90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
import itertools
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from itertools import compress
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from itertools import starmap
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from operator import __and__
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import cv2
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import numpy as np
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from cv_analysis.utils.connect_rects import connect_related_rects
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from cv_analysis.utils.display import show_image_mpl
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from cv_analysis.utils.draw import draw_rectangles
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from cv_analysis.utils.label_rects import label_rects
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from cv_analysis.utils.structures import Rectangle
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from cv_analysis.utils.postprocessing import (
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remove_overlapping,
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remove_included,
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has_no_parent,
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)
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from cv_analysis.utils.visual_logging import vizlogger
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#could be dynamic parameter is the scan is noisy
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def is_likely_segment(rect, min_area=100):
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return cv2.contourArea(rect, False) > min_area
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def find_segments(image):
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contours, hierarchies = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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mask1 = map(is_likely_segment, contours)
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mask2 = map(has_no_parent, hierarchies[0])
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mask = starmap(__and__, zip(mask1, mask2))
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contours = compress(contours, mask)
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rectangles = (cv2.boundingRect(c) for c in contours)
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return rectangles
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def dilate_page_components(image):
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#if text is detected in words make kernel bigger
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image = cv2.GaussianBlur(image, (7, 7), 0)
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thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
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return cv2.dilate(thresh, kernel, iterations=4)
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def fill_in_component_area(image, rect):
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x, y, w, h = rect
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cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 0), -1)
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cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), 7)
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_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
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return ~image
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def parse_layout(image: np.array):
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image = image.copy()
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image_ = image.copy()
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#show_image_mpl(image)
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if len(image_.shape) > 2:
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image_ = cv2.cvtColor(image_, cv2.COLOR_BGR2GRAY)
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dilate = dilate_page_components(image_)
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# show_mpl(dilate)
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rects = list(find_segments(dilate))
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# -> Run meta detection on the previous detections TODO: refactor
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for rect in rects:
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x, y, w, h = rect
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cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 0), -1)
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cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), 7)
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# show_mpl(image)
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_, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
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image = ~image
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# show_mpl(image)
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if len(image.shape) > 2:
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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rects = find_segments(image)
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# <- End of meta detection
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rects = list(map(Rectangle.from_xywh, rects))
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rects = remove_included(rects)
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rects = map(lambda r: r.xywh(), rects)
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rects = connect_related_rects(rects)
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rects = list(map(Rectangle.from_xywh, rects))
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# rects = remove_included(rects)
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return rects
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