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helpers.py
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import math, os, csv
from config import checkpoint_path, csv_log_path, log_path, image_dir_path, dataset_output_path, \
augmented_dataset_output_path, results_path
import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt
def print_section(message):
print('-' * 50)
print(message, '...')
print('-' * 50)
def calculated_steps_per_epoch(num_samples, batch_size):
return int(math.ceil(1. * num_samples / batch_size))
def generate_path_prefix(model, batch_size, epochs, n_filters, multi_modal, augmented, early_fusion, late_fusion,
pe_block, dt, stage=None):
aug = "augmented" if augmented else "non_augmented"
stage_str = "_stage_" + str(stage) if stage else ""
pe_block_str = "pe_block_all" if pe_block else ""
fusion_type = ""
if multi_modal:
fusion_type = "multi_stage_fusion"
if multi_modal and early_fusion:
fusion_type = "early_fusion"
elif multi_modal and late_fusion:
fusion_type = "late_fusion"
return model + "_e" + str(epochs) + "_b" + str(batch_size) + "_nf" + str(
n_filters[0]) + "_" + aug + "_" + pe_block_str + "_" + fusion_type + "_" + dt + stage_str
def generate_model_image_path(path_prefix):
return os.path.join(image_dir_path, path_prefix + ".png")
def generate_csv_log_path(path_prefix):
return os.path.join(csv_log_path, path_prefix + ".csv")
def generate_checkpoint_path(path_prefix):
return os.path.join(checkpoint_path, path_prefix + ".hdf5")
def generate_tensorboard_path(path_prefix):
return os.path.join(log_path, path_prefix)
def save_test_images(imgs_mask_test, path_prefix):
imgs_mask_test = np.around(imgs_mask_test, decimals=0)
imgs_mask_test = (imgs_mask_test * 1.).astype(np.uint8)
pred_dir = os.path.join('preds', path_prefix)
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
pred_list = []
for k, img in enumerate(imgs_mask_test):
pred_path = os.path.join(pred_dir, str(k) + ".nii.gz")
img = np.squeeze(img, axis=-1)
img_vol = sitk.GetImageFromArray(img)
sitk.WriteImage(img_vol, pred_path)
pred_list.append(pred_path)
return pred_list
def create_dataset_csv(dataset_type, volume_list, augmented):
folder = augmented_dataset_output_path if augmented else dataset_output_path
if not os.path.exists(folder):
os.makedirs(folder)
file_path = os.path.join(folder, dataset_type + ".csv")
with open(file_path, 'w', newline='') as out:
csv_out = csv.writer(out)
csv_out.writerow(['bmode', 'pd', 'label'])
for row in volume_list:
csv_out.writerow(row)
def create_results_csv(path_prefix, perf_metrics, pred_ground_truth_tuple_list, total_time):
if len(perf_metrics) != len(pred_ground_truth_tuple_list):
return
if not os.path.exists(results_path):
os.mkdir(results_path)
file_path = os.path.join(results_path, path_prefix + ".csv")
with open(file_path, 'w', newline='') as out:
csv_out = csv.writer(out)
csv_out.writerow(['Prediction', 'Ground Truth', 'DSC', 'Jaccard', 'Hausdorff', 'Mean Surface Distance', 'Total Time (s)'])
for i in range(len(pred_ground_truth_tuple_list)):
row = list()
row.extend(list(pred_ground_truth_tuple_list[i]))
row.extend(list(perf_metrics[i]))
row.append(total_time)
print(tuple(row))
csv_out.writerow(tuple(row))
def get_record_path(dataset_type, i=None, augmented=False):
folder = os.path.join(augmented_dataset_output_path, dataset_type) if augmented else os.path.join(
dataset_output_path, dataset_type)
if not os.path.exists(folder):
os.makedirs(folder)
if i != None:
return os.path.join(folder, str(i) + '.tfrecords')
else:
return folder
def generate_slices(dataset_type, i, bmode, pd, label, augmented=False):
folder = os.path.join(augmented_dataset_output_path, "images") if augmented else os.path.join(dataset_output_path,
"images")
if not os.path.exists(folder):
os.makedirs(folder)
sub_dir = os.path.join(folder, dataset_type)
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
mask = np.ma.masked_where(label == 0, label)
y_mins = np.where(mask == np.amin(mask))[1]
mask_min = y_mins[0]
mask_max = y_mins[-1]
plt.imshow(bmode[:, (mask_min + mask_max) // 2, :], cmap="gray")
plt.imshow(mask[:, (mask_min + mask_max) // 2, :], cmap="autumn", alpha=0.3)
plt.savefig(os.path.join(sub_dir, str(i) + ".png"))
plt.close()
def generate_nifti(dataset_type, i, bmode, pd, label, augmented=False):
folder = os.path.join(augmented_dataset_output_path, "volumes") if augmented else os.path.join(dataset_output_path,
"volumes")
if not os.path.exists(folder):
os.makedirs(folder)
sub_dir = os.path.join(folder, dataset_type)
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
child_dir = os.path.join(sub_dir, str(i))
if not os.path.exists(child_dir):
os.makedirs(child_dir)
bmode_vol = sitk.GetImageFromArray(bmode)
pd_vol = sitk.GetImageFromArray(pd)
label_vol = sitk.GetImageFromArray(label)
sitk.WriteImage(bmode_vol, os.path.join(child_dir, str(i) + "_bmode.nii.gz"))
sitk.WriteImage(pd_vol, os.path.join(child_dir, str(i) + "_pd.nii.gz"))
sitk.WriteImage(label_vol, os.path.join(child_dir, str(i) + "_label.nii.gz"))