qurator-spk/sbb_pixelwise_segmentation
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visulizing textline detection from eynollah page-xml output

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vahidrezanezhad 2025-05-12 18:31:40 +02:00
parent d554d26739
commit fc75770b73
2 changed files with 136 additions and 0 deletions
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88
gt_gen_utils.py
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@ -16,6 +16,52 @@ KERNEL = np.ones((5, 5), np.uint8)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
def visualize_model_output(prediction, img, task):
if task == "binarization":
prediction = prediction * -1
prediction = prediction + 1
added_image = prediction * 255
layout_only = None
else:
unique_classes = np.unique(prediction[:,:,0])
rgb_colors = {'0' : [255, 255, 255],
'1' : [255, 0, 0],
'2' : [255, 125, 0],
'3' : [255, 0, 125],
'4' : [125, 125, 125],
'5' : [125, 125, 0],
'6' : [0, 125, 255],
'7' : [0, 125, 0],
'8' : [125, 125, 125],
'9' : [0, 125, 255],
'10' : [125, 0, 125],
'11' : [0, 255, 0],
'12' : [0, 0, 255],
'13' : [0, 255, 255],
'14' : [255, 125, 125],
'15' : [255, 0, 255]}
layout_only = np.zeros(prediction.shape)
for unq_class in unique_classes:
rgb_class_unique = rgb_colors[str(int(unq_class))]
layout_only[:,:,0][prediction[:,:,0]==unq_class] = rgb_class_unique[0]
layout_only[:,:,1][prediction[:,:,0]==unq_class] = rgb_class_unique[1]
layout_only[:,:,2][prediction[:,:,0]==unq_class] = rgb_class_unique[2]
img = resize_image(img, layout_only.shape[0], layout_only.shape[1])
layout_only = layout_only.astype(np.int32)
img = img.astype(np.int32)
added_image = cv2.addWeighted(img,0.5,layout_only,0.1,0)
return added_image, layout_only
def get_content_of_dir(dir_in):
"""
Listing all ground truth page xml files. All files are needed to have xml format.
@ -138,6 +184,48 @@ def update_region_contours(co_text, img_boundary, erosion_rate, dilation_rate, y
img_boundary[:,:][boundary[:,:]==1] =1
return co_text_eroded, img_boundary
def get_textline_contours_for_visualization(xml_file):
tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding = 'iso-8859-5'))
root1=tree1.getroot()
alltags=[elem.tag for elem in root1.iter()]
link=alltags[0].split('}')[0]+'}'
for jj in root1.iter(link+'Page'):
y_len=int(jj.attrib['imageHeight'])
x_len=int(jj.attrib['imageWidth'])
region_tags = np.unique([x for x in alltags if x.endswith('TextLine')])
tag_endings = ['}TextLine','}textline']
co_use_case = []
for tag in region_tags:
if tag.endswith(tag_endings[0]) or tag.endswith(tag_endings[1]):
for nn in root1.iter(tag):
c_t_in = []
sumi = 0
for vv in nn.iter():
if vv.tag == link + 'Coords':
coords = bool(vv.attrib)
if coords:
p_h = vv.attrib['points'].split(' ')
c_t_in.append(
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
break
else:
pass
if vv.tag == link + 'Point':
c_t_in.append([int(float(vv.attrib['x'])), int(float(vv.attrib['y']))])
sumi += 1
elif vv.tag != link + 'Point' and sumi >= 1:
break
co_use_case.append(np.array(c_t_in))
return co_use_case, y_len, x_len
def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_file, config_params, printspace, dir_images, dir_out_images):
"""
Reading the page xml files and write the ground truth images into given output directory.