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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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unifying-training-models
vahidrezanezhad 7 days ago
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.", 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) xml_files_ind = os.listdir(dir_xml)
input_height = int(input_height) input_height = int(input_height)
input_width = int(input_width) input_width = int(input_width)
min_area = float(min_area_size) min_area = float(min_area_size)
if min_area_early:
min_area_early = float(min_area_early)
indexer_start= 0#55166 indexer_start= 0#55166
max_area = 1 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] 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))) 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[:,:,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 i in range(len(texts_corr_order_index_int)):
for j in range(len(texts_corr_order_index_int)): for j in range(len(texts_corr_order_index_int)):
if i!=j: if i!=j:
input_multi_visual_modal = np.zeros((input_height,input_width,3)).astype(np.int8) if regions_ar_less_than_early_min:
final_f_name = f_name+'_'+str(indexer+indexer_start) if regions_ar_less_than_early_min[i]==1:
order_class_condition = texts_corr_order_index_int[i]-texts_corr_order_index_int[j] input_multi_visual_modal = np.zeros((input_height,input_width,3)).astype(np.int8)
if order_class_condition<0: final_f_name = f_name+'_'+str(indexer+indexer_start)
class_type = 1 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: 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[:,:,0] = labels_con[:,:,i]
input_multi_visual_modal[:,:,1] = resize_image(img_poly[:,:,0], input_height, input_width) input_multi_visual_modal[:,:,1] = img_poly[:,:,0]
input_multi_visual_modal[:,:,2] = resize_image(labels_con[:,:,j], input_height, input_width) input_multi_visual_modal[:,:,2] = labels_con[:,:,j]
np.save(os.path.join(dir_out_classes,final_f_name+'.npy' ), class_type) 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) cv2.imwrite(os.path.join(dir_out_modal_image,final_f_name+'.png' ), input_multi_visual_modal)
indexer = indexer+1 indexer = indexer+1
@main.command() @main.command()

13
gt_gen_utils.py
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@ -51,9 +51,10 @@ def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, m
jv += 1 jv += 1
return found_polygons_early return found_polygons_early
def filter_contours_area_of_image(image, contours, order_index, max_area, min_area): def filter_contours_area_of_image(image, contours, order_index, max_area, min_area, min_early):
found_polygons_early = list() found_polygons_early = list()
order_index_filtered = list() order_index_filtered = list()
regions_ar_less_than_early_min = list()
#jv = 0 #jv = 0
for jv, c in enumerate(contours): for jv, c in enumerate(contours):
if len(np.shape(c)) == 3: if len(np.shape(c)) == 3:
@ -68,8 +69,16 @@ def filter_contours_area_of_image(image, contours, order_index, max_area, min_ar
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 : if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 :
found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint)) found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint))
order_index_filtered.append(order_index[jv]) order_index_filtered.append(order_index[jv])
if min_early:
if area < min_early * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and hierarchy[0][jv][3]==-1 :
regions_ar_less_than_early_min.append(1)
else:
regions_ar_less_than_early_min.append(0)
else:
regions_ar_less_than_early_min = None
#jv += 1 #jv += 1
return found_polygons_early, order_index_filtered return found_polygons_early, order_index_filtered, regions_ar_less_than_early_min
def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002): def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):

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