def masks_to_annotation(datasets_dir, save_path):
masks_dir = os.path.join(datasets_dir, "labels_all")
nucleis_dir = os.path.join(datasets_dir, "images_all")
# %% Process one folder and save as one json file allowing multiple annotation types
simplify_tol = 0 # Tolerance for polygon simplification with shapely (0 to not simplify)
# outputs_dir = os.path.abspath(os.path.join('..', 'data', 'postProcessing', 'mask2json'))
if os.path.exists(masks_dir):
print(f'Analyzing folder:{masks_dir}')
for file in [f for f in os.listdir(masks_dir)]:
file_id = os.path.splitext(file)[0]
# Read png with mask
print(f'Analyzing file:{file}')
file_full = os.path.join(masks_dir, file)
mask_img = io.imread(file_full)
print("mask_img.shape:", mask_img.shape)
mask = measure.label(mask_img)
label = "nuclei"
print("label:", label)
sample_path = os.path.join(save_path, file_id)
if not os.path.exists(sample_path):
os.makedirs(sample_path)
io.imsave(os.path.join(sample_path, "mask_labels.png"), mask_img)
shutil.copyfile(
os.path.join(nucleis_dir, file.replace(".tif", ".png")),
os.path.join(sample_path, "nuclei.png"))
segmentationUtils.masks_to_polygon(mask,
label=label,
simplify_tol=simplify_tol,
save_name=os.path.join(
sample_path,
"annotation.json"))
def masks_to_annotation(self, outputs_dir, outputs=None):
# %% Process one folder and save as one json file allowing multiple annotation types
simplify_tol = 0 # Tolerance for polygon simplification with shapely (0 to not simplify)
if os.path.exists(outputs_dir):
print(f'Analyzing folder:{outputs_dir}')
features = [] # For geojson
image_size = None
for out in outputs:
file_full = os.path.join(outputs_dir, out.get("name") + "_output.png")
if os.path.exists(file_full):
print("get output file path:", file_full)
mask_img = io.imread(file_full)
print("mask_img.shape:", mask_img.shape)
mask = measure.label(mask_img)
post_mask = DRFNStoolbox.seedless_segment(mask, 15, p_thresh=0.5)
img = Image.fromarray(post_mask.astype('uint8'))
img.save(os.path.join(outputs_dir, out.get("name") + '_noSeeds_OBJECTS.png'))
if out.get("postProcessing").get("type") == "withseed":
seed_mask_file = os.path.join(outputs_dir, out.get("postProcessing").get("seed") + "_output.png")
if os.path.exists(seed_mask_file):
print("get seed mask file :", seed_mask_file)
seed_mask_img = io.imread(seed_mask_file)
print("seed_mask_img.shape:", seed_mask_img.shape)
seed_mask = measure.label(seed_mask_img)
post_mask = DRFNStoolbox.segment_with_seed(seed_mask, post_mask, 15, p_thresh=0.5)
img = Image.fromarray(post_mask.astype('uint8'))
img.save(os.path.join(outputs_dir, out.get("name") + '_wSeeds_OBJECTS.png'))
else:
print("warming: seed mask file not exist:", seed_mask_file)
# Here summarizing the geojson should occur
image_size = mask_img.shape # This might cause problems if any kind of binning was performed
# Get label from file name
label = out.get("name").split('_', 1)[0]
print("label:", label)
# print(mask_img[0:1, :100])
# Call function to transform segmentation masks into (geojson) polygons
feature, contours = segmentationUtils.masks_to_polygon(post_mask,
label=label,
simplify_tol=simplify_tol)
# save_name=file_full.replace(".png", ".json"))
features = features + feature
else:
print("warming: output file not exist:", file_full)
# feature_collection = FeatureCollection(features, bbox=[0, 0.0, image_size[0], image_size[1]])
feature_collection = FeatureCollection(features, bbox=[0, 0.0, image_size[0], image_size[1]])
# Save to json file
save_name_json = os.path.join(outputs_dir, 'prediction.json')
with open(save_name_json, 'w') as f:
dump(feature_collection, f)
f.close()
return feature_collection