shape from plantseg.pipeline import gui_logger from plantseg.pipeline.steps import GenericPipelineStep -from plantseg.predictions.utils import get_loader_config, get_model_config, get_predictor_config, set_device +from plantseg.predictions.utils import get_dataset_config, get_model_config, get_predictor_config, set_device def _check_patch_size(paths, patch_size): @@ -59,10 +59,10 @@ def __init__(self, predictor, predictor_config = get_predictor_config(model_name) self.predictor = predictor(model=model, config=model_config, device=device, **predictor_config) - self.loader, self.loader_config = get_loader_config(model_name, - patch=patch, - stride=stride, - mirror_padding=mirror_padding) + self.dataset_builder, self.dataset_config = get_dataset_config(model_name, + patch=patch, + stride=stride, + mirror_padding=mirror_padding) super().__init__(valid_paths, input_type=input_type, @@ -74,6 +74,6 @@ def __init__(self, h5_output_key=h5_output_key) def process(self, raw): - raw_loader = self.loader(raw, **self.loader_config) - pmaps = self.predictor(raw_loader) + dataset = self.dataset_builder(raw, **self.dataset_config) + pmaps = self.predictor(dataset) return pmaps[0] diff --git a/plantseg/predictions/utils.py b/plantseg/predictions/utils.py index 42cb284c..7b159d5e 100644 --- a/plantseg/predictions/utils.py +++ b/plantseg/predictions/utils.py @@ -70,52 +70,52 @@ def set_device(device): return device -def get_loader_config(model_name, patch, stride, mirror_padding, num_workers=8, global_normalization=True): +def get_dataset_config(model_name, patch, stride, mirror_padding, num_workers=8, global_normalization=True): predict_template = get_predict_template() - loader_config = predict_template.pop('loaders') + dataset_config = predict_template.pop('loaders') - loader_config["num_workers"] = num_workers - loader_config["mirror_padding"] = mirror_padding + dataset_config["num_workers"] = num_workers + dataset_config["mirror_padding"] = mirror_padding # Add patch and stride to the config - loader_config["test"]["slice_builder"]["patch_shape"] = patch + dataset_config["test"]["slice_builder"]["patch_shape"] = patch stride_key, stride_shape = stride, get_stride_shape(patch, "Balanced") if type(stride_key) is list: - loader_config["test"]["slice_builder"]["stride_shape"] = stride_key + dataset_config["test"]["slice_builder"]["stride_shape"] = stride_key elif type(stride_key) is str: stride_shape = get_stride_shape(patch, stride_key) - loader_config["test"]["slice_builder"]["stride_shape"] = stride_shape + dataset_config["test"]["slice_builder"]["stride_shape"] = stride_shape else: raise RuntimeError(f"Unsupported stride type: {type(stride_key)}") # Set paths to None - loader_config["test"]["file_paths"] = None + dataset_config["test"]["file_paths"] = None config_train = get_train_config(model_name) if config_train["model"]["name"] == "UNet2D": # make sure that z-pad is 0 for 2d UNet - mirror_padding[0] = 0 + dataset_config["mirror_padding"] = [0, mirror_padding[1], mirror_padding[2]] # make sure to skip the patch size validation for 2d unet - loader_config["test"]["slice_builder"]["skip_shape_check"] = True + dataset_config["test"]["slice_builder"]["skip_shape_check"] = True # z-dim of patch and stride has to be one - patch_shape = loader_config["test"]["slice_builder"]["patch_shape"] - stride_shape = loader_config["test"]["slice_builder"]["stride_shape"] + patch_shape = dataset_config["test"]["slice_builder"]["patch_shape"] + stride_shape = dataset_config["test"]["slice_builder"]["stride_shape"] if patch_shape[0] != 1: gui_logger.warning(f"Incorrect z-dimension in the patch_shape for the 2D UNet prediction. {patch_shape[0]}" f" was given, but has to be 1. Defaulting default value: 1") - loader_config["test"]["slice_builder"]["patch_shape"][0] = 1 + dataset_config["test"]["slice_builder"]["patch_shape"][0] = 1 if stride_shape[0] != 1: gui_logger.warning(f"Incorrect z-dimension in the stride_shape for the 2D UNet prediction. " f"{stride_shape[0]} was given, but has to be 1. Defaulting default value: 1") - loader_config["test"]["slice_builder"]["stride_shape"][0] = 1 + dataset_config["test"]["slice_builder"]["stride_shape"][0] = 1 - dataset_config = {'slice_builder_config': loader_config['test']['slice_builder'], - 'transformer_config': loader_config['test']['transformer'], - 'mirror_padding': loader_config['mirror_padding'], + dataset_config = {'slice_builder_config': dataset_config['test']['slice_builder'], + 'transformer_config': dataset_config['test']['transformer'], + 'mirror_padding': dataset_config['mirror_padding'], 'global_normalization': global_normalization } From 8d43912b9aa3ef5f01fbf75a6b3563f2a06c0527 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 3 Aug 2022 16:43:33 +0200 Subject: [PATCH 13/77] add entry point for napari --- plantseg/napari/viewer.py | 8 ++++++++ plantseg/run_plantseg.py | 5 +++++ 2 files changed, 13 insertions(+) diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index e69de29b..fc2a067a 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -0,0 +1,8 @@ +import napari + + +def run_viewer(): + viewer = napari.Viewer() + napari.run() + + diff --git a/plantseg/run_plantseg.py b/plantseg/run_plantseg.py index 24290051..b6046e05 100644 --- a/plantseg/run_plantseg.py +++ b/plantseg/run_plantseg.py @@ -4,12 +4,14 @@ from plantseg.gui.plantsegapp import PlantSegApp from plantseg.pipeline.raw2seg import raw2seg +from plantseg.napari.viewer import run_viewer def parser(): parser = argparse.ArgumentParser(description='Plant cell instance segmentation script') parser.add_argument('--config', type=str, help='Path to the YAML config file', required=False) parser.add_argument('--gui', action='store_true', help='Launch GUI configurator', required=False) + parser.add_argument('--napari', action='store_true', help='Napari Viewer', required=False) args = parser.parse_args() return args @@ -25,6 +27,9 @@ def main(): if args.gui: PlantSegApp() + if args.napari: + run_viewer() + elif args.config is not None: config = load_config(args) raw2seg(config) From 76085e4b0426d0d9e52129df653d4ef76f2950f5 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Sat, 6 Aug 2022 17:32:07 +0200 Subject: [PATCH 14/77] base introductions of napari viewer and widget --- .../dataprocessing/functional/__init__.py | 4 + plantseg/napari/containers.py | 29 ++++++++ plantseg/napari/stack_manager.py | 41 +++++++++++ plantseg/napari/viewer.py | 14 ++++ plantseg/napari/widget/__init__.py | 0 plantseg/napari/widget/dataprocessing.py | 73 +++++++++++++++++++ plantseg/napari/widget/io.py | 40 ++++++++++ plantseg/napari/widget/predictions.py | 22 ++++++ plantseg/napari/widget/segmentation.py | 31 ++++++++ plantseg/napari/widget/utils.py | 29 ++++++++ .../predictions/functional/predictions.py | 7 +- .../segmentation/functional/segmentation.py | 9 +++ 12 files changed, 298 insertions(+), 1 deletion(-) create mode 100644 plantseg/napari/containers.py create mode 100644 plantseg/napari/stack_manager.py create mode 100644 plantseg/napari/widget/__init__.py create mode 100644 plantseg/napari/widget/dataprocessing.py create mode 100644 plantseg/napari/widget/io.py create mode 100644 plantseg/napari/widget/predictions.py create mode 100644 plantseg/napari/widget/segmentation.py create mode 100644 plantseg/napari/widget/utils.py diff --git a/plantseg/dataprocessing/functional/__init__.py b/plantseg/dataprocessing/functional/__init__.py index e69de29b..ad9a3add 100644 --- a/plantseg/dataprocessing/functional/__init__.py +++ b/plantseg/dataprocessing/functional/__init__.py @@ -0,0 +1,4 @@ +from plantseg.dataprocessing.functional.dataprocessing import image_gaussian_smoothing +from plantseg.dataprocessing.functional.dataprocessing import image_rescale +from plantseg.dataprocessing.functional.dataprocessing import image_crop +from plantseg.dataprocessing.functional.dataprocessing import image_median diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py new file mode 100644 index 00000000..f593f2d3 --- /dev/null +++ b/plantseg/napari/containers.py @@ -0,0 +1,29 @@ +from magicgui.widgets import Container + +from plantseg.napari.widget.dataprocessing import widget_generic_preprocessing, widget_cropping, widget_add_layers +from plantseg.napari.widget.predictions import widget_unet_predictions +from plantseg.napari.widget.segmentation import widget_dt_ws, widget_gasp +from plantseg.napari.widget.io import open_file, export_stack + + +def get_main(): + container = Container(widgets=[open_file, + export_stack], + labels=False) + return container + + +def get_preprocessing_workflow(): + container = Container(widgets=[widget_generic_preprocessing, + widget_cropping, + widget_cropping], + labels=False) + return container + + +def get_gasp_workflow(): + container = Container(widgets=[widget_unet_predictions, + widget_dt_ws, + widget_gasp], + labels=False) + return container diff --git a/plantseg/napari/stack_manager.py b/plantseg/napari/stack_manager.py new file mode 100644 index 00000000..ea93a3c5 --- /dev/null +++ b/plantseg/napari/stack_manager.py @@ -0,0 +1,41 @@ +from dataclasses import dataclass, field +from typing import Tuple + + +@dataclass +class GenericStack: + name: str + pipe: list = field(default_factory=list) + original_voxel_size: list = field(default_factory=lambda: [1., 1., 1.]) + current_voxel_size: list = field(default_factory=lambda: [1., 1., 1.]) + + +class ImageStack(GenericStack): + data_range: Tuple[float, float] = (0., 1.) + data_type: str = 'float32' + + +class LabelStack: + data_type: str = 'uint16' + + +class StackState: + + def __int__(self): + self._active_stacks = {} + self.stack_types = {'ImageStack': ImageStack, + 'LabelStack': LabelStack} + + def list_active_stacks(self): + return list(self._active_stacks.keys()) + + def set_stack_state(self, stack_name, stack_type: str = 'ImageStack'): + if stack_name not in self._active_stacks: + self._active_stacks[stack_name] = self.stack_types[stack_type](name=stack_name) + + def rm_stack_state(self, stack_name): + if stack_name not in self._active_stacks: + del self._active_stacks[stack_name] + + +stack_state = StackState() diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index fc2a067a..ed75ed61 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -1,8 +1,22 @@ import napari +from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main + def run_viewer(): viewer = napari.Viewer() + + main_container = get_main() + container1 = get_preprocessing_workflow() + container2 = get_gasp_workflow() + + main_w = viewer.window.add_dock_widget(main_container, name='Main') + container1_w = viewer.window.add_dock_widget(container1, name='Processing') + container2_w = viewer.window.add_dock_widget(container2, name='GASP') + + viewer.window._qt_window.tabifyDockWidget(main_w, container1_w) + viewer.window._qt_window.tabifyDockWidget(main_w, container2_w) napari.run() + diff --git a/plantseg/napari/widget/__init__.py b/plantseg/napari/widget/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py new file mode 100644 index 00000000..c40510b9 --- /dev/null +++ b/plantseg/napari/widget/dataprocessing.py @@ -0,0 +1,73 @@ +from concurrent.futures import Future + +from magicgui import magicgui +from napari.types import ImageData, LayerDataTuple +from napari.layers import Image, Labels, Shapes +from typing import Tuple, Union + +from plantseg.dataprocessing.functional import image_gaussian_smoothing, image_crop, image_rescale +from plantseg.napari.widget.utils import start_threading_process, choices_of_image_layers + + +@magicgui(call_button='Run Gaussian Smoothing', + sigma={"widget_type": "FloatSlider", "max": 5., 'min': 0.1}) +def widget_image_gaussian_smoothing(image: Image, + sigma: float = 0.5) -> Future[LayerDataTuple]: + def func(): + _image = image.data + out = image_gaussian_smoothing(image=_image, sigma=sigma) + return out, {'name': 'Cell Boundary Raw'}, 'image' + + return start_threading_process(func) + + +@magicgui(call_button='Run Data Preprocessing', + sigma={"widget_type": "FloatSlider", "max": 5., 'min': 0.1}) +def widget_generic_preprocessing(image: Image, + gaussian_smoothing: bool = False, + sigma: float = 1., + rescale: bool = False, + rescaling_factors: Tuple[float, float, float] = (1., 1., 1.), + ) -> Future[LayerDataTuple]: + def func(): + _image = image.data + if gaussian_smoothing: + _image = image_gaussian_smoothing(image=_image, sigma=sigma) + if rescale: + _image = image_rescale(image=_image, factor=rescaling_factors, order=1) + return _image, {'name': 'Cell Boundary Raw'}, 'image' + + return start_threading_process(func) + + +@magicgui(call_button='Run Cropping', ) +def widget_cropping(data: Labels, + crop_roi: Union[Shapes, None] = None, + crop_z: int = 1, + ) -> LayerDataTuple: + + assert len(crop_roi.shape_type) == 1, "Only one rectangle should be used for cropping" + assert crop_roi.shape_type[0] == 'rectangle', "Only a rectangle shape should be used for cropping" + rectangle = crop_roi.data[0].astype('int64') + crop_slices = [slice(rectangle[0, 0], rectangle[2, 0]), slice(rectangle[0, 1], rectangle[2, 1])] + data_image = data.data + if data_image.ndim == 2: + crop_slices = tuple(crop_slices) + elif data_image.ndim == 3: + crop_slices = tuple([1] + crop_slices) + + out = data.data[crop_slices] + return out, {'name': f'cropped_{data.name}'}, 'image' + + +@magicgui(call_button='Run Add Layers', weights={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) +def widget_add_layers(image1: Image, + image2: Image, + weights: float = 0.5, + ) -> Future[LayerDataTuple]: + + def func(): + out = weights * image1.data + (1. - weights) * image2.data + return out, {'name': 'Cell Boundary Raw'}, 'image' + + return start_threading_process(func) diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py new file mode 100644 index 00000000..2bc7702f --- /dev/null +++ b/plantseg/napari/widget/io.py @@ -0,0 +1,40 @@ +from concurrent.futures import Future + +from magicgui import magicgui +from napari.types import LayerDataTuple +from pathlib import Path +from typing import List, Union +from plantseg.io import smart_load +from napari.layers import Image, Layer + + +@magicgui( + call_button="Open file", + path={"label": "Pick a file to open (tiff or h5)"}, + stack_type={ + "label": "Data type", + "widget_type": "RadioButtons", + "orientation": "horizontal", + "choices": ['image', 'labels']}, + key={"label": "key (h5 only)"}, + channel={"label": "channel (tiff only)"} + +) +def open_file(path: Path = Path.home(), + stack_type: str = 'image', + open_all: bool = True, + key: str = None, + channel: int = 0 + ) -> LayerDataTuple: + raw, info = smart_load(path, key=key) + return raw, {}, stack_type + + +@magicgui( + call_button="Export stack", + path={"label": "Pick a file to open (tiff or h5)"}, +) +def export_stack(images: List[Layer], + path: Path = Path.home(), + ) -> None: + pass diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py new file mode 100644 index 00000000..39946675 --- /dev/null +++ b/plantseg/napari/widget/predictions.py @@ -0,0 +1,22 @@ +from concurrent.futures import Future +from typing import Tuple + +from magicgui import magicgui +from napari.types import ImageData, LayerDataTuple + +from plantseg.gui import list_models +from plantseg.napari.widget.utils import start_threading_process +from plantseg.predictions.functional import unet_predictions + + +@magicgui(call_button='Run Predictions', model_name={"choices": list_models()}, ) +def widget_unet_predictions(image: ImageData, + model_name, + patch_size: Tuple[int, int, int] = (80, 160, 160)) -> Future[LayerDataTuple]: + image = image.astype('float32') + + def func(): + out = unet_predictions(raw=image, model_name=model_name, patch=patch_size, device='cpu') + return out, {'name': model_name}, 'image' + + return start_threading_process(func) diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py new file mode 100644 index 00000000..28d6cf33 --- /dev/null +++ b/plantseg/napari/widget/segmentation.py @@ -0,0 +1,31 @@ +from concurrent.futures import Future + +from magicgui import magicgui +from napari.types import ImageData, LayerDataTuple, LabelsData + +from plantseg.napari.widget.utils import start_threading_process +from plantseg.segmentation.functional import gasp, dt_watershed + + +@magicgui(call_button='Run GASP', beta={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) +def widget_gasp(image: ImageData, labels: LabelsData, + beta: float = 0.5, + minsize: int = 100) -> Future[LayerDataTuple]: + image = image.astype('float32') + def func(): + out = gasp(boundary_pmaps=image, superpixels=labels, beta=beta, post_minsize=minsize) + return out, {'name': 'GASP'}, 'labels' + + return start_threading_process(func) + + +@magicgui(call_button='Run WS', threshold={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) +def widget_dt_ws(image: ImageData, threshold: float = 0.5) -> Future[LayerDataTuple]: + + image = image.astype('float32') + + def func(): + out = dt_watershed(boundary_pmaps=image, threshold=threshold) + return out, {'name': 'dt WS'}, 'labels' + + return start_threading_process(func) diff --git a/plantseg/napari/widget/utils.py b/plantseg/napari/widget/utils.py new file mode 100644 index 00000000..164255c1 --- /dev/null +++ b/plantseg/napari/widget/utils.py @@ -0,0 +1,29 @@ +from concurrent.futures import Future +from typing import Callable + +from napari.qt.threading import thread_worker +import napari + + +def start_threading_process(func: Callable) -> Future: + func = thread_worker(func) + + future = Future() + + def on_done(result): + future.set_result(result) + + worker = func() + worker.returned.connect(on_done) + worker.start() + return future + + +def choices_of_image_layers(viewer: napari.Viewer): + return [layer.name for layer in viewer.layers if isinstance(layer, + napari.layers.image.image.Image)] + + +def choices_of_label_layers(viewer: napari.Viewer): + return [layer.name for layer in viewer.layers if isinstance(layer, + napari.layers.labels.labels.Labels)] diff --git a/plantseg/predictions/functional/predictions.py b/plantseg/predictions/functional/predictions.py index 6731cd24..4e153e19 100644 --- a/plantseg/predictions/functional/predictions.py +++ b/plantseg/predictions/functional/predictions.py @@ -1,5 +1,6 @@ from typing import Tuple, Union +import numpy as np from numpy.typing import ArrayLike from pytorch3dunet.unet3d import utils @@ -31,6 +32,10 @@ def unet_predictions(raw: ArrayLike, stride=stride, mirror_padding=mirror_padding) + if raw.ndim == 2: + raw = raw[None, ...] + raw_loader = dataset_builder(raw, verbose_logging=False, **dataset_config) pmaps = predictor(raw_loader) - return pmaps[0] + pmaps = np.squeeze(pmaps[0]) + return pmaps diff --git a/plantseg/segmentation/functional/segmentation.py b/plantseg/segmentation/functional/segmentation.py index 893c2dd3..d5d599be 100644 --- a/plantseg/segmentation/functional/segmentation.py +++ b/plantseg/segmentation/functional/segmentation.py @@ -70,11 +70,20 @@ def gasp(boundary_pmaps: ArrayLike, n_threads: int = 6) -> ArrayLike: if superpixels is not None: + assert boundary_pmaps.shape == superpixels.shape + + if superpixels.ndim == 2: + superpixels = superpixels[None, ...] + def superpixel_gen(*args, **kwargs): return superpixels else: superpixel_gen = None + if boundary_pmaps.ndim == 2: + boundary_pmaps = boundary_pmaps[None, ...] + + run_GASP_kwargs = {'linkage_criteria': gasp_linkage_criteria, 'add_cannot_link_constraints': False, 'use_efficient_implementations': False} From 531eca5f898c87d215e7d19b5dade88f8760f088 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Mon, 8 Aug 2022 17:25:12 +0200 Subject: [PATCH 15/77] standardize widgets and add dask tracking of workflows --- .../functional/dataprocessing.py | 18 +++ plantseg/io/io.py | 20 ++- plantseg/napari/containers.py | 6 +- plantseg/napari/dag_manager.py | 37 +++++ plantseg/napari/stack_manager.py | 41 ----- plantseg/napari/viewer.py | 4 +- plantseg/napari/widget/dataprocessing.py | 98 ++++++++---- plantseg/napari/widget/io.py | 144 ++++++++++++++---- plantseg/napari/widget/predictions.py | 8 +- plantseg/napari/widget/utils.py | 26 +++- plantseg/pipeline/steps.py | 22 +-- 11 files changed, 289 insertions(+), 135 deletions(-) create mode 100644 plantseg/napari/dag_manager.py delete mode 100644 plantseg/napari/stack_manager.py diff --git a/plantseg/dataprocessing/functional/dataprocessing.py b/plantseg/dataprocessing/functional/dataprocessing.py index bd626063..ccae747f 100644 --- a/plantseg/dataprocessing/functional/dataprocessing.py +++ b/plantseg/dataprocessing/functional/dataprocessing.py @@ -33,3 +33,21 @@ def image_crop(image, crop_str): if i else None for i in part.strip().split(':'))) if ':' in part else int(part.strip())) for part in crop_str.split(',')) return image[slices] + + +def fix_input_shape(data): + if data.ndim == 2: + return data.reshape(1, data.shape[0], data.shape[1]) + + elif data.ndim == 3: + return data + + elif data.ndim == 4: + return data[0] + + else: + raise RuntimeError(f"Expected input data to be 2d, 3d or 4d, but got {data.ndim}d input") + + +def normalize_01(data): + return (data - np.min(data)) / (np.max(data) - np.max(data) + 1e-12) diff --git a/plantseg/io/io.py b/plantseg/io/io.py index d8c3bd4d..9d5431d0 100644 --- a/plantseg/io/io.py +++ b/plantseg/io/io.py @@ -78,7 +78,6 @@ def visitor_func(name, node): def load_h5(path, key, slices=None, info_only=False): - with h5py.File(path, 'r') as f: if key is None: key = _find_input_key(f) @@ -116,7 +115,7 @@ def smart_load(path, key=None, info_only=False, default=load_tiff): if ext in H5_EXTENSIONS: return load_h5(path, key, info_only=info_only) - elif ext in H5_EXTENSIONS: + elif ext in TIFF_EXTENSIONS: return load_tiff(path, info_only=info_only) else: @@ -136,6 +135,11 @@ def create_h5(path, stack, key, voxel_size=(1.0, 1.0, 1.0), mode='a'): f[key].attrs['element_size_um'] = voxel_size +def list_keys(path, mode='r'): + with h5py.File(path, mode) as f: + return [key for key in f.keys() if isinstance(f[key], h5py.Dataset)] + + def del_h5_key(path, key, mode='a'): with h5py.File(path, mode) as f: if key in f: @@ -159,9 +163,9 @@ def create_tiff(path, stack, voxel_size): spacing, y, x = voxel_size resolution = (1. / x, 1. / y) # Save output results as tiff - tifffile.imsave(path, - data=stack, - dtype=stack.dtype, - imagej=True, - resolution=resolution, - metadata={'axes': 'TZCYXS', 'spacing': spacing, 'unit': 'um'}) \ No newline at end of file + tifffile.imwrite(path, + data=stack, + dtype=stack.dtype, + imagej=True, + resolution=resolution, + metadata={'axes': 'TZCYXS', 'spacing': spacing, 'unit': 'um'}) diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index f593f2d3..fd5ba877 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -3,12 +3,12 @@ from plantseg.napari.widget.dataprocessing import widget_generic_preprocessing, widget_cropping, widget_add_layers from plantseg.napari.widget.predictions import widget_unet_predictions from plantseg.napari.widget.segmentation import widget_dt_ws, widget_gasp -from plantseg.napari.widget.io import open_file, export_stack +from plantseg.napari.widget.io import open_file, export_stacks def get_main(): container = Container(widgets=[open_file, - export_stack], + export_stacks], labels=False) return container @@ -16,7 +16,7 @@ def get_main(): def get_preprocessing_workflow(): container = Container(widgets=[widget_generic_preprocessing, widget_cropping, - widget_cropping], + widget_add_layers], labels=False) return container diff --git a/plantseg/napari/dag_manager.py b/plantseg/napari/dag_manager.py new file mode 100644 index 00000000..403a6dfb --- /dev/null +++ b/plantseg/napari/dag_manager.py @@ -0,0 +1,37 @@ +import pickle + +import dask +from dask.threaded import get + + +class DagManager: + def __init__(self): + self.dag = {} + self.inputs = [] + self.outputs = [] + self.outputs_suffixes = [] + + def __repr__(self): + return f'inputs: {self.inputs} \n ' \ + f'dag: {self.dag} \n' \ + f'outputs: {self.outputs}' + + def get_dag(self, inputs): + for key, _input in zip(self.inputs, inputs): + self.dag[key] = _input + return get(self.dag, self.outputs) + + def add_step(self, function, input_keys, output_key): + self.inputs += [input_key for input_key in input_keys + if input_key not in self.dag.keys()] + self.dag[output_key] = (dask.delayed(function), *input_keys) + + def export_dag(self, path, outputs, outputs_suffixes): + self.outputs = outputs + self.outputs_suffixes = outputs_suffixes + + with open(path, 'wb') as f: + pickle.dump(self, f) + + +dag = DagManager() diff --git a/plantseg/napari/stack_manager.py b/plantseg/napari/stack_manager.py deleted file mode 100644 index ea93a3c5..00000000 --- a/plantseg/napari/stack_manager.py +++ /dev/null @@ -1,41 +0,0 @@ -from dataclasses import dataclass, field -from typing import Tuple - - -@dataclass -class GenericStack: - name: str - pipe: list = field(default_factory=list) - original_voxel_size: list = field(default_factory=lambda: [1., 1., 1.]) - current_voxel_size: list = field(default_factory=lambda: [1., 1., 1.]) - - -class ImageStack(GenericStack): - data_range: Tuple[float, float] = (0., 1.) - data_type: str = 'float32' - - -class LabelStack: - data_type: str = 'uint16' - - -class StackState: - - def __int__(self): - self._active_stacks = {} - self.stack_types = {'ImageStack': ImageStack, - 'LabelStack': LabelStack} - - def list_active_stacks(self): - return list(self._active_stacks.keys()) - - def set_stack_state(self, stack_name, stack_type: str = 'ImageStack'): - if stack_name not in self._active_stacks: - self._active_stacks[stack_name] = self.stack_types[stack_type](name=stack_name) - - def rm_stack_state(self, stack_name): - if stack_name not in self._active_stacks: - del self._active_stacks[stack_name] - - -stack_state = StackState() diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index ed75ed61..17e60954 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -11,8 +11,8 @@ def run_viewer(): container2 = get_gasp_workflow() main_w = viewer.window.add_dock_widget(main_container, name='Main') - container1_w = viewer.window.add_dock_widget(container1, name='Processing') - container2_w = viewer.window.add_dock_widget(container2, name='GASP') + container1_w = viewer.window.add_dock_widget(container1, name='Data - Processing') + container2_w = viewer.window.add_dock_widget(container2, name='GASP Workflow') viewer.window._qt_window.tabifyDockWidget(main_w, container1_w) viewer.window._qt_window.tabifyDockWidget(main_w, container2_w) diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index c40510b9..58f7dfd3 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -1,24 +1,22 @@ from concurrent.futures import Future +from functools import partial +from typing import Tuple, Union from magicgui import magicgui -from napari.types import ImageData, LayerDataTuple -from napari.layers import Image, Labels, Shapes -from typing import Tuple, Union +from napari.layers import Image, Labels, Shapes, Layer +from napari.types import LayerDataTuple -from plantseg.dataprocessing.functional import image_gaussian_smoothing, image_crop, image_rescale -from plantseg.napari.widget.utils import start_threading_process, choices_of_image_layers +from plantseg.dataprocessing.functional import image_gaussian_smoothing, image_rescale +from plantseg.napari.widget.utils import start_threading_process -@magicgui(call_button='Run Gaussian Smoothing', - sigma={"widget_type": "FloatSlider", "max": 5., 'min': 0.1}) -def widget_image_gaussian_smoothing(image: Image, - sigma: float = 0.5) -> Future[LayerDataTuple]: - def func(): - _image = image.data - out = image_gaussian_smoothing(image=_image, sigma=sigma) - return out, {'name': 'Cell Boundary Raw'}, 'image' +def _generic_preprocessing(image_data, sigma, gaussian_smoothing, rescale, rescaling_factors): + if gaussian_smoothing: + image_data = image_gaussian_smoothing(image=image_data, sigma=sigma) + if rescale: + image_data = image_rescale(image=image_data, factor=rescaling_factors, order=1) - return start_threading_process(func) + return image_data @magicgui(call_button='Run Data Preprocessing', @@ -29,35 +27,63 @@ def widget_generic_preprocessing(image: Image, rescale: bool = False, rescaling_factors: Tuple[float, float, float] = (1., 1., 1.), ) -> Future[LayerDataTuple]: - def func(): - _image = image.data - if gaussian_smoothing: - _image = image_gaussian_smoothing(image=_image, sigma=sigma) - if rescale: - _image = image_rescale(image=_image, factor=rescaling_factors, order=1) - return _image, {'name': 'Cell Boundary Raw'}, 'image' - return start_threading_process(func) + out_name = f'{image.name}_processed' + inputs_kwarg = {'image_data': image.data} + inputs_names = (image.name, ) + layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_type = 'image' + func = partial(_generic_preprocessing, sigma=sigma, + gaussian_smoothing=gaussian_smoothing, + rescale=rescale, + rescaling_factors=rescaling_factors) + return start_threading_process(func, + func_kwargs=inputs_kwarg, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) + + +def _cropping(data, crop_slices): + return data[crop_slices] @magicgui(call_button='Run Cropping', ) -def widget_cropping(data: Labels, +def widget_cropping(image: Layer, crop_roi: Union[Shapes, None] = None, crop_z: int = 1, - ) -> LayerDataTuple: + ) -> Future[LayerDataTuple]: assert len(crop_roi.shape_type) == 1, "Only one rectangle should be used for cropping" assert crop_roi.shape_type[0] == 'rectangle', "Only a rectangle shape should be used for cropping" + + out_name = f'{image.name}_cropped' + inputs_names = (image.name,) + layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_type = 'image' + rectangle = crop_roi.data[0].astype('int64') crop_slices = [slice(rectangle[0, 0], rectangle[2, 0]), slice(rectangle[0, 1], rectangle[2, 1])] - data_image = data.data + data_image = image.data if data_image.ndim == 2: crop_slices = tuple(crop_slices) elif data_image.ndim == 3: crop_slices = tuple([1] + crop_slices) - out = data.data[crop_slices] - return out, {'name': f'cropped_{data.name}'}, 'image' + func = partial(_cropping, crop_slices=crop_slices) + return start_threading_process(func, + func_kwargs={'data': image.data}, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) + + +def _add_two_layers(data1, data2, weights: float = 0.5): + return weights * data1 + (1. - weights) * data2 @magicgui(call_button='Run Add Layers', weights={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) @@ -66,8 +92,16 @@ def widget_add_layers(image1: Image, weights: float = 0.5, ) -> Future[LayerDataTuple]: - def func(): - out = weights * image1.data + (1. - weights) * image2.data - return out, {'name': 'Cell Boundary Raw'}, 'image' - - return start_threading_process(func) + out_name = f'{image1.name}_{image2.name}' + inputs_names = (image1.name, image2.name) + layer_kwargs = {'name': out_name, 'scale': image1.scale} + layer_type = 'image' + + func = partial(_add_two_layers, weights=weights) + return start_threading_process(func, + func_kwargs={'data1': image1.data, 'data2': image2.data}, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index 2bc7702f..6b1997c7 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -1,40 +1,126 @@ -from concurrent.futures import Future +from functools import partial +from pathlib import Path +from typing import List, Tuple from magicgui import magicgui +from napari.layers import Layer from napari.types import LayerDataTuple -from pathlib import Path -from typing import List, Union -from plantseg.io import smart_load -from napari.layers import Image, Layer + +from plantseg.dataprocessing.functional.dataprocessing import fix_input_shape +from plantseg.io import H5_EXTENSIONS, TIFF_EXTENSIONS, allowed_data_format +from plantseg.io.io import load_tiff, load_h5, create_tiff +from plantseg.napari.dag_manager import dag +from plantseg.napari.widget.utils import layer_properties + + +def _check_layout_string(layout): + n_c = 0 + for l in layout: + if l not in ['x', 'c']: + raise ValueError(f'letter {l} found in layout [{layout}], layout should contain only x and a single c') + if l == 'c': + n_c += 1 + + if n_c != 1: + raise ValueError(f'letter c found in layout {n_c} times, but should be present only once') + + +def _filter_channel(data, channel, layout): + slices = [] + for i, l in enumerate(layout): + if l == 'x': + slices.append(slice(None, None)) + else: + if channel > data.shape[i]: + raise ValueError(f'image has only {data.shape[i]} channels along {layout}') + slices.append(slice(channel, channel + 1)) + + return data[tuple(slices)] + + +def _advanced_load(path, key, channel, advanced_load=False, headless=False): + base, ext = path.stem, path.suffix + if ext not in allowed_data_format: + raise ValueError(f'File extension is {ext} but should be one of {allowed_data_format}') + + if ext in H5_EXTENSIONS: + key = key if advanced_load else None + data, (voxel_size, _, _) = load_h5(path, key=key) + + elif ext in TIFF_EXTENSIONS: + channel, layout = channel + data, (voxel_size, _, _) = load_tiff(path) + if advanced_load: + assert data.ndim == len(layout) + _check_layout_string(layout) + data = _filter_channel(data, channel=channel, layout=layout) + + else: + raise NotImplementedError() + + data = fix_input_shape(data) + + if headless: + return data + return data, voxel_size @magicgui( - call_button="Open file", - path={"label": "Pick a file to open (tiff or h5)"}, - stack_type={ - "label": "Data type", - "widget_type": "RadioButtons", - "orientation": "horizontal", - "choices": ['image', 'labels']}, - key={"label": "key (h5 only)"}, - channel={"label": "channel (tiff only)"} - -) + call_button='Open file', + path={'label': 'Pick a file (tiff or h5)'}, + name={'label': 'Layer Name'}, + layer_type={ + 'label': 'Layer type', + 'widget_type': 'RadioButtons', + 'orientation': 'horizontal', + 'choices': ['image', 'labels']}, + advanced_load={'label': 'Advanced load a specific h5-key / tiff-channel'}, + key={'label': 'key/layout (h5 only)'}, + channel={'label': 'channel/layout (tiff only)'}) def open_file(path: Path = Path.home(), - stack_type: str = 'image', - open_all: bool = True, - key: str = None, - channel: int = 0 + layer_type: str = 'image', + name: str = '', + advanced_load: bool = False, + key: str = 'raw', + channel: Tuple[int, str] = (0, 'xcxx'), ) -> LayerDataTuple: - raw, info = smart_load(path, key=key) - return raw, {}, stack_type + name = layer_type if name == '' else name + _func_gui = partial(_advanced_load, key=key, channel=channel, advanced_load=advanced_load, headless=False) + _func_dask = partial(_advanced_load, key=key, channel=channel, advanced_load=advanced_load, headless=True) + + data, voxel_size = _func_gui(path) + dag.add_step(_func_dask, input_keys=(f'{name}_path',), output_key=name) + return data, layer_properties(name=name, scale=voxel_size), layer_type + + +def export_stack_as_tiff(data, name, voxel_size, directory, suffix): + stack_name = f'{name}_{suffix}' + out_path = directory / f'{stack_name}.tiff' + data = fix_input_shape(data) + print(data.shape, data.dtype, voxel_size) + data = data.astype('float32') + create_tiff(path=out_path, stack=data[...], voxel_size=voxel_size) + return stack_name @magicgui( - call_button="Export stack", - path={"label": "Pick a file to open (tiff or h5)"}, -) -def export_stack(images: List[Layer], - path: Path = Path.home(), - ) -> None: - pass + call_button='Export stack', + images={'label': 'Layers to export', 'layout': 'vertical'}, + data_type={'label': 'Data Type', 'choices': ['float32', 'uint8', 'uint16']}, + directory={'label': 'Directory to export files'}) +def export_stacks(images: List[Tuple[Layer, str]], + directory: Path = Path.home(), + data_type: str = 'float32', + ) -> None: + names, suffixes = [], [] + for image, image_suffix in images: + out_name = export_stack_as_tiff(data=image.data, + name=image.name, + voxel_size=image.scale, + directory=directory, + suffix=image_suffix) + names.append(out_name) + suffixes.append(image_suffix) + + out_path = directory / 'workflow.pkl' + dag.export_dag(out_path, names, suffixes) diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py index 39946675..75390c26 100644 --- a/plantseg/napari/widget/predictions.py +++ b/plantseg/napari/widget/predictions.py @@ -9,9 +9,15 @@ from plantseg.predictions.functional import unet_predictions -@magicgui(call_button='Run Predictions', model_name={"choices": list_models()}, ) +@magicgui(call_button='Run Predictions', + model_name={"choices": list_models()}, + stride={"choices": ['Accurate', 'Balanced', 'Draft']}, + device={"choices": ['cpu', 'cuda']} + ) def widget_unet_predictions(image: ImageData, model_name, + device: str = 'cpu', + stride: str = 'Accurate', patch_size: Tuple[int, int, int] = (80, 160, 160)) -> Future[LayerDataTuple]: image = image.astype('float32') diff --git a/plantseg/napari/widget/utils.py b/plantseg/napari/widget/utils.py index 164255c1..6397fd60 100644 --- a/plantseg/napari/widget/utils.py +++ b/plantseg/napari/widget/utils.py @@ -1,24 +1,42 @@ from concurrent.futures import Future -from typing import Callable +from typing import Callable, Tuple from napari.qt.threading import thread_worker import napari +from functools import partial +from plantseg.napari.dag_manager import dag -def start_threading_process(func: Callable) -> Future: - func = thread_worker(func) +def identity(x): + return x + +def start_threading_process(func: Callable, + func_kwargs: dict, + out_name: str, + input_keys: Tuple[str, ...], + layer_kwarg: dict, + layer_type='image', + skip_dag=False) -> Future: + thread_func = thread_worker(partial(func, **func_kwargs)) future = Future() def on_done(result): + _func = func if not skip_dag else identity + dag.add_step(_func, input_keys=input_keys, output_key=out_name) + result = result, layer_kwarg, layer_type future.set_result(result) - worker = func() + worker = thread_func() worker.returned.connect(on_done) worker.start() return future +def layer_properties(name, scale): + return {'name': name, 'scale': scale} + + def choices_of_image_layers(viewer: napari.Viewer): return [layer.name for layer in viewer.layers if isinstance(layer, napari.layers.image.image.Image)] diff --git a/plantseg/pipeline/steps.py b/plantseg/pipeline/steps.py index 1699f8fe..4302964b 100644 --- a/plantseg/pipeline/steps.py +++ b/plantseg/pipeline/steps.py @@ -1,4 +1,5 @@ import os +from abc import ABC import numpy as np import yaml @@ -6,11 +7,12 @@ from plantseg.pipeline import gui_logger from plantseg.pipeline.utils import SUPPORTED_TYPES from plantseg.io import smart_load, create_tiff, create_h5 +from plantseg.dataprocessing.functional.dataprocessing import normalize_01, fix_input_shape class GenericPipelineStep: """ - Base class for the a single step of a pipeline + Base class for a single step of a pipeline Args: input_paths (iterable): paths to the files to be processed @@ -40,7 +42,7 @@ def __init__(self, input_paths, input_type, output_type, save_directory, self.state = state self.save_raw = save_raw - # create save_directory if doesn't exist + # create save_directory if it doesn't exist self.save_directory = os.path.join(os.path.dirname(input_paths[0]), save_directory) if self.state: os.makedirs(self.save_directory, exist_ok=True) @@ -126,17 +128,7 @@ def save_raw_dataset(self, input_path, output_path, voxel_size): @staticmethod def _fix_input_shape(data): - if data.ndim == 2: - return data.reshape(1, data.shape[0], data.shape[1]) - - elif data.ndim == 3: - return data - - elif data.ndim == 4: - return data[0] - - else: - raise RuntimeError(f"Expected input data to be 2d, 3d or 4d, but got {data.ndim}d input") + return fix_input_shape(data) def _adjust_input_type(self, data): if self.input_type == "labels": @@ -149,7 +141,7 @@ def _adjust_input_type(self, data): @staticmethod def _normalize_01(data): - return (data - data.min()) / (data.max() - data.min() + 1e-12) + return normalize_01(data) def _log_params(self, file): file = os.path.splitext(file)[0] + ".yaml" @@ -208,7 +200,7 @@ def _raw_path(self, input_path): return os.path.join(base, filename) -class AbstractSegmentationStep(GenericPipelineStep): +class AbstractSegmentationStep(GenericPipelineStep, ABC): def __init__(self, input_paths, save_directory, file_suffix, state): super().__init__(input_paths=input_paths, input_type="data_float32", From f0569d0ba9af65276725b08be3af93702249e6aa Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 9 Aug 2022 15:24:19 +0200 Subject: [PATCH 16/77] add remaining widgets and fix export widget --- .../functional/dataprocessing.py | 3 +- plantseg/napari/containers.py | 12 +- plantseg/napari/viewer.py | 20 +-- plantseg/napari/widget/io.py | 57 ++++++--- plantseg/napari/widget/predictions.py | 32 +++-- plantseg/napari/widget/segmentation.py | 118 +++++++++++++++--- .../predictions/functional/predictions.py | 8 +- 7 files changed, 193 insertions(+), 57 deletions(-) diff --git a/plantseg/dataprocessing/functional/dataprocessing.py b/plantseg/dataprocessing/functional/dataprocessing.py index ccae747f..02301314 100644 --- a/plantseg/dataprocessing/functional/dataprocessing.py +++ b/plantseg/dataprocessing/functional/dataprocessing.py @@ -22,6 +22,7 @@ def image_median(image, radius): def image_gaussian_smoothing(image, sigma): + image = image.astype('float32') max_sigma = (np.array(image.shape) - 1) / 3 sigma = np.minimum(max_sigma, np.ones(max_sigma.ndim) * sigma) return gaussianSmoothing(image, sigma) @@ -50,4 +51,4 @@ def fix_input_shape(data): def normalize_01(data): - return (data - np.min(data)) / (np.max(data) - np.max(data) + 1e-12) + return (data - np.min(data)) / (np.max(data) - np.min(data) + 1e-12).astype('float32') diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index fd5ba877..e08a5c3a 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -1,15 +1,17 @@ from magicgui.widgets import Container from plantseg.napari.widget.dataprocessing import widget_generic_preprocessing, widget_cropping, widget_add_layers +from plantseg.napari.widget.io import open_file, export_stacks from plantseg.napari.widget.predictions import widget_unet_predictions from plantseg.napari.widget.segmentation import widget_dt_ws, widget_gasp -from plantseg.napari.widget.io import open_file, export_stacks +from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut def get_main(): container = Container(widgets=[open_file, export_stacks], labels=False) + return container @@ -27,3 +29,11 @@ def get_gasp_workflow(): widget_gasp], labels=False) return container + + +def get_extra(): + container = Container(widgets=[widget_multicut, + widget_lifted_multicut, + ], + labels=False) + return container diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index 17e60954..24eb17d2 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -1,22 +1,22 @@ import napari - -from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main +from magicgui import magicgui +from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main, get_extra +from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut def run_viewer(): viewer = napari.Viewer() - main_container = get_main() - container1 = get_preprocessing_workflow() - container2 = get_gasp_workflow() - main_w = viewer.window.add_dock_widget(main_container, name='Main') - container1_w = viewer.window.add_dock_widget(container1, name='Data - Processing') - container2_w = viewer.window.add_dock_widget(container2, name='GASP Workflow') - viewer.window._qt_window.tabifyDockWidget(main_w, container1_w) - viewer.window._qt_window.tabifyDockWidget(main_w, container2_w) + for _containers, name in [(get_preprocessing_workflow(), 'Data - Processing'), + (get_gasp_workflow(), 'UNet + GASP Workflow'), + (get_extra(), 'Extra')]: + _container_w = viewer.window.add_dock_widget(_containers, name=name) + viewer.window._qt_window.tabifyDockWidget(main_w, _container_w) + napari.run() + diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index 6b1997c7..950fb867 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -3,10 +3,11 @@ from typing import List, Tuple from magicgui import magicgui -from napari.layers import Layer +from napari.layers import Layer, Image, Labels from napari.types import LayerDataTuple +from warnings import warn -from plantseg.dataprocessing.functional.dataprocessing import fix_input_shape +from plantseg.dataprocessing.functional.dataprocessing import fix_input_shape, normalize_01 from plantseg.io import H5_EXTENSIONS, TIFF_EXTENSIONS, allowed_data_format from plantseg.io.io import load_tiff, load_h5, create_tiff from plantseg.napari.dag_manager import dag @@ -38,7 +39,7 @@ def _filter_channel(data, channel, layout): return data[tuple(slices)] -def _advanced_load(path, key, channel, advanced_load=False, headless=False): +def _advanced_load(path, key, channel, advanced_load=False, layer_type='image', headless=False): base, ext = path.stem, path.suffix if ext not in allowed_data_format: raise ValueError(f'File extension is {ext} but should be one of {allowed_data_format}') @@ -60,6 +61,12 @@ def _advanced_load(path, key, channel, advanced_load=False, headless=False): data = fix_input_shape(data) + if layer_type == 'image': + data = normalize_01(data) + + elif layer_type == 'labels': + data = data.astype('uint16') + if headless: return data return data, voxel_size @@ -85,20 +92,29 @@ def open_file(path: Path = Path.home(), channel: Tuple[int, str] = (0, 'xcxx'), ) -> LayerDataTuple: name = layer_type if name == '' else name - _func_gui = partial(_advanced_load, key=key, channel=channel, advanced_load=advanced_load, headless=False) - _func_dask = partial(_advanced_load, key=key, channel=channel, advanced_load=advanced_load, headless=True) + _func_gui = partial(_advanced_load, + key=key, + channel=channel, + advanced_load=advanced_load, + layer_type=layer_type, + headless=False) + _func_dask = partial(_advanced_load, + key=key, + channel=channel, + advanced_load=advanced_load, + layer_type=layer_type, + headless=True) data, voxel_size = _func_gui(path) dag.add_step(_func_dask, input_keys=(f'{name}_path',), output_key=name) return data, layer_properties(name=name, scale=voxel_size), layer_type -def export_stack_as_tiff(data, name, voxel_size, directory, suffix): +def export_stack_as_tiff(data, name, voxel_size, directory, dtype, suffix): stack_name = f'{name}_{suffix}' out_path = directory / f'{stack_name}.tiff' data = fix_input_shape(data) - print(data.shape, data.dtype, voxel_size) - data = data.astype('float32') + data = data.astype(dtype) create_tiff(path=out_path, stack=data[...], voxel_size=voxel_size) return stack_name @@ -114,12 +130,25 @@ def export_stacks(images: List[Tuple[Layer, str]], ) -> None: names, suffixes = [], [] for image, image_suffix in images: - out_name = export_stack_as_tiff(data=image.data, - name=image.name, - voxel_size=image.scale, - directory=directory, - suffix=image_suffix) - names.append(out_name) + if isinstance(image, Image): + dtype = data_type + + elif isinstance(image, Labels): + if data_type in ['uint8', 'uint16']: + dtype = data_type + else: + dtype = 'uint16' + warn(f"{data_type} is not a valid type for Labels, please use uint8 or uint16") + else: + raise ValueError(f'{type(image)} cannot be exported, please use Image layers or Labels layers') + + _ = export_stack_as_tiff(data=image.data, + name=image.name, + voxel_size=image.scale, + directory=directory, + dtype=dtype, + suffix=image_suffix) + names.append(image.name) suffixes.append(image_suffix) out_path = directory / 'workflow.pkl' diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py index 75390c26..b9efcc44 100644 --- a/plantseg/napari/widget/predictions.py +++ b/plantseg/napari/widget/predictions.py @@ -1,28 +1,38 @@ from concurrent.futures import Future from typing import Tuple - +from functools import partial from magicgui import magicgui +from napari.layers import Image from napari.types import ImageData, LayerDataTuple from plantseg.gui import list_models from plantseg.napari.widget.utils import start_threading_process from plantseg.predictions.functional import unet_predictions +from plantseg.predictions.utils import STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE @magicgui(call_button='Run Predictions', model_name={"choices": list_models()}, - stride={"choices": ['Accurate', 'Balanced', 'Draft']}, + stride={"choices": [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, device={"choices": ['cpu', 'cuda']} ) -def widget_unet_predictions(image: ImageData, +def widget_unet_predictions(image: Image, model_name, - device: str = 'cpu', - stride: str = 'Accurate', - patch_size: Tuple[int, int, int] = (80, 160, 160)) -> Future[LayerDataTuple]: - image = image.astype('float32') + patch_size: Tuple[int, int, int] = (80, 160, 160), + stride: str = STRIDE_ACCURATE, + device: str = 'cuda',) -> Future[LayerDataTuple]: + + out_name = f'{image.name}_{model_name}' + inputs_names = (image.name, ) + layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_type = 'image' - def func(): - out = unet_predictions(raw=image, model_name=model_name, patch=patch_size, device='cpu') - return out, {'name': model_name}, 'image' + func = partial(unet_predictions, model_name=model_name, stride=stride, patch=patch_size, device=device) - return start_threading_process(func) + return start_threading_process(func, + func_kwargs={'raw': image.data}, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index 28d6cf33..74ec3652 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -1,31 +1,117 @@ from concurrent.futures import Future - +from functools import partial from magicgui import magicgui from napari.types import ImageData, LayerDataTuple, LabelsData - +from napari.layers import Labels, Image, Layer +from typing import Union, Tuple, Callable from plantseg.napari.widget.utils import start_threading_process -from plantseg.segmentation.functional import gasp, dt_watershed +from plantseg.segmentation.functional import gasp, multicut, dt_watershed +from plantseg.segmentation.functional import lifted_multicut_from_nuclei_segmentation, lifted_multicut_from_nuclei_pmaps + + +def _generic_clustering(image: Image, labels: Labels, + beta: float = 0.5, + minsize: int = 100, + name: str = "GASP", + agg_func: Callable = gasp) -> Future[LayerDataTuple]: + out_name = f'{image.name}_{name}' + inputs_names = (image.name, labels.name) + layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_type = 'labels' + + func = partial(agg_func, beta=beta, post_minsize=minsize) + + return start_threading_process(func, + func_kwargs={'boundary_pmaps': image.data, + 'superpixels': labels.data}, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) @magicgui(call_button='Run GASP', beta={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) -def widget_gasp(image: ImageData, labels: LabelsData, +def widget_gasp(image: Image, labels: Labels, beta: float = 0.5, minsize: int = 100) -> Future[LayerDataTuple]: - image = image.astype('float32') - def func(): - out = gasp(boundary_pmaps=image, superpixels=labels, beta=beta, post_minsize=minsize) - return out, {'name': 'GASP'}, 'labels' + return _generic_clustering(image, labels, beta=beta, minsize=minsize, name='GASP', agg_func=gasp) + + +@magicgui(call_button='Run MultiCut', beta={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) +def widget_multicut(image: Image, labels: Labels, + beta: float = 0.5, + minsize: int = 100) -> Future[LayerDataTuple]: + return _generic_clustering(image, labels, beta=beta, minsize=minsize, name='MultiCut', agg_func=multicut) + + +@magicgui(call_button='Run Lifted MultiCut', beta={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) +def widget_lifted_multicut(image: Image, + nuclei: Layer, + labels: Labels, + beta: float = 0.5, + minsize: int = 100) -> Future[LayerDataTuple]: + if isinstance(nuclei, Image): + lmc = lifted_multicut_from_nuclei_pmaps + extra_key = 'nuclei_pmaps' + elif isinstance(nuclei, Labels): + lmc = lifted_multicut_from_nuclei_segmentation + extra_key = 'nuclei_seg' + else: + raise ValueError(f'{nuclei} must be either an image or a labels layer') - return start_threading_process(func) + out_name = f'{image.name}_Lifted_MultiCut' + inputs_names = (image.name, nuclei.name, labels.name) + layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_type = 'labels' + func = partial(lmc, superpixels=labels, beta=beta, post_minsize=minsize) -@magicgui(call_button='Run WS', threshold={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) -def widget_dt_ws(image: ImageData, threshold: float = 0.5) -> Future[LayerDataTuple]: + return start_threading_process(func, + func_kwargs={'boundary_pmaps': image.data, + extra_key: nuclei.data, + 'superpixels': labels.data}, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) - image = image.astype('float32') - def func(): - out = dt_watershed(boundary_pmaps=image, threshold=threshold) - return out, {'name': 'dt WS'}, 'labels' +@magicgui(call_button='Run WS', + stacked={'widget_type': 'RadioButtons', 'orientation': 'horizontal', 'choices': ['2D', '3D']}, + threshold={"widget_type": "FloatSlider", "max": 1., 'min': 0.}, ) +def widget_dt_ws(image: Image, + stacked: str = '2D', + threshold: float = 0.5, + min_size: int = 100, + sigma_seeds: float = .2, + sigma_weights: float = 2., + alpha: float = 1., + use_pixel_pitch: bool = False, + pixel_pitch: Tuple[int, int, int] = (1, 1, 1), + apply_nonmax_suppression: bool = False) -> Future[LayerDataTuple]: + out_name = f'{image.name}_dt_WS' + inputs_names = (image.name,) + layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_type = 'labels' - return start_threading_process(func) + stacked = False if stacked == '3D' else True + pixel_pitch = pixel_pitch if use_pixel_pitch else None + func = partial(dt_watershed, + threshold=threshold, + min_size=min_size, + stacked=stacked, + sigma_seeds=sigma_seeds, + sigma_weights=sigma_weights, + alpha=alpha, + pixel_pitch=pixel_pitch, + apply_nonmax_suppression=apply_nonmax_suppression + ) + return start_threading_process(func, + func_kwargs={'boundary_pmaps': image.data}, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) diff --git a/plantseg/predictions/functional/predictions.py b/plantseg/predictions/functional/predictions.py index 4e153e19..dee27f50 100644 --- a/plantseg/predictions/functional/predictions.py +++ b/plantseg/predictions/functional/predictions.py @@ -3,7 +3,7 @@ import numpy as np from numpy.typing import ArrayLike from pytorch3dunet.unet3d import utils - +from plantseg.dataprocessing.functional.dataprocessing import fix_input_shape from plantseg.predictions.utils import get_dataset_config, get_model_config, get_predictor_config, set_device @@ -32,10 +32,10 @@ def unet_predictions(raw: ArrayLike, stride=stride, mirror_padding=mirror_padding) - if raw.ndim == 2: - raw = raw[None, ...] + raw = fix_input_shape(raw) + raw = raw.astype('float32') raw_loader = dataset_builder(raw, verbose_logging=False, **dataset_config) pmaps = predictor(raw_loader) - pmaps = np.squeeze(pmaps[0]) + pmaps = fix_input_shape(pmaps[0]) return pmaps From 9e5eeb25c9db0c2802a8e71a1ed6d70259a0e637 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 9 Aug 2022 16:00:08 +0200 Subject: [PATCH 17/77] fix cropping --- plantseg/napari/widget/dataprocessing.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index 58f7dfd3..435d7ba2 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -2,6 +2,7 @@ from functools import partial from typing import Tuple, Union +import math from magicgui import magicgui from napari.layers import Image, Labels, Shapes, Layer from napari.types import LayerDataTuple @@ -65,12 +66,9 @@ def widget_cropping(image: Layer, layer_type = 'image' rectangle = crop_roi.data[0].astype('int64') - crop_slices = [slice(rectangle[0, 0], rectangle[2, 0]), slice(rectangle[0, 1], rectangle[2, 1])] - data_image = image.data - if data_image.ndim == 2: - crop_slices = tuple(crop_slices) - elif data_image.ndim == 3: - crop_slices = tuple([1] + crop_slices) + crop_slices = [slice(rectangle[0, 0] - crop_z//2, rectangle[0, 0] + math.ceil(crop_z/2)), + slice(rectangle[0, 1], rectangle[2, 1]), + slice(rectangle[0, 2], rectangle[2, 2])] func = partial(_cropping, crop_slices=crop_slices) return start_threading_process(func, @@ -79,6 +77,7 @@ def widget_cropping(image: Layer, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + skip_dag=True, ) From 2bed35c4745d0bf6a160c3e85720e9f8fd534962 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 10 Aug 2022 15:03:02 +0200 Subject: [PATCH 18/77] add additional widget for predictions --- plantseg/napari/containers.py | 5 +- plantseg/napari/viewer.py | 2 - plantseg/napari/widget/dataprocessing.py | 7 +- plantseg/napari/widget/io.py | 10 ++- plantseg/napari/widget/predictions.py | 107 +++++++++++++++++++++-- plantseg/napari/widget/segmentation.py | 10 ++- plantseg/napari/widget/utils.py | 27 ++++-- 7 files changed, 141 insertions(+), 27 deletions(-) diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index e08a5c3a..8441eff8 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -2,7 +2,8 @@ from plantseg.napari.widget.dataprocessing import widget_generic_preprocessing, widget_cropping, widget_add_layers from plantseg.napari.widget.io import open_file, export_stacks -from plantseg.napari.widget.predictions import widget_unet_predictions +from plantseg.napari.widget.predictions import widget_unet_predictions, widget_test_all_unet_predictions +from plantseg.napari.widget.predictions import widget_iterative_unet_predictions from plantseg.napari.widget.segmentation import widget_dt_ws, widget_gasp from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut @@ -34,6 +35,8 @@ def get_gasp_workflow(): def get_extra(): container = Container(widgets=[widget_multicut, widget_lifted_multicut, + widget_test_all_unet_predictions, + widget_iterative_unet_predictions, ], labels=False) return container diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index 24eb17d2..1ae757e3 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -1,7 +1,5 @@ import napari -from magicgui import magicgui from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main, get_extra -from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut def run_viewer(): diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index 435d7ba2..f9e6f701 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -8,7 +8,7 @@ from napari.types import LayerDataTuple from plantseg.dataprocessing.functional import image_gaussian_smoothing, image_rescale -from plantseg.napari.widget.utils import start_threading_process +from plantseg.napari.widget.utils import start_threading_process, build_nice_name def _generic_preprocessing(image_data, sigma, gaussian_smoothing, rescale, rescaling_factors): @@ -29,7 +29,7 @@ def widget_generic_preprocessing(image: Image, rescaling_factors: Tuple[float, float, float] = (1., 1., 1.), ) -> Future[LayerDataTuple]: - out_name = f'{image.name}_processed' + out_name = build_nice_name(image.name, 'processed') inputs_kwarg = {'image_data': image.data} inputs_names = (image.name, ) layer_kwargs = {'name': out_name, 'scale': image.scale} @@ -38,6 +38,7 @@ def widget_generic_preprocessing(image: Image, gaussian_smoothing=gaussian_smoothing, rescale=rescale, rescaling_factors=rescaling_factors) + return start_threading_process(func, func_kwargs=inputs_kwarg, out_name=out_name, @@ -60,7 +61,7 @@ def widget_cropping(image: Layer, assert len(crop_roi.shape_type) == 1, "Only one rectangle should be used for cropping" assert crop_roi.shape_type[0] == 'rectangle', "Only a rectangle shape should be used for cropping" - out_name = f'{image.name}_cropped' + out_name = build_nice_name(image.name, 'cropped') inputs_names = (image.name,) layer_kwargs = {'name': out_name, 'scale': image.scale} layer_type = 'image' diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index 950fb867..02e14442 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -123,13 +123,16 @@ def export_stack_as_tiff(data, name, voxel_size, directory, dtype, suffix): call_button='Export stack', images={'label': 'Layers to export', 'layout': 'vertical'}, data_type={'label': 'Data Type', 'choices': ['float32', 'uint8', 'uint16']}, - directory={'label': 'Directory to export files'}) + directory={'label': 'Directory to export files'}, + workflow_name={'label': 'Workflow name'}, +) def export_stacks(images: List[Tuple[Layer, str]], directory: Path = Path.home(), data_type: str = 'float32', + workflow_name: str = 'workflow', ) -> None: names, suffixes = [], [] - for image, image_suffix in images: + for i, (image, image_suffix) in enumerate(images): if isinstance(image, Image): dtype = data_type @@ -142,6 +145,7 @@ def export_stacks(images: List[Tuple[Layer, str]], else: raise ValueError(f'{type(image)} cannot be exported, please use Image layers or Labels layers') + image_suffix = f'export_{i}' if image_suffix == '' else image_suffix _ = export_stack_as_tiff(data=image.data, name=image.name, voxel_size=image.scale, @@ -151,5 +155,5 @@ def export_stacks(images: List[Tuple[Layer, str]], names.append(image.name) suffixes.append(image_suffix) - out_path = directory / 'workflow.pkl' + out_path = directory / f'{workflow_name}.pkl' dag.export_dag(out_path, names, suffixes) diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py index b9efcc44..0cb10f02 100644 --- a/plantseg/napari/widget/predictions.py +++ b/plantseg/napari/widget/predictions.py @@ -1,13 +1,14 @@ from concurrent.futures import Future -from typing import Tuple +from typing import Tuple, List from functools import partial from magicgui import magicgui -from napari.layers import Image +from napari.layers import Image, Points from napari.types import ImageData, LayerDataTuple - +from napari.qt.threading import thread_worker from plantseg.gui import list_models -from plantseg.napari.widget.utils import start_threading_process +from plantseg.napari.widget.utils import start_threading_process, build_nice_name from plantseg.predictions.functional import unet_predictions +from plantseg.dataprocessing.functional import image_gaussian_smoothing from plantseg.predictions.utils import STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE @@ -17,13 +18,13 @@ device={"choices": ['cpu', 'cuda']} ) def widget_unet_predictions(image: Image, - model_name, + model_name: str, patch_size: Tuple[int, int, int] = (80, 160, 160), stride: str = STRIDE_ACCURATE, - device: str = 'cuda',) -> Future[LayerDataTuple]: + device: str = 'cuda', ) -> Future[LayerDataTuple]: + out_name = build_nice_name(image.name, model_name) - out_name = f'{image.name}_{model_name}' - inputs_names = (image.name, ) + inputs_names = (image.name,) layer_kwargs = {'name': out_name, 'scale': image.scale} layer_type = 'image' @@ -36,3 +37,93 @@ def widget_unet_predictions(image: Image, layer_kwarg=layer_kwargs, layer_type=layer_type, ) + + +def _compute_multiple_predictions(image, patch_size, stride, device): + out_layers = [] + for model_name in list_models(): + out_name = build_nice_name(image.name, model_name) + layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_type = 'image' + try: + pmap = unet_predictions(raw=image.data, + model_name=model_name, + stride=stride, + patch=patch_size, + device=device) + out_layers.append((pmap, layer_kwargs, layer_type)) + + except Exception as e: + print(f'Error while processing: {model_name}') + + return out_layers + + +@magicgui(call_button='Run Test Predictions', + stride={"choices": [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, + device={"choices": ['cpu', 'cuda']} + ) +def widget_test_all_unet_predictions(image: Image, + patch_size: Tuple[int, int, int] = (80, 160, 160), + stride: str = STRIDE_ACCURATE, + device: str = 'cuda', ) -> Future[List[LayerDataTuple]]: + func = thread_worker(partial(_compute_multiple_predictions, + image=image, + patch_size=patch_size, + stride=stride, + device=device)) + + future = Future() + + def on_done(result): + future.set_result(result) + + worker = func() + worker.returned.connect(on_done) + worker.start() + return future + + +def _compute_iterative_predictions(pmap, model_name, num_iterations, sigma, patch_size, stride, device): + func = partial(unet_predictions, model_name=model_name, stride=stride, patch=patch_size, device=device) + for i in range(num_iterations - 1): + pmap = func(pmap) + pmap = image_gaussian_smoothing(image=pmap, sigma=sigma) + + pmap = func(pmap) + return pmap + + +@magicgui(call_button='Run Iterative Predictions', + model_name={"choices": list_models()}, + stride={"choices": [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, + device={"choices": ['cpu', 'cuda']} + ) +def widget_iterative_unet_predictions(image: Image, + model_name: str, + num_iterations: int = 2, + sigma: float = 1., + patch_size: Tuple[int, int, int] = (80, 160, 160), + stride: str = STRIDE_ACCURATE, + device: str = 'cuda', ) -> Future[LayerDataTuple]: + + out_name = build_nice_name(image.name, f'iterative-{model_name}-x{num_iterations}') + inputs_names = (image.name,) + layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_type = 'image' + + func = partial(_compute_iterative_predictions, + model_name=model_name, + num_iterations=num_iterations, + sigma=sigma, + patch_size=patch_size, + stride=stride, + device=device) + + return start_threading_process(func, + func_kwargs={'pmap': image.data}, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index 74ec3652..dbef9443 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -4,7 +4,7 @@ from napari.types import ImageData, LayerDataTuple, LabelsData from napari.layers import Labels, Image, Layer from typing import Union, Tuple, Callable -from plantseg.napari.widget.utils import start_threading_process +from plantseg.napari.widget.utils import start_threading_process, build_nice_name from plantseg.segmentation.functional import gasp, multicut, dt_watershed from plantseg.segmentation.functional import lifted_multicut_from_nuclei_segmentation, lifted_multicut_from_nuclei_pmaps @@ -14,7 +14,8 @@ def _generic_clustering(image: Image, labels: Labels, minsize: int = 100, name: str = "GASP", agg_func: Callable = gasp) -> Future[LayerDataTuple]: - out_name = f'{image.name}_{name}' + + out_name = build_nice_name(image.name, name) inputs_names = (image.name, labels.name) layer_kwargs = {'name': out_name, 'scale': image.scale} layer_type = 'labels' @@ -60,7 +61,7 @@ def widget_lifted_multicut(image: Image, else: raise ValueError(f'{nuclei} must be either an image or a labels layer') - out_name = f'{image.name}_Lifted_MultiCut' + out_name = build_nice_name(image.name, 'LiftedMultiCut') inputs_names = (image.name, nuclei.name, labels.name) layer_kwargs = {'name': out_name, 'scale': image.scale} layer_type = 'labels' @@ -91,7 +92,8 @@ def widget_dt_ws(image: Image, use_pixel_pitch: bool = False, pixel_pitch: Tuple[int, int, int] = (1, 1, 1), apply_nonmax_suppression: bool = False) -> Future[LayerDataTuple]: - out_name = f'{image.name}_dt_WS' + + out_name = build_nice_name(image.name, 'dtWS') inputs_names = (image.name,) layer_kwargs = {'name': out_name, 'scale': image.scale} layer_type = 'labels' diff --git a/plantseg/napari/widget/utils.py b/plantseg/napari/widget/utils.py index 6397fd60..34db67ae 100644 --- a/plantseg/napari/widget/utils.py +++ b/plantseg/napari/widget/utils.py @@ -37,11 +37,26 @@ def layer_properties(name, scale): return {'name': name, 'scale': scale} -def choices_of_image_layers(viewer: napari.Viewer): - return [layer.name for layer in viewer.layers if isinstance(layer, - napari.layers.image.image.Image)] +def _find_version(old_suffix, new_suffix): + s_idx = old_suffix.find(new_suffix) + if s_idx != -1: + v_idx = s_idx + len(new_suffix) + old_suffix, current_version = old_suffix[:v_idx], old_suffix[v_idx:] + current_version = 0 if current_version == '' else int(current_version[1:-1]) + current_version += 1 + new_version = f'[{current_version}]' + return old_suffix, new_version -def choices_of_label_layers(viewer: napari.Viewer): - return [layer.name for layer in viewer.layers if isinstance(layer, - napari.layers.labels.labels.Labels)] + return f'{old_suffix}_{new_suffix}', '' + + +def build_nice_name(base, new_suffix): + if base.find('_') == -1: + return f'{base}_{new_suffix}' + + *base_without_suffix, old_suffix = base.split('_') + base_without_suffix = '_'.join(base_without_suffix) + + new_suffix, version = _find_version(old_suffix, new_suffix) + return f'{base_without_suffix}_{new_suffix}{version}' From 0f101a9995dae6af8c3f2e3409bf50217324e64e Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 10 Aug 2022 15:55:57 +0200 Subject: [PATCH 19/77] fix minor issue with unet config --- plantseg/predictions/utils.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/plantseg/predictions/utils.py b/plantseg/predictions/utils.py index 7b159d5e..a827aa28 100644 --- a/plantseg/predictions/utils.py +++ b/plantseg/predictions/utils.py @@ -59,10 +59,12 @@ def get_model_config(model_name, model_update=False, version='best'): return model, model_config, model_path -def set_device(device): +def set_device(device, device_id=0): + device = device if torch.cuda.is_available() else 'cpu' + # Add correct device for inference if device == 'cuda': - device = torch.device("cuda:0") + device = torch.device(f"cuda:{device_id}") elif device == 'cpu': device = torch.device("cpu") else: @@ -76,7 +78,6 @@ def get_dataset_config(model_name, patch, stride, mirror_padding, num_workers=8, dataset_config["num_workers"] = num_workers dataset_config["mirror_padding"] = mirror_padding - # Add patch and stride to the config dataset_config["test"]["slice_builder"]["patch_shape"] = patch stride_key, stride_shape = stride, get_stride_shape(patch, "Balanced") @@ -106,12 +107,12 @@ def get_dataset_config(model_name, patch, stride, mirror_padding, num_workers=8, if patch_shape[0] != 1: gui_logger.warning(f"Incorrect z-dimension in the patch_shape for the 2D UNet prediction. {patch_shape[0]}" f" was given, but has to be 1. Defaulting default value: 1") - dataset_config["test"]["slice_builder"]["patch_shape"][0] = 1 + dataset_config["test"]["slice_builder"]["patch_shape"] = (1, patch_shape[1], patch_shape[2]) if stride_shape[0] != 1: gui_logger.warning(f"Incorrect z-dimension in the stride_shape for the 2D UNet prediction. " f"{stride_shape[0]} was given, but has to be 1. Defaulting default value: 1") - dataset_config["test"]["slice_builder"]["stride_shape"][0] = 1 + dataset_config["test"]["slice_builder"]["stride_shape"] = (1, stride_shape[1], stride_shape[2]) dataset_config = {'slice_builder_config': dataset_config['test']['slice_builder'], 'transformer_config': dataset_config['test']['transformer'], From fb33d4dbdb79491fb1a0af792df77b6da2e56b0f Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 10 Aug 2022 15:59:54 +0200 Subject: [PATCH 20/77] add new instructions to README.md --- README.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 7506a54d..14f36092 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ The `Miniconda3-latest-Linux-x86_64.sh` file can be safely deleted. #### Install PlantSeg using conda The tool can be installed directly by executing in the terminal: ```bash -conda create -n plant-seg -c pytorch -c conda-forge -c lcerrone -c awolny pytorch=1.9 pytorch-3dunet=1.3.7 plantseg +conda create -n plant-seg -c pytorch -c conda-forge -c lcerrone -c awolny python=3.9 pytorch-3dunet=1.3.7 plantseg napari ``` Above command will create new conda environment `plant-seg` together with all required dependencies. @@ -62,7 +62,7 @@ executable `.exe` for your Windows version and follow the installation instructi The tool can be installed directly by executing in the anaconda prompt (***For installing and running plantseg this is equivalent to a linux terminal***): ```bash -conda create -n plant-seg -c pytorch -c conda-forge -c lcerrone -c awolny plantseg pillow=8.4 +conda create -n plant-seg -c pytorch -c conda-forge -c lcerrone -c awolny python=3.9 pillow=8.4 plantseg napari ``` Above command will create new conda environment `plant-seg` together with all required dependencies. @@ -83,6 +83,18 @@ conda activate plant-seg pip install SimpleITK ``` +## Pipeline Usage (Napari viewer) +PlantSeg app can also be started using napari as a viewer. +First, activate the newly created conda environment with: +```bash +conda activate plant-seg +``` + +then, start the plantseg in napari +```bash +$ plantseg --napari +``` + ## Pipeline Usage (GUI) PlantSeg app can also be started in a GUI mode, where basic user interface allows to configure and run the pipeline. First, activate the newly created conda environment with: From 92a33484a46613083b5389763bb522638fa64d9e Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 10 Aug 2022 16:31:50 +0200 Subject: [PATCH 21/77] fix version README.md --- conda-recipe/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/conda-recipe/README.md b/conda-recipe/README.md index 558a683e..2e9aafd4 100644 --- a/conda-recipe/README.md +++ b/conda-recipe/README.md @@ -3,7 +3,7 @@ In order to create conda package manually: Run: ``` -conda build conda-recipe -c abailoni -c cpape -c awolny -c conda-forge +conda build -c conda-forge -c awolny conda-recipe ``` ## Release new version From 698468ba467552ec3c82892ba1844406993dbd5b Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Thu, 11 Aug 2022 00:51:47 +0200 Subject: [PATCH 22/77] fix resolution issue --- .../functional/dataprocessing.py | 17 ++- plantseg/gui/__init__.py | 59 ++++++++- plantseg/gui/gui_tools.py | 35 +---- plantseg/napari/containers.py | 23 ++-- plantseg/napari/viewer.py | 6 +- plantseg/napari/widget/dataprocessing.py | 123 ++++++++++++++---- plantseg/napari/widget/io.py | 7 + plantseg/napari/widget/predictions.py | 27 +++- plantseg/resources/models_zoo.yaml | 8 +- 9 files changed, 229 insertions(+), 76 deletions(-) diff --git a/plantseg/dataprocessing/functional/dataprocessing.py b/plantseg/dataprocessing/functional/dataprocessing.py index 02301314..8232c09e 100644 --- a/plantseg/dataprocessing/functional/dataprocessing.py +++ b/plantseg/dataprocessing/functional/dataprocessing.py @@ -5,8 +5,23 @@ from vigra import gaussianSmoothing +def compute_scaling_factor(input_voxel_size, output_voxel_size): + scaling = [i_size / o_size for i_size, o_size in zip(input_voxel_size, output_voxel_size)] + return scaling + + +def compute_scaling_voxelsize(input_voxel_size, scaling_factor): + output_voxel_size = [i_size / s_size for i_size, s_size in zip(input_voxel_size, scaling_factor)] + return output_voxel_size + + +def scale_image_to_voxelsize(image, input_voxel_size, output_voxel_size, order=0): + factor = compute_scaling_factor(input_voxel_size, output_voxel_size) + return image_rescale(image, factor, order=order) + + def image_rescale(image, factor, order): - if np.array_equal(factor, [1, 1, 1]): + if np.array_equal(factor, [1., 1., 1.]): return image else: return zoom(image, zoom=factor, order=order) diff --git a/plantseg/gui/__init__.py b/plantseg/gui/__init__.py index f43902e5..9df2a94a 100644 --- a/plantseg/gui/__init__.py +++ b/plantseg/gui/__init__.py @@ -1,9 +1,14 @@ +import glob import os import tkinter +from pathlib import Path +from shutil import copy2 +from typing import Tuple import yaml -from plantseg import custom_zoo, model_zoo_path +from plantseg import custom_zoo, home_path, PLANTSEG_MODELS_DIR +from plantseg import model_zoo_path stick_all = tkinter.N + tkinter.S + tkinter.E + tkinter.W stick_ew = tkinter.E + tkinter.W @@ -38,8 +43,7 @@ def convert_rgb(rgb=(0, 0, 0)): return "#%02x%02x%02x" % rgb -def list_models(): - """ list model zoo """ +def get_model_config(): zoo_config = os.path.join(model_zoo_path) zoo_config = yaml.load(open(zoo_config, 'r'), Loader=yaml.FullLoader) @@ -51,5 +55,54 @@ def list_models(): custom_zoo_config = {} zoo_config.update(custom_zoo_config) + return zoo_config + + +def list_models(): + """ list model zoo """ + zoo_config = get_model_config() models = list(zoo_config.keys()) return models + + +def get_model_resolution(model): + """ list model zoo """ + zoo_config = get_model_config() + resolution = zoo_config[model].get('resolution', [1., 1., 1.]) + return resolution + + +def add_custom_model(new_model_name: str, + location: Path = Path.home(), + resolution: Tuple[float, float, float] = (1., 1., 1.), + description: str = ''): + + dest_dir = os.path.join(home_path, PLANTSEG_MODELS_DIR, new_model_name) + os.makedirs(dest_dir, exist_ok=True) + all_files = glob.glob(os.path.join(location, "*")) + all_expected_files = ['config_train.yml', + 'last_checkpoint.pytorch', + 'best_checkpoint.pytorch'] + for file in all_files: + if os.path.basename(file) in all_expected_files: + copy2(file, dest_dir) + all_expected_files.remove(os.path.basename(file)) + + if len(all_expected_files) != 0: + msg = f'It was not possible to find in the directory specified {all_expected_files}, ' \ + f'the model can not be loaded.' + return False, msg + + custom_zoo_dict = yaml.load(open(custom_zoo, 'r'), Loader=yaml.FullLoader) + if custom_zoo_dict is None: + custom_zoo_dict = {} + + custom_zoo_dict[new_model_name] = {} + custom_zoo_dict[new_model_name]["path"] = location + custom_zoo_dict[new_model_name]["resolution"] = resolution + custom_zoo_dict[new_model_name]["description"] = description + + with open(custom_zoo, 'w') as f: + yaml.dump(custom_zoo_dict, f) + + return True, None diff --git a/plantseg/gui/gui_tools.py b/plantseg/gui/gui_tools.py index ecdd6dcd..62b99642 100644 --- a/plantseg/gui/gui_tools.py +++ b/plantseg/gui/gui_tools.py @@ -11,6 +11,7 @@ from plantseg.gui import stick_all, stick_ew, var_to_tkinter, convert_rgb, PLANTSEG_GREEN from plantseg.io import read_tiff_voxel_size, read_h5_voxel_size, TIFF_EXTENSIONS, H5_EXTENSIONS from plantseg.pipeline import gui_logger +from plantseg.gui import add_custom_model current_model = None current_segmentation = None @@ -853,37 +854,13 @@ def load_model(self): # Get description description = str(self.simple_entry2.tk_value.get()) - dest_dir = os.path.join(home_path, PLANTSEG_MODELS_DIR, model_name) - os.makedirs(dest_dir, exist_ok=True) - all_files = glob.glob(os.path.join(path, "*")) - all_expected_files = ['config_train.yml', - 'last_checkpoint.pytorch', - 'best_checkpoint.pytorch'] - for file in all_files: - if os.path.basename(file) in all_expected_files: - copy2(file, dest_dir) - all_expected_files.remove(os.path.basename(file)) - - if len(all_expected_files) != 0: - msg = f'It was not possible to find in the directory specified {all_expected_files}, ' \ - f'the model can not be loaded.' - gui_logger.error(msg) + success = add_custom_model(new_model_name=model_name, location=path, resolution=resolution, + description=description) + if not success[0]: + gui_logger.error(success[1]) self.popup.destroy() - raise RuntimeError(msg) - - custom_zoo_dict = yaml.load(open(custom_zoo, 'r'), Loader=yaml.FullLoader) - if custom_zoo_dict is None: - custom_zoo_dict = {} - - custom_zoo_dict[model_name] = {} - custom_zoo_dict[model_name]["path"] = path - custom_zoo_dict[model_name]["resolution"] = resolution - custom_zoo_dict[model_name]["description"] = description - - with open(custom_zoo, 'w') as f: - yaml.dump(custom_zoo_dict, f) + raise RuntimeError(success[1]) - gui_logger.info("Model successfully added!") self.restart() diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index 8441eff8..7cb5a4ff 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -1,9 +1,10 @@ from magicgui.widgets import Container -from plantseg.napari.widget.dataprocessing import widget_generic_preprocessing, widget_cropping, widget_add_layers +from plantseg.napari.widget.dataprocessing import widget_rescaling, widget_gaussian_smoothing +from plantseg.napari.widget.dataprocessing import widget_cropping, widget_add_layers from plantseg.napari.widget.io import open_file, export_stacks from plantseg.napari.widget.predictions import widget_unet_predictions, widget_test_all_unet_predictions -from plantseg.napari.widget.predictions import widget_iterative_unet_predictions +from plantseg.napari.widget.predictions import widget_iterative_unet_predictions, widget_add_custom_model from plantseg.napari.widget.segmentation import widget_dt_ws, widget_gasp from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut @@ -17,7 +18,8 @@ def get_main(): def get_preprocessing_workflow(): - container = Container(widgets=[widget_generic_preprocessing, + container = Container(widgets=[widget_gaussian_smoothing, + widget_rescaling, widget_cropping, widget_add_layers], labels=False) @@ -32,11 +34,16 @@ def get_gasp_workflow(): return container -def get_extra(): +def get_extra_seg(): container = Container(widgets=[widget_multicut, - widget_lifted_multicut, - widget_test_all_unet_predictions, - widget_iterative_unet_predictions, - ], + widget_lifted_multicut], labels=False) return container + + +def get_extra_pred(): + container = Container(widgets=[widget_test_all_unet_predictions, + widget_iterative_unet_predictions, + widget_add_custom_model], + labels=False) + return container \ No newline at end of file diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index 1ae757e3..aa410e42 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -1,5 +1,6 @@ import napari -from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main, get_extra +from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main +from plantseg.napari.containers import get_extra_seg, get_extra_pred def run_viewer(): @@ -9,7 +10,8 @@ def run_viewer(): for _containers, name in [(get_preprocessing_workflow(), 'Data - Processing'), (get_gasp_workflow(), 'UNet + GASP Workflow'), - (get_extra(), 'Extra')]: + (get_extra_pred(), 'Extra-Pred'), + (get_extra_seg(), 'Extra-Seg')]: _container_w = viewer.window.add_dock_widget(_containers, name=name) viewer.window._qt_window.tabifyDockWidget(main_w, _container_w) diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index f9e6f701..5a3f402f 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -1,13 +1,17 @@ +import math from concurrent.futures import Future +from enum import Enum from functools import partial from typing import Tuple, Union -import math +import numpy as np from magicgui import magicgui from napari.layers import Image, Labels, Shapes, Layer from napari.types import LayerDataTuple from plantseg.dataprocessing.functional import image_gaussian_smoothing, image_rescale +from plantseg.dataprocessing.functional.dataprocessing import compute_scaling_factor, compute_scaling_voxelsize +from plantseg.gui import list_models, get_model_resolution from plantseg.napari.widget.utils import start_threading_process, build_nice_name @@ -20,24 +24,77 @@ def _generic_preprocessing(image_data, sigma, gaussian_smoothing, rescale, resca return image_data -@magicgui(call_button='Run Data Preprocessing', +@magicgui(call_button='Run Gaussian Smoothing', sigma={"widget_type": "FloatSlider", "max": 5., 'min': 0.1}) -def widget_generic_preprocessing(image: Image, - gaussian_smoothing: bool = False, - sigma: float = 1., - rescale: bool = False, - rescaling_factors: Tuple[float, float, float] = (1., 1., 1.), - ) -> Future[LayerDataTuple]: - - out_name = build_nice_name(image.name, 'processed') - inputs_kwarg = {'image_data': image.data} - inputs_names = (image.name, ) +def widget_gaussian_smoothing(image: Image, + sigma: float = 1., + ) -> Future[LayerDataTuple]: + out_name = build_nice_name(image.name, 'GaussianSmoothing') + inputs_kwarg = {'image': image.data} + inputs_names = (image.name,) layer_kwargs = {'name': out_name, 'scale': image.scale} layer_type = 'image' - func = partial(_generic_preprocessing, sigma=sigma, - gaussian_smoothing=gaussian_smoothing, - rescale=rescale, - rescaling_factors=rescaling_factors) + func = partial(image_gaussian_smoothing, sigma=sigma) + + return start_threading_process(func, + func_kwargs=inputs_kwarg, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) + + +@magicgui(call_button='Run Image Rescaling', + type_of_refactor={'widget_type': 'RadioButtons', + 'orientation': 'vertical', + 'choices': ['Rescaling factor', + 'Voxel size (um)', + 'Same as Reference Layer', + 'Same as Reference Model']}, + reference_model={"choices": list_models()}) +def widget_rescaling(image: Layer, + type_of_refactor: str = 'Rescaling factor', + rescaling_factor: Tuple[float, float, float] = (1., 1., 1.), + out_voxel_size: Tuple[float, float, float] = (1., 1., 1.), + reference_layer: Union[None, Layer] = None, + reference_model: str = list_models()[0], + order: int = 1, + ) -> Future[LayerDataTuple]: + + if isinstance(image, Image): + pass + + elif isinstance(image, Labels): + order = 0 + + else: + raise ValueError(f'{type(image)} cannot be rescaled, please use Image layers or Labels layers') + + current_resolution = image.scale + if type_of_refactor == 'Voxel size (um)': + rescaling_factor = compute_scaling_factor(current_resolution, out_voxel_size) + + elif type_of_refactor == 'Same as Reference Layer': + out_voxel_size = reference_layer.scale + rescaling_factor = compute_scaling_factor(current_resolution, reference_layer.scale) + + elif type_of_refactor == 'Same as Reference Model': + out_voxel_size = get_model_resolution(reference_model) + print(out_voxel_size) + rescaling_factor = compute_scaling_factor(current_resolution, out_voxel_size) + + else: + out_voxel_size = compute_scaling_voxelsize(current_resolution, scaling_factor=rescaling_factor) + + out_name = build_nice_name(image.name, 'Rescaled') + inputs_kwarg = {'image': image.data} + inputs_names = (image.name, ) + layer_kwargs = {'name': out_name, + 'scale': out_voxel_size, + 'metadata': {'original_voxel_size': current_resolution}} + layer_type = 'image' + func = partial(image_rescale, factor=rescaling_factor, order=order) return start_threading_process(func, func_kwargs=inputs_kwarg, @@ -57,7 +114,6 @@ def widget_cropping(image: Layer, crop_roi: Union[Shapes, None] = None, crop_z: int = 1, ) -> Future[LayerDataTuple]: - assert len(crop_roi.shape_type) == 1, "Only one rectangle should be used for cropping" assert crop_roi.shape_type[0] == 'rectangle', "Only a rectangle shape should be used for cropping" @@ -67,7 +123,7 @@ def widget_cropping(image: Layer, layer_type = 'image' rectangle = crop_roi.data[0].astype('int64') - crop_slices = [slice(rectangle[0, 0] - crop_z//2, rectangle[0, 0] + math.ceil(crop_z/2)), + crop_slices = [slice(rectangle[0, 0] - crop_z // 2, rectangle[0, 0] + math.ceil(crop_z / 2)), slice(rectangle[0, 1], rectangle[2, 1]), slice(rectangle[0, 2], rectangle[2, 2])] @@ -82,22 +138,43 @@ def widget_cropping(image: Layer, ) -def _add_two_layers(data1, data2, weights: float = 0.5): - return weights * data1 + (1. - weights) * data2 +class Operation(Enum): + add = np.add + subtract = np.subtract + maximum = np.max + minimum = np.min + + +def _two_layers_operation(data1, data2, operation, weights: float = 0.5): + if operation == 'Mean': + return weights * data1 + (1. - weights) * data2 + elif operation == 'Maximum': + return np.maximum(data1, data2) + else: + return np.minimum(data1, data2) -@magicgui(call_button='Run Add Layers', weights={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) +@magicgui(call_button='Run Merge Layers', + operation={'widget_type': 'RadioButtons', + 'orientation': 'horizontal', + 'choices': ['Mean', + 'Maximum', + 'Minimum']}, + weights={"widget_type": "FloatSlider", "max": 1., 'min': 0.}, + ) def widget_add_layers(image1: Image, image2: Image, + operation: str, weights: float = 0.5, ) -> Future[LayerDataTuple]: - out_name = f'{image1.name}_{image2.name}' + out_name = build_nice_name(f'{image1.name}-{image2.name}', operation) inputs_names = (image1.name, image2.name) layer_kwargs = {'name': out_name, 'scale': image1.scale} layer_type = 'image' - func = partial(_add_two_layers, weights=weights) + func = partial(_two_layers_operation, weights=weights, operation=operation) + assert image1.data.shape == image2.data.shape return start_threading_process(func, func_kwargs={'data1': image1.data, 'data2': image2.data}, out_name=out_name, diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index 02e14442..243d068a 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -7,6 +7,7 @@ from napari.types import LayerDataTuple from warnings import warn +from plantseg.dataprocessing.functional.dataprocessing import scale_image_to_voxelsize from plantseg.dataprocessing.functional.dataprocessing import fix_input_shape, normalize_01 from plantseg.io import H5_EXTENSIONS, TIFF_EXTENSIONS, allowed_data_format from plantseg.io.io import load_tiff, load_h5, create_tiff @@ -128,11 +129,17 @@ def export_stack_as_tiff(data, name, voxel_size, directory, dtype, suffix): ) def export_stacks(images: List[Tuple[Layer, str]], directory: Path = Path.home(), + to_original_resolution: bool = True, data_type: str = 'float32', workflow_name: str = 'workflow', ) -> None: names, suffixes = [], [] for i, (image, image_suffix) in enumerate(images): + if 'original_voxel_size' in image.metadata.keys(): + output_resolution = image.metadata['original_voxel_size'] + image.data = scale_image_to_voxelsize(image.data, image.scale, output_resolution) + image.scale = output_resolution + if isinstance(image, Image): dtype = data_type diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py index 0cb10f02..ae2388d4 100644 --- a/plantseg/napari/widget/predictions.py +++ b/plantseg/napari/widget/predictions.py @@ -1,14 +1,17 @@ from concurrent.futures import Future -from typing import Tuple, List from functools import partial +from typing import Tuple, List + from magicgui import magicgui -from napari.layers import Image, Points -from napari.types import ImageData, LayerDataTuple +from napari.layers import Image from napari.qt.threading import thread_worker -from plantseg.gui import list_models +from napari.types import LayerDataTuple + +from pathlib import Path +from plantseg.dataprocessing.functional import image_gaussian_smoothing +from plantseg.gui import list_models, add_custom_model from plantseg.napari.widget.utils import start_threading_process, build_nice_name from plantseg.predictions.functional import unet_predictions -from plantseg.dataprocessing.functional import image_gaussian_smoothing from plantseg.predictions.utils import STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE @@ -106,7 +109,6 @@ def widget_iterative_unet_predictions(image: Image, patch_size: Tuple[int, int, int] = (80, 160, 160), stride: str = STRIDE_ACCURATE, device: str = 'cuda', ) -> Future[LayerDataTuple]: - out_name = build_nice_name(image.name, f'iterative-{model_name}-x{num_iterations}') inputs_names = (image.name,) layer_kwargs = {'name': out_name, 'scale': image.scale} @@ -127,3 +129,16 @@ def widget_iterative_unet_predictions(image: Image, layer_kwarg=layer_kwargs, layer_type=layer_type, ) + + +@magicgui(call_button='Add Custom Model', + ) +def widget_add_custom_model(new_model_name: str = '', + model_location: Path = Path.home(), + resolution: Tuple[float, float, float] = (1., 1., 1.), + description: str = '') -> None: + new_model_name = 'custom_model' if new_model_name == '' else new_model_name + add_custom_model(new_model_name=new_model_name, + location=model_location, + resolution=resolution, + description=description) diff --git a/plantseg/resources/models_zoo.yaml b/plantseg/resources/models_zoo.yaml index 7788df5b..cf86d9f4 100644 --- a/plantseg/resources/models_zoo.yaml +++ b/plantseg/resources/models_zoo.yaml @@ -26,15 +26,15 @@ confocal_unet_bce_dice_ds3x: description: "Unet trained on confocal images on 1/3-resolution in XY with BCEDiceLoss." confocal_2D_unet_bce_dice_ds1x: path: https://oc.embl.de/index.php/s/61s67Mg5VQy7dh9/download?path=%2FArabidopsis-Ovules%2F2dunet_bce_dice_ds1x&files= - resolution: [0.075, 0.075] + resolution: [1., 0.075, 0.075] description: "2D Unet trained on z-slices of confocal images on original resolution with BCEDiceLoss." confocal_2D_unet_bce_dice_ds2x: path: https://oc.embl.de/index.php/s/61s67Mg5VQy7dh9/download?path=%2FArabidopsis-Ovules%2F2dunet_bce_dice_ds2x&files= - resolution: [0.150, 0.150] + resolution: [1., 0.150, 0.150] description: "2D Unet trained on z-slices of confocal images (1/2-resolution in XY) with BCEDiceLoss." confocal_2D_unet_bce_dice_ds3x: path: https://oc.embl.de/index.php/s/61s67Mg5VQy7dh9/download?path=%2FArabidopsis-Ovules%2F2dunet_bce_dice_ds3x&files= - resolution: [0.225, 0.225] + resolution: [1., 0.225, 0.225] description: "2D Unet trained on z-slices of confocal images (1/3-resolution in XY) with BCEDiceLoss." confocal_unet_bce_dice_nuclei_stain_ds1x: path: https://oc.embl.de/index.php/s/61s67Mg5VQy7dh9/download?path=%2FArabidopsis-Ovules%2Funet_bce_dice_nuclei_stain_ds1x&files= @@ -62,7 +62,7 @@ lightsheet_unet_bce_dice_ds3x: # PNAS confocal_PNAS_2d: path: https://oc.embl.de/index.php/s/61s67Mg5VQy7dh9/download?path=%2FPNAS%2Fconfocal_PNAS_2d&files= - resolution: [0.25, 0.25, 0.25] + resolution: [1., 0.25, 0.25] description: "2D Unet trained on z-slices of confocal images of Arabidopsis thaliana apical stem cell: https://www.repository.cam.ac.uk/handle/1810/262530" confocal_PNAS_3d: path: https://oc.embl.de/index.php/s/61s67Mg5VQy7dh9/download?path=%2FPNAS%2Fconfocal_PNAS_3d&files= From a523fe3d718cad54a5334aadc309fee6b55ec7b7 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Thu, 11 Aug 2022 23:15:54 +0200 Subject: [PATCH 23/77] add proofreading to plantseg --- .../functional/labelprocessing.py | 18 +++ plantseg/napari/__init__.py | 1 + plantseg/napari/containers.py | 11 +- plantseg/napari/viewer.py | 16 ++- plantseg/napari/widget/dataprocessing.py | 48 +++++-- plantseg/napari/widget/io.py | 18 ++- .../napari/widget/proofreading/__init__.py | 0 .../widget/proofreading/proofreading.py | 134 ++++++++++++++++++ plantseg/napari/widget/proofreading/utils.py | 62 ++++++++ plantseg/napari/widget/segmentation.py | 42 +++++- plantseg/napari/widget/utils.py | 6 +- .../segmentation/functional/segmentation.py | 11 +- 12 files changed, 330 insertions(+), 37 deletions(-) create mode 100644 plantseg/dataprocessing/functional/labelprocessing.py create mode 100644 plantseg/napari/widget/proofreading/__init__.py create mode 100644 plantseg/napari/widget/proofreading/proofreading.py create mode 100644 plantseg/napari/widget/proofreading/utils.py diff --git a/plantseg/dataprocessing/functional/labelprocessing.py b/plantseg/dataprocessing/functional/labelprocessing.py new file mode 100644 index 00000000..fc5d8bd5 --- /dev/null +++ b/plantseg/dataprocessing/functional/labelprocessing.py @@ -0,0 +1,18 @@ +import numpy as np +from skimage import measure + + +def relabel_segmentation(segmentation_image): + """ + Relabel contiguously a segmentation image, non-touching instances with same id will be relabeled differently. + To be noted that measure.label is different from ndimage.label + """ + return measure.label(segmentation_image) + + +def set_background_to_value(segmentation_image, value: int = 0): + segmentation_image += 1 + idx, counts = np.unique(segmentation_image, return_counts=True) + bg_idx = idx[np.argmax(counts)] + return np.where(segmentation_image == bg_idx, value, segmentation_image) + diff --git a/plantseg/napari/__init__.py b/plantseg/napari/__init__.py index e69de29b..8b137891 100644 --- a/plantseg/napari/__init__.py +++ b/plantseg/napari/__init__.py @@ -0,0 +1 @@ + diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index 7cb5a4ff..e9865890 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -7,11 +7,15 @@ from plantseg.napari.widget.predictions import widget_iterative_unet_predictions, widget_add_custom_model from plantseg.napari.widget.segmentation import widget_dt_ws, widget_gasp from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut +from plantseg.napari.widget.proofreading.proofreading import widget_split_and_merge_from_scribbles +from plantseg.napari.widget.dataprocessing import widget_label_processing def get_main(): container = Container(widgets=[open_file, - export_stacks], + export_stacks, + widget_split_and_merge_from_scribbles + ], labels=False) return container @@ -21,7 +25,8 @@ def get_preprocessing_workflow(): container = Container(widgets=[widget_gaussian_smoothing, widget_rescaling, widget_cropping, - widget_add_layers], + widget_add_layers, + widget_label_processing], labels=False) return container @@ -46,4 +51,4 @@ def get_extra_pred(): widget_iterative_unet_predictions, widget_add_custom_model], labels=False) - return container \ No newline at end of file + return container diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index aa410e42..be89d35e 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -1,6 +1,9 @@ import napari -from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main +from napari.utils.notifications import show_info + from plantseg.napari.containers import get_extra_seg, get_extra_pred +from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main +from plantseg.napari.widget.proofreading.proofreading import widget_split_and_merge_from_scribbles, clean_scribble def run_viewer(): @@ -8,15 +11,16 @@ def run_viewer(): main_container = get_main() main_w = viewer.window.add_dock_widget(main_container, name='Main') + viewer.bind_key('p', widget_split_and_merge_from_scribbles) + viewer.bind_key('c', clean_scribble) + for _containers, name in [(get_preprocessing_workflow(), 'Data - Processing'), (get_gasp_workflow(), 'UNet + GASP Workflow'), (get_extra_pred(), 'Extra-Pred'), - (get_extra_seg(), 'Extra-Seg')]: + (get_extra_seg(), 'Extra-Seg'), + ]: _container_w = viewer.window.add_dock_widget(_containers, name=name) viewer.window._qt_window.tabifyDockWidget(main_w, _container_w) + show_info('Napari - Plantseg is ready!') napari.run() - - - - diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index 5a3f402f..9c54d888 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -11,6 +11,8 @@ from plantseg.dataprocessing.functional import image_gaussian_smoothing, image_rescale from plantseg.dataprocessing.functional.dataprocessing import compute_scaling_factor, compute_scaling_voxelsize +from plantseg.dataprocessing.functional.labelprocessing import set_background_to_value +from plantseg.dataprocessing.functional.labelprocessing import relabel_segmentation as _relabel_segmentation from plantseg.gui import list_models, get_model_resolution from plantseg.napari.widget.utils import start_threading_process, build_nice_name @@ -61,7 +63,6 @@ def widget_rescaling(image: Layer, reference_model: str = list_models()[0], order: int = 1, ) -> Future[LayerDataTuple]: - if isinstance(image, Image): pass @@ -89,7 +90,7 @@ def widget_rescaling(image: Layer, out_name = build_nice_name(image.name, 'Rescaled') inputs_kwarg = {'image': image.data} - inputs_names = (image.name, ) + inputs_names = (image.name,) layer_kwargs = {'name': out_name, 'scale': out_voxel_size, 'metadata': {'original_voxel_size': current_resolution}} @@ -138,13 +139,6 @@ def widget_cropping(image: Layer, ) -class Operation(Enum): - add = np.add - subtract = np.subtract - maximum = np.max - minimum = np.min - - def _two_layers_operation(data1, data2, operation, weights: float = 0.5): if operation == 'Mean': return weights * data1 + (1. - weights) * data2 @@ -167,7 +161,6 @@ def widget_add_layers(image1: Image, operation: str, weights: float = 0.5, ) -> Future[LayerDataTuple]: - out_name = build_nice_name(f'{image1.name}-{image2.name}', operation) inputs_names = (image1.name, image2.name) layer_kwargs = {'name': out_name, 'scale': image1.scale} @@ -182,3 +175,38 @@ def widget_add_layers(image1: Image, layer_kwarg=layer_kwargs, layer_type=layer_type, ) + + +def _label_processing(segmentation, set_bg_to_0, relabel_segmentation): + + if relabel_segmentation: + segmentation = _relabel_segmentation(segmentation) + + if set_bg_to_0: + segmentation = set_background_to_value(segmentation, value=0) + + return segmentation + + +@magicgui(call_button='Run Label processing') +def widget_label_processing(segmentation: Labels, + set_bg_to_0: bool = True, + relabel_segmentation: bool = True, + ) -> Future[LayerDataTuple]: + out_name = build_nice_name(segmentation.name, 'Processed') + inputs_kwarg = {'segmentation': segmentation.data} + inputs_names = (segmentation.name,) + layer_kwargs = {'name': out_name, + 'scale': segmentation.scale} + layer_type = 'labels' + func = partial(_label_processing, + set_bg_to_0=set_bg_to_0, + relabel_segmentation=relabel_segmentation) + + return start_threading_process(func, + func_kwargs=inputs_kwarg, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index 243d068a..4a5d7d86 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -13,6 +13,7 @@ from plantseg.io.io import load_tiff, load_h5, create_tiff from plantseg.napari.dag_manager import dag from plantseg.napari.widget.utils import layer_properties +from napari.utils.notifications import show_info def _check_layout_string(layout): @@ -108,6 +109,7 @@ def open_file(path: Path = Path.home(), data, voxel_size = _func_gui(path) dag.add_step(_func_dask, input_keys=(f'{name}_path',), output_key=name) + show_info(f'Napari - PlantSeg info: {name} correctly imported') return data, layer_properties(name=name, scale=voxel_size), layer_type @@ -124,26 +126,24 @@ def export_stack_as_tiff(data, name, voxel_size, directory, dtype, suffix): call_button='Export stack', images={'label': 'Layers to export', 'layout': 'vertical'}, data_type={'label': 'Data Type', 'choices': ['float32', 'uint8', 'uint16']}, - directory={'label': 'Directory to export files'}, + directory={'label': 'Directory to export files', 'mode': 'd'}, workflow_name={'label': 'Workflow name'}, ) def export_stacks(images: List[Tuple[Layer, str]], directory: Path = Path.home(), - to_original_resolution: bool = True, + rescale_to_original_resolution: bool = True, data_type: str = 'float32', workflow_name: str = 'workflow', ) -> None: names, suffixes = [], [] for i, (image, image_suffix) in enumerate(images): - if 'original_voxel_size' in image.metadata.keys(): - output_resolution = image.metadata['original_voxel_size'] - image.data = scale_image_to_voxelsize(image.data, image.scale, output_resolution) - image.scale = output_resolution if isinstance(image, Image): + order = 1 dtype = data_type elif isinstance(image, Labels): + order = 0 if data_type in ['uint8', 'uint16']: dtype = data_type else: @@ -152,6 +152,11 @@ def export_stacks(images: List[Tuple[Layer, str]], else: raise ValueError(f'{type(image)} cannot be exported, please use Image layers or Labels layers') + if rescale_to_original_resolution and 'original_voxel_size' in image.metadata.keys(): + output_resolution = image.metadata['original_voxel_size'] + image.data = scale_image_to_voxelsize(image.data, image.scale, output_resolution, order=order) + image.scale = output_resolution + image_suffix = f'export_{i}' if image_suffix == '' else image_suffix _ = export_stack_as_tiff(data=image.data, name=image.name, @@ -164,3 +169,4 @@ def export_stacks(images: List[Tuple[Layer, str]], out_path = directory / f'{workflow_name}.pkl' dag.export_dag(out_path, names, suffixes) + show_info(f'Napari - PlantSeg info: workflow correctly exported') diff --git a/plantseg/napari/widget/proofreading/__init__.py b/plantseg/napari/widget/proofreading/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/plantseg/napari/widget/proofreading/proofreading.py b/plantseg/napari/widget/proofreading/proofreading.py new file mode 100644 index 00000000..374143bc --- /dev/null +++ b/plantseg/napari/widget/proofreading/proofreading.py @@ -0,0 +1,134 @@ +import napari +import numpy as np +from magicgui import magicgui +from napari.layers import Labels, Image +from skimage.segmentation import watershed +from vigra.analysis import watershedsNew +from napari.utils.notifications import show_info +from plantseg.napari.widget.proofreading.utils import get_bboxes, get_idx_slice +from napari.qt.threading import thread_worker + +current_label_layer = None + + +def _merge_from_seeds(segmentation, region_slice, region_bbox, bboxes, all_idx, max_label): + region_segmentation = segmentation[region_slice] + + mask = [region_segmentation == idx for idx in all_idx] + + new_label = 0 if 0 in all_idx else all_idx[0] + + mask = np.logical_or.reduce(mask) + region_segmentation[mask] = new_label + bboxes[new_label] = region_bbox + show_info('merge complete') + return region_segmentation, region_slice, {'bboxes': bboxes, 'max_label': max_label} + + +def _split_from_seed(segmentation, sz, sx, sy, region_slice, all_idx, offsets, bboxes, image, seeds_values, max_label): + local_sz, local_sx, local_sy = sz - offsets[0], sx - offsets[1], sy - offsets[2] + + region_image = image[region_slice] + region_segmentation = segmentation[region_slice] + + region_seeds = np.zeros_like(region_segmentation) + + seeds_values += max_label + region_seeds[local_sz, local_sx, local_sy] = seeds_values + + mask = [region_segmentation == idx for idx in all_idx] + mask = np.logical_or.reduce(mask) + + new_seg = watershed(region_image, + markers=region_seeds, + mask=mask, compactness=0.01) + #new_seg, _ = watershedsNew(region_image.astype('float32'), + # seeds=region_seeds.astype('uint32')) + new_seg[~mask] = region_segmentation[~mask] + + new_bboxes = get_bboxes(new_seg) + for idx in np.unique(seeds_values): + values = new_bboxes[idx] + values = values + offsets[None, :] + bboxes[idx] = values + + show_info('split_complete') + return new_seg, region_slice, {'bboxes': bboxes, 'max_label': max_label} + + +def split_merge_from_seeds(seeds, segmentation, image, bboxes, max_label): + # find seeds location ad label value + sz, sx, sy = np.nonzero(seeds) + + seeds_values = seeds[sz, sx, sy] + seeds_idx = np.unique(seeds_values) + + all_idx = segmentation[sz, sx, sy] + all_idx = np.unique(all_idx) + + region_slice, region_bbox, offsets = get_idx_slice(all_idx, bboxes_dict=bboxes) + + if len(seeds_idx) == 1: + return _merge_from_seeds(segmentation, + region_slice, + region_bbox, + bboxes, + all_idx, + max_label) + else: + return _split_from_seed(segmentation, + sz, sx, sy, + region_slice, + all_idx, + offsets, + bboxes, + image, + seeds_values, + max_label) + + +def clean_scribble(viewer): + if current_label_layer is None: + show_info('Scribble not defined. Run the proofreading widget tool once first') + return None + + viewer.layers[current_label_layer].data = np.zeros_like(viewer.layers[current_label_layer].data) + viewer.layers[current_label_layer].refresh() + + +@magicgui(call_button='Split and Merge from scribbles - p') +def widget_split_and_merge_from_scribbles(viewer: napari.Viewer, + scribbles: Labels, + segmentation: Labels, + image: Image) -> None: + + if scribbles.name == segmentation.name: + show_info('Scribbles layer and segmentation layer cannot be the same') + return None + + if 'bboxes' in segmentation.metadata.keys(): + bboxes = segmentation.metadata['bboxes'] + else: + bboxes = get_bboxes(segmentation.data) + + if 'max_label' in segmentation.metadata.keys(): + max_label = segmentation.metadata['max_label'] + else: + max_label = np.max(segmentation.data) + + global current_label_layer + current_label_layer = scribbles.name + + @thread_worker + def func(): + new_seg, region_slice, meta = split_merge_from_seeds(scribbles.data, + segmentation.data, + image=image.data, + bboxes=bboxes, + max_label=max_label) + viewer.layers[segmentation.name].data[region_slice] = new_seg + viewer.layers[segmentation.name].metadata = meta + viewer.layers[segmentation.name].refresh() + + worker = func() + worker.start() diff --git a/plantseg/napari/widget/proofreading/utils.py b/plantseg/napari/widget/proofreading/utils.py new file mode 100644 index 00000000..39188e11 --- /dev/null +++ b/plantseg/napari/widget/proofreading/utils.py @@ -0,0 +1,62 @@ +import numba +import numpy as np + + +@numba.njit(parallel=True) +def _get_bboxes(segmentation, labels_idx): + shape = segmentation.shape + + bboxes = {} + for idx in labels_idx: + _x = np.array([[shape[0], shape[1], shape[2]], [0, 0, 0]]) + bboxes[idx] = _x + + for z in numba.prange(shape[0]): + for x in range(shape[1]): + for y in range(shape[2]): + idx = segmentation[z, x, y] + if idx > 0: + zmin, xmin, ymin = bboxes[idx][0] + zmax, xmax, ymax = bboxes[idx][1] + + if z < zmin: + bboxes[idx][0, 0] = z + if x < xmin: + bboxes[idx][0, 1] = x + if y < ymin: + bboxes[idx][0, 2] = y + + if z > zmax: + bboxes[idx][1, 0] = z + if x > xmax: + bboxes[idx][1, 1] = x + if y > ymax: + bboxes[idx][1, 2] = y + return bboxes + + +def get_bboxes(segmentation, slack=(1, 3, 3)): + segmentation = segmentation.astype('int64') + labels_idx = np.unique(segmentation) + bboxes = _get_bboxes(segmentation, labels_idx) + + slack = np.array(slack) + bboxes_out = {} + for key, values in bboxes.items(): + values[0] = np.maximum(values[0] - slack, [0, 0, 0]) + values[1] = np.minimum(values[1] + slack, segmentation.shape) + bboxes_out[int(key)] = values + return bboxes_out + + +def get_idx_slice(indices, bboxes_dict: dict): + if isinstance(indices, int): + indices = [indices] + + list_min_values = [bboxes_dict[idx][0] for idx in indices] + list_max_values = [bboxes_dict[idx][1] for idx in indices] + min_values = np.min(np.stack(list_min_values), axis=0) + max_values = np.max(np.stack(list_max_values), axis=0) + values = np.stack([min_values, max_values]) + + return tuple([slice(_min, _max) for _min, _max in zip(*values)]), values, min_values diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index dbef9443..bf9bc8ea 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -7,6 +7,7 @@ from plantseg.napari.widget.utils import start_threading_process, build_nice_name from plantseg.segmentation.functional import gasp, multicut, dt_watershed from plantseg.segmentation.functional import lifted_multicut_from_nuclei_segmentation, lifted_multicut_from_nuclei_pmaps +from plantseg.dataprocessing.functional.dataprocessing import normalize_01 def _generic_clustering(image: Image, labels: Labels, @@ -14,7 +15,6 @@ def _generic_clustering(image: Image, labels: Labels, minsize: int = 100, name: str = "GASP", agg_func: Callable = gasp) -> Future[LayerDataTuple]: - out_name = build_nice_name(image.name, name) inputs_names = (image.name, labels.name) layer_kwargs = {'name': out_name, 'scale': image.scale} @@ -79,6 +79,36 @@ def widget_lifted_multicut(image: Image, ) +def _nuclei_aware_dtws_wrapper(boundary_pmaps, + stacked: bool = True, + threshold: float = 0.5, + min_size: int = 100, + sigma_seeds: float = .2, + sigma_weights: float = 2., + alpha: float = 1., + pixel_pitch: Tuple[int, int, int] = (1, 1, 1), + apply_nonmax_suppression: bool = False, + nuclei: bool = False): + if nuclei: + boundary_pmaps = normalize_01(boundary_pmaps) + boundary_pmaps = 1. - boundary_pmaps + mask = boundary_pmaps < threshold + else: + mask = None + + return dt_watershed(boundary_pmaps=boundary_pmaps, + threshold=threshold, + min_size=min_size, + stacked=stacked, + sigma_seeds=sigma_seeds, + sigma_weights=sigma_weights, + alpha=alpha, + pixel_pitch=pixel_pitch, + apply_nonmax_suppression=apply_nonmax_suppression, + mask=mask + ) + + @magicgui(call_button='Run WS', stacked={'widget_type': 'RadioButtons', 'orientation': 'horizontal', 'choices': ['2D', '3D']}, threshold={"widget_type": "FloatSlider", "max": 1., 'min': 0.}, ) @@ -91,8 +121,8 @@ def widget_dt_ws(image: Image, alpha: float = 1., use_pixel_pitch: bool = False, pixel_pitch: Tuple[int, int, int] = (1, 1, 1), - apply_nonmax_suppression: bool = False) -> Future[LayerDataTuple]: - + apply_nonmax_suppression: bool = False, + nuclei: bool = False) -> Future[LayerDataTuple]: out_name = build_nice_name(image.name, 'dtWS') inputs_names = (image.name,) layer_kwargs = {'name': out_name, 'scale': image.scale} @@ -100,7 +130,8 @@ def widget_dt_ws(image: Image, stacked = False if stacked == '3D' else True pixel_pitch = pixel_pitch if use_pixel_pitch else None - func = partial(dt_watershed, + + func = partial(_nuclei_aware_dtws_wrapper, threshold=threshold, min_size=min_size, stacked=stacked, @@ -108,7 +139,8 @@ def widget_dt_ws(image: Image, sigma_weights=sigma_weights, alpha=alpha, pixel_pitch=pixel_pitch, - apply_nonmax_suppression=apply_nonmax_suppression + apply_nonmax_suppression=apply_nonmax_suppression, + nuclei=nuclei ) return start_threading_process(func, func_kwargs={'boundary_pmaps': image.data}, diff --git a/plantseg/napari/widget/utils.py b/plantseg/napari/widget/utils.py index 34db67ae..1577e500 100644 --- a/plantseg/napari/widget/utils.py +++ b/plantseg/napari/widget/utils.py @@ -1,9 +1,10 @@ from concurrent.futures import Future +from functools import partial from typing import Callable, Tuple from napari.qt.threading import thread_worker -import napari -from functools import partial +from napari.utils.notifications import show_info + from plantseg.napari.dag_manager import dag @@ -22,6 +23,7 @@ def start_threading_process(func: Callable, future = Future() def on_done(result): + show_info(f'Napari - PlantSeg info: widget computation complete') _func = func if not skip_dag else identity dag.add_step(_func, input_keys=input_keys, output_key=out_name) result = result, layer_kwarg, layer_type diff --git a/plantseg/segmentation/functional/segmentation.py b/plantseg/segmentation/functional/segmentation.py index d5d599be..ad59b809 100644 --- a/plantseg/segmentation/functional/segmentation.py +++ b/plantseg/segmentation/functional/segmentation.py @@ -23,7 +23,8 @@ def dt_watershed(boundary_pmaps: ArrayLike, alpha: float = 1.0, pixel_pitch: List[int] = None, apply_nonmax_suppression: bool = False, - n_threads: int = None) -> ArrayLike: + n_threads: int = None, + mask: ArrayLike = None) -> ArrayLike: """ Wrapper around elf.distance_transform_watershed Args: @@ -39,6 +40,7 @@ def dt_watershed(boundary_pmaps: ArrayLike, apply_nonmax_suppression (bool): whether to apply non-maximum suppression to filter out seeds. Needs nifty. (default: False) n_threads (int): if not None, parallelize the 2D stacked ws. (default: None) + mask (np.ndarray) Returns: np.ndarray: watershed segmentation @@ -48,7 +50,9 @@ def dt_watershed(boundary_pmaps: ArrayLike, ws_kwargs = dict(threshold=threshold, sigma_seeds=sigma_seeds, sigma_weights=sigma_weights, min_size=min_size, alpha=alpha, - pixel_pitch=pixel_pitch, apply_nonmax_suppression=apply_nonmax_suppression) + pixel_pitch=pixel_pitch, + apply_nonmax_suppression=apply_nonmax_suppression, + mask=mask) if stacked: # WS in 2D ws, _ = stacked_watershed(boundary_pmaps, @@ -68,7 +72,6 @@ def gasp(boundary_pmaps: ArrayLike, beta: float = 0.5, post_minsize: int = 100, n_threads: int = 6) -> ArrayLike: - if superpixels is not None: assert boundary_pmaps.shape == superpixels.shape @@ -83,7 +86,6 @@ def superpixel_gen(*args, **kwargs): if boundary_pmaps.ndim == 2: boundary_pmaps = boundary_pmaps[None, ...] - run_GASP_kwargs = {'linkage_criteria': gasp_linkage_criteria, 'add_cannot_link_constraints': False, 'use_efficient_implementations': False} @@ -118,7 +120,6 @@ def multicut(boundary_pmaps: ArrayLike, superpixels: ArrayLike, beta: float = 0.5, post_minsize: int = 50) -> ArrayLike: - rag = compute_rag(superpixels) # Prob -> edge costs From a3049c8ece26304c9f1949cb6e59e7eadff2c4d4 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Thu, 11 Aug 2022 23:29:00 +0200 Subject: [PATCH 24/77] add segmentation processing from nuclei --- .../functional/advanced_dataprocessing.py | 183 ++++++++++++++++++ plantseg/napari/containers.py | 4 +- plantseg/napari/widget/segmentation.py | 34 ++++ 3 files changed, 220 insertions(+), 1 deletion(-) create mode 100644 plantseg/dataprocessing/functional/advanced_dataprocessing.py diff --git a/plantseg/dataprocessing/functional/advanced_dataprocessing.py b/plantseg/dataprocessing/functional/advanced_dataprocessing.py new file mode 100644 index 00000000..db08d6ad --- /dev/null +++ b/plantseg/dataprocessing/functional/advanced_dataprocessing.py @@ -0,0 +1,183 @@ +import numpy as np +import tqdm +import copy +import numba +from skimage.segmentation import watershed +from skimage.filters import gaussian + + +def get_bbox(mask, pixel_toll=0): + max_shape = mask.shape + coords = np.nonzero(mask) + z_min, z_max = max(coords[0].min() - pixel_toll, 0), min(coords[0].max() + pixel_toll, max_shape[0]) + z_max = z_max if z_max - z_min > 0 else 1 + x_min, x_max = max(coords[1].min() - pixel_toll, 0), min(coords[1].max() + pixel_toll, max_shape[1]) + y_min, y_max = max(coords[2].min() - pixel_toll, 0), min(coords[2].max() + pixel_toll, max_shape[2]) + return (slice(z_min, z_max), slice(x_min, x_max), slice(y_min, y_max)), z_min, x_min, y_min + + +def get_quantile_mask(counts, quantile=(0.2, 0.99)): + mask_low = np.quantile(counts, quantile[0]) < counts + mask_up = np.quantile(counts, quantile[1]) > counts + mask = np.logical_and(mask_low, mask_up) + return mask + + +@numba.njit(parallel=True) +def numba_find_overlaps(cell_seg, n_seg): + shape_x, shape_y, shape_z = cell_seg.shape + + cell_counts = np.zeros(cell_seg.max() + 1).astype(np.uint16) + n_counts = np.zeros(n_seg.max() + 1).astype(np.uint16) + overlap_counts = np.zeros((cell_seg.max() + 1, n_seg.max() + 1)).astype(np.uint16) + + for x in numba.prange(shape_x): + for y in range(shape_y): + for z in range(shape_z): + seg_id = cell_seg[x, y, z] + if seg_id > 0: + cell_counts[seg_id] += 1 + + n_id = n_seg[x, y, z] + if n_id > 0: + n_counts[n_id] += 1 + + if n_id > 0 and seg_id > 0: + overlap_counts[seg_id, n_id] += 1 + + return cell_counts, n_counts, overlap_counts + + +def find_potential_under_seg(nuclei_counts, + cell_counts, + intersection_counts, + threshold=0.9, + quantiles_clip=(0.2, 0.99)): + nuclei_counts_mask = get_quantile_mask(nuclei_counts, quantiles_clip) + cell_assigment = {} + for idx in range(cell_counts.shape[0]): + n_idx = np.nonzero(intersection_counts[idx])[0] + r_intersection = [intersection_counts[idx, _n_idx] / nuclei_counts[_n_idx] for _n_idx in n_idx] + + under_seg_n_idx = [] + for _n_idx, r_i in zip(n_idx, r_intersection): + if r_i > threshold and nuclei_counts_mask[_n_idx]: + under_seg_n_idx.append(_n_idx) + + is_under_seg = True if len(under_seg_n_idx) > 1 else False + + if is_under_seg: + cell_assigment[idx] = {'n_idx': n_idx, + 'under_seg_idx': under_seg_n_idx, + 'is_under_seg': is_under_seg, + 'r_intersection': r_intersection} + return cell_assigment + + +def find_potential_over_seg(nuclei_counts, + intersection_counts, + threshold=0.3): + + nuclei_assigment = {} + for idx in range(nuclei_counts.shape[0]): + c_idx = np.nonzero(intersection_counts[:, idx])[0] + + r_intersection = [intersection_counts[_c_idx, idx] / nuclei_counts[idx] for _c_idx in c_idx] + + over_seg_c_idx = [] + for _c_idx, r_i in zip(c_idx, r_intersection): + if r_i > threshold: + over_seg_c_idx.append(_c_idx) + + is_over_seg = True if len(over_seg_c_idx) > 1 else False + if is_over_seg: + nuclei_assigment[idx] = {'c_idx': c_idx, + 'over_seg_idx': over_seg_c_idx, + 'is_over_seg': is_over_seg, + 'r_intersection': r_intersection} + return nuclei_assigment + + +def split_from_seeds(segmentation, boundary_pmap, seeds, all_idx): + # find seeds location ad label value + c_segmentation = copy.deepcopy(segmentation) + + # create bbox from mask + mask = np.logical_or.reduce([c_segmentation == label_idx for label_idx in all_idx]) + bbox, z_min, x_min, y_min = get_bbox(mask) + + _boundary_pmap = boundary_pmap[bbox] + _seeds = seeds[bbox] + _mask = np.logical_or.reduce([c_segmentation[bbox] == label_idx for label_idx in all_idx]) + + # tobe refactored watershed segmentation + _boundary_pmap = gaussian(_boundary_pmap / _boundary_pmap.max(), 2.0) + local_seg = watershed(_boundary_pmap, markers=_seeds, compactness=0.001) + max_id = c_segmentation.max() + + # copy unique labels in the source data + local_seg += max_id + 1 + c_segmentation[bbox][_mask] = local_seg[_mask].ravel() + return c_segmentation + + +def fix_under_segmentation(segmentation, + nuclei_segmentation, + boundary_pmap, + cell_assignments, + cell_idx=None): + _segmentation = copy.deepcopy(segmentation) + print(" -fixing under segmentation") + for c_idx, value in tqdm.tqdm(cell_assignments.items()): + + if cell_idx is None or c_idx in cell_idx: + _nuclei_seeds = np.zeros_like(_segmentation) + for i, n_idx in enumerate(value['under_seg_idx']): + _nuclei_seeds[n_idx == nuclei_segmentation] = i + 1 + + _segmentation = split_from_seeds(_segmentation, boundary_pmap, _nuclei_seeds, all_idx=[c_idx]) + return _segmentation + + +def fix_over_segmentation(segmentation, + nuclei_assignments, + nuclei_idx=None): + _segmentation = copy.deepcopy(segmentation) + print(" -fixing over segmentation") + for n_idx, value in tqdm.tqdm(nuclei_assignments.items()): + + if nuclei_idx is None or n_idx in nuclei_idx: + new_value = value['over_seg_idx'][0] + for i, c_idx in enumerate(value['over_seg_idx']): + _segmentation[segmentation == c_idx] = new_value + return _segmentation + + +def fix_over_under_segmentation_from_nuclei(cell_seg, + nuclei_seg, + threshold_merge=0.33, + threshold_split=0.66, + quantiles_nuclei=(0.3, 0.99), + boundary=None): + + cell_counts, nuclei_counts, cell_nuclei_counts = numba_find_overlaps(cell_seg, nuclei_seg) + nuclei_assignments = find_potential_over_seg(nuclei_counts, + cell_nuclei_counts, + threshold=threshold_merge) + + _cell_seg = fix_over_segmentation(cell_seg, nuclei_assignments, nuclei_idx=None) + + cell_counts, nuclei_counts, cell_nuclei_counts = numba_find_overlaps(_cell_seg, nuclei_seg) + cell_assignments = find_potential_under_seg(nuclei_counts, + cell_counts, + cell_nuclei_counts, + threshold=threshold_split, + quantiles_clip=quantiles_nuclei) + + boundary_pmap = np.ones_like(cell_seg) if boundary is None else boundary + _cell_seg = fix_under_segmentation(_cell_seg, + nuclei_seg, + boundary_pmap, + cell_assignments, + cell_idx=None) + return _cell_seg diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index e9865890..f173ce21 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -6,6 +6,7 @@ from plantseg.napari.widget.predictions import widget_unet_predictions, widget_test_all_unet_predictions from plantseg.napari.widget.predictions import widget_iterative_unet_predictions, widget_add_custom_model from plantseg.napari.widget.segmentation import widget_dt_ws, widget_gasp +from plantseg.napari.widget.segmentation import widget_fix_over_under_segmentation_from_nuclei from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut from plantseg.napari.widget.proofreading.proofreading import widget_split_and_merge_from_scribbles from plantseg.napari.widget.dataprocessing import widget_label_processing @@ -41,7 +42,8 @@ def get_gasp_workflow(): def get_extra_seg(): container = Container(widgets=[widget_multicut, - widget_lifted_multicut], + widget_lifted_multicut, + widget_fix_over_under_segmentation_from_nuclei], labels=False) return container diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index bf9bc8ea..3be1d7b6 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -8,6 +8,7 @@ from plantseg.segmentation.functional import gasp, multicut, dt_watershed from plantseg.segmentation.functional import lifted_multicut_from_nuclei_segmentation, lifted_multicut_from_nuclei_pmaps from plantseg.dataprocessing.functional.dataprocessing import normalize_01 +from plantseg.dataprocessing.functional.advanced_dataprocessing import fix_over_under_segmentation_from_nuclei def _generic_clustering(image: Image, labels: Labels, @@ -149,3 +150,36 @@ def widget_dt_ws(image: Image, layer_kwarg=layer_kwargs, layer_type=layer_type, ) + + +@magicgui(call_button='Run Segmentation Fix from Nuclei') +def widget_fix_over_under_segmentation_from_nuclei(cell_segmentation: Labels, + nuclei_segmentation: Labels, + boundary_pmaps: Union[None, Image], + threshold_merge=0.33, + threshold_split=0.66) -> Future[LayerDataTuple]: + out_name = build_nice_name(cell_segmentation.name, 'NucleiSegFix') + + if boundary_pmaps is not None: + inputs_names = (cell_segmentation.name, nuclei_segmentation.name, boundary_pmaps.name) + func_kwargs = {'cell_seg': cell_segmentation.data, + 'nuclei_seg': nuclei_segmentation.data, + 'boundary': boundary_pmaps.data} + else: + inputs_names = (cell_segmentation.name, nuclei_segmentation.name) + func_kwargs = {'cell_seg': cell_segmentation.data, + 'nuclei_seg': nuclei_segmentation.data} + layer_kwargs = {'name': out_name, 'scale': cell_segmentation.scale} + layer_type = 'labels' + + func = partial(fix_over_under_segmentation_from_nuclei, + threshold_merge=threshold_merge, + threshold_split=threshold_split, + ) + return start_threading_process(func, + func_kwargs=func_kwargs, + out_name=out_name, + input_keys=inputs_names, + layer_kwarg=layer_kwargs, + layer_type=layer_type, + ) From 812c88260ba22156112a37b9f779aa031a89c63f Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 12 Aug 2022 12:11:31 +0200 Subject: [PATCH 25/77] fix bug on voxel_size import/export and improved units handling --- plantseg/gui/gui_tools.py | 2 +- plantseg/io/io.py | 17 ++++++++------ plantseg/napari/widget/dataprocessing.py | 26 +++++++++++++--------- plantseg/napari/widget/io.py | 28 +++++++++++++++++------- plantseg/napari/widget/predictions.py | 14 ++++++++---- plantseg/napari/widget/segmentation.py | 19 +++++++++++----- plantseg/napari/widget/utils.py | 9 ++++++-- plantseg/pipeline/steps.py | 4 ++-- 8 files changed, 80 insertions(+), 39 deletions(-) diff --git a/plantseg/gui/gui_tools.py b/plantseg/gui/gui_tools.py index 62b99642..36cc4a55 100644 --- a/plantseg/gui/gui_tools.py +++ b/plantseg/gui/gui_tools.py @@ -731,7 +731,7 @@ def read_from_file(self): if ext in H5_EXTENSIONS: file_resolution = read_h5_voxel_size(file_dialog.config["path"]) elif ext in TIFF_EXTENSIONS: - file_resolution = read_tiff_voxel_size(file_dialog.config["path"]) + file_resolution, _ = read_tiff_voxel_size(file_dialog.config["path"]) else: raise NotImplementedError diff --git a/plantseg/io/io.py b/plantseg/io/io.py index 9d5431d0..28817011 100644 --- a/plantseg/io/io.py +++ b/plantseg/io/io.py @@ -29,16 +29,18 @@ def _xy_voxel_size(tags, key): image_metadata = tiff.imagej_metadata if image_metadata is not None: z = image_metadata.get('spacing', 1.) + voxel_size_unit = image_metadata.get('unit', 'um') else: # default voxel size z = 1. + voxel_size_unit = 'um' tags = tiff.pages[0].tags # parse X, Y resolution y = _xy_voxel_size(tags, 'YResolution') x = _xy_voxel_size(tags, 'XResolution') # return voxel size - return [z, y, x] + return [z, y, x], voxel_size_unit def read_h5_voxel_size(f, h5key): @@ -85,7 +87,7 @@ def load_h5(path, key, slices=None, info_only=False): voxel_size = read_h5_voxel_size(f, key) file_shape = f[key].shape - infos = (voxel_size, file_shape, key) + infos = (voxel_size, file_shape, key, 'um') if info_only: return infos @@ -97,13 +99,14 @@ def load_h5(path, key, slices=None, info_only=False): def load_tiff(path, info_only=False): file = tifffile.imread(path) try: - voxel_size = read_tiff_voxel_size(path) + voxel_size, voxel_size_unit = read_tiff_voxel_size(path) except: # ZeroDivisionError could happen while reading the voxel size warnings.warn('Voxel size not found, returning default [1.0, 1.0. 1.0]', RuntimeWarning) voxel_size = [1.0, 1.0, 1.0] + voxel_size_unit = 'um' - infos = (voxel_size, file.shape, None) + infos = (voxel_size, file.shape, None, voxel_size_unit) if info_only: return infos else: @@ -124,7 +127,7 @@ def smart_load(path, key=None, info_only=False, default=load_tiff): def load_shape(path, key=None): - _, data_shape, _ = smart_load(path, key=key, info_only=True) + _, data_shape, _, _ = smart_load(path, key=key, info_only=True) return data_shape @@ -156,7 +159,7 @@ def rename_h5_key(path, old_key, new_key, mode='r+'): f.close() -def create_tiff(path, stack, voxel_size): +def create_tiff(path, stack, voxel_size, voxel_size_unit='um'): # taken from: https://pypi.org/project/tifffile docs z, y, x = stack.shape stack.shape = 1, z, 1, y, x, 1 # dimensions in TZCYXS order @@ -168,4 +171,4 @@ def create_tiff(path, stack, voxel_size): dtype=stack.dtype, imagej=True, resolution=resolution, - metadata={'axes': 'TZCYXS', 'spacing': spacing, 'unit': 'um'}) + metadata={'axes': 'TZCYXS', 'spacing': spacing, 'unit': voxel_size_unit}) diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index 9c54d888..b87b6b31 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -14,7 +14,7 @@ from plantseg.dataprocessing.functional.labelprocessing import set_background_to_value from plantseg.dataprocessing.functional.labelprocessing import relabel_segmentation as _relabel_segmentation from plantseg.gui import list_models, get_model_resolution -from plantseg.napari.widget.utils import start_threading_process, build_nice_name +from plantseg.napari.widget.utils import start_threading_process, build_nice_name, layer_properties def _generic_preprocessing(image_data, sigma, gaussian_smoothing, rescale, rescaling_factors): @@ -34,7 +34,7 @@ def widget_gaussian_smoothing(image: Image, out_name = build_nice_name(image.name, 'GaussianSmoothing') inputs_kwarg = {'image': image.data} inputs_names = (image.name,) - layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_kwargs = layer_properties(name=out_name, scale=image.scale, metadata=image.metadata) layer_type = 'image' func = partial(image_gaussian_smoothing, sigma=sigma) @@ -51,7 +51,7 @@ def widget_gaussian_smoothing(image: Image, type_of_refactor={'widget_type': 'RadioButtons', 'orientation': 'vertical', 'choices': ['Rescaling factor', - 'Voxel size (um)', + 'Voxel size', 'Same as Reference Layer', 'Same as Reference Model']}, reference_model={"choices": list_models()}) @@ -91,9 +91,10 @@ def widget_rescaling(image: Layer, out_name = build_nice_name(image.name, 'Rescaled') inputs_kwarg = {'image': image.data} inputs_names = (image.name,) - layer_kwargs = {'name': out_name, - 'scale': out_voxel_size, - 'metadata': {'original_voxel_size': current_resolution}} + layer_kwargs = layer_properties(name=out_name, + scale=out_voxel_size, + metadata={**image.metadata, + **{'original_voxel_size': current_resolution}}) layer_type = 'image' func = partial(image_rescale, factor=rescaling_factor, order=order) @@ -120,7 +121,9 @@ def widget_cropping(image: Layer, out_name = build_nice_name(image.name, 'cropped') inputs_names = (image.name,) - layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_kwargs = layer_properties(name=out_name, + scale=image.scale, + metadata=image.metadata) layer_type = 'image' rectangle = crop_roi.data[0].astype('int64') @@ -163,7 +166,9 @@ def widget_add_layers(image1: Image, ) -> Future[LayerDataTuple]: out_name = build_nice_name(f'{image1.name}-{image2.name}', operation) inputs_names = (image1.name, image2.name) - layer_kwargs = {'name': out_name, 'scale': image1.scale} + layer_kwargs = layer_properties(name=out_name, + scale=image1.scale, + metadata=image1.metadata) layer_type = 'image' func = partial(_two_layers_operation, weights=weights, operation=operation) @@ -196,8 +201,9 @@ def widget_label_processing(segmentation: Labels, out_name = build_nice_name(segmentation.name, 'Processed') inputs_kwarg = {'segmentation': segmentation.data} inputs_names = (segmentation.name,) - layer_kwargs = {'name': out_name, - 'scale': segmentation.scale} + layer_kwargs = layer_properties(name=out_name, + scale=segmentation.scale, + metadata=segmentation.metadata) layer_type = 'labels' func = partial(_label_processing, set_bg_to_0=set_bg_to_0, diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index 4a5d7d86..99d9853f 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -48,11 +48,11 @@ def _advanced_load(path, key, channel, advanced_load=False, layer_type='image', if ext in H5_EXTENSIONS: key = key if advanced_load else None - data, (voxel_size, _, _) = load_h5(path, key=key) + data, (voxel_size, _, _, voxel_size_unit) = load_h5(path, key=key) elif ext in TIFF_EXTENSIONS: channel, layout = channel - data, (voxel_size, _, _) = load_tiff(path) + data, (voxel_size, _, _, voxel_size_unit) = load_tiff(path) if advanced_load: assert data.ndim == len(layout) _check_layout_string(layout) @@ -71,7 +71,7 @@ def _advanced_load(path, key, channel, advanced_load=False, layer_type='image', if headless: return data - return data, voxel_size + return data, voxel_size, voxel_size_unit @magicgui( @@ -107,18 +107,22 @@ def open_file(path: Path = Path.home(), layer_type=layer_type, headless=True) - data, voxel_size = _func_gui(path) + data, voxel_size, voxel_size_unit = _func_gui(path) dag.add_step(_func_dask, input_keys=(f'{name}_path',), output_key=name) - show_info(f'Napari - PlantSeg info: {name} correctly imported') - return data, layer_properties(name=name, scale=voxel_size), layer_type + show_info(f'Napari - PlantSeg info: {name} correctly imported, voxel_size: {voxel_size} {voxel_size_unit}') + layer_kwargs = layer_properties(name=name, + scale=voxel_size, + metadata={'original_voxel_size': voxel_size, + 'voxel_size_unit': voxel_size_unit}) + return data, layer_kwargs, layer_type -def export_stack_as_tiff(data, name, voxel_size, directory, dtype, suffix): +def export_stack_as_tiff(data, name, voxel_size, voxel_size_unit, directory, dtype, suffix): stack_name = f'{name}_{suffix}' out_path = directory / f'{stack_name}.tiff' data = fix_input_shape(data) data = data.astype(dtype) - create_tiff(path=out_path, stack=data[...], voxel_size=voxel_size) + create_tiff(path=out_path, stack=data[...], voxel_size=voxel_size, voxel_size_unit=voxel_size_unit) return stack_name @@ -157,15 +161,23 @@ def export_stacks(images: List[Tuple[Layer, str]], image.data = scale_image_to_voxelsize(image.data, image.scale, output_resolution, order=order) image.scale = output_resolution + if 'voxel_size_unit' in image.metadata.keys(): + voxel_size_unit = image.metadata['voxel_size_unit'] + else: + voxel_size_unit = 'um' + image_suffix = f'export_{i}' if image_suffix == '' else image_suffix _ = export_stack_as_tiff(data=image.data, name=image.name, voxel_size=image.scale, + voxel_size_unit=voxel_size_unit, directory=directory, dtype=dtype, suffix=image_suffix) names.append(image.name) suffixes.append(image_suffix) + show_info(f'Napari - PlantSeg info: {image.name} correctly exported,' + f' voxel_size: {image.scale} {voxel_size_unit}') out_path = directory / f'{workflow_name}.pkl' dag.export_dag(out_path, names, suffixes) diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py index ae2388d4..48386903 100644 --- a/plantseg/napari/widget/predictions.py +++ b/plantseg/napari/widget/predictions.py @@ -10,7 +10,7 @@ from pathlib import Path from plantseg.dataprocessing.functional import image_gaussian_smoothing from plantseg.gui import list_models, add_custom_model -from plantseg.napari.widget.utils import start_threading_process, build_nice_name +from plantseg.napari.widget.utils import start_threading_process, build_nice_name, layer_properties from plantseg.predictions.functional import unet_predictions from plantseg.predictions.utils import STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE @@ -28,7 +28,9 @@ def widget_unet_predictions(image: Image, out_name = build_nice_name(image.name, model_name) inputs_names = (image.name,) - layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_kwargs = layer_properties(name=out_name, + scale=image.scale, + metadata=image.metadata) layer_type = 'image' func = partial(unet_predictions, model_name=model_name, stride=stride, patch=patch_size, device=device) @@ -46,7 +48,9 @@ def _compute_multiple_predictions(image, patch_size, stride, device): out_layers = [] for model_name in list_models(): out_name = build_nice_name(image.name, model_name) - layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_kwargs = layer_properties(name=out_name, + scale=image.scale, + metadata=image.metadata) layer_type = 'image' try: pmap = unet_predictions(raw=image.data, @@ -111,7 +115,9 @@ def widget_iterative_unet_predictions(image: Image, device: str = 'cuda', ) -> Future[LayerDataTuple]: out_name = build_nice_name(image.name, f'iterative-{model_name}-x{num_iterations}') inputs_names = (image.name,) - layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_kwargs = layer_properties(name=out_name, + scale=image.scale, + metadata=image.metadata) layer_type = 'image' func = partial(_compute_iterative_predictions, diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index 3be1d7b6..2e752aeb 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -4,7 +4,7 @@ from napari.types import ImageData, LayerDataTuple, LabelsData from napari.layers import Labels, Image, Layer from typing import Union, Tuple, Callable -from plantseg.napari.widget.utils import start_threading_process, build_nice_name +from plantseg.napari.widget.utils import start_threading_process, build_nice_name, layer_properties from plantseg.segmentation.functional import gasp, multicut, dt_watershed from plantseg.segmentation.functional import lifted_multicut_from_nuclei_segmentation, lifted_multicut_from_nuclei_pmaps from plantseg.dataprocessing.functional.dataprocessing import normalize_01 @@ -18,7 +18,9 @@ def _generic_clustering(image: Image, labels: Labels, agg_func: Callable = gasp) -> Future[LayerDataTuple]: out_name = build_nice_name(image.name, name) inputs_names = (image.name, labels.name) - layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_kwargs = layer_properties(name=out_name, + scale=image.scale, + metadata=image.metadata) layer_type = 'labels' func = partial(agg_func, beta=beta, post_minsize=minsize) @@ -64,7 +66,9 @@ def widget_lifted_multicut(image: Image, out_name = build_nice_name(image.name, 'LiftedMultiCut') inputs_names = (image.name, nuclei.name, labels.name) - layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_kwargs = layer_properties(name=out_name, + scale=image.scale, + metadata=image.metadata) layer_type = 'labels' func = partial(lmc, superpixels=labels, beta=beta, post_minsize=minsize) @@ -126,7 +130,9 @@ def widget_dt_ws(image: Image, nuclei: bool = False) -> Future[LayerDataTuple]: out_name = build_nice_name(image.name, 'dtWS') inputs_names = (image.name,) - layer_kwargs = {'name': out_name, 'scale': image.scale} + layer_kwargs = layer_properties(name=out_name, + scale=image.scale, + metadata=image.metadata) layer_type = 'labels' stacked = False if stacked == '3D' else True @@ -169,7 +175,10 @@ def widget_fix_over_under_segmentation_from_nuclei(cell_segmentation: Labels, inputs_names = (cell_segmentation.name, nuclei_segmentation.name) func_kwargs = {'cell_seg': cell_segmentation.data, 'nuclei_seg': nuclei_segmentation.data} - layer_kwargs = {'name': out_name, 'scale': cell_segmentation.scale} + + layer_kwargs = layer_properties(name=out_name, + scale=cell_segmentation.scale, + metadata=cell_segmentation.metadata) layer_type = 'labels' func = partial(fix_over_under_segmentation_from_nuclei, diff --git a/plantseg/napari/widget/utils.py b/plantseg/napari/widget/utils.py index 1577e500..47130272 100644 --- a/plantseg/napari/widget/utils.py +++ b/plantseg/napari/widget/utils.py @@ -35,8 +35,13 @@ def on_done(result): return future -def layer_properties(name, scale): - return {'name': name, 'scale': scale} +def layer_properties(name, scale, metadata: dict = None): + keys_to_save = {'original_voxel_size', 'voxel_size_unit'} + if metadata is not None: + _new_metadata = {key: metadata[key] for key in keys_to_save if key in metadata} + else: + _new_metadata = {} + return {'name': name, 'scale': scale, 'metadata': _new_metadata} def _find_version(old_suffix, new_suffix): diff --git a/plantseg/pipeline/steps.py b/plantseg/pipeline/steps.py index 4302964b..6298af07 100644 --- a/plantseg/pipeline/steps.py +++ b/plantseg/pipeline/steps.py @@ -101,7 +101,7 @@ def load_stack(self, file_path, check_input_type=True): tuple(nd.array, tuple(float)): (numpy array containing stack's data, stack's data voxel size) """ _, ext = os.path.splitext(file_path) - data, (voxel_size, data_shape, key) = smart_load(file_path, key=None) + data, (voxel_size, _, key, _) = smart_load(file_path, key=None) if self.h5_output_key is None: self.h5_output_key = key @@ -120,7 +120,7 @@ def save_raw_dataset(self, input_path, output_path, voxel_size): """ raw_path = self._raw_path(input_path) if os.path.exists(raw_path): - raw, _, _ = smart_load(raw_path, key='raw') + raw, _ = smart_load(raw_path, key='raw') create_h5(output_path, stack=raw, key='raw', voxel_size=voxel_size, mode='a') else: From f15fb84fa8036172c24a837f2daa0af5650f610d Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 12 Aug 2022 12:38:52 +0200 Subject: [PATCH 26/77] fix minor bug in predictions and mc workflow from gui --- plantseg/io/io.py | 1 + plantseg/predictions/predict.py | 20 +++++++++---------- .../segmentation/functional/segmentation.py | 4 +++- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/plantseg/io/io.py b/plantseg/io/io.py index 28817011..afee6628 100644 --- a/plantseg/io/io.py +++ b/plantseg/io/io.py @@ -71,6 +71,7 @@ def visitor_func(name, node): if len(found_datasets) == 1: return found_datasets[0] else: + print(found_datasets) for h5_key in H5_KEYS: if h5_key in found_datasets: return h5_key diff --git a/plantseg/predictions/predict.py b/plantseg/predictions/predict.py index 06cf8ab5..4aaed255 100755 --- a/plantseg/predictions/predict.py +++ b/plantseg/predictions/predict.py @@ -48,7 +48,16 @@ def __init__(self, state=True): h5_output_key = "predictions" - valid_paths = _check_patch_size(input_paths, patch_size=patch) + valid_paths = _check_patch_size(input_paths, patch_size=patch) if state else input_paths + + super().__init__(valid_paths, + input_type=input_type, + output_type=output_type, + save_directory=model_name, + out_ext=out_ext, + state=state, + file_suffix='_predictions', + h5_output_key=h5_output_key) model, model_config, model_path = get_model_config(model_name, model_update=model_update, version=version) utils.load_checkpoint(model_path, model) @@ -64,15 +73,6 @@ def __init__(self, stride=stride, mirror_padding=mirror_padding) - super().__init__(valid_paths, - input_type=input_type, - output_type=output_type, - save_directory=model_name, - out_ext=out_ext, - state=state, - file_suffix='_predictions', - h5_output_key=h5_output_key) - def process(self, raw): dataset = self.dataset_builder(raw, **self.dataset_config) pmaps = self.predictor(dataset) diff --git a/plantseg/segmentation/functional/segmentation.py b/plantseg/segmentation/functional/segmentation.py index ad59b809..9c76c70b 100644 --- a/plantseg/segmentation/functional/segmentation.py +++ b/plantseg/segmentation/functional/segmentation.py @@ -136,7 +136,9 @@ def multicut(boundary_pmaps: ArrayLike, # run size threshold if post_minsize > 0: - segmentation, _ = apply_size_filter(segmentation, boundary_pmaps, post_minsize) + segmentation, _ = apply_size_filter(segmentation.astype('uint32'), + boundary_pmaps, + post_minsize) return segmentation From 8c734752e6014bc9a43c26c7374f8a711d11092d Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 12 Aug 2022 15:33:14 +0200 Subject: [PATCH 27/77] add partial support for omero tiff files --- plantseg/io/io.py | 88 ++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 76 insertions(+), 12 deletions(-) diff --git a/plantseg/io/io.py b/plantseg/io/io.py index afee6628..2b9ace9c 100644 --- a/plantseg/io/io.py +++ b/plantseg/io/io.py @@ -2,7 +2,10 @@ import warnings import h5py +import numpy as np import tifffile +import warnings +from xml.etree import cElementTree as ElementTree TIFF_EXTENSIONS = [".tiff", ".tif"] H5_EXTENSIONS = [".hdf", ".h5", ".hd5", "hdf5"] @@ -12,11 +15,10 @@ allowed_data_format = TIFF_EXTENSIONS + H5_EXTENSIONS -def read_tiff_voxel_size(file_path): +def _read_imagej_meta(tiff): """ Implemented based on information found in https://pypi.org/project/tifffile """ - def _xy_voxel_size(tags, key): assert key in ['XResolution', 'YResolution'] if key in tags: @@ -25,21 +27,83 @@ def _xy_voxel_size(tags, key): # return default return 1. + image_metadata = tiff.imagej_metadata + z = image_metadata.get('spacing', 1.) + voxel_size_unit = image_metadata.get('unit', 'um') + + tags = tiff.pages[0].tags + # parse X, Y resolution + y = _xy_voxel_size(tags, 'YResolution') + x = _xy_voxel_size(tags, 'XResolution') + # return voxel size + return [z, y, x], voxel_size_unit + + +def _read_ome_meta(tiff): + xml_om = tiff.ome_metadata + tree = ElementTree.fromstring(xml_om) + + image_element = [image for image in tree if image.tag.find('Image') != -1] + if len(image_element) == 1: + image_element = image_element[0] + else: + warnings.warn(f'Error parsing omero tiff meta. ' + f'Reverting to default voxel size (1., 1., 1.) um') + return [1., 1., 1.], 'um' + + pixels_element = [pixels for pixels in image_element if pixels.tag.find('Pixels') != -1] + if len(pixels_element) == 1: + pixels_element = pixels_element[0] + else: + warnings.warn(f'Error parsing omero tiff meta. ' + f'Reverting to default voxel size (1., 1., 1.) um') + return [1., 1., 1.], 'um' + + units = [] + x, y, z, voxel_size_unit = None, None, None, 'um' + + for key, value in pixels_element.items(): + if key == 'PhysicalSizeX': + x = float(value) + + elif key == 'PhysicalSizeY': + y = float(value) + + elif key == 'PhysicalSizeZ': + z = float(value) + + if key in ['PhysicalSizeXUnit', 'PhysicalSizeYUnit', 'PhysicalSizeZUnit']: + units.append(value) + + if len(units) == 3: + voxel_size_unit = units[0] + if not np.alltrue([_value == units[0] for _value in units]): + warnings.warn(f'Units are not homogeneous: {units}') + + for value in [x, y, z]: + if value is None: + warnings.warn(f'Error parsing omero tiff meta. ' + f'Reverting to default voxel size (1., 1., 1.) um') + return [1., 1., 1.], 'um' + + return [z, y, x], voxel_size_unit + + +def read_tiff_voxel_size(file_path): with tifffile.TiffFile(file_path) as tiff: - image_metadata = tiff.imagej_metadata - if image_metadata is not None: - z = image_metadata.get('spacing', 1.) - voxel_size_unit = image_metadata.get('unit', 'um') + if tiff.imagej_metadata is not None: + [z, y, x], voxel_size_unit = _read_imagej_meta(tiff) + + elif tiff.ome_metadata is not None: + [z, y, x], voxel_size_unit = _read_ome_meta(tiff) + else: # default voxel size - z = 1. + warnings.warn(f'No metadata found. ' + f'Reverting to default voxel size (1., 1., 1.) um') + x, y, z = 1., 1., 1. voxel_size_unit = 'um' - tags = tiff.pages[0].tags - # parse X, Y resolution - y = _xy_voxel_size(tags, 'YResolution') - x = _xy_voxel_size(tags, 'XResolution') - # return voxel size return [z, y, x], voxel_size_unit From da66c4d057bbcccd22680f959c327bb1500271e8 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 12 Aug 2022 16:00:35 +0200 Subject: [PATCH 28/77] add partial support for omero tiff files --- plantseg/io/io.py | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/plantseg/io/io.py b/plantseg/io/io.py index 2b9ace9c..19c8376f 100644 --- a/plantseg/io/io.py +++ b/plantseg/io/io.py @@ -44,18 +44,18 @@ def _read_ome_meta(tiff): tree = ElementTree.fromstring(xml_om) image_element = [image for image in tree if image.tag.find('Image') != -1] - if len(image_element) == 1: + if image_element: image_element = image_element[0] else: - warnings.warn(f'Error parsing omero tiff meta. ' + warnings.warn(f'Error parsing omero tiff meta Image. ' f'Reverting to default voxel size (1., 1., 1.) um') return [1., 1., 1.], 'um' pixels_element = [pixels for pixels in image_element if pixels.tag.find('Pixels') != -1] - if len(pixels_element) == 1: + if pixels_element: pixels_element = pixels_element[0] else: - warnings.warn(f'Error parsing omero tiff meta. ' + warnings.warn(f'Error parsing omero tiff meta Pixels. ' f'Reverting to default voxel size (1., 1., 1.) um') return [1., 1., 1.], 'um' @@ -75,16 +75,25 @@ def _read_ome_meta(tiff): if key in ['PhysicalSizeXUnit', 'PhysicalSizeYUnit', 'PhysicalSizeZUnit']: units.append(value) - if len(units) == 3: + if units: voxel_size_unit = units[0] if not np.alltrue([_value == units[0] for _value in units]): warnings.warn(f'Units are not homogeneous: {units}') - for value in [x, y, z]: - if value is None: - warnings.warn(f'Error parsing omero tiff meta. ' - f'Reverting to default voxel size (1., 1., 1.) um') - return [1., 1., 1.], 'um' + if x is None: + x = 1. + warnings.warn(f'Error parsing omero tiff meta. ' + f'Reverting to default voxel size x = 1.') + + if y is None: + y = 1. + warnings.warn(f'Error parsing omero tiff meta. ' + f'Reverting to default voxel size y = 1.') + + if z is None: + z = 1. + warnings.warn(f'Error parsing omero tiff meta. ' + f'Reverting to default voxel size z = 1.') return [z, y, x], voxel_size_unit From 31b85a0924bd51217e39a94903672fe56f02b0fa Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 16 Aug 2022 13:54:51 +0200 Subject: [PATCH 29/77] minor bug fixes --- plantseg/napari/containers.py | 4 +++- plantseg/napari/viewer.py | 4 ++-- plantseg/napari/widget/io.py | 3 ++- plantseg/napari/widget/proofreading/proofreading.py | 5 +++-- 4 files changed, 10 insertions(+), 6 deletions(-) diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index f173ce21..58bd5489 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -9,13 +9,15 @@ from plantseg.napari.widget.segmentation import widget_fix_over_under_segmentation_from_nuclei from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut from plantseg.napari.widget.proofreading.proofreading import widget_split_and_merge_from_scribbles +from plantseg.napari.widget.proofreading.proofreading import widget_clean_scribble from plantseg.napari.widget.dataprocessing import widget_label_processing def get_main(): container = Container(widgets=[open_file, export_stacks, - widget_split_and_merge_from_scribbles + widget_split_and_merge_from_scribbles, + widget_clean_scribble ], labels=False) diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index be89d35e..199fddff 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -3,7 +3,7 @@ from plantseg.napari.containers import get_extra_seg, get_extra_pred from plantseg.napari.containers import get_gasp_workflow, get_preprocessing_workflow, get_main -from plantseg.napari.widget.proofreading.proofreading import widget_split_and_merge_from_scribbles, clean_scribble +from plantseg.napari.widget.proofreading.proofreading import widget_split_and_merge_from_scribbles, widget_clean_scribble def run_viewer(): @@ -12,7 +12,7 @@ def run_viewer(): main_w = viewer.window.add_dock_widget(main_container, name='Main') viewer.bind_key('p', widget_split_and_merge_from_scribbles) - viewer.bind_key('c', clean_scribble) + viewer.bind_key('c', widget_clean_scribble) for _containers, name in [(get_preprocessing_workflow(), 'Data - Processing'), (get_gasp_workflow(), 'UNet + GASP Workflow'), diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index 99d9853f..e1ce323c 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -2,6 +2,7 @@ from pathlib import Path from typing import List, Tuple +import numpy as np from magicgui import magicgui from napari.layers import Layer, Image, Labels from napari.types import LayerDataTuple @@ -38,7 +39,7 @@ def _filter_channel(data, channel, layout): raise ValueError(f'image has only {data.shape[i]} channels along {layout}') slices.append(slice(channel, channel + 1)) - return data[tuple(slices)] + return np.squeeze(data[tuple(slices)]) def _advanced_load(path, key, channel, advanced_load=False, layer_type='image', headless=False): diff --git a/plantseg/napari/widget/proofreading/proofreading.py b/plantseg/napari/widget/proofreading/proofreading.py index 374143bc..e46eb53a 100644 --- a/plantseg/napari/widget/proofreading/proofreading.py +++ b/plantseg/napari/widget/proofreading/proofreading.py @@ -87,7 +87,8 @@ def split_merge_from_seeds(seeds, segmentation, image, bboxes, max_label): max_label) -def clean_scribble(viewer): +@magicgui(call_button='Clean scribbles < c >') +def widget_clean_scribble(viewer): if current_label_layer is None: show_info('Scribble not defined. Run the proofreading widget tool once first') return None @@ -96,7 +97,7 @@ def clean_scribble(viewer): viewer.layers[current_label_layer].refresh() -@magicgui(call_button='Split and Merge from scribbles - p') +@magicgui(call_button='Split and Merge from scribbles < p >') def widget_split_and_merge_from_scribbles(viewer: napari.Viewer, scribbles: Labels, segmentation: Labels, From efcd34308371442d12f06ccda32644feaea642e0 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 16 Aug 2022 16:10:24 +0200 Subject: [PATCH 30/77] improve widget names --- plantseg/gui/__init__.py | 2 +- plantseg/napari/widget/dataprocessing.py | 61 ++++++++++-- plantseg/napari/widget/io.py | 59 ++++++++--- plantseg/napari/widget/predictions.py | 53 ++++++++-- .../widget/proofreading/proofreading.py | 7 +- plantseg/napari/widget/segmentation.py | 98 +++++++++++++++---- 6 files changed, 225 insertions(+), 55 deletions(-) diff --git a/plantseg/gui/__init__.py b/plantseg/gui/__init__.py index 9df2a94a..a043c102 100644 --- a/plantseg/gui/__init__.py +++ b/plantseg/gui/__init__.py @@ -98,7 +98,7 @@ def add_custom_model(new_model_name: str, custom_zoo_dict = {} custom_zoo_dict[new_model_name] = {} - custom_zoo_dict[new_model_name]["path"] = location + custom_zoo_dict[new_model_name]["path"] = str(location) custom_zoo_dict[new_model_name]["resolution"] = resolution custom_zoo_dict[new_model_name]["description"] = description diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index b87b6b31..f6fb4336 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -27,7 +27,14 @@ def _generic_preprocessing(image_data, sigma, gaussian_smoothing, rescale, resca @magicgui(call_button='Run Gaussian Smoothing', - sigma={"widget_type": "FloatSlider", "max": 5., 'min': 0.1}) + image={'label': 'Image', + 'tooltip': 'Image layer to apply the smoothing.'}, + sigma={'label': 'Sigma', + 'widget_type': 'FloatSlider', + 'tooltip': 'Define the size of the gaussian smoothing kernel. ' + 'The larger the more blurred will be the output image.', + 'max': 5., + 'min': 0.1}) def widget_gaussian_smoothing(image: Image, sigma: float = 1., ) -> Future[LayerDataTuple]: @@ -48,13 +55,29 @@ def widget_gaussian_smoothing(image: Image, @magicgui(call_button='Run Image Rescaling', - type_of_refactor={'widget_type': 'RadioButtons', + image={'label': 'Image or Label', + 'tooltip': 'Layer to apply the rescaling.'}, + type_of_refactor={'label': 'Type of refactor', + 'tooltip': 'Select the mode of finding the right rescaling factor.', + 'widget_type': 'RadioButtons', 'orientation': 'vertical', 'choices': ['Rescaling factor', 'Voxel size', 'Same as Reference Layer', 'Same as Reference Model']}, - reference_model={"choices": list_models()}) + rescaling_factor={'label': 'Rescaling factor', + 'tooltip': 'Define the scaling factor to use for resizing the input image.'}, + out_voxel_size={'label': 'Out voxel size', + 'tooltip': 'Define the output voxel size. Units are same as imported, ' + '(if units are missing default is "um").'}, + reference_layer={'label': 'Reference layer', + 'tooltip': 'Rescale to same voxel size as selected layer.'}, + reference_model={'label': 'Reference model', + 'tooltip': 'Rescale to same voxel size as selected model.', + 'choices': list_models()}, + order={'label': 'Interpolation order', + 'tooltip': '0 for nearest neighbours (default for labels), 1 for linear, 2 for bilinear.', + }) def widget_rescaling(image: Layer, type_of_refactor: str = 'Rescaling factor', rescaling_factor: Tuple[float, float, float] = (1., 1., 1.), @@ -111,7 +134,14 @@ def _cropping(data, crop_slices): return data[crop_slices] -@magicgui(call_button='Run Cropping', ) +@magicgui(call_button='Run Cropping', + image={'label': 'Image or Label', + 'tooltip': 'Layer to apply the rescaling.'}, + crop_roi={'label': 'Crop ROI', + 'tooltip': 'This must be a shape layer with a rectangle XY overlaying the area to crop.'}, + crop_z={'label': 'Z slices', + 'tooltip': 'Numer of z slices to take next to the current selection.'}, + ) def widget_cropping(image: Layer, crop_roi: Union[Shapes, None] = None, crop_z: int = 1, @@ -152,12 +182,17 @@ def _two_layers_operation(data1, data2, operation, weights: float = 0.5): @magicgui(call_button='Run Merge Layers', - operation={'widget_type': 'RadioButtons', + image1={'label': 'Image 1'}, + image2={'label': 'Image 2'}, + operation={'label': 'Operation', + 'tooltip': 'Operation used to merge the two layers.', + 'widget_type': 'RadioButtons', 'orientation': 'horizontal', 'choices': ['Mean', 'Maximum', 'Minimum']}, - weights={"widget_type": "FloatSlider", "max": 1., 'min': 0.}, + weights={'label': 'Mean weights', + 'widget_type': 'FloatSlider', 'max': 1., 'min': 0.}, ) def widget_add_layers(image1: Image, image2: Image, @@ -183,7 +218,6 @@ def widget_add_layers(image1: Image, def _label_processing(segmentation, set_bg_to_0, relabel_segmentation): - if relabel_segmentation: segmentation = _relabel_segmentation(segmentation) @@ -193,11 +227,22 @@ def _label_processing(segmentation, set_bg_to_0, relabel_segmentation): return segmentation -@magicgui(call_button='Run Label processing') +@magicgui(call_button='Run Label processing', + segmentation={'label': 'Segmentation', + 'tooltip': 'Segmentation can be any label layer.'}, + set_bg_to_0={'label': 'Set background to 0', + 'tooltip': 'Set the largest idx in the image to zero.'}, + relabel_segmentation={'label': 'Relabel Segmentation', + 'tooltip': 'Relabel segmentation contiguously to avoid labels clash.'} + ) def widget_label_processing(segmentation: Labels, set_bg_to_0: bool = True, relabel_segmentation: bool = True, ) -> Future[LayerDataTuple]: + + if relabel_segmentation and 'bboxes' in segmentation.metadata.keys(): + del segmentation.metadata['bboxes'] + out_name = build_nice_name(segmentation.name, 'Processed') inputs_kwarg = {'segmentation': segmentation.data} inputs_names = (segmentation.name,) diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index e1ce323c..b0dca1bc 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -77,16 +77,23 @@ def _advanced_load(path, key, channel, advanced_load=False, layer_type='image', @magicgui( call_button='Open file', - path={'label': 'Pick a file (tiff or h5)'}, - name={'label': 'Layer Name'}, + path={'label': 'Pick a file (tiff or h5)', + 'tooltip': 'Select a file to be imported, the file can be a tiff or h5.'}, + name={'label': 'Layer Name', + 'tooltip': 'Define the name of the output layer, default is either image or label.'}, layer_type={ 'label': 'Layer type', + 'tooltip': 'Select if the image is a normal image or a segmentation', 'widget_type': 'RadioButtons', 'orientation': 'horizontal', 'choices': ['image', 'labels']}, - advanced_load={'label': 'Advanced load a specific h5-key / tiff-channel'}, - key={'label': 'key/layout (h5 only)'}, - channel={'label': 'channel/layout (tiff only)'}) + advanced_load={'label': 'Advanced load a specific h5-key / tiff-channel', + 'tooltip': 'If specified allows to select specific h5 dataset in a file,' + ' or specific channels in a tiff.'}, + key={'label': 'Key/layout (h5 only)', + 'tooltip': 'Key to be loaded from h5'}, + channel={'label': 'Channel/layout (tiff only)', + 'tooltip': 'Channel to select and channels layout'}) def open_file(path: Path = Path.home(), layer_type: str = 'image', name: str = '', @@ -94,6 +101,7 @@ def open_file(path: Path = Path.home(), key: str = 'raw', channel: Tuple[int, str] = (0, 'xcxx'), ) -> LayerDataTuple: + name = layer_type if name == '' else name _func_gui = partial(_advanced_load, key=key, @@ -118,21 +126,41 @@ def open_file(path: Path = Path.home(), return data, layer_kwargs, layer_type -def export_stack_as_tiff(data, name, voxel_size, voxel_size_unit, directory, dtype, suffix): +def export_stack_as_tiff(data, name, voxel_size, voxel_size_unit, directory, func_typecast, dtype, suffix): stack_name = f'{name}_{suffix}' out_path = directory / f'{stack_name}.tiff' data = fix_input_shape(data) - data = data.astype(dtype) + data = func_typecast(data, dtype) create_tiff(path=out_path, stack=data[...], voxel_size=voxel_size, voxel_size_unit=voxel_size_unit) return stack_name +def _image_typecast(data, dtype): + data = normalize_01(data) + if dtype != 'float32': + data = (data * np.iinfo(dtype).max) + + data = data.astype(dtype) + return data + + +def _label_typecast(data, dtype): + return data.astype(dtype) + + @magicgui( call_button='Export stack', - images={'label': 'Layers to export', 'layout': 'vertical'}, - data_type={'label': 'Data Type', 'choices': ['float32', 'uint8', 'uint16']}, - directory={'label': 'Directory to export files', 'mode': 'd'}, - workflow_name={'label': 'Workflow name'}, + images={'label': 'Layers to export', + 'layout': 'vertical', + 'tooltip': 'Select all layer to be exported, and (optional) set a suffix to append to each file name.'}, + data_type={'label': 'Data Type', + 'choices': ['float32', 'uint8', 'uint16'], + 'tooltip': 'Export datatype (uint16 for segmentation) and all others for images.'}, + directory={'label': 'Directory to export files', + 'mode': 'd', + 'tooltip': 'Select the directory where the files will be exported'}, + workflow_name={'label': 'Workflow name', + 'tooltip': 'Name of the workflow object.'}, ) def export_stacks(images: List[Tuple[Layer, str]], directory: Path = Path.home(), @@ -145,14 +173,14 @@ def export_stacks(images: List[Tuple[Layer, str]], if isinstance(image, Image): order = 1 + func_typecast = _image_typecast dtype = data_type elif isinstance(image, Labels): order = 0 - if data_type in ['uint8', 'uint16']: - dtype = data_type - else: - dtype = 'uint16' + func_typecast = _image_typecast + dtype = 'uint16' + if data_type != 'uint16': warn(f"{data_type} is not a valid type for Labels, please use uint8 or uint16") else: raise ValueError(f'{type(image)} cannot be exported, please use Image layers or Labels layers') @@ -173,6 +201,7 @@ def export_stacks(images: List[Tuple[Layer, str]], voxel_size=image.scale, voxel_size_unit=voxel_size_unit, directory=directory, + func_typecast=func_typecast, dtype=dtype, suffix=image_suffix) names.append(image.name) diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py index 48386903..7b290e89 100644 --- a/plantseg/napari/widget/predictions.py +++ b/plantseg/napari/widget/predictions.py @@ -16,9 +16,17 @@ @magicgui(call_button='Run Predictions', - model_name={"choices": list_models()}, - stride={"choices": [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, - device={"choices": ['cpu', 'cuda']} + image={'label': 'Image', + 'tooltip': 'Raw image to be processed with a neural network.'}, + model_name={'label': 'Select model', + 'tooltip': 'Select a pretrained model.', + 'choices': list_models()}, + patch_size={'label': 'Patch size', + 'tooltip': 'Patch size use to processed the data.'}, + stride={'label': 'Stride', + 'choices': [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, + device={'label': 'Device', + 'choices': ['cpu', 'cuda']} ) def widget_unet_predictions(image: Image, model_name: str, @@ -66,9 +74,15 @@ def _compute_multiple_predictions(image, patch_size, stride, device): return out_layers -@magicgui(call_button='Run Test Predictions', - stride={"choices": [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, - device={"choices": ['cpu', 'cuda']} +@magicgui(call_button='Try all Available Models', + image={'label': 'Image', + 'tooltip': 'Raw image to be processed with a neural network.'}, + patch_size={'label': 'Patch size', + 'tooltip': 'Patch size use to processed the data.'}, + stride={'label': 'Stride', + 'choices': [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, + device={'label': 'Device', + 'choices': ['cpu', 'cuda']} ) def widget_test_all_unet_predictions(image: Image, patch_size: Tuple[int, int, int] = (80, 160, 160), @@ -102,9 +116,25 @@ def _compute_iterative_predictions(pmap, model_name, num_iterations, sigma, patc @magicgui(call_button='Run Iterative Predictions', - model_name={"choices": list_models()}, - stride={"choices": [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, - device={"choices": ['cpu', 'cuda']} + image={'label': 'Image', + 'tooltip': 'Raw image to be processed with a neural network.'}, + model_name={'label': 'Select model', + 'tooltip': 'Select a pretrained model.', + 'choices': list_models()}, + num_iterations={'label': 'Num. of iterations', + 'tooltip': 'Nuber of iterations the model will run.'}, + sigma={'label': 'Sigma', + 'widget_type': 'FloatSlider', + 'tooltip': 'Define the size of the gaussian smoothing kernel. ' + 'The larger the more blurred will be the output image.', + 'max': 5., + 'min': 0.1}, + patch_size={'label': 'Patch size', + 'tooltip': 'Patch size use to processed the data.'}, + stride={'label': 'Stride', + 'choices': [STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE]}, + device={'label': 'Device', + 'choices': ['cpu', 'cuda']} ) def widget_iterative_unet_predictions(image: Image, model_name: str, @@ -138,6 +168,11 @@ def widget_iterative_unet_predictions(image: Image, @magicgui(call_button='Add Custom Model', + new_model_name={'label': 'New model name'}, + model_location={'label': 'Model location', + 'mode': 'd'}, + resolution={'label': 'Resolution'}, + description={'label': 'Description'}, ) def widget_add_custom_model(new_model_name: str = '', model_location: Path = Path.home(), diff --git a/plantseg/napari/widget/proofreading/proofreading.py b/plantseg/napari/widget/proofreading/proofreading.py index e46eb53a..cfa9c8ab 100644 --- a/plantseg/napari/widget/proofreading/proofreading.py +++ b/plantseg/napari/widget/proofreading/proofreading.py @@ -87,7 +87,7 @@ def split_merge_from_seeds(seeds, segmentation, image, bboxes, max_label): max_label) -@magicgui(call_button='Clean scribbles < c >') +@magicgui(call_button='Clean scribbles - < c >') def widget_clean_scribble(viewer): if current_label_layer is None: show_info('Scribble not defined. Run the proofreading widget tool once first') @@ -97,7 +97,10 @@ def widget_clean_scribble(viewer): viewer.layers[current_label_layer].refresh() -@magicgui(call_button='Split and Merge from scribbles < p >') +@magicgui(call_button='Split/Merge from scribbles - < p >', + scribbles={'label': 'Scribbles'}, + segmentation={'label': 'Segmentation'}, + image={'label': 'Image'}) def widget_split_and_merge_from_scribbles(viewer: napari.Viewer, scribbles: Labels, segmentation: Labels, diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index 2e752aeb..61940a09 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -14,7 +14,7 @@ def _generic_clustering(image: Image, labels: Labels, beta: float = 0.5, minsize: int = 100, - name: str = "GASP", + name: str = 'GASP', agg_func: Callable = gasp) -> Future[LayerDataTuple]: out_name = build_nice_name(image.name, name) inputs_names = (image.name, labels.name) @@ -35,24 +35,56 @@ def _generic_clustering(image: Image, labels: Labels, ) -@magicgui(call_button='Run GASP', beta={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) -def widget_gasp(image: Image, labels: Labels, +@magicgui(call_button='Run GASP', + image={'label': 'Image', + 'tooltip': 'Raw or boundary image to use as input for clustering.'}, + _labels={'label': 'Over-segmentation', + 'tooltip': 'Over-segmentation labels layer to use as input for clustering.'}, + beta={'label': 'Under/Over segmentation factor', + 'tooltip': 'A low value will increase under-segmentation tendency ' + 'and a large value increase over-segmentation tendency.', + 'widget_type': 'FloatSlider', 'max': 1., 'min': 0.}, + minsize={'label': 'Min-size', + 'tooltip': 'Minimum segment size allowed in voxels.'}) +def widget_gasp(image: Image, _labels: Labels, beta: float = 0.5, minsize: int = 100) -> Future[LayerDataTuple]: - return _generic_clustering(image, labels, beta=beta, minsize=minsize, name='GASP', agg_func=gasp) - - -@magicgui(call_button='Run MultiCut', beta={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) -def widget_multicut(image: Image, labels: Labels, + return _generic_clustering(image, _labels, beta=beta, minsize=minsize, name='GASP', agg_func=gasp) + + +@magicgui(call_button='Run MultiCut', + image={'label': 'Image', + 'tooltip': 'Raw or boundary image to use as input for clustering.'}, + _labels={'label': 'Over-segmentation', + 'tooltip': 'Over-segmentation labels layer to use as input for clustering.'}, + beta={'label': 'Under/Over segmentation factor', + 'tooltip': 'A low value will increase under-segmentation tendency ' + 'and a large value increase over-segmentation tendency.', + 'widget_type': 'FloatSlider', 'max': 1., 'min': 0.}, + minsize={'label': 'Min-size', + 'tooltip': 'Minimum segment size allowed in voxels.'}) +def widget_multicut(image: Image, _labels: Labels, beta: float = 0.5, minsize: int = 100) -> Future[LayerDataTuple]: - return _generic_clustering(image, labels, beta=beta, minsize=minsize, name='MultiCut', agg_func=multicut) - - -@magicgui(call_button='Run Lifted MultiCut', beta={"widget_type": "FloatSlider", "max": 1., 'min': 0.}) + return _generic_clustering(image, _labels, beta=beta, minsize=minsize, name='MultiCut', agg_func=multicut) + + +@magicgui(call_button='Run Lifted MultiCut', + image={'label': 'Image', + 'tooltip': 'Raw or boundary image to use as input for clustering.'}, + nuclei={'label': 'Nuclei', + 'tooltip': 'Nuclei binary predictions or Nuclei segmentation.'}, + _labels={'label': 'Over-segmentation', + 'tooltip': 'Over-segmentation labels layer to use as input for clustering.'}, + beta={'label': 'Under/Over segmentation factor', + 'tooltip': 'A low value will increase under-segmentation tendency ' + 'and a large value increase over-segmentation tendency.', + 'widget_type': 'FloatSlider', 'max': 1., 'min': 0.}, + minsize={'label': 'Min-size', + 'tooltip': 'Minimum segment size allowed in voxels.'}) def widget_lifted_multicut(image: Image, nuclei: Layer, - labels: Labels, + _labels: Labels, beta: float = 0.5, minsize: int = 100) -> Future[LayerDataTuple]: if isinstance(nuclei, Image): @@ -65,18 +97,18 @@ def widget_lifted_multicut(image: Image, raise ValueError(f'{nuclei} must be either an image or a labels layer') out_name = build_nice_name(image.name, 'LiftedMultiCut') - inputs_names = (image.name, nuclei.name, labels.name) + inputs_names = (image.name, nuclei.name, _labels.name) layer_kwargs = layer_properties(name=out_name, scale=image.scale, metadata=image.metadata) layer_type = 'labels' - func = partial(lmc, superpixels=labels, beta=beta, post_minsize=minsize) + func = partial(lmc, superpixels=_labels, beta=beta, post_minsize=minsize) return start_threading_process(func, func_kwargs={'boundary_pmaps': image.data, extra_key: nuclei.data, - 'superpixels': labels.data}, + 'superpixels': _labels.data}, out_name=out_name, input_keys=inputs_names, layer_kwarg=layer_kwargs, @@ -114,9 +146,30 @@ def _nuclei_aware_dtws_wrapper(boundary_pmaps, ) -@magicgui(call_button='Run WS', - stacked={'widget_type': 'RadioButtons', 'orientation': 'horizontal', 'choices': ['2D', '3D']}, - threshold={"widget_type": "FloatSlider", "max": 1., 'min': 0.}, ) +@magicgui(call_button='Run Watershed', + image={'label': 'Image', + 'tooltip': 'Raw or boundary image to use as input for Watershed.'}, + stacked={'label': 'Stacked', + 'tooltip': 'Define if the Watershed will run slice by slice (faster) ' + 'or on the full volume (slower).', + 'widget_type': 'RadioButtons', + 'orientation': 'horizontal', + 'choices': ['2D', '3D']}, + threshold={'label': 'Threshold', + 'tooltip': 'A low value will increase over-segmentation tendency ' + 'and a large value increase under-segmentation tendency.', + 'widget_type': 'FloatSlider', 'max': 1., 'min': 0.}, + min_size={'label': 'Min-size', + 'tooltip': 'Minimum segment size allowed in voxels.'}, + sigma_seeds={'label': 'Sigma seeds'}, + sigma_weights={'label': 'Sigma weights'}, + alpha={'label': 'Alpha'}, + use_pixel_pitch={'label': 'Use pixel pitch'}, + pixel_pitch={'label': 'Pixel pitch'}, + apply_nonmax_suppression={'label': 'Apply nonmax suppression'}, + nuclei={'label': 'Is image Nuclei'} + + ) def widget_dt_ws(image: Image, stacked: str = '2D', threshold: float = 0.5, @@ -158,7 +211,12 @@ def widget_dt_ws(image: Image, ) -@magicgui(call_button='Run Segmentation Fix from Nuclei') +@magicgui(call_button='Run Segmentation Fix from Nuclei', + cell_segmentation={'label': 'Cell Segmentation'}, + nuclei_segmentation={'label': 'Nuclei Segmentation'}, + boundary_pmaps={'label': 'Boundary Image'}, + threshold_merge={'label': 'Threshold merge'}, + threshold_split={'label': 'Threshold split'}) def widget_fix_over_under_segmentation_from_nuclei(cell_segmentation: Labels, nuclei_segmentation: Labels, boundary_pmaps: Union[None, Image], From e8b6016c2f9cf768667ed11917263b4610778d53 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 19 Aug 2022 15:51:50 +0200 Subject: [PATCH 31/77] fix bung in itk implementation --- .../widget/proofreading/proofreading.py | 26 +++++++++++++------ .../segmentation/functional/segmentation.py | 22 ++++++++++++++-- 2 files changed, 38 insertions(+), 10 deletions(-) diff --git a/plantseg/napari/widget/proofreading/proofreading.py b/plantseg/napari/widget/proofreading/proofreading.py index cfa9c8ab..b27a9087 100644 --- a/plantseg/napari/widget/proofreading/proofreading.py +++ b/plantseg/napari/widget/proofreading/proofreading.py @@ -1,12 +1,26 @@ +from functools import partial + import napari import numpy as np from magicgui import magicgui from napari.layers import Labels, Image -from skimage.segmentation import watershed -from vigra.analysis import watershedsNew +from napari.qt.threading import thread_worker from napari.utils.notifications import show_info + from plantseg.napari.widget.proofreading.utils import get_bboxes, get_idx_slice -from napari.qt.threading import thread_worker +from skimage.segmentation import watershed + +""" +try: + import SimpleITK as sitk + from plantseg.segmentation.functional.segmentation import simple_itk_watershed_from_markers as watershed + +except ImportError: + from skimage.segmentation import watershed as skimage_ws + watershed = partial(skimage_ws, compactness=0.01) + +""" + current_label_layer = None @@ -39,11 +53,7 @@ def _split_from_seed(segmentation, sz, sx, sy, region_slice, all_idx, offsets, b mask = [region_segmentation == idx for idx in all_idx] mask = np.logical_or.reduce(mask) - new_seg = watershed(region_image, - markers=region_seeds, - mask=mask, compactness=0.01) - #new_seg, _ = watershedsNew(region_image.astype('float32'), - # seeds=region_seeds.astype('uint32')) + new_seg = watershed(region_image, region_seeds, mask=mask, compactness=0.001) new_seg[~mask] = region_segmentation[~mask] new_bboxes = get_bboxes(new_seg) diff --git a/plantseg/segmentation/functional/segmentation.py b/plantseg/segmentation/functional/segmentation.py index 9c76c70b..e040a73c 100644 --- a/plantseg/segmentation/functional/segmentation.py +++ b/plantseg/segmentation/functional/segmentation.py @@ -10,9 +10,16 @@ from elf.segmentation.multicut import multicut_kernighan_lin from elf.segmentation.watershed import distance_transform_watershed, apply_size_filter from numpy.typing import ArrayLike +from vigra.filters import gaussianSmoothing from plantseg.segmentation.functional.utils import shift_affinities, compute_mc_costs +try: + import SimpleITK as sitk + sitk_installed = True +except ImportError: + sitk_installed = False + def dt_watershed(boundary_pmaps: ArrayLike, threshold: float = 0.5, @@ -210,8 +217,8 @@ def simple_itk_watershed(boundary_pmaps: ArrayLike, threshold: float, sigma: float, minsize: int): - import SimpleITK as sitk - from vigra.filters import gaussianSmoothing + if not sitk_installed: + raise ValueError('please install sitk before running this process') if sigma > 0: # fix ws sigma length @@ -229,3 +236,14 @@ def simple_itk_watershed(boundary_pmaps: ArrayLike, itk_segmentation = sitk.RelabelComponent(itk_segmentation, minsize) segmentation = sitk.GetArrayFromImage(itk_segmentation).astype(np.uint16) return segmentation + + +def simple_itk_watershed_from_markers(boundary_pmaps: ArrayLike, + seeds: ArrayLike): + if not sitk_installed: + raise ValueError('please install sitk before running this process') + + itk_pmaps = sitk.GetImageFromArray(boundary_pmaps) + itk_seeds = sitk.GetImageFromArray(seeds) + segmentation = sitk.MorphologicalWatershedFromMarkers(itk_pmaps, itk_seeds, markWatershedLine=False, fullyConnected=False) + return sitk.GetArrayFromImage(segmentation).astype('uint32') From 7161bbb067577f61d7002568012e0f0fdc6d9962 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 19 Aug 2022 17:28:12 +0200 Subject: [PATCH 32/77] fix bug in cropping + add more logging on widget execution --- plantseg/napari/widget/dataprocessing.py | 26 ++++++++++++++++++++---- plantseg/napari/widget/predictions.py | 7 ++++++- plantseg/napari/widget/segmentation.py | 4 ++++ plantseg/napari/widget/utils.py | 4 +++- 4 files changed, 35 insertions(+), 6 deletions(-) diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index f6fb4336..0eabc747 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -51,6 +51,7 @@ def widget_gaussian_smoothing(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name='Gaussian Smoothing', ) @@ -126,10 +127,27 @@ def widget_rescaling(image: Layer, out_name=out_name, input_keys=inputs_names, layer_kwarg=layer_kwargs, + process_name='Rescaling', layer_type=layer_type, ) +def _compute_slices(rectangle, crop_z, shape): + z_start = max(rectangle[0, 0] - crop_z // 2, 0) + z_end = min(rectangle[0, 0] + math.ceil(crop_z / 2), shape[0]) + z_slice = slice(z_start, z_end) + + x_start = max(rectangle[0, 1], 0) + x_end = min(rectangle[2, 1], shape[1]) + x_slice = slice(x_start, x_end) + + y_start = max(rectangle[0, 2], 0) + y_end = min(rectangle[2, 2], shape[2]) + y_slice = slice(y_start, y_end) + + return [z_slice, x_slice, y_slice] + + def _cropping(data, crop_slices): return data[crop_slices] @@ -157,10 +175,8 @@ def widget_cropping(image: Layer, layer_type = 'image' rectangle = crop_roi.data[0].astype('int64') - crop_slices = [slice(rectangle[0, 0] - crop_z // 2, rectangle[0, 0] + math.ceil(crop_z / 2)), - slice(rectangle[0, 1], rectangle[2, 1]), - slice(rectangle[0, 2], rectangle[2, 2])] + crop_slices = _compute_slices(rectangle, crop_z, image.data.shape) func = partial(_cropping, crop_slices=crop_slices) return start_threading_process(func, func_kwargs={'data': image.data}, @@ -168,6 +184,7 @@ def widget_cropping(image: Layer, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name='Cropping', skip_dag=True, ) @@ -214,6 +231,7 @@ def widget_add_layers(image1: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name='Merge Layers', ) @@ -239,7 +257,6 @@ def widget_label_processing(segmentation: Labels, set_bg_to_0: bool = True, relabel_segmentation: bool = True, ) -> Future[LayerDataTuple]: - if relabel_segmentation and 'bboxes' in segmentation.metadata.keys(): del segmentation.metadata['bboxes'] @@ -260,4 +277,5 @@ def widget_label_processing(segmentation: Labels, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name='Label Processing', ) diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py index 7b290e89..1eb73aa0 100644 --- a/plantseg/napari/widget/predictions.py +++ b/plantseg/napari/widget/predictions.py @@ -13,6 +13,7 @@ from plantseg.napari.widget.utils import start_threading_process, build_nice_name, layer_properties from plantseg.predictions.functional import unet_predictions from plantseg.predictions.utils import STRIDE_DRAFT, STRIDE_BALANCED, STRIDE_ACCURATE +from napari.utils.notifications import show_info @magicgui(call_button='Run Predictions', @@ -49,12 +50,15 @@ def widget_unet_predictions(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name='UNet Predictions', ) def _compute_multiple_predictions(image, patch_size, stride, device): out_layers = [] - for model_name in list_models(): + for i, model_name in enumerate(list_models()): + show_info(f'Napari - PlantSeg info: running UNet Predictions: {model_name} {i}/{len(list_models())}') + out_name = build_nice_name(image.name, model_name) layer_kwargs = layer_properties(name=out_name, scale=image.scale, @@ -164,6 +168,7 @@ def widget_iterative_unet_predictions(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name='UNet Iterative Predictions', ) diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index 61940a09..5de76c09 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -32,6 +32,7 @@ def _generic_clustering(image: Image, labels: Labels, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name=f'{name} Clustering', ) @@ -113,6 +114,7 @@ def widget_lifted_multicut(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name=f'Lifted Multicut Clustering', ) @@ -208,6 +210,7 @@ def widget_dt_ws(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name=f'Watershed Segmentation', ) @@ -249,4 +252,5 @@ def widget_fix_over_under_segmentation_from_nuclei(cell_segmentation: Labels, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, + process_name=f'Fix Over / Under segmentation', ) diff --git a/plantseg/napari/widget/utils.py b/plantseg/napari/widget/utils.py index 47130272..1a2f5963 100644 --- a/plantseg/napari/widget/utils.py +++ b/plantseg/napari/widget/utils.py @@ -18,12 +18,13 @@ def start_threading_process(func: Callable, input_keys: Tuple[str, ...], layer_kwarg: dict, layer_type='image', + process_name='', skip_dag=False) -> Future: thread_func = thread_worker(partial(func, **func_kwargs)) future = Future() def on_done(result): - show_info(f'Napari - PlantSeg info: widget computation complete') + show_info(f'Napari - PlantSeg info: widget {process_name} computation complete') _func = func if not skip_dag else identity dag.add_step(_func, input_keys=input_keys, output_key=out_name) result = result, layer_kwarg, layer_type @@ -32,6 +33,7 @@ def on_done(result): worker = thread_func() worker.returned.connect(on_done) worker.start() + show_info(f'Napari - PlantSeg info: widget {process_name} computation started') return future From 0c34ddda4bb6a64690c9237dc6f63cca17a0f306 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Mon, 22 Aug 2022 16:24:11 +0200 Subject: [PATCH 33/77] fix bugs in the dask headless mode pipeline --- plantseg/napari/dag_manager.py | 57 ++++++--- plantseg/napari/widget/dataprocessing.py | 30 +++-- plantseg/napari/widget/io.py | 154 +++++++++++++++++------ plantseg/napari/widget/predictions.py | 24 ++-- plantseg/napari/widget/segmentation.py | 43 ++++--- plantseg/napari/widget/utils.py | 19 +-- 6 files changed, 221 insertions(+), 106 deletions(-) diff --git a/plantseg/napari/dag_manager.py b/plantseg/napari/dag_manager.py index 403a6dfb..d0b658be 100644 --- a/plantseg/napari/dag_manager.py +++ b/plantseg/napari/dag_manager.py @@ -1,35 +1,60 @@ import pickle import dask -from dask.threaded import get +from dask.threaded import get as tget +from dask.multiprocessing import get as mpget +import warnings class DagManager: def __init__(self): - self.dag = {} + self.dask_dag = {} + self.human_readable_dag = {} self.inputs = [] self.outputs = [] self.outputs_suffixes = [] def __repr__(self): return f'inputs: {self.inputs} \n ' \ - f'dag: {self.dag} \n' \ + f'dag: {self.human_readable_dag} \n' \ f'outputs: {self.outputs}' - def get_dag(self, inputs): - for key, _input in zip(self.inputs, inputs): - self.dag[key] = _input - return get(self.dag, self.outputs) - - def add_step(self, function, input_keys, output_key): - self.inputs += [input_key for input_key in input_keys - if input_key not in self.dag.keys()] - self.dag[output_key] = (dask.delayed(function), *input_keys) - - def export_dag(self, path, outputs, outputs_suffixes): + def get_dag(self, inputs_dict, get_type='threaded'): + for _input in self.inputs: + if _input in inputs_dict: + self.dask_dag[_input] = inputs_dict[_input] + else: + self.dask_dag[_input] = None + warnings.warn(f'{_input} is not in {list(inputs_dict.keys())}, this might compromise the pipeline.') + + if get_type == 'threaded': + get = tget + elif get_type == 'multiprocessing': + get = mpget + else: + raise ValueError('get_type must me either threaded or multiprocessing.') + return get(self.dask_dag, self.outputs) + + def add_step(self, function, input_keys, output_key, step_name=None, step_params=None): + step_name = step_name if step_name is not None else 'unknown step' + step_params = step_params if step_params is not None else {} + + _inputs = [input_key for input_key in input_keys if input_key not in self.dask_dag.keys()] + self.inputs += _inputs + self.dask_dag[output_key] = (dask.delayed(function, name=step_name), *input_keys) + + self.dag_add_step_description(step_name, step_params, input_keys, output_key) + + def dag_add_step_description(self, step_name, step_params, input_keys, output_key): + if isinstance(input_keys, tuple): + input_keys = list(input_keys) + + self.human_readable_dag[output_key] = {'step-name': step_name, + 'step_inputs': input_keys, + 'step_params': step_params} + + def export_dag(self, path, outputs): self.outputs = outputs - self.outputs_suffixes = outputs_suffixes - with open(path, 'wb') as f: pickle.dump(self, f) diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index 0eabc747..91063a27 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -40,10 +40,11 @@ def widget_gaussian_smoothing(image: Image, ) -> Future[LayerDataTuple]: out_name = build_nice_name(image.name, 'GaussianSmoothing') inputs_kwarg = {'image': image.data} + step_kwargs = {'sigma': sigma} inputs_names = (image.name,) layer_kwargs = layer_properties(name=out_name, scale=image.scale, metadata=image.metadata) layer_type = 'image' - func = partial(image_gaussian_smoothing, sigma=sigma) + func = partial(image_gaussian_smoothing, **step_kwargs) return start_threading_process(func, func_kwargs=inputs_kwarg, @@ -51,7 +52,8 @@ def widget_gaussian_smoothing(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name='Gaussian Smoothing', + step_name='Gaussian Smoothing', + step_kwargs=step_kwargs, ) @@ -106,7 +108,6 @@ def widget_rescaling(image: Layer, elif type_of_refactor == 'Same as Reference Model': out_voxel_size = get_model_resolution(reference_model) - print(out_voxel_size) rescaling_factor = compute_scaling_factor(current_resolution, out_voxel_size) else: @@ -115,19 +116,21 @@ def widget_rescaling(image: Layer, out_name = build_nice_name(image.name, 'Rescaled') inputs_kwarg = {'image': image.data} inputs_names = (image.name,) + step_kwargs = {'factor': rescaling_factor, 'order': order} layer_kwargs = layer_properties(name=out_name, scale=out_voxel_size, metadata={**image.metadata, **{'original_voxel_size': current_resolution}}) layer_type = 'image' - func = partial(image_rescale, factor=rescaling_factor, order=order) + func = partial(image_rescale, **step_kwargs) return start_threading_process(func, func_kwargs=inputs_kwarg, out_name=out_name, input_keys=inputs_names, layer_kwarg=layer_kwargs, - process_name='Rescaling', + step_name='Rescaling', + step_kwargs=step_kwargs, layer_type=layer_type, ) @@ -184,7 +187,7 @@ def widget_cropping(image: Layer, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name='Cropping', + step_name='Cropping', skip_dag=True, ) @@ -222,16 +225,18 @@ def widget_add_layers(image1: Image, scale=image1.scale, metadata=image1.metadata) layer_type = 'image' - - func = partial(_two_layers_operation, weights=weights, operation=operation) + step_kwargs = dict(weights=weights, operation=operation) + func = partial(_two_layers_operation, **step_kwargs) assert image1.data.shape == image2.data.shape + return start_threading_process(func, func_kwargs={'data1': image1.data, 'data2': image2.data}, out_name=out_name, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name='Merge Layers', + step_name='Merge Layers', + step_kwargs=step_kwargs ) @@ -267,9 +272,9 @@ def widget_label_processing(segmentation: Labels, scale=segmentation.scale, metadata=segmentation.metadata) layer_type = 'labels' + step_kwargs = dict(set_bg_to_0=set_bg_to_0, relabel_segmentation=relabel_segmentation) func = partial(_label_processing, - set_bg_to_0=set_bg_to_0, - relabel_segmentation=relabel_segmentation) + **step_kwargs) return start_threading_process(func, func_kwargs=inputs_kwarg, @@ -277,5 +282,6 @@ def widget_label_processing(segmentation: Labels, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name='Label Processing', + step_name='Label Processing', + step_kwargs=step_kwargs ) diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index b0dca1bc..8321460a 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -8,7 +8,7 @@ from napari.types import LayerDataTuple from warnings import warn -from plantseg.dataprocessing.functional.dataprocessing import scale_image_to_voxelsize +from plantseg.dataprocessing.functional.dataprocessing import image_rescale, compute_scaling_factor from plantseg.dataprocessing.functional.dataprocessing import fix_input_shape, normalize_01 from plantseg.io import H5_EXTENSIONS, TIFF_EXTENSIONS, allowed_data_format from plantseg.io.io import load_tiff, load_h5, create_tiff @@ -42,7 +42,8 @@ def _filter_channel(data, channel, layout): return np.squeeze(data[tuple(slices)]) -def _advanced_load(path, key, channel, advanced_load=False, layer_type='image', headless=False): +def _advanced_load(path, key, channel, advanced_load=False, layer_type='image'): + path = Path(path) base, ext = path.stem, path.suffix if ext not in allowed_data_format: raise ValueError(f'File extension is {ext} but should be one of {allowed_data_format}') @@ -70,9 +71,14 @@ def _advanced_load(path, key, channel, advanced_load=False, layer_type='image', elif layer_type == 'labels': data = data.astype('uint16') - if headless: - return data - return data, voxel_size, voxel_size_unit + return {'data': data, + 'voxel_size': voxel_size, + 'voxel_size_unit': voxel_size_unit + } + + +def _unpack_load(loaded_dict, key): + return loaded_dict.get(key) @magicgui( @@ -101,38 +107,64 @@ def open_file(path: Path = Path.home(), key: str = 'raw', channel: Tuple[int, str] = (0, 'xcxx'), ) -> LayerDataTuple: - name = layer_type if name == '' else name - _func_gui = partial(_advanced_load, - key=key, - channel=channel, - advanced_load=advanced_load, - layer_type=layer_type, - headless=False) - _func_dask = partial(_advanced_load, - key=key, - channel=channel, - advanced_load=advanced_load, - layer_type=layer_type, - headless=True) - - data, voxel_size, voxel_size_unit = _func_gui(path) - dag.add_step(_func_dask, input_keys=(f'{name}_path',), output_key=name) + + # wrap load routine and add it to the dag + step_params = {'key': key, + 'channel': channel, + 'advanced_load': advanced_load, + 'layer_type': layer_type} + _func = partial(_advanced_load, + **step_params) + + dag.add_step(_func, + input_keys=(f'{name}_path',), + output_key=f'_loaded_dict', + step_name='Load stack', + step_params=step_params) + + # locally unwrap the result + load_dict = _func(path) + data = load_dict['data'] + voxel_size = load_dict['voxel_size'] + voxel_size_unit = load_dict['voxel_size_unit'] + + # add the key unwrapping to the dag + for key, out_name in [('data', name), + ('voxel_size', f'{name}_voxel_size'), + ('voxel_size_unit', f'{name}_voxel_size_unit')]: + step_params = {'key': key} + dag.add_step(partial(_unpack_load, **step_params), + input_keys=(f'_loaded_dict',), + output_key=out_name, + step_name=f'Unpack stack: {key}', + step_params=step_params + ) + + # return layer show_info(f'Napari - PlantSeg info: {name} correctly imported, voxel_size: {voxel_size} {voxel_size_unit}') layer_kwargs = layer_properties(name=name, scale=voxel_size, metadata={'original_voxel_size': voxel_size, - 'voxel_size_unit': voxel_size_unit}) + 'voxel_size_unit': voxel_size_unit, + 'root_name': name}) return data, layer_kwargs, layer_type -def export_stack_as_tiff(data, name, voxel_size, voxel_size_unit, directory, func_typecast, dtype, suffix): +def export_stack_as_tiff(data, name, directory, + voxel_size, voxel_size_unit, + func_scale, func_typecast, dtype, suffix): + + if func_scale is not None: + data = func_scale(data) + stack_name = f'{name}_{suffix}' + directory = Path(directory) out_path = directory / f'{stack_name}.tiff' data = fix_input_shape(data) data = func_typecast(data, dtype) create_tiff(path=out_path, stack=data[...], voxel_size=voxel_size, voxel_size_unit=voxel_size_unit) - return stack_name + return out_path def _image_typecast(data, dtype): @@ -148,6 +180,12 @@ def _label_typecast(data, dtype): return data.astype(dtype) +def checkout(*args): + for stack in args: + stack = Path(stack) + assert stack.is_file() + + @magicgui( call_button='Export stack', images={'label': 'Layers to export', @@ -168,9 +206,9 @@ def export_stacks(images: List[Tuple[Layer, str]], data_type: str = 'float32', workflow_name: str = 'workflow', ) -> None: - names, suffixes = [], [] + export_name = [] for i, (image, image_suffix) in enumerate(images): - + # parse type and casting function to use if isinstance(image, Image): order = 1 func_typecast = _image_typecast @@ -185,10 +223,17 @@ def export_stacks(images: List[Tuple[Layer, str]], else: raise ValueError(f'{type(image)} cannot be exported, please use Image layers or Labels layers') + # parse metadata in the layer if rescale_to_original_resolution and 'original_voxel_size' in image.metadata.keys(): output_resolution = image.metadata['original_voxel_size'] - image.data = scale_image_to_voxelsize(image.data, image.scale, output_resolution, order=order) - image.scale = output_resolution + input_resolution = image.scale + scaling_factor = compute_scaling_factor(input_voxel_size=input_resolution, + output_voxel_size=output_resolution) + func_scaling = partial(image_rescale, factor=scaling_factor, order=order) + + else: + output_resolution = image.scale + func_scaling = None if 'voxel_size_unit' in image.metadata.keys(): voxel_size_unit = image.metadata['voxel_size_unit'] @@ -196,19 +241,46 @@ def export_stacks(images: List[Tuple[Layer, str]], voxel_size_unit = 'um' image_suffix = f'export_{i}' if image_suffix == '' else image_suffix - _ = export_stack_as_tiff(data=image.data, - name=image.name, - voxel_size=image.scale, - voxel_size_unit=voxel_size_unit, - directory=directory, - func_typecast=func_typecast, - dtype=dtype, - suffix=image_suffix) - names.append(image.name) - suffixes.append(image_suffix) + step_params = {'func_scale': func_scaling, + 'func_typecast': func_typecast, + 'dtype': dtype, + 'suffix': image_suffix + } + + func_export_tiff = partial(export_stack_as_tiff, **step_params) + _ = func_export_tiff(data=image.data, + name=image.name, + directory=directory, + voxel_size=output_resolution, + voxel_size_unit=voxel_size_unit, ) + + root_name = image.metadata['root_name'] + input_keys = (image.name, + 'out_stack_name', + 'out_directory', + f'{root_name}_voxel_size', + f'{root_name}_voxel_size_unit' + ) + + _export_name = f'{image.name}_export' + dag.add_step(func_export_tiff, + input_keys=input_keys, + output_key=_export_name, + step_name='Export', + step_params=step_params) + export_name.append(_export_name) + show_info(f'Napari - PlantSeg info: {image.name} correctly exported,' f' voxel_size: {image.scale} {voxel_size_unit}') - out_path = directory / f'{workflow_name}.pkl' - dag.export_dag(out_path, names, suffixes) - show_info(f'Napari - PlantSeg info: workflow correctly exported') + if export_name: + final_export_check = 'final_export_check' + dag.add_step(checkout, + input_keys=export_name, + output_key=final_export_check, + step_name='Checkout Execution', + step_params={}) + + out_path = directory / f'{workflow_name}.pkl' + dag.export_dag(out_path, final_export_check) + show_info(f'Napari - PlantSeg info: workflow correctly exported') diff --git a/plantseg/napari/widget/predictions.py b/plantseg/napari/widget/predictions.py index 1eb73aa0..ae984dd2 100644 --- a/plantseg/napari/widget/predictions.py +++ b/plantseg/napari/widget/predictions.py @@ -41,8 +41,8 @@ def widget_unet_predictions(image: Image, scale=image.scale, metadata=image.metadata) layer_type = 'image' - - func = partial(unet_predictions, model_name=model_name, stride=stride, patch=patch_size, device=device) + step_kwargs = dict(model_name=model_name, stride=stride, patch=patch_size, device=device) + func = partial(unet_predictions, **step_kwargs) return start_threading_process(func, func_kwargs={'raw': image.data}, @@ -50,7 +50,8 @@ def widget_unet_predictions(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name='UNet Predictions', + step_name='UNet Predictions', + step_kwargs=step_kwargs ) @@ -153,14 +154,14 @@ def widget_iterative_unet_predictions(image: Image, scale=image.scale, metadata=image.metadata) layer_type = 'image' + step_kwargs = dict(model_name=model_name, + num_iterations=num_iterations, + sigma=sigma, + patch_size=patch_size, + stride=stride, + device=device) - func = partial(_compute_iterative_predictions, - model_name=model_name, - num_iterations=num_iterations, - sigma=sigma, - patch_size=patch_size, - stride=stride, - device=device) + func = partial(_compute_iterative_predictions, **step_kwargs) return start_threading_process(func, func_kwargs={'pmap': image.data}, @@ -168,7 +169,8 @@ def widget_iterative_unet_predictions(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name='UNet Iterative Predictions', + step_name='UNet Iterative Predictions', + step_kwargs=step_kwargs ) diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index 5de76c09..60182b15 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -22,8 +22,8 @@ def _generic_clustering(image: Image, labels: Labels, scale=image.scale, metadata=image.metadata) layer_type = 'labels' - - func = partial(agg_func, beta=beta, post_minsize=minsize) + step_kwargs = dict(beta=beta, post_minsize=minsize) + func = partial(agg_func, **step_kwargs) return start_threading_process(func, func_kwargs={'boundary_pmaps': image.data, @@ -32,7 +32,8 @@ def _generic_clustering(image: Image, labels: Labels, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name=f'{name} Clustering', + step_name=f'{name} Clustering', + step_kwargs=step_kwargs ) @@ -103,8 +104,8 @@ def widget_lifted_multicut(image: Image, scale=image.scale, metadata=image.metadata) layer_type = 'labels' - - func = partial(lmc, superpixels=_labels, beta=beta, post_minsize=minsize) + step_kwargs = dict(beta=beta, post_minsize=minsize) + func = partial(lmc, step_kwargs) return start_threading_process(func, func_kwargs={'boundary_pmaps': image.data, @@ -114,7 +115,8 @@ def widget_lifted_multicut(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name=f'Lifted Multicut Clustering', + step_name=f'Lifted Multicut Clustering', + step_kwargs=step_kwargs ) @@ -192,17 +194,18 @@ def widget_dt_ws(image: Image, stacked = False if stacked == '3D' else True pixel_pitch = pixel_pitch if use_pixel_pitch else None + step_kwargs = dict(threshold=threshold, + min_size=min_size, + stacked=stacked, + sigma_seeds=sigma_seeds, + sigma_weights=sigma_weights, + alpha=alpha, + pixel_pitch=pixel_pitch, + apply_nonmax_suppression=apply_nonmax_suppression, + nuclei=nuclei) func = partial(_nuclei_aware_dtws_wrapper, - threshold=threshold, - min_size=min_size, - stacked=stacked, - sigma_seeds=sigma_seeds, - sigma_weights=sigma_weights, - alpha=alpha, - pixel_pitch=pixel_pitch, - apply_nonmax_suppression=apply_nonmax_suppression, - nuclei=nuclei + **step_kwargs ) return start_threading_process(func, func_kwargs={'boundary_pmaps': image.data}, @@ -210,7 +213,8 @@ def widget_dt_ws(image: Image, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name=f'Watershed Segmentation', + step_name=f'Watershed Segmentation', + step_kwargs=step_kwargs ) @@ -241,10 +245,10 @@ def widget_fix_over_under_segmentation_from_nuclei(cell_segmentation: Labels, scale=cell_segmentation.scale, metadata=cell_segmentation.metadata) layer_type = 'labels' + step_kwargs = dict(threshold_merge=threshold_merge, threshold_split=threshold_split) func = partial(fix_over_under_segmentation_from_nuclei, - threshold_merge=threshold_merge, - threshold_split=threshold_split, + **step_kwargs ) return start_threading_process(func, func_kwargs=func_kwargs, @@ -252,5 +256,6 @@ def widget_fix_over_under_segmentation_from_nuclei(cell_segmentation: Labels, input_keys=inputs_names, layer_kwarg=layer_kwargs, layer_type=layer_type, - process_name=f'Fix Over / Under segmentation', + step_name=f'Fix Over / Under segmentation', + step_kwargs=step_kwargs ) diff --git a/plantseg/napari/widget/utils.py b/plantseg/napari/widget/utils.py index 1a2f5963..01563e1e 100644 --- a/plantseg/napari/widget/utils.py +++ b/plantseg/napari/widget/utils.py @@ -17,28 +17,33 @@ def start_threading_process(func: Callable, out_name: str, input_keys: Tuple[str, ...], layer_kwarg: dict, - layer_type='image', - process_name='', - skip_dag=False) -> Future: + layer_type: str = 'image', + step_name: str = '', + step_kwargs: dict = None, + skip_dag: bool = False) -> Future: + thread_func = thread_worker(partial(func, **func_kwargs)) future = Future() def on_done(result): - show_info(f'Napari - PlantSeg info: widget {process_name} computation complete') + show_info(f'Napari - PlantSeg info: widget {step_name} computation complete') _func = func if not skip_dag else identity - dag.add_step(_func, input_keys=input_keys, output_key=out_name) + dag.add_step(_func, input_keys=input_keys, + output_key=out_name, + step_name=step_name, + step_params=step_kwargs) result = result, layer_kwarg, layer_type future.set_result(result) worker = thread_func() worker.returned.connect(on_done) worker.start() - show_info(f'Napari - PlantSeg info: widget {process_name} computation started') + show_info(f'Napari - PlantSeg info: widget {step_name} computation started') return future def layer_properties(name, scale, metadata: dict = None): - keys_to_save = {'original_voxel_size', 'voxel_size_unit'} + keys_to_save = {'original_voxel_size', 'voxel_size_unit', 'root_name'} if metadata is not None: _new_metadata = {key: metadata[key] for key in keys_to_save if key in metadata} else: From ee80b134f9b0843570ce8e19ca13f940ff32df49 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 23 Aug 2022 18:56:02 +0200 Subject: [PATCH 34/77] move ci to from conda to mamba --- .github/workflows/build-deploy-on-conda.yml | 33 +++++++++++++-------- conda-recipe/meta.yaml | 6 ++-- 2 files changed, 22 insertions(+), 17 deletions(-) diff --git a/.github/workflows/build-deploy-on-conda.yml b/.github/workflows/build-deploy-on-conda.yml index 4bc0e302..ce1b96e1 100644 --- a/.github/workflows/build-deploy-on-conda.yml +++ b/.github/workflows/build-deploy-on-conda.yml @@ -13,29 +13,36 @@ jobs: - name: Print PlantSeg version name run: | echo $RELEASE_VERSION - - name: Set up Python 3.8 - uses: actions/setup-python@v2 + - uses: conda-incubator/setup-miniconda@v2 with: - python-version: 3.8 - - name: Add conda to system path + mamba-version: "*" + auto-activate-base: true + activate-environment: "" + channels: local,conda-forge,defaults + channel-priority: false + + - shell: bash -l {0} run: | - # $CONDA is an environment variable pointing to the root of the miniconda directory - echo $CONDA/bin >> $GITHUB_PATH - - name: Build PlantSeg + conda info --envs + - name: Build PlantSeg using Boa + shell: bash -l {0} run: | - conda install -q conda-build - conda build -c conda-forge -c awolny conda-recipe - - name: Install dependencies + mamba install -q boa + mamba mambabuild -c conda-forge -c awolny conda-recipe + - name: Create PlantSeg env run: | - conda install -c conda-forge -c awolny --use-local pytorch-3dunet=1.3.7 plantseg + mamba create -n plant-seg -c local -c conda-forge -c awolny plantseg pytest - name: Run pytest + shell: bash -l {0} run: | - conda install pytest + conda activate plant-seg pytest + conda deactivate - name: Deploy on conda if: ${{ startsWith( github.ref, 'refs/tags/') && success() }} env: ANACONDA_SECRET: ${{ secrets.ANACONDA_TOKEN }} + shell: bash -l {0} run: | - conda install -q anaconda-client + mamba install -q anaconda-client anaconda -t $ANACONDA_SECRET upload $CONDA/conda-bld/**/plantseg-*.tar.bz2 diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml index 3fb07bdb..48f2b41c 100644 --- a/conda-recipe/meta.yaml +++ b/conda-recipe/meta.yaml @@ -19,18 +19,16 @@ requirements: - pip run: - - python - - pillow + - python => 3.9 - h5py - requests - pyyaml - - scipy - scikit-image - tifffile - vigra - pytorch - python-elf - - pytorch-3dunet + - pytorch-3dunet => 1.3.7 test: imports: From 04d46de11b9b52060c2e691c1093a0d56689b6cd Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Tue, 23 Aug 2022 19:07:45 +0200 Subject: [PATCH 35/77] move ci to from conda to mamba --- conda-recipe/meta.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml index 48f2b41c..f8fbf5b9 100644 --- a/conda-recipe/meta.yaml +++ b/conda-recipe/meta.yaml @@ -19,7 +19,7 @@ requirements: - pip run: - - python => 3.9 + - python =3.9 - h5py - requests - pyyaml @@ -28,7 +28,7 @@ requirements: - vigra - pytorch - python-elf - - pytorch-3dunet => 1.3.7 + - pytorch-3dunet =>1.3.7 test: imports: From 95865e2258c345b7987673a836ef87f652b24397 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 24 Aug 2022 13:41:54 +0200 Subject: [PATCH 36/77] fix minor bug in cropping tool --- plantseg/napari/widget/dataprocessing.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/plantseg/napari/widget/dataprocessing.py b/plantseg/napari/widget/dataprocessing.py index 91063a27..fd14ac2e 100644 --- a/plantseg/napari/widget/dataprocessing.py +++ b/plantseg/napari/widget/dataprocessing.py @@ -148,7 +148,7 @@ def _compute_slices(rectangle, crop_z, shape): y_end = min(rectangle[2, 2], shape[2]) y_slice = slice(y_start, y_end) - return [z_slice, x_slice, y_slice] + return z_slice, x_slice, y_slice def _cropping(data, crop_slices): @@ -181,6 +181,7 @@ def widget_cropping(image: Layer, crop_slices = _compute_slices(rectangle, crop_z, image.data.shape) func = partial(_cropping, crop_slices=crop_slices) + return start_threading_process(func, func_kwargs={'data': image.data}, out_name=out_name, From f2d453658b9e423fbaf29e6465105351afc21a4e Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 24 Aug 2022 15:09:51 +0200 Subject: [PATCH 37/77] add napari in the dependencies --- conda-recipe/meta.yaml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml index f8fbf5b9..18076676 100644 --- a/conda-recipe/meta.yaml +++ b/conda-recipe/meta.yaml @@ -29,6 +29,8 @@ requirements: - pytorch - python-elf - pytorch-3dunet =>1.3.7 + - napari + - python-graphviz test: imports: From f480f17475c81ea6f961ee78ff4c12556576444f Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Wed, 24 Aug 2022 17:18:29 +0200 Subject: [PATCH 38/77] nice dag representation --- plantseg/napari/dag_manager.py | 20 +++++++++++++++++--- plantseg/napari/widget/io.py | 2 +- 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/plantseg/napari/dag_manager.py b/plantseg/napari/dag_manager.py index d0b658be..af661b24 100644 --- a/plantseg/napari/dag_manager.py +++ b/plantseg/napari/dag_manager.py @@ -15,9 +15,23 @@ def __init__(self): self.outputs_suffixes = [] def __repr__(self): - return f'inputs: {self.inputs} \n ' \ - f'dag: {self.human_readable_dag} \n' \ - f'outputs: {self.outputs}' + max_key_len = max([len(key) for key in self.human_readable_dag.keys()]) + out_str = 'PlantSeg Computation Graph Manager\n\n' + out_str += f'\033[1mInputs\033[0m: {", ".join(self.inputs)} \n\n' + out_str += '\033[1mDAG\033[0m: \n' + for key, value in self.human_readable_dag.items(): + key += ' ' * (1 + max_key_len - len(key)) + process = "\033[92m {}\033[00m" .format(value["step-name"]) + process = f' {key} <-- {process}(' + len_process = len(process) - 2 * len('\033[92m') + + for i, ikey in enumerate(value["step_inputs"]): + off_set = '' if i == 0 else len_process * ' ' + end = ')\n' if i == (len(value["step_inputs"]) - 1) else ',\n' + process += f'{off_set}{ikey}{end}' + + out_str += process + return out_str def get_dag(self, inputs_dict, get_type='threaded'): for _input in self.inputs: diff --git a/plantseg/napari/widget/io.py b/plantseg/napari/widget/io.py index 8321460a..182730c5 100644 --- a/plantseg/napari/widget/io.py +++ b/plantseg/napari/widget/io.py @@ -137,7 +137,7 @@ def open_file(path: Path = Path.home(), dag.add_step(partial(_unpack_load, **step_params), input_keys=(f'_loaded_dict',), output_key=out_name, - step_name=f'Unpack stack: {key}', + step_name=f'Unpack {key}', step_params=step_params ) From f26ee3f5a672735eece838198ccd6b3533e2b006 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 26 Aug 2022 12:28:25 +0200 Subject: [PATCH 39/77] add mutex and help functionality --- plantseg/napari/containers.py | 57 ++++++++++++------- plantseg/napari/viewer.py | 2 +- plantseg/napari/widget/segmentation.py | 37 +++++++++--- plantseg/segmentation/functional/__init__.py | 1 + .../segmentation/functional/segmentation.py | 19 ++++++- 5 files changed, 86 insertions(+), 30 deletions(-) diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index 58bd5489..9d3f8dc6 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -1,4 +1,4 @@ -from magicgui.widgets import Container +from magicgui.widgets import Container, MainWindow from plantseg.napari.widget.dataprocessing import widget_rescaling, widget_gaussian_smoothing from plantseg.napari.widget.dataprocessing import widget_cropping, widget_add_layers @@ -11,48 +11,67 @@ from plantseg.napari.widget.proofreading.proofreading import widget_split_and_merge_from_scribbles from plantseg.napari.widget.proofreading.proofreading import widget_clean_scribble from plantseg.napari.widget.dataprocessing import widget_label_processing +from PyQt5.QtCore import Qt +import webbrowser -def get_main(): - container = Container(widgets=[open_file, - export_stacks, - widget_split_and_merge_from_scribbles, - widget_clean_scribble - ], - labels=False) +def setup_menu(container, path=None): + def _callback(): + if path is not None: + webbrowser.open(path) + + container.create_menu_item(menu_name='Help', + item_name='Open Documentation', + callback=_callback) + container._widget._layout.setAlignment(Qt.AlignTop) + return container + +def get_main(): + container = MainWindow(widgets=[open_file, + export_stacks, + widget_split_and_merge_from_scribbles, + widget_clean_scribble + ], + labels=False) + container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki') return container def get_preprocessing_workflow(): - container = Container(widgets=[widget_gaussian_smoothing, - widget_rescaling, - widget_cropping, - widget_add_layers, - widget_label_processing], - labels=False) + + container = MainWindow(widgets=[widget_gaussian_smoothing, + widget_rescaling, + widget_cropping, + widget_add_layers, + widget_label_processing], + labels=False) + container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki/Data-Processing') return container def get_gasp_workflow(): - container = Container(widgets=[widget_unet_predictions, + container = MainWindow(widgets=[widget_unet_predictions, widget_dt_ws, widget_gasp], - labels=False) + labels=False) + container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki/UNet-GASP-Workflow') return container def get_extra_seg(): - container = Container(widgets=[widget_multicut, + container = MainWindow(widgets=[widget_multicut, widget_lifted_multicut, widget_fix_over_under_segmentation_from_nuclei], - labels=False) + labels=False) + container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki/Extra-Seg') return container def get_extra_pred(): - container = Container(widgets=[widget_test_all_unet_predictions, + container = MainWindow(widgets=[widget_test_all_unet_predictions, widget_iterative_unet_predictions, widget_add_custom_model], labels=False) + container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki/Extra-Pred') return container diff --git a/plantseg/napari/viewer.py b/plantseg/napari/viewer.py index 199fddff..46b1e6ce 100644 --- a/plantseg/napari/viewer.py +++ b/plantseg/napari/viewer.py @@ -15,7 +15,7 @@ def run_viewer(): viewer.bind_key('c', widget_clean_scribble) for _containers, name in [(get_preprocessing_workflow(), 'Data - Processing'), - (get_gasp_workflow(), 'UNet + GASP Workflow'), + (get_gasp_workflow(), 'UNet + Segmentation'), (get_extra_pred(), 'Extra-Pred'), (get_extra_seg(), 'Extra-Seg'), ]: diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index 60182b15..027db586 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -1,15 +1,22 @@ from concurrent.futures import Future from functools import partial +from typing import Union, Tuple, Callable + from magicgui import magicgui -from napari.types import ImageData, LayerDataTuple, LabelsData from napari.layers import Labels, Image, Layer -from typing import Union, Tuple, Callable +from napari.types import LayerDataTuple +from enum import Enum + +from plantseg.dataprocessing.functional.advanced_dataprocessing import fix_over_under_segmentation_from_nuclei +from plantseg.dataprocessing.functional.dataprocessing import normalize_01 from plantseg.napari.widget.utils import start_threading_process, build_nice_name, layer_properties -from plantseg.segmentation.functional import gasp, multicut, dt_watershed +from plantseg.segmentation.functional import gasp, multicut, dt_watershed, mutex_ws from plantseg.segmentation.functional import lifted_multicut_from_nuclei_segmentation, lifted_multicut_from_nuclei_pmaps -from plantseg.dataprocessing.functional.dataprocessing import normalize_01 -from plantseg.dataprocessing.functional.advanced_dataprocessing import fix_over_under_segmentation_from_nuclei +class ClusteringOptions(Enum): + gasp = gasp + multicut = multicut + mutex_ws = mutex_ws def _generic_clustering(image: Image, labels: Labels, beta: float = 0.5, @@ -37,11 +44,15 @@ def _generic_clustering(image: Image, labels: Labels, ) -@magicgui(call_button='Run GASP', +@magicgui(call_button='Run Clustering', image={'label': 'Image', 'tooltip': 'Raw or boundary image to use as input for clustering.'}, _labels={'label': 'Over-segmentation', 'tooltip': 'Over-segmentation labels layer to use as input for clustering.'}, + mode={'label': 'Aggl. Mode', + 'choices': ['GASP', 'MutexWS', 'MultiCut'], + 'tooltip': 'Which agglomeration algorithm to use.' + }, beta={'label': 'Under/Over segmentation factor', 'tooltip': 'A low value will increase under-segmentation tendency ' 'and a large value increase over-segmentation tendency.', @@ -49,9 +60,19 @@ def _generic_clustering(image: Image, labels: Labels, minsize={'label': 'Min-size', 'tooltip': 'Minimum segment size allowed in voxels.'}) def widget_gasp(image: Image, _labels: Labels, - beta: float = 0.5, + mode: str = "GASP", + beta: float = 0.6, minsize: int = 100) -> Future[LayerDataTuple]: - return _generic_clustering(image, _labels, beta=beta, minsize=minsize, name='GASP', agg_func=gasp) + if mode == 'GASP': + func = gasp + + elif mode == 'MutexWS': + func = mutex_ws + + else: + func = multicut + + return _generic_clustering(image, _labels, beta=beta, minsize=minsize, name=mode, agg_func=func) @magicgui(call_button='Run MultiCut', diff --git a/plantseg/segmentation/functional/__init__.py b/plantseg/segmentation/functional/__init__.py index 6ba98796..b4efe65b 100644 --- a/plantseg/segmentation/functional/__init__.py +++ b/plantseg/segmentation/functional/__init__.py @@ -3,3 +3,4 @@ from plantseg.segmentation.functional.segmentation import lifted_multicut_from_nuclei_pmaps from plantseg.segmentation.functional.segmentation import lifted_multicut_from_nuclei_segmentation from plantseg.segmentation.functional.segmentation import multicut +from plantseg.segmentation.functional.segmentation import mutex_ws diff --git a/plantseg/segmentation/functional/segmentation.py b/plantseg/segmentation/functional/segmentation.py index e040a73c..2a942614 100644 --- a/plantseg/segmentation/functional/segmentation.py +++ b/plantseg/segmentation/functional/segmentation.py @@ -1,5 +1,5 @@ from typing import List - +from functools import partial import nifty import nifty.graph.rag as nrag import numpy as np @@ -16,6 +16,7 @@ try: import SimpleITK as sitk + sitk_installed = True except ImportError: sitk_installed = False @@ -123,6 +124,19 @@ def superpixel_gen(*args, **kwargs): return segmentation +def mutex_ws(boundary_pmaps: ArrayLike, + superpixels: ArrayLike = None, + beta: float = 0.5, + post_minsize: int = 100, + n_threads: int = 6) -> ArrayLike: + return gasp(boundary_pmaps=boundary_pmaps, + superpixels=superpixels, + gasp_linkage_criteria='mutex_watershed', + beta=beta, + post_minsize=post_minsize, + n_threads=n_threads) + + def multicut(boundary_pmaps: ArrayLike, superpixels: ArrayLike, beta: float = 0.5, @@ -245,5 +259,6 @@ def simple_itk_watershed_from_markers(boundary_pmaps: ArrayLike, itk_pmaps = sitk.GetImageFromArray(boundary_pmaps) itk_seeds = sitk.GetImageFromArray(seeds) - segmentation = sitk.MorphologicalWatershedFromMarkers(itk_pmaps, itk_seeds, markWatershedLine=False, fullyConnected=False) + segmentation = sitk.MorphologicalWatershedFromMarkers(itk_pmaps, itk_seeds, markWatershedLine=False, + fullyConnected=False) return sitk.GetArrayFromImage(segmentation).astype('uint32') From 53a9b4fc35f6ac558c86e541575b0e987c8af392 Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 26 Aug 2022 13:28:56 +0200 Subject: [PATCH 40/77] add mutex and help functionality --- plantseg/napari/containers.py | 21 +++++++++---------- plantseg/napari/widget/segmentation.py | 29 +++++++------------------- 2 files changed, 17 insertions(+), 33 deletions(-) diff --git a/plantseg/napari/containers.py b/plantseg/napari/containers.py index 9d3f8dc6..875b2e46 100644 --- a/plantseg/napari/containers.py +++ b/plantseg/napari/containers.py @@ -5,9 +5,9 @@ from plantseg.napari.widget.io import open_file, export_stacks from plantseg.napari.widget.predictions import widget_unet_predictions, widget_test_all_unet_predictions from plantseg.napari.widget.predictions import widget_iterative_unet_predictions, widget_add_custom_model -from plantseg.napari.widget.segmentation import widget_dt_ws, widget_gasp +from plantseg.napari.widget.segmentation import widget_dt_ws, widget_agglomeration from plantseg.napari.widget.segmentation import widget_fix_over_under_segmentation_from_nuclei -from plantseg.napari.widget.segmentation import widget_multicut, widget_lifted_multicut +from plantseg.napari.widget.segmentation import widget_lifted_multicut from plantseg.napari.widget.proofreading.proofreading import widget_split_and_merge_from_scribbles from plantseg.napari.widget.proofreading.proofreading import widget_clean_scribble from plantseg.napari.widget.dataprocessing import widget_label_processing @@ -34,7 +34,7 @@ def get_main(): widget_clean_scribble ], labels=False) - container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki') + container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki/Napari-Main') return container @@ -52,17 +52,16 @@ def get_preprocessing_workflow(): def get_gasp_workflow(): container = MainWindow(widgets=[widget_unet_predictions, - widget_dt_ws, - widget_gasp], + widget_dt_ws, + widget_agglomeration], labels=False) container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki/UNet-GASP-Workflow') return container def get_extra_seg(): - container = MainWindow(widgets=[widget_multicut, - widget_lifted_multicut, - widget_fix_over_under_segmentation_from_nuclei], + container = MainWindow(widgets=[widget_lifted_multicut, + widget_fix_over_under_segmentation_from_nuclei], labels=False) container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki/Extra-Seg') return container @@ -70,8 +69,8 @@ def get_extra_seg(): def get_extra_pred(): container = MainWindow(widgets=[widget_test_all_unet_predictions, - widget_iterative_unet_predictions, - widget_add_custom_model], - labels=False) + widget_iterative_unet_predictions, + widget_add_custom_model], + labels=False) container = setup_menu(container, path='https://github.com/hci-unihd/plant-seg/wiki/Extra-Pred') return container diff --git a/plantseg/napari/widget/segmentation.py b/plantseg/napari/widget/segmentation.py index 027db586..5bf57e70 100644 --- a/plantseg/napari/widget/segmentation.py +++ b/plantseg/napari/widget/segmentation.py @@ -13,11 +13,13 @@ from plantseg.segmentation.functional import gasp, multicut, dt_watershed, mutex_ws from plantseg.segmentation.functional import lifted_multicut_from_nuclei_segmentation, lifted_multicut_from_nuclei_pmaps + class ClusteringOptions(Enum): gasp = gasp multicut = multicut mutex_ws = mutex_ws + def _generic_clustering(image: Image, labels: Labels, beta: float = 0.5, minsize: int = 100, @@ -51,7 +53,7 @@ def _generic_clustering(image: Image, labels: Labels, 'tooltip': 'Over-segmentation labels layer to use as input for clustering.'}, mode={'label': 'Aggl. Mode', 'choices': ['GASP', 'MutexWS', 'MultiCut'], - 'tooltip': 'Which agglomeration algorithm to use.' + 'tooltip': 'Select which agglomeration algorithm to use.' }, beta={'label': 'Under/Over segmentation factor', 'tooltip': 'A low value will increase under-segmentation tendency ' @@ -59,10 +61,10 @@ def _generic_clustering(image: Image, labels: Labels, 'widget_type': 'FloatSlider', 'max': 1., 'min': 0.}, minsize={'label': 'Min-size', 'tooltip': 'Minimum segment size allowed in voxels.'}) -def widget_gasp(image: Image, _labels: Labels, - mode: str = "GASP", - beta: float = 0.6, - minsize: int = 100) -> Future[LayerDataTuple]: +def widget_agglomeration(image: Image, _labels: Labels, + mode: str = "GASP", + beta: float = 0.6, + minsize: int = 100) -> Future[LayerDataTuple]: if mode == 'GASP': func = gasp @@ -75,23 +77,6 @@ def widget_gasp(image: Image, _labels: Labels, return _generic_clustering(image, _labels, beta=beta, minsize=minsize, name=mode, agg_func=func) -@magicgui(call_button='Run MultiCut', - image={'label': 'Image', - 'tooltip': 'Raw or boundary image to use as input for clustering.'}, - _labels={'label': 'Over-segmentation', - 'tooltip': 'Over-segmentation labels layer to use as input for clustering.'}, - beta={'label': 'Under/Over segmentation factor', - 'tooltip': 'A low value will increase under-segmentation tendency ' - 'and a large value increase over-segmentation tendency.', - 'widget_type': 'FloatSlider', 'max': 1., 'min': 0.}, - minsize={'label': 'Min-size', - 'tooltip': 'Minimum segment size allowed in voxels.'}) -def widget_multicut(image: Image, _labels: Labels, - beta: float = 0.5, - minsize: int = 100) -> Future[LayerDataTuple]: - return _generic_clustering(image, _labels, beta=beta, minsize=minsize, name='MultiCut', agg_func=multicut) - - @magicgui(call_button='Run Lifted MultiCut', image={'label': 'Image', 'tooltip': 'Raw or boundary image to use as input for clustering.'}, From 9a11d862f6b7ecc85b01c4ee9c608d055fe0cb2b Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 26 Aug 2022 13:50:09 +0200 Subject: [PATCH 41/77] fix readme for new assets --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 14f36092..6972f830 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ # PlantSeg -![alt text](./Documentation-GUI/images/main_figure_nologo.png) +![alt text](../assets/images/main_figure_nologo.png) [PlantSeg](plantseg) is a tool for cell instance aware segmentation in densely packed 3D volumetric images. The pipeline uses a two stages segmentation strategy (Neural Network + Segmentation). The pipeline is tuned for plant cell tissue acquired with confocal and light sheet microscopy. From c4ecaa80fb869d0eda835466011c641389885bed Mon Sep 17 00:00:00 2001 From: lorenzocerrone Date: Fri, 26 Aug 2022 13:50:55 +0200 Subject: [PATCH 42/77] remove unused assets --- Documentation-GUI/Data-Processing.md | 44 ---- Documentation-GUI/Predictions.md | 29 --- Documentation-GUI/README.md | 205 ------------------ Documentation-GUI/Segmentation.md | 49 ----- Documentation-GUI/images/3datlas_leaf_raw.png | Bin 534792 -> 0 bytes Documentation-GUI/images/3datlas_root_raw.png | Bin 136055 -> 0 bytes Documentation-GUI/images/cnn-predictions.png | Bin 527965 -> 0 bytes Documentation-GUI/images/confocal.png | Bin 6951767 -> 0 bytes Documentation-GUI/images/cos_root_mc_raw.png | Bin 64018 -> 0 bytes Documentation-GUI/images/main_figure.png | Bin 593408 -> 0 bytes .../images/main_figure_nologo.png | Bin 542209 -> 0 bytes Documentation-GUI/images/meristem.png | Bin 318317 -> 0 bytes .../images/plantseg_overview.png | Bin 540061 -> 0 bytes Documentation-GUI/images/preprocessing.png | Bin 528593 -> 0 bytes Documentation-GUI/images/segmentation.png | Bin 528012 -> 0 bytes .../images/tum_ovules_mc_raw.png | Bin 419618 -> 0 bytes 16 files changed, 327 deletions(-) delete mode 100644 Documentation-GUI/Data-Processing.md delete mode 100644 Documentation-GUI/Predictions.md delete mode 100644 Documentation-GUI/README.md delete mode 100644 Documentation-GUI/Segmentation.md delete mode 100644 Documentation-GUI/images/3datlas_leaf_raw.png delete mode 100644 Documentation-GUI/images/3datlas_root_raw.png delete mode 100644 Documentation-GUI/images/cnn-predictions.png delete mode 100644 Documentation-GUI/images/confocal.png delete mode 100644 Documentation-GUI/images/cos_root_mc_raw.png delete mode 100644 Documentation-GUI/images/main_figure.png delete mode 100644 Documentation-GUI/images/main_figure_nologo.png delete mode 100644 Documentation-GUI/images/meristem.png delete mode 100644 Documentation-GUI/images/plantseg_overview.png delete mode 100644 Documentation-GUI/images/preprocessing.png delete mode 100644 Documentation-GUI/images/segmentation.png delete mode 100644 Documentation-GUI/images/tum_ovules_mc_raw.png diff --git a/Documentation-GUI/Data-Processing.md b/Documentation-GUI/Data-Processing.md deleted file mode 100644 index f3abd3cb..00000000 --- a/Documentation-GUI/Data-Processing.md +++ /dev/null @@ -1,44 +0,0 @@ -# Data Processing -![alt text](./images/preprocessing.png) -**PlantSeg** includes basic utilities for data pre-processing and post-processing. - -## Pre-Processing - -The input for this widget can be either a "raw" image or "prediction" image. -Input format allowed are tiff and h5 while output is always h5. - -* The **Save Directory** entry can be used to define output directory. - -* The most important setting is the **Rescaling**. It is very important to rescale the image to - match the resolution of the data used for training the Neural Network. -This operation can be done automatically by clicking in the gui on **Guided**. -Be careful to use this function only in case of data considerably different from -the reference resolution. -``` -As and example: - - if your data has the voxel size of 0.3 x 0.1 x 0.1 (zxy). - - and the networks was trained on 0.3 x 0.2 x 0.2 data (reference resolution). - -The required voxel size can be obtained by computing the ratio between your data and the -reference at train. In the example the rescaling factor = 1 x 2 x 2. -``` - -* The **Interpolation** field control the interpolation type (0 for nearest neighbors, 1 for linear spline, -2 for quadratic). - -* The last field defines a **Filter** operation. Implemented there are: - 1. **Gaussian** Filtering: The parameters is float and define the sigma value for the gaussian smoothing. -The higher the wider is filtering kernel. - 2. **Median** Filtering: Apply median operation convolutionally over the image. - The kernel is a sphere of size defined in the parameter field. - -## Post-Processing - -A post processing widget can be applied after the **CNN-Predictions** and the **Segmentation**. -Two type of operations can be performed: - * Converting output to tiff file (default is h5). - - * Casting the **CNN-Predictions** output to *data_uint8* drastically reduce the memory foot print of the output - file. - -Additionally, the post processing will scale back your outputs to the original voxels resolutions. \ No newline at end of file diff --git a/Documentation-GUI/Predictions.md b/Documentation-GUI/Predictions.md deleted file mode 100644 index a86e0e40..00000000 --- a/Documentation-GUI/Predictions.md +++ /dev/null @@ -1,29 +0,0 @@ -# CNN Predictions -![alt text](./images/cnn-predictions.png) - -The CNN predictions widget process the stacks at hand with a Convolutional Neural Network. The output is -a boundary classification image, where every voxel gets a value between 0 (not a cell boundary) and 1 (cell boundary). - -The input image can be a raw stack "tiff"/"h5" or the output of the PreProcessing widget. - -* The **Model Name** menu shows all available models. There are two main basic models available - 1. **Generic confocal**, this a generic model for all confocal dataset. - Some examples: - ![alt text](./images/confocal.png) - - 2. **Generic lightsheet** this a generic model for all lightsheet dataset. - Some examples: - ![alt text](./images/cos_root_mc_raw.png) - -* Due to memory constrains, usually a complete stacks does not fit the GPUs memory, - therefore the **Patch size** can be used to optimize the performance of the pipeline. - Usually a bigger patches cost more memory but can give a slight improvement in performance. - For 2D segmentation the **Patch size** relative to the z axis has to be set to 1. - -* To minimize the boundary effect due to the sliding windows patching we can use different **stride**: - 1. Accurate: corresponding to a stride 50% of the patch size (yield best predictions/segmentation accuracy) - 2. Balanced: corresponding to a stride 75% of the patch size - 3. Draft: corresponding to a stride 95% of the patch size (yield fastest runtime) - -* The **Device type** menu can be used to enable or not gpu acceleration. The networks predictions are -greatly accelerated by CUDA on Nvidia GPUs, at the moment we don't support other GPUs manufacturers. diff --git a/Documentation-GUI/README.md b/Documentation-GUI/README.md deleted file mode 100644 index 6639027d..00000000 --- a/Documentation-GUI/README.md +++ /dev/null @@ -1,205 +0,0 @@ - -# PlantSeg introduction - -PlantSeg is a tool for 3D and 2D segmentation. The methods used are very generic and can be used -for any type of instance segmentation workflow, but they are tuned towards cell segmentation in plant tissue. - The tools is fundamentally composed of two main steps. - -![alt text](./images/main_figure.png) - -* ***Cell boundary predictions***: Where a convolutional neural network is used to extract a -voxel wise boundary classification. The neural network is capable of filtering out very different types/intensity of -noise, homogenising the signal strength and fixing imaging defect (such as missing/blurred cell boundaries). - -* ***Cell Segmentation as graph partitioning***: The output of the fist step can be used directly for automated -segmentation. We implemented 4 different algorithms for segmentation, each with peculiar features. - This type of approach is especially well suited for segmentation of densely packed cell. - -For a complete description of the methods used please check out our -[manuscript](https://www.biorxiv.org/content/10.1101/2020.01.17.910562v1). - -# PlantSeg from GUI -The graphical user interface is the easiest way to configure and run PlantSeg. -Currently the GUI does not allow to visualize or interact with the data. -We recommend using [MorphographX](https://www.mpipz.mpg.de/MorphoGraphX) or -[Fiji](https://fiji.sc/) in order to assert the success and quality of the pipeline results. - -![alt text](./images/plantseg_overview.png) - -### File Browser Widget -The file browser can be used to select the input files for the pipeline. -PlantSeg can run on a single file (button A) or in batch mode for all files inside a directory (button B). -If a directory is selected PlantSeg will run on all compatible files inside the directory. - -### Main Pipeline Configurator -The central panel of PlantSeg (C) is the core of the pipeline configuration. -It can be used for customizing and tuning the pipeline accordingly to the data at hand. -Detailed information for each stage can be found at: -* [Data-Processing](Data-Processing.md) -* [CNN-Predictions](Predictions.md) -* [Segmentation](Segmentation.md) - -Any of the above widgets can be run singularly or in sequence (left to right). The order of execution can not be -modified. - -### Run -The last panel has two main functions. -Running the pipeline (D), once the run button is pressed the -pipeline starts. The button is inactive until the process is finished. -Adding a custom model (E). Custom trained model can be done by using the dedicated popup. Training a new model can be -done following the instruction at [pytorch-3dunet](https://github.com/wolny/pytorch-3dunet). - -### Results - -The results are stored together with the source input files inside a nested directory structure. -As example, if we want to run PlantSeg inside a directory with 2 stacks, we will obtain the following -outputs: -``` -/file1.tif -/file2.tif -/PreProcesing/ ------------->/file1.h5 ------------->/file1.yaml ------------->/file2.h5 ------------->/file2.yaml ------------->/generic_confocal_3d_unet/ -------------------------------------->/file1_predictions.h5 -------------------------------------->/file1_predictions.yaml -------------------------------------->/file2_predictions.h5 -------------------------------------->/file2_predictions.yaml -------------------------------------->/GASP/ ------------------------------------------->/file_1_predions_gasp_average.h5 ------------------------------------------->/file_1_predions_gasp_average.yaml ------------------------------------------->/file_2_predions_gasp_average.h5 ------------------------------------------->/file_2_predions_gasp_average.yaml ------------------------------------------->/PostProcessing/ ---------------------------------------------------------->/file_1_predions_gasp_average.tiff ---------------------------------------------------------->/file_1_predions_gasp_average.yaml ---------------------------------------------------------->/file_2_predions_gasp_average.tiff ---------------------------------------------------------->/file_2_predions_gasp_average.yaml -``` -The use of this hierarchical directory structure allows PlantSeg to easily find the necessary files and can be used -to test different combination of segmentation algorithms/parameters minimizing the memory overhead on the disk. -For sake of reproducibility, every file is associated with a configuration file ".yaml" that saves all parameters used -to produce the result. - -### Start PlantSeg GUI -In order to start the PlantSeg app in GUI mode: -First, activate the newly created conda environment with: -```bash -conda activate plant-seg -``` - -then, run the GUI by simply typing: -```bash -$ plantseg --gui -``` - -# PlantSeg from configuration file -This modality of using PlantSeg is particularly suited for high throughput processing and for running -PlantSeg on a remote server. -In order to use PlantSeg from command line mode, you will need to create a configuration file using a normal text editor - or using the save option of the PlantSeg gui. - -Here an example configuration: - -``` -path: /home/USERNAME/DATA.tiff # Contains the path to the directory or file to process - -preprocessing: - # enable/disable preprocessing - state: True - # create a new sub folder where all results will be stored - save_directory: "PreProcessing" - # rescaling the volume is essential for the generalization of the networks. The rescaling factor can be computed as the resolution - # of the volume at hand divided by the resolution of the dataset used in training. Be careful, if the difference is too large check for a different model. - factor: [1.0, 1.0, 1.0] - # the order of the spline interpolation - order: 2 - # optional: perform Gaussian smoothing or median filtering on the input. - filter: - # enable/disable filtering - state: False - # Accepted values: 'gaussian'/'median' - type: gaussian - # sigma (gaussian) or disc radius (median) - param: 1.0 - -cnn_prediction: - # enable/disable UNet prediction - state: True - # Trained model name, more info on available models and custom models in the README - model_name: "generic_confocal_3d_unet" - # If a CUDA capable gpu is available and corrected setup use "cuda", if not you can use "cpu" for cpu only inference (slower) - device: "cpu" - # (int or tuple) mirror pad the input stack in each axis for best prediction performance - mirror_padding: [16, 32, 32] - # how many subprocesses to use for data loading - num_workers: 8 - # patch size given to the network (adapt to fit in your GPU mem) - patch: [32, 128, 128] - # stride between patches (make sure the the patches overlap in order to get smoother prediction maps) - stride: [20, 100, 100] - # "best" refers to best performing on the val set (recommended), alternatively "last" refers to the last version before interruption - version: best - # If "True" forces downloading networks from the online repos - model_update: False - -cnn_postprocessing: - # enable/disable cnn post processing - state: False - # if True convert to result to tiff - tiff: False - # rescaling factor - factor: [1, 1, 1] - # spline order for rescaling - order: 2 - -segmentation: - # enable/disable segmentation - state: True - # Name of the algorithm to use for inferences. Options: MultiCut, MutexWS, GASP, DtWatershed - name: "MultiCut" - # Segmentation specific parameters here - # balance under-/over-segmentation; 0 - aim for undersegmentation, 1 - aim for oversegmentation. (Not active for DtWatershed) - beta: 0.5 - # directory where to save the results - save_directory: "MultiCut" - # enable/disable watershed - run_ws: True - # use 2D instead of 3D watershed - ws_2D: True - # probability maps threshold - ws_threshold: 0.5 - # set the minimum superpixels size - ws_minsize: 50 - # sigma for the gaussian smoothing of the distance transform - ws_sigma: 2.0 - # sigma for the gaussian smoothing of boundary - ws_w_sigma: 0 - # set the minimum segment size in the final segmentation. (Not active for DtWatershed) - post_minsize: 50 - -segmentation_postprocessing: - # enable/disable segmentation post processing - state: False - # if True convert to result to tiff - tiff: False - # rescaling factor - factor: [1, 1, 1] - # spline order for rescaling (keep 0 for segmentation post processing - order: 0 -``` -This configuration can be found at [config.yaml](examples/config.yaml). - -### Pipeline Usage (command line) -In order to start PlantSeg from command line: -First, activate the newly created conda environment with: -```bash -conda activate plant-seg -``` -then, one can just start the pipeline with -```bash -plantseg --config CONFIG_PATH -``` -where `CONFIG_PATH` is the path to the YAML configuration file. \ No newline at end of file diff --git a/Documentation-GUI/Segmentation.md b/Documentation-GUI/Segmentation.md deleted file mode 100644 index f1b84a93..00000000 --- a/Documentation-GUI/Segmentation.md +++ /dev/null @@ -1,49 +0,0 @@ -# Segmentation -![alt text](./images/segmentation.png) - -The segmentation widget allows to use very powerful graph partitioning techniques to obtain a segmentation from the -input stacks. -The input of this widget should be the output of the [CNN-predictions widget](./Predictions.md). -If the boundary prediction staged failed for any reason, a raw image can be used (especially if the cell boundaries are - very sharp and the noise is low) but this usually does not yields satisfactory results. - -* The **Algorithm** menu can be used to choose the segmentation algorithm, available choice are: - 1. GASP (average): is a generalization of the classical hierarchical clustering. It usually deliver very - reliable and accurate segmentation. It is the default in PlantSeg. - 2. MutexWS: Mutex Watershed is a derivative of the standard Watershed where we do not need seeds for the - segmentation. This algorithm performs very well in certain types of complex morphology (like ) - 3. MultiCut: in contrast to the other algorithms is not based on a greedy agglomeration but tries to find the - global optimal segmentation. This is in practice very hard and it can infeasible for very large stacks. - 4. DtWatershed: is our implementation of the distance transform Watershed. From the imput we extract a distance map - from the boundaries. Based this distance map, seeds are placed at local minima. Then those seeds are used for - computing the Watershed segmentation. To speed up the computation of GASP, MutexWS and MultiCut an over segmentation - is obtained using Dt Watershed. - -* **Save Directory** defines the name of the sub-directory where the segmentation results will be stored. - -* The **Under/Over- segmentation factor** is the most important parameters for tuning the segmentation of GASP, -MutexWS and MultiCut. A small value will steer the segmentation towards under-segmentation. While a high value bias the -segmentation towards the over-segmentation. This parameter has no effect on the distance transform Watershed. - -* If **Run Watershed in 2D** value is True the superpixels are created in 2D (over the z slice). While if False creates -the superpixels in the whole 3D volume. 3D superpixels are much slower and memory intensive, but can improve - the segmentation accuracy. - -* The **CNN Predictions Threshold** is used for the superpixels extraction and Distance Transform Watershed. It has a -crucial role for the watershed seeds extraction and can be used similarly to the "Unde/Over segmentation factor" -to bias the final result. -An high value translate to less seeds being placed (more under segmentation), while with a low value more seeds are - placed (more over segmentation). - -* The input is used by the distance transform Watershed for extracting the seed and - for finding the segmentation boundaries. If **Watershed Seeds Sigma** and **Watershed Boundary Sigma** are larger than - zero a gaussian smoothing is applied on the input before the aforementioned operations. This is mainly helpful for - the seeds computation, but in most cases does not impact segmentation quality. - -* The **Superpixels Minimum Size** applies a size filter to the initial superpixels over segmentation, this remove -small segments that are often produced by Watershed. This is usually helpful for the subsequent agglomeration. - Segments smaller than the threshold will be merged with a the nearest neighbour segment. - -* Even tough GASP, MutexWS and MultiCut are not very prone to produce small segments, the **Cell Minimum Size** can -be uses a final size processing filter. 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