New Segmentation model

Pretraining on artificial data, training on JNatProd + patent data

decimer_segmentation/decimer_segmentation.py

@@ -205,7 +205,7 @@ def load_model() -> modellib.MaskRCNN:
     # Download trained weights if needed
     if not os.path.exists(model_path):
         print("Downloading model weights...")
-        url = "https://zenodo.org/records/10142866/files/mask_rcnn_molecule.h5?download=1"
+        url = "https://zenodo.org/record/10663579/files/mask_rcnn_molecule.h5?download=1"
         req = requests.get(url, allow_redirects=True)
         with open(model_path, "wb") as model_file:
             model_file.write(req.content)

tests/test_mask_expansion.py

@@ -1,4 +1,5 @@
 import numpy as np
+
 from decimer_segmentation.complete_structure import (
     binarize_image,
     get_seeds,
@@ -7,15 +8,13 @@
     detect_horizontal_and_vertical_lines,
 )
 
-
 def test_binarize_image():
     test_image_array = np.array([[255, 255, 255],[0, 0, 0],[255, 255, 255]])
     test_threshold = "otsu"
     expected_result = np.array([True, False, True])
     actual_result = binarize_image(test_image_array, test_threshold)
     assert np.array_equal(expected_result, actual_result)
 
-
 def test_get_seeds():
     test_image_array = np.array([[0, 1, 0],[1, 0, 1],[0, 1, 0]])
     test_mask_array = np.ones(test_image_array.shape)
@@ -25,7 +24,6 @@ def test_get_seeds():
     for index in range(len(expected_result)):
         assert expected_result[index] == actual_result[index]
 
-
 def test_expand_masks():
     test_image_array = np.array([(False, False, True, True, True)])
     test_seed_pixels = [(2, 0)]