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