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98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
import cv2
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def extract_cell_images_from_table(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_TREE, cv2.CHAIN_APPROX_SIMPLE,
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)
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perimeter_lengths = [cv2.arcLength(c, True) for c in contours]
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epsilons = [0.05 * 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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# Filter out contours that aren't rectangular. Those that aren't rectangular
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# are probably noise.
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approx_rects = [p for p in approx_polys if len(p) == 4]
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bounding_rects = [cv2.boundingRect(a) for a in approx_polys]
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# Filter out rectangles that are too narrow or too short.
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MIN_RECT_WIDTH = 40
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MIN_RECT_HEIGHT = 10
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bounding_rects = [
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r for r in bounding_rects if MIN_RECT_WIDTH < r[2] and MIN_RECT_HEIGHT < r[3]
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]
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# The largest bounding rectangle is assumed to be the entire table.
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# Remove it from the list. We don't want to accidentally try to OCR
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# the entire table.
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largest_rect = max(bounding_rects, key=lambda r: r[2] * r[3])
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bounding_rects = [b for b in bounding_rects if b is not largest_rect]
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cells = [c for c in bounding_rects]
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def cell_in_same_row(c1, c2):
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c1_center = c1[1] + c1[3] - c1[3] / 2
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c2_bottom = c2[1] + c2[3]
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c2_top = c2[1]
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return c2_top < c1_center < c2_bottom
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orig_cells = [c for c in cells]
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rows = []
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while cells:
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first = cells[0]
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rest = cells[1:]
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cells_in_same_row = sorted(
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[
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c for c in rest
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if cell_in_same_row(c, first)
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],
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key=lambda c: c[0]
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)
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row_cells = sorted([first] + cells_in_same_row, key=lambda c: c[0])
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rows.append(row_cells)
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cells = [
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c for c in rest
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if not cell_in_same_row(c, first)
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]
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# Sort rows by average height of their center.
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def avg_height_of_center(row):
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centers = [y + h - h / 2 for x, y, w, h in row]
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return sum(centers) / len(centers)
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rows.sort(key=avg_height_of_center)
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cell_images_rows = []
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for row in rows:
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cell_images_row = []
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for x, y, w, h in row:
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cell_images_row.append(image[y:y+h, x:x+w])
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cell_images_rows.append(cell_images_row)
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return cell_images_rows
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