Add gitignore, rename modules, remove unused code
Eric Ihli
5 years ago
parent
8546902e64
commit
78e9cdb3f5
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.DS_Store
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.idea
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*.log
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tmp/
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*.py[cod]
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*.egg
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build
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htmlcov
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dist
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import setuptools
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import setuptools
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with open("README.md", "r") as fh:
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long_description = """
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long_description = fh.read()
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Utilities for turning images of tables into CSV data. Uses Tesseract and OpenCV.
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Requires binaries for tesseract and pdfimages (from Poppler).
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"""
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setuptools.setup(
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setuptools.setup(
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name="example-pkg-YOUR-USERNAME-HERE", # Replace with your own username
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name="table_ocr",
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version="0.0.1",
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version="0.0.1",
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author="Example Author",
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author="Eric Ihli",
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author_email="author@example.com",
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author_email="eihli@owoga.com",
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description="A small example package",
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description="Turn images of tables into CSV data.",
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long_description=long_description,
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long_description=long_description,
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long_description_content_type="text/markdown",
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long_description_content_type="text/plain",
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url="https://github.com/pypa/sampleproject",
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url="https://github.com/eihli/image-table-ocr",
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packages=setuptools.find_packages(),
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packages=setuptools.find_packages(),
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classifiers=[
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classifiers=[
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3",
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"License :: OSI Approved :: MIT License",
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"License :: OSI Approved :: MIT License",
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"Operating System :: OS Independent",
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"Operating System :: OS Independent",
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],
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],
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install_requires=[
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"pytesseract~=0.3",
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"opencv-python~=4.2",
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],
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python_requires='>=3.6',
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python_requires='>=3.6',
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)
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)
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Metadata-Version: 2.1
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Name: table-ocr
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Version: 0.0.1
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Summary: Turn images of tables into CSV data.
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Home-page: https://github.com/eihli/image-table-ocr
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Author: Eric Ihli
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Author-email: eihli@owoga.com
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License: UNKNOWN
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Description:
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Utilities for turning images of tables into CSV data. Uses Tesseract and OpenCV.
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Requires binaries for tesseract and pdfimages (from Poppler).
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Platform: UNKNOWN
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Classifier: Programming Language :: Python :: 3
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Operating System :: OS Independent
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Requires-Python: >=3.6
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Description-Content-Type: text/plain
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setup.py
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table_ocr/__init__.py
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table_ocr/extract_cells_from_table.py
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table_ocr/extract_tables.py
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table_ocr/prepare_pdfs.py
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table_ocr/util.py
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table_ocr.egg-info/PKG-INFO
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table_ocr.egg-info/SOURCES.txt
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table_ocr.egg-info/dependency_links.txt
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table_ocr.egg-info/requires.txt
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table_ocr.egg-info/top_level.txt
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pytesseract~=0.3
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opencv-python~=4.2
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table_ocr
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import os
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import sys
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import cv2
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import pytesseract
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def main(f):
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results = []
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directory, filename = os.path.split(f)
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table = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
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rows = extract_cell_images_from_table(table)
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cell_img_dir = os.path.join(directory, "cells")
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os.makedirs(cell_img_dir, exist_ok=True)
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for i, row in enumerate(rows):
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for j, cell in enumerate(row):
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cell_filename = "{:03d}-{:03d}.png".format(i, j)
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path = os.path.join(cell_img_dir, cell_filename)
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cv2.imwrite(path, cell)
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print(cell_filename)
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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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if __name__ == "__main__":
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main(sys.argv[1])
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import argparse
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import os
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import cv2
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parser = argparse.ArgumentParser()
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parser.add_argument("files", nargs="+")
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def main(files):
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results = []
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for f in files:
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directory, filename = os.path.split(f)
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image = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
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print("Reading {}".format(f))
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tables = find_tables(image)
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files = []
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for i, table in enumerate(tables):
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filename_sans_extension = os.path.splitext(filename)[0]
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table_filename = "{}-table-{:03d}.png".format(filename_sans_extension, i)
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table_filepath = os.path.join(directory, table_filename)
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files.append(table_filepath)
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cv2.imwrite(table_filepath, table)
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results.append((f, files))
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for image_filename, table_filenames in results:
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print("{}\n{}\n".format(image_filename, "\n".join(table_filenames)))
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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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if __name__ == "__main__":
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args = parser.parse_args()
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files = args.files
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main(files)
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import argparse
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import logging
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import os
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import re
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import subprocess
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import sys
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from table_ocr.util import working_dir, make_tempdir
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def get_logger():
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logger = logging.getLogger(__name__)
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lvl = os.environ.get("PY_LOG_LVL", "info").upper()
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handler = logging.StreamHandler()
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formatter = logging.Formatter(logging.BASIC_FORMAT)
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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handler.setLevel(lvl)
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logger.setLevel(lvl)
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return logger
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logger = get_logger()
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parser = argparse.ArgumentParser()
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parser.add_argument("files", nargs="+")
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def main(files):
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pdf_images = []
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for f in files:
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pdf_images.append((f, pdf_to_images(f)))
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for pdf, images in pdf_images:
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for image in images:
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preprocess_img(image)
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for pdf, images in pdf_images:
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print("{}\n{}\n".format(pdf, "\n".join(images)))
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def pdf_to_images(pdf_filepath):
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"""
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Turn a pdf into images
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"""
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directory, filename = os.path.split(pdf_filepath)
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with working_dir(directory):
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image_filenames = pdfimages(pdf_filepath)
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# Since pdfimages creates a number of files named each for there page number
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# and doesn't return us the list that it created
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return [os.path.join(directory, f) for f in image_filenames]
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def pdfimages(pdf_filepath):
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"""
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Uses the `pdfimages` utility from Poppler
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(https://poppler.freedesktop.org/). Creates images out of each page. Images
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are prefixed by their name sans extension and suffixed by their page number.
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"""
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directory, filename = os.path.split(pdf_filepath)
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filename_sans_ext = filename.split(".pdf")[0]
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subprocess.run(["pdfimages", "-png", pdf_filepath, filename.split(".pdf")[0]])
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image_filenames = find_matching_files_in_dir(filename_sans_ext, directory)
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logger.debug("Converted {} into files:\n{}".format(pdf_filepath, "\n".join(image_filenames)))
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return image_filenames
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def find_matching_files_in_dir(file_prefix, directory):
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files = [
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filename
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for filename in os.listdir(directory)
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if re.match(r"{}.*\.png".format(re.escape(file_prefix)), filename)
|
||||||
|
]
|
||||||
|
return files
|
||||||
|
def preprocess_img(filepath):
|
||||||
|
"""
|
||||||
|
Processing that involves running shell executables,
|
||||||
|
like mogrify to rotate.
|
||||||
|
"""
|
||||||
|
rotate = get_rotate(filepath)
|
||||||
|
logger.debug("Rotating {} by {}.".format(filepath, rotate))
|
||||||
|
mogrify(filepath, rotate)
|
||||||
|
|
||||||
|
|
||||||
|
def get_rotate(image_filepath):
|
||||||
|
output = (
|
||||||
|
subprocess.check_output(["tesseract", "--psm", "0", image_filepath, "-"])
|
||||||
|
.decode("utf-8")
|
||||||
|
.split("\n")
|
||||||
|
)
|
||||||
|
output = next(l for l in output if "Rotate: " in l)
|
||||||
|
output = output.split(": ")[1]
|
||||||
|
return output
|
||||||
|
|
||||||
|
|
||||||
|
def mogrify(image_filepath, rotate):
|
||||||
|
subprocess.run(["mogrify", "-rotate", rotate, image_filepath])
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
args = parser.parse_args()
|
||||||
|
main(args.files)
|
||||||
Loading…
Reference in New Issue