157 lines
5.2 KiB
Python
157 lines
5.2 KiB
Python
from functools import partial
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from itertools import chain, starmap
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from operator import attrgetter
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import cv2
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import numpy as np
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from pdf2image import pdf2image
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from cv_analysis.utils.display import show_mpl
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from cv_analysis.utils.draw import draw_rectangles
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from cv_analysis.utils.post_processing import xywh_to_vecs, xywh_to_vec_rect, adjacent1d, remove_isolated
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from cv_analysis.utils.deskew import deskew_histbased
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from cv_analysis.layout_parsing import parse_layout
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def add_external_contours(image, img):
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contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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# contours = filter(partial(is_large_enough, min_area=5000000), contours)
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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 isolate_vertical_and_horizontal_components(img_bin, bounding_rects, show=False):
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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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if show:
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show_mpl(img_bin_h | img_bin_v)
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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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# show_mpl(img_bin_h | img_bin_v)
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# reduced filtersize from 100 to 80 to minimize splitting narrow cells
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img_bin_h = apply_motion_blur(img_bin_h, 80, 0)
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img_bin_v = apply_motion_blur(img_bin_v, 80, 90)
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img_bin_final = img_bin_h | img_bin_v
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if show:
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show_mpl(img_bin_final)
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# changed threshold from 110 to 120 to minimize cell splitting
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th1, img_bin_final = cv2.threshold(img_bin_final, 120, 255, cv2.THRESH_BINARY)
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img_bin_final = cv2.dilate(img_bin_final, np.ones((1, 1), np.uint8), iterations=1)
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# show_mpl(img_bin_final)
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# problem if layout parser detects too big of a layout box as in VV-748542.pdf p.22
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img_bin_final = disconnect_non_existing_cells(img_bin_final, bounding_rects)
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# show_mpl(img_bin_final)
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return img_bin_final
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def disconnect_non_existing_cells(img_bin, bounding_rects):
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for rect in bounding_rects:
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x, y, w, h = rect
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img_bin = cv2.rectangle(img_bin, (x, y), (x + w, y + h), (0, 0, 0), 5)
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return img_bin
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# FIXME: does not work yet
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def has_table_shape(rects):
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assert isinstance(rects, list)
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points = list(chain(*map(xywh_to_vecs, rects)))
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brect = xywh_to_vec_rect(cv2.boundingRect(np.vstack(points)))
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rects = list(map(xywh_to_vec_rect, rects))
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def matches_bounding_rect_corner(rect, x, y):
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corresp_coords = list(zip(*map(attrgetter(x, y), [brect, rect])))
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ret = all(starmap(partial(adjacent1d, tolerance=30), corresp_coords))
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return ret
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return all(
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(
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any(matches_bounding_rect_corner(r, "xmin", "ymin") for r in rects),
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any(matches_bounding_rect_corner(r, "xmin", "ymax") for r in rects),
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any(matches_bounding_rect_corner(r, "xmax", "ymax") for r in rects),
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any(matches_bounding_rect_corner(r, "xmax", "ymin") for r in rects),
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)
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)
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def apply_motion_blur(image, size, angle):
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k = np.zeros((size, size), dtype=np.float32)
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k[(size - 1) // 2, :] = np.ones(size, dtype=np.float32)
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k = cv2.warpAffine(k, cv2.getRotationMatrix2D((size / 2 - 0.5, size / 2 - 0.5), angle, 1.0), (size, size))
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k = k * (1.0 / np.sum(k))
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return cv2.filter2D(image, -1, k)
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def find_table_layout_boxes(image: np.array):
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layout_boxes = parse_layout(image)
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table_boxes = []
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for box in layout_boxes:
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(x, y, w, h) = box
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if w * h >= 100000:
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table_boxes.append(box)
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return table_boxes
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def preprocess(image: np.array):
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if len(image.shape) > 2:
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if len(image.shape) > 2 else image
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th1, image = cv2.threshold(image, 195, 255, cv2.THRESH_BINARY)
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image = ~image
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return image
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def parse_table(image: np.array, show=False):
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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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image = preprocess(image)
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if show:
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show_mpl(image)
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table_layout_boxes = find_table_layout_boxes(image)
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image = isolate_vertical_and_horizontal_components(image, table_layout_boxes)
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image = add_external_contours(image, image)
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_, _, stats, _ = cv2.connectedComponentsWithStats(~image, connectivity=8, ltype=cv2.CV_32S)
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stats = np.vstack(list(filter(is_large_enough, stats)))
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rects = stats[:, :-1][2:]
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# FIXME: produces false negatives for `data0/043d551b4c4c768b899eaece4466c836.pdf 1 --type table`
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rects = remove_isolated(rects, input_sorted=True)
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return list(rects)
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def annotate_tables_in_pdf(pdf_path, page_index=0, deskew=False, show=True):
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page = pdf2image.convert_from_path(pdf_path, first_page=page_index + 1, last_page=page_index + 1)[0]
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page = np.array(page)
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if deskew:
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page, _ = deskew_histbased(page)
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stats = parse_table(page)
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page = draw_rectangles(page, stats, annotate=True)
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if show:
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show_mpl(page)
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else:
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return page
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