3669b6b341
layout_parsing.py added condition so fig_detection_with_layout.py works table_parsing.py uncommented line for better table parsing text.py changed kernel sizes
62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
import cv2
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from cv_analysis.utils.display import show_mpl
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def remove_primary_text_regions(image):
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"""Removes regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.
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Args:
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image: Image to remove primary text from.
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Returns:
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Image with primary text removed.
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"""
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image = image.copy()
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cnts = find_primary_text_regions(image)
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for cnt in cnts:
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x, y, w, h = cv2.boundingRect(cnt)
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print(x,y,w,h, w*h, w/h)
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cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), -1)
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return image
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def find_primary_text_regions(image):
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"""Finds regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.
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Args:
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image: Image to remove primary text from.
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Returns:
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Image with primary text removed.
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References:
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https://stackoverflow.com/questions/58349726/opencv-how-to-remove-text-from-background
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"""
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def is_likely_primary_text_segments(cnt):
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x,y,w,h = cv2.boundingRect(cnt)
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print(cv2.contourArea(cnt))
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return 800 < cv2.contourArea(cnt) < 16000 or w/h > 3
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image = image.copy()
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if len(image.shape) > 2:
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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image = cv2.threshold(image, 253, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
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close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17, 7))
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close = cv2.morphologyEx(image, cv2.MORPH_CLOSE, close_kernel, iterations=1)
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show_mpl(close)
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dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 5))
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dilate = cv2.dilate(close, dilate_kernel, iterations=1)
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show_mpl(dilate)
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cnts, _ = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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cnts = filter(is_likely_primary_text_segments, cnts)
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return cnts
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