60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
import json
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from itertools import count
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import cv2
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import numpy as np
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import pdf2image
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from matplotlib import pyplot as plt
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def parse(image: np.array):
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gray_scale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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th1, img_bin = cv2.threshold(gray_scale, 150, 225, cv2.THRESH_BINARY)
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img_bin = ~img_bin
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line_min_width = 4
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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_bin_final = img_bin_h | img_bin_v
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_, labels, stats, _ = cv2.connectedComponentsWithStats(~img_bin_final, connectivity=8, ltype=cv2.CV_32S)
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return labels, stats
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def parse_tables_in_pdf(pages):
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return zip(map(parse, pages), count())
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def annotate_image(image, stats):
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for x, y, w, h, area in stats[2:]:
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if w > 10 and h > 10:
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cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 255), 2)
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for i, (s, v) in enumerate(zip(["x", "y", "w", "h"], [x, y, w, h])):
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anno = f"{s} = {v}"
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xann = int(x + 5)
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yann = int(y + h - (20 * (i + 1)))
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cv2.putText(image, anno, (xann, yann), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 255), 2)
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return image
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def annotate_tables_in_pdf(pdf_path, page_index=1):
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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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_, stats = parse(page)
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page = annotate_image(page, stats)
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fig, ax = plt.subplots(1, 1)
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fig.set_size_inches(20, 20)
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ax.imshow(page)
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plt.show()
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