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50 lines
2.1 KiB
Python
50 lines
2.1 KiB
Python
5 years ago
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import cv2
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def find_tables(image):
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BLUR_KERNEL_SIZE = (17, 17)
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STD_DEV_X_DIRECTION = 0
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STD_DEV_Y_DIRECTION = 0
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blurred = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
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MAX_COLOR_VAL = 255
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BLOCK_SIZE = 15
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SUBTRACT_FROM_MEAN = -2
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img_bin = cv2.adaptiveThreshold(
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~blurred,
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MAX_COLOR_VAL,
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cv2.ADAPTIVE_THRESH_MEAN_C,
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cv2.THRESH_BINARY,
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BLOCK_SIZE,
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SUBTRACT_FROM_MEAN,
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)
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vertical = horizontal = img_bin.copy()
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SCALE = 5
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image_width, image_height = horizontal.shape
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horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (int(image_width / SCALE), 1))
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horizontally_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
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vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, int(image_height / SCALE)))
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vertically_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
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horizontally_dilated = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1)))
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vertically_dilated = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 60)))
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mask = horizontally_dilated + vertically_dilated
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contours, heirarchy = cv2.findContours(
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mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE,
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)
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MIN_TABLE_AREA = 1e5
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contours = [c for c in contours if cv2.contourArea(c) > MIN_TABLE_AREA]
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perimeter_lengths = [cv2.arcLength(c, True) for c in contours]
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epsilons = [0.1 * p for p in perimeter_lengths]
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approx_polys = [cv2.approxPolyDP(c, e, True) for c, e in zip(contours, epsilons)]
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bounding_rects = [cv2.boundingRect(a) for a in approx_polys]
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# The link where a lot of this code was borrowed from recommends an
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# additional step to check the number of "joints" inside this bounding rectangle.
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# A table should have a lot of intersections. We might have a rectangular image
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# here though which would only have 4 intersections, 1 at each corner.
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# Leaving that step as a future TODO if it is ever necessary.
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images = [image[y:y+h, x:x+w] for x, y, w, h in bounding_rects]
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return images
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