qurator-spk/sbb_pixelwise_segmentation
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adding min_early parameter for generating training dataset for machine based reading order model

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vahidrezanezhad 2025-05-03 19:25:32 +02:00
parent d57de478eb
commit 8ae42b7c6e
2 changed files with 60 additions and 17 deletions
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64
generate_gt_for_training.py
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@ -147,11 +147,20 @@ def image_enhancement(dir_imgs, dir_out_images, dir_out_labels, scales):
help="min area size of regions considered for reading order training.",
)
def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, input_height, input_width, min_area_size):
@click.option(
"--min_area_early",
"-min_early",
help="If you have already generated a training dataset using a specific minimum area value and now wish to create a dataset with a smaller minimum area value, you can avoid regenerating the previous dataset by providing the earlier minimum area value. This will ensure that only the missing data is generated.",
)
def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, input_height, input_width, min_area_size, min_area_early):
xml_files_ind = os.listdir(dir_xml)
input_height = int(input_height)
input_width = int(input_width)
min_area = float(min_area_size)
if min_area_early:
min_area_early = float(min_area_early)
indexer_start= 0#55166
max_area = 1
@ -181,7 +190,8 @@ def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, i
texts_corr_order_index_int = [int(x) for x in texts_corr_order_index]
co_text_all, texts_corr_order_index_int = filter_contours_area_of_image(img_poly, co_text_all, texts_corr_order_index_int, max_area, min_area)
co_text_all, texts_corr_order_index_int, regions_ar_less_than_early_min = filter_contours_area_of_image(img_poly, co_text_all, texts_corr_order_index_int, max_area, min_area, min_area_early)
arg_array = np.array(range(len(texts_corr_order_index_int)))
@ -195,25 +205,49 @@ def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, i
labels_con[:,:,i] = img_label[:,:,0]
labels_con = resize_image(labels_con, input_height, input_width)
img_poly = resize_image(img_poly, input_height, input_width)
for i in range(len(texts_corr_order_index_int)):
for j in range(len(texts_corr_order_index_int)):
if i!=j:
input_multi_visual_modal = np.zeros((input_height,input_width,3)).astype(np.int8)
final_f_name = f_name+'_'+str(indexer+indexer_start)
order_class_condition = texts_corr_order_index_int[i]-texts_corr_order_index_int[j]
if order_class_condition<0:
class_type = 1
if regions_ar_less_than_early_min:
if regions_ar_less_than_early_min[i]==1:
input_multi_visual_modal = np.zeros((input_height,input_width,3)).astype(np.int8)
final_f_name = f_name+'_'+str(indexer+indexer_start)
order_class_condition = texts_corr_order_index_int[i]-texts_corr_order_index_int[j]
if order_class_condition<0:
class_type = 1
else:
class_type = 0
input_multi_visual_modal[:,:,0] = labels_con[:,:,i]
input_multi_visual_modal[:,:,1] = img_poly[:,:,0]
input_multi_visual_modal[:,:,2] = labels_con[:,:,j]
np.save(os.path.join(dir_out_classes,final_f_name+'_missed.npy' ), class_type)
cv2.imwrite(os.path.join(dir_out_modal_image,final_f_name+'_missed.png' ), input_multi_visual_modal)
indexer = indexer+1
else:
class_type = 0
input_multi_visual_modal = np.zeros((input_height,input_width,3)).astype(np.int8)
final_f_name = f_name+'_'+str(indexer+indexer_start)
order_class_condition = texts_corr_order_index_int[i]-texts_corr_order_index_int[j]
if order_class_condition<0:
class_type = 1
else:
class_type = 0
input_multi_visual_modal[:,:,0] = resize_image(labels_con[:,:,i], input_height, input_width)
input_multi_visual_modal[:,:,1] = resize_image(img_poly[:,:,0], input_height, input_width)
input_multi_visual_modal[:,:,2] = resize_image(labels_con[:,:,j], input_height, input_width)
input_multi_visual_modal[:,:,0] = labels_con[:,:,i]
input_multi_visual_modal[:,:,1] = img_poly[:,:,0]
input_multi_visual_modal[:,:,2] = labels_con[:,:,j]
np.save(os.path.join(dir_out_classes,final_f_name+'.npy' ), class_type)
cv2.imwrite(os.path.join(dir_out_modal_image,final_f_name+'.png' ), input_multi_visual_modal)
indexer = indexer+1
np.save(os.path.join(dir_out_classes,final_f_name+'.npy' ), class_type)
cv2.imwrite(os.path.join(dir_out_modal_image,final_f_name+'.png' ), input_multi_visual_modal)
indexer = indexer+1
@main.command()