识别图像中的网格并使用图像处理更改其颜色

Question

我必须确定这张图片中的表格网格，并将其更改为格里姆森红色。我是图像处理的初学者。你知道吗

img_arr = mpimg.imread("1.jpg")

plt.imshow(img_arr)

grid = img_arr[470:800,42:670,(0,1,2)]

plt.imshow(grid.data)

基于图像尺寸，我能够看到图像的网格部分，但我不知道如何识别网格并改变其颜色。如果有人对此有任何想法，请回复。你知道吗

Answer 1

这里有一个方法：

• 将图像转换为灰度和阈值
• 找到轮廓并使用轮廓区域进行过滤以隔离网格
• 找出水平线和垂直线
• 在图像上画线

---

结果是这样的

import cv2
import numpy as np

image = cv2.imread('1.png')
mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Detect only grid
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area > 10000:
        cv2.drawContours(mask, [c], -1, (255,255,255), -1)

mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
mask = cv2.bitwise_and(mask, thresh)

# Find horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (55,1))
detect_horizontal = cv2.morphologyEx(mask, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(image, [c], -1, (0,0,255), 2)

# Find vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,25))
detect_vertical = cv2.morphologyEx(mask, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(image, [c], -1, (0,0,255), 2)

cv2.imshow('thresh', thresh)
cv2.imshow('mask', mask)
cv2.imshow('image', image)
cv2.waitKey()