使用 OpenCV 检测表格图像中的行数和列数

Question

我们如何通过 Opencv 获取 Image 表中的行数和列数。

用于在我正确的表格中获取框的代码

contours, hierarchy = cv2.findContours(img_final_bin, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

def sort_contours(cnts, method="left-to-right"):
# initialize the reverse flag and sort index
reverse = False
i = 0
# handle if we need to sort in reverse
if method == "right-to-left" or method == "bottom-to-top":
    reverse = True
# handle if we are sorting against the y-coordinate rather than
# the x-coordinate of the bounding box
if method == "top-to-bottom" or method == "bottom-to-top":
    i = 1
# construct the list of bounding boxes and sort them from top to
# bottom
boundingBoxes = [cv2.boundingRect(c) for c in cnts]
(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
    key=lambda b:b[1][i], reverse=reverse))
# return the list of sorted contours and bounding boxes
return (cnts, boundingBoxes)

(contours, boundingBoxes) = sort_contours(contours, method="top-to-bottom")

Answer 1

这是一个潜在的方法：

1. 获取二值图像。加载图像，转换为灰度，高斯模糊，然后是大津阈值。

2. 删除单元格内的文本。查找轮廓并使用过滤器cv2.contourArea通过填充轮廓来删除文本cv2.drawContours

3. 反转图像。我们反转图像，使单元格为白色，背景为黑色

4. 对单元格进行排序并对行/列求和。我们找到轮廓然后轮廓从排序top-to-bottom使用imutils.contours.sort_contours。接下来我们遍历轮廓并找到质心以获得(cX, cY)坐标。这个想法是我们可以cY通过使用偏移量来比较每个单元格的值来确定它是新行还是同一行中的单元格。如果cY值是 +/- 某个偏移值，则单元格应该在同一行中。如果它更大，则意味着该单元格在新行中。我们构建了一个模型表，其中表的长度为您提供行，而任何索引的长度为您提供列数。

二进制图像

去除文字轮廓+倒置图像

这是迭代每个单元格以计算行数和列数的可视化

结果

Rows: 7
Columns: 4

代码

import numpy as np
from imutils import contours
import cv2

# Load image, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Find contours and remove text inside cells
cnts = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 4000:
        cv2.drawContours(thresh, [c], -1, 0, -1)

# Invert image
invert = 255 - thresh
offset, old_cY, first = 10, 0, True
visualize = cv2.cvtColor(invert, cv2.COLOR_GRAY2BGR)

# Find contours, sort from top-to-bottom and then sum up column/rows
cnts = cv2.findContours(invert, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
(cnts, _) = contours.sort_contours(cnts, method="top-to-bottom")
for c in cnts:
    # Find centroid
    M = cv2.moments(c)
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])

    # New row
    if (abs(cY) - abs(old_cY)) > offset:
        if first:
            row, table = [], []
            first = False
        old_cY = cY
        table.append(row)
        row = []

    # Cell in same row
    if ((abs(cY) - abs(old_cY)) <= offset) or first:
        row.append(1)

    # Uncomment to visualize 
    '''
    cv2.circle(visualize, (cX, cY), 10, (36, 255, 12), -1) 
    cv2.imshow('visualize', visualize)
    cv2.waitKey(200)
    '''

print('Rows: {}'.format(len(table)))
print('Columns: {}'.format(len(table[1])))

cv2.imshow('invert', invert)
cv2.imshow('thresh', thresh)
cv2.waitKey()