272 lines
8.3 KiB
Python
272 lines
8.3 KiB
Python
from itertools import groupby
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from operator import itemgetter
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import cv2
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import numpy as np
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from .utils import merge_tuples
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def adaptive_threshold(imagename, invert=False, blocksize=15, c=-2):
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"""Thresholds an image using OpenCV's adaptiveThreshold.
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Parameters
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----------
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imagename : string
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Path to image file.
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invert : bool
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Whether or not to invert the image. Useful when pdfs have
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tables with lines in background.
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(optional, default: False)
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blocksize: int
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Size of a pixel neighborhood that is used to calculate a
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threshold value for the pixel: 3, 5, 7, and so on.
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c: float
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Constant subtracted from the mean or weighted mean
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(see the details below). Normally, it is positive but may be
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zero or negative as well.
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Returns
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-------
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img : object
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numpy.ndarray representing the original image.
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threshold : object
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numpy.ndarray representing the thresholded image.
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"""
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img = cv2.imread(imagename)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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if invert:
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threshold = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY, blocksize, c)
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else:
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threshold = cv2.adaptiveThreshold(np.invert(gray), 255,
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cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, blocksize, c)
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return img, threshold
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def find_lines(threshold, direction='horizontal', scale=15, iterations=0):
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"""Finds horizontal and vertical lines by applying morphological
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transformations on an image.
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Parameters
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----------
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threshold : object
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numpy.ndarray representing the thresholded image.
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direction : string
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Specifies whether to find vertical or horizontal lines.
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(default: 'horizontal')
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scale : int
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Used to divide the height/width to get a structuring element
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for morph transform.
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(optional, default: 15)
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iterations : int
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Number of iterations for dilation.
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(optional, default: 2)
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Returns
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-------
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dmask : object
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numpy.ndarray representing pixels where vertical/horizontal
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lines lie.
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lines : list
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List of tuples representing vertical/horizontal lines with
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coordinates relative to a left-top origin in
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OpenCV's coordinate space.
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"""
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lines = []
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if direction == 'vertical':
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size = threshold.shape[0] // scale
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el = cv2.getStructuringElement(cv2.MORPH_RECT, (1, size))
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elif direction == 'horizontal':
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size = threshold.shape[1] // scale
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el = cv2.getStructuringElement(cv2.MORPH_RECT, (size, 1))
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elif direction is None:
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raise ValueError("Specify direction as either 'vertical' or"
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" 'horizontal'")
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threshold = cv2.erode(threshold, el)
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threshold = cv2.dilate(threshold, el)
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dmask = cv2.dilate(threshold, el, iterations=iterations)
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try:
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_, contours, _ = cv2.findContours(
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threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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contours, _ = cv2.findContours(
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threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for c in contours:
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x, y, w, h = cv2.boundingRect(c)
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x1, x2 = x, x + w
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y1, y2 = y, y + h
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if direction == 'vertical':
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lines.append(((x1 + x2) / 2, y2, (x1 + x2) / 2, y1))
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elif direction == 'horizontal':
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lines.append((x1, (y1 + y2) / 2, x2, (y1 + y2) / 2))
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return dmask, lines
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def find_table_contours(vertical, horizontal):
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"""Finds table boundaries using OpenCV's findContours.
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Parameters
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----------
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vertical : object
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numpy.ndarray representing pixels where vertical lines lie.
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horizontal : object
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numpy.ndarray representing pixels where horizontal lines lie.
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Returns
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-------
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cont : list
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List of tuples representing table boundaries. Each tuple is of
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the form (x, y, w, h) where (x, y) -> left-top, w -> width and
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h -> height in OpenCV's coordinate space.
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"""
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mask = vertical + horizontal
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try:
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__, contours, __ = cv2.findContours(
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mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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contours, __ = cv2.findContours(
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mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
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cont = []
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for c in contours:
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c_poly = cv2.approxPolyDP(c, 3, True)
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x, y, w, h = cv2.boundingRect(c_poly)
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cont.append((x, y, w, h))
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return cont
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def find_table_joints(contours, vertical, horizontal):
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"""Finds joints/intersections present inside each table boundary.
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Parameters
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----------
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contours : list
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List of tuples representing table boundaries. Each tuple is of
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the form (x, y, w, h) where (x, y) -> left-top, w -> width and
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h -> height in OpenCV's coordinate space.
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vertical : object
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numpy.ndarray representing pixels where vertical lines lie.
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horizontal : object
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numpy.ndarray representing pixels where horizontal lines lie.
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Returns
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-------
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tables : dict
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Dict with table boundaries as keys and list of intersections
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in that boundary as their value.
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Keys are of the form (x1, y1, x2, y2) where (x1, y1) -> lb
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and (x2, y2) -> rt in OpenCV's coordinate space.
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"""
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joints = np.bitwise_and(vertical, horizontal)
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tables = {}
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for c in contours:
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x, y, w, h = c
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roi = joints[y : y + h, x : x + w]
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try:
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__, jc, __ = cv2.findContours(
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roi, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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jc, __ = cv2.findContours(
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roi, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
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if len(jc) <= 4: # remove contours with less than 4 joints
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continue
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joint_coords = []
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for j in jc:
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jx, jy, jw, jh = cv2.boundingRect(j)
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c1, c2 = x + (2 * jx + jw) / 2, y + (2 * jy + jh) / 2
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joint_coords.append((c1, c2))
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tables[(x, y + h, x + w, y)] = joint_coords
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return tables
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def remove_lines(threshold, line_scale=15):
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"""Removes lines from a thresholded image.
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Parameters
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----------
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threshold : object
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numpy.ndarray representing the thresholded image.
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line_scale : int
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Line scaling factor.
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(optional, default: 15)
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Returns
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-------
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threshold : object
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numpy.ndarray representing the thresholded image
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with horizontal and vertical lines removed.
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"""
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size = threshold.shape[0] // line_scale
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vertical_erode_el = cv2.getStructuringElement(cv2.MORPH_RECT, (1, size))
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horizontal_erode_el = cv2.getStructuringElement(cv2.MORPH_RECT, (size, 1))
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dilate_el = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
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vertical = cv2.erode(threshold, vertical_erode_el)
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vertical = cv2.dilate(vertical, dilate_el)
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horizontal = cv2.erode(threshold, horizontal_erode_el)
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horizontal = cv2.dilate(horizontal, dilate_el)
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threshold = np.bitwise_and(threshold, np.invert(vertical))
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threshold = np.bitwise_and(threshold, np.invert(horizontal))
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return threshold
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def find_cuts(threshold, char_scale=200):
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"""Finds cuts made by text projections on y-axis.
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Parameters
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----------
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threshold : object
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numpy.ndarray representing the thresholded image.
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char_scale : int
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Char scaling factor.
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(optional, default: 200)
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Returns
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-------
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y_cuts : list
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List of cuts on y-axis.
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"""
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size = threshold.shape[0] // char_scale
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char_el = cv2.getStructuringElement(cv2.MORPH_RECT, (1, size))
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threshold = cv2.erode(threshold, char_el)
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threshold = cv2.dilate(threshold, char_el)
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try:
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__, contours, __ = cv2.findContours(threshold, cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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contours, __ = cv2.findContours(threshold, cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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contours = [cv2.boundingRect(c) for c in contours]
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y_cuts = [(c[1], c[1] + c[3]) for c in contours]
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y_cuts = list(merge_tuples(sorted(y_cuts)))
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y_cuts = [(y_cuts[i][0] + y_cuts[i - 1][1]) / 2 for i in range(1, len(y_cuts))]
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return sorted(y_cuts, reverse=True) |