qurator-spk/sbb_pixelwise_segmentation
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Merge pull request #24 from johnlockejrr/unifying-training-models

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Unifying training models
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Clemens Neudecker 2025-06-03 09:00:56 +02:00 • committed by GitHub
commit d6ccb83bf5
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2 changed files with 9 additions and 9 deletions
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12
gt_gen_utils.py
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@ -125,7 +125,7 @@ def get_content_of_dir(dir_in):
"""
gt_all=os.listdir(dir_in)
gt_list=[file for file in gt_all if file.split('.')[ len(file.split('.'))-1 ]=='xml' ]
gt_list = [file for file in gt_all if os.path.splitext(file)[1] == '.xml']
return gt_list
def return_parent_contours(contours, hierarchy):
@ -555,7 +555,7 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
if dir_images:
ls_org_imgs = os.listdir(dir_images)
ls_org_imgs_stem = [item.split('.')[0] for item in ls_org_imgs]
ls_org_imgs_stem = [os.path.splitext(item)[0] for item in ls_org_imgs]
for index in tqdm(range(len(gt_list))):
#try:
print(gt_list[index])
@ -722,10 +722,10 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
img_poly = resize_image(img_poly, y_new, x_new)
try:
xml_file_stem = gt_list[index].split('-')[1].split('.')[0]
xml_file_stem = os.path.splitext(gt_list[index])[0]
cv2.imwrite(os.path.join(output_dir, xml_file_stem + '.png'), img_poly)
except:
xml_file_stem = gt_list[index].split('.')[0]
xml_file_stem = os.path.splitext(gt_list[index])[0]
cv2.imwrite(os.path.join(output_dir, xml_file_stem + '.png'), img_poly)
if dir_images:
@ -1185,10 +1185,10 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
img_poly = resize_image(img_poly, y_new, x_new)
try:
xml_file_stem = gt_list[index].split('-')[1].split('.')[0]
xml_file_stem = os.path.splitext(gt_list[index])[0]
cv2.imwrite(os.path.join(output_dir, xml_file_stem + '.png'), img_poly)
except:
xml_file_stem = gt_list[index].split('.')[0]
xml_file_stem = os.path.splitext(gt_list[index])[0]
cv2.imwrite(os.path.join(output_dir, xml_file_stem + '.png'), img_poly)

6
utils.py
View file

@ -434,7 +434,7 @@ def generate_arrays_from_folder_reading_order(classes_file_dir, modal_dir, batch
batchcount = 0
while True:
for i in all_labels_files:
file_name = i.split('.')[0]
file_name = os.path.splitext(i)[0]
img = cv2.imread(os.path.join(modal_dir,file_name+'.png'))
label_class = int( np.load(os.path.join(classes_file_dir,i)) )
@ -479,7 +479,7 @@ def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_c
for i in range(c, c + batch_size): # initially from 0 to 16, c = 0.
try:
filename = n[i].split('.')[0]
filename = os.path.splitext(n[i])[0]
train_img = cv2.imread(img_folder + '/' + n[i]) / 255.
train_img = cv2.resize(train_img, (input_width, input_height),
@ -745,7 +745,7 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
indexer = 0
for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)):
img_name = im.split('.')[0]
img_name = os.path.splitext(im)[0]
if task == "segmentation" or task == "binarization":
dir_of_label_file = os.path.join(dir_seg, img_name + '.png')
elif task=="enhancement":