qurator-spk/sbb_pixelwise_segmentation
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min_area size of regions considered for reading order detection passed as an argument for inference

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vahidrezanezhad 2024-05-29 13:07:06 +02:00
parent f6abefb0a8
commit 785033536a
2 changed files with 35 additions and 9 deletions
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13
gt_gen_utils.py
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@ -32,10 +32,16 @@ def filter_contours_area_of_image_tables(image, contours, hierarchy, max_area, m
jv = 0 jv = 0
for c in contours: for c in contours:
if len(np.shape(c)) == 3:
c = c[0]
elif len(np.shape(c)) == 2:
pass
#c = c[0]
if len(c) < 3: # A polygon cannot have less than 3 points if len(c) < 3: # A polygon cannot have less than 3 points
continue continue
polygon = geometry.Polygon([point[0] for point in c]) c_e = [point for point in c]
polygon = geometry.Polygon(c_e)
# area = cv2.contourArea(c) # area = cv2.contourArea(c)
area = polygon.area area = polygon.area
# Check that polygon has area greater than minimal area # Check that polygon has area greater than minimal area
@ -49,7 +55,10 @@ def filter_contours_area_of_image(image, contours, order_index, max_area, min_ar
order_index_filtered = list() order_index_filtered = list()
#jv = 0 #jv = 0
for jv, c in enumerate(contours): for jv, c in enumerate(contours):
c = c[0] if len(np.shape(c)) == 3:
c = c[0]
elif len(np.shape(c)) == 2:
pass
if len(c) < 3: # A polygon cannot have less than 3 points if len(c) < 3: # A polygon cannot have less than 3 points
continue continue
c_e = [point for point in c] c_e = [point for point in c]

31
inference.py
View file

@ -28,7 +28,7 @@ Tool to load model and predict for given image.
""" """
class sbb_predict: class sbb_predict:
def __init__(self,image, model, task, config_params_model, patches, save, ground_truth, xml_file, out): def __init__(self,image, model, task, config_params_model, patches, save, ground_truth, xml_file, out, min_area):
self.image=image self.image=image
self.patches=patches self.patches=patches
self.save=save self.save=save
@ -38,6 +38,10 @@ class sbb_predict:
self.config_params_model=config_params_model self.config_params_model=config_params_model
self.xml_file = xml_file self.xml_file = xml_file
self.out = out self.out = out
if min_area:
self.min_area = float(min_area)
else:
self.min_area = 0
def resize_image(self,img_in,input_height,input_width): def resize_image(self,img_in,input_height,input_width):
return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST) return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST)
@ -255,11 +259,18 @@ class sbb_predict:
##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]
texts_corr_order_index_int = list(np.array(range(len(co_text_all)))) texts_corr_order_index_int = list(np.array(range(len(co_text_all))))
min_area = 0 #print(texts_corr_order_index_int)
max_area = 1
max_area = 1
##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) #print(np.shape(co_text_all[0]), len( np.shape(co_text_all[0]) ),'co_text_all')
#co_text_all = filter_contours_area_of_image_tables(img_poly, co_text_all, _, max_area, min_area)
#print(co_text_all,'co_text_all')
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, self.min_area)
#print(texts_corr_order_index_int)
#co_text_all = [co_text_all[index] for index in texts_corr_order_index_int]
id_all_text = [id_all_text[index] for index in texts_corr_order_index_int]
labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8') labels_con = np.zeros((y_len,x_len,len(co_text_all)),dtype='uint8')
for i in range(len(co_text_all)): for i in range(len(co_text_all)):
@ -596,7 +607,13 @@ class sbb_predict:
"-xml", "-xml",
help="xml file with layout coordinates that reading order detection will be implemented on. The result will be written in the same xml file.", help="xml file with layout coordinates that reading order detection will be implemented on. The result will be written in the same xml file.",
) )
def main(image, model, patches, save, ground_truth, xml_file, out):
@click.option(
"--min_area",
"-min",
help="min area size of regions considered for reading order detection. The default value is zero and means that all text regions are considered for reading order.",
)
def main(image, model, patches, save, ground_truth, xml_file, out, min_area):
with open(os.path.join(model,'config.json')) as f: with open(os.path.join(model,'config.json')) as f:
config_params_model = json.load(f) config_params_model = json.load(f)
task = config_params_model['task'] task = config_params_model['task']
@ -604,7 +621,7 @@ def main(image, model, patches, save, ground_truth, xml_file, out):
if not save: if not save:
print("Error: You used one of segmentation or binarization task but not set -s, you need a filename to save visualized output with -s") print("Error: You used one of segmentation or binarization task but not set -s, you need a filename to save visualized output with -s")
sys.exit(1) sys.exit(1)
x=sbb_predict(image, model, task, config_params_model, patches, save, ground_truth, xml_file, out) x=sbb_predict(image, model, task, config_params_model, patches, save, ground_truth, xml_file, out, min_area)
x.run() x.run()
if __name__=="__main__": if __name__=="__main__":