130 lines
4.0 KiB
Python
130 lines
4.0 KiB
Python
import cv2
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import numpy as np
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from funcy import lmap, lfilter
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from cv_analysis.layout_parsing import parse_layout
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from cv_analysis.utils.conversion import box_to_rectangle
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from cv_analysis.utils.postprocessing import remove_isolated
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from cv_analysis.utils.visual_logger import vizlogger
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def add_external_contours(image, image_h_w_lines_only):
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contours, _ = cv2.findContours(image_h_w_lines_only, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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for cnt in contours:
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x, y, w, h = cv2.boundingRect(cnt)
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cv2.rectangle(image, (x, y), (x + w, y + h), 255, 1)
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return image
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def apply_motion_blur(image: np.array, angle, size=80):
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"""Solidifies and slightly extends detected lines.
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Args:
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image (np.array): page image as array
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angle: direction in which to apply blur, 0 or 90
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size (int): kernel size; 80 found empirically to work well
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Returns:
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np.ndarray
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"""
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k = np.zeros((size, size), dtype=np.float32)
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vizlogger.debug(k, "tables08_blur_kernel1.png")
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k[(size - 1) // 2, :] = np.ones(size, dtype=np.float32)
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vizlogger.debug(k, "tables09_blur_kernel2.png")
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k = cv2.warpAffine(
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k,
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cv2.getRotationMatrix2D((size / 2 - 0.5, size / 2 - 0.5), angle, 1.0),
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(size, size),
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)
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vizlogger.debug(k, "tables10_blur_kernel3.png")
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k = k * (1.0 / np.sum(k))
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vizlogger.debug(k, "tables11_blur_kernel4.png")
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blurred = cv2.filter2D(image, -1, k)
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return blurred
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def isolate_vertical_and_horizontal_components(img_bin):
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"""Identifies and reinforces horizontal and vertical lines in a binary image.
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Args:
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img_bin (np.array): array corresponding to single binarized page image
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Returns:
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np.ndarray
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"""
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line_min_width = 48
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kernel_h = np.ones((1, line_min_width), np.uint8)
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kernel_v = np.ones((line_min_width, 1), np.uint8)
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img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
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img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
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img_lines_raw = img_bin_v | img_bin_h
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kernel_h = np.ones((1, 30), np.uint8)
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kernel_v = np.ones((30, 1), np.uint8)
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img_bin_h = cv2.dilate(img_bin_h, kernel_h, iterations=2)
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img_bin_v = cv2.dilate(img_bin_v, kernel_v, iterations=2)
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img_bin_h = apply_motion_blur(img_bin_h, 0)
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img_bin_v = apply_motion_blur(img_bin_v, 90)
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img_bin_extended = img_bin_h | img_bin_v
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th1, img_bin_extended = cv2.threshold(img_bin_extended, 120, 255, cv2.THRESH_BINARY)
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img_bin_final = cv2.dilate(img_bin_extended, np.ones((1, 1), np.uint8), iterations=1)
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# add contours before lines are extended by blurring
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img_bin_final = add_external_contours(img_bin_final, img_lines_raw)
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return img_bin_final
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def find_table_layout_boxes(image: np.array):
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def is_large_enough(box):
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(x, y, w, h) = box
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if w * h >= 100000:
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return box_to_rectangle(box)
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layout_boxes = parse_layout(image)
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return lmap(is_large_enough, layout_boxes)
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def preprocess(image: np.array):
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) > 2 else image
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_, image = cv2.threshold(image, 195, 255, cv2.THRESH_BINARY)
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return ~image
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def turn_connected_components_into_rectangles(image: np.array):
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def is_large_enough(stat):
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x1, y1, w, h, area = stat
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return area > 2000 and w > 35 and h > 25
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_, _, stats, _ = cv2.connectedComponentsWithStats(~image, connectivity=8, ltype=cv2.CV_32S)
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stats = lfilter(is_large_enough, stats)
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if stats:
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stats = np.vstack(stats)
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return stats[:, :-1][2:]
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return []
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def parse_tables(image: np.array):
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"""Runs the full table parsing process.
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Args:
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image (np.array): single PDF page, converted to a numpy array
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Returns:
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list: list of rectangles corresponding to table cells
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"""
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image = preprocess(image)
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image = isolate_vertical_and_horizontal_components(image)
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boxes = turn_connected_components_into_rectangles(image)
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rectangles = lmap(box_to_rectangle, boxes)
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rectangles = remove_isolated(rectangles)
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return rectangles
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