如何在图像中找到白色边框的矩形？

Question

我正在尝试获取图像中白边矩形的坐标。传统的方法是找到轮廓来找到图像中存在的所有矩形，但我只想得到某些边框颜色，如下图

如何仅使用 opencv 获取白色边框矩形的坐标？ 对此非常有帮助的任何建议，谢谢

编辑：我尝试了guivi使用python给出的建议，如下所示

import cv2
import numpy as np
import random as rng
image = cv2.imread('strawberry.png')
threshold = 100
grayscale= cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
canny_output = cv2.Canny(grayscale, threshold, threshold * 2)

ret, thresh= cv2.threshold(grayscale,200,255,cv2.THRESH_BINARY_INV)
contours, _ = cv2.findContours(canny_output,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
contours_poly = [None]*len(contours)
#boundRect = [None]*len(contours)
boundRect = list()
centers = [None]*len(contours)
radius = [None]*len(contours)
for i, c in enumerate(contours):
    contours_poly[i] = cv2.approxPolyDP(c, 3, True)
    if len(contours_poly[i]) == 4:
        boundRect.append(cv2.boundingRect(contours_poly[i]))
    #boundRect[i] = cv.boundingRect(contours_poly[i])
    #centers[i], radius[i] = cv.minEnclosingCircle(contours_poly[i])


drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)

print(len(boundRect))
#for i in range(len(contours)):
#    color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
#    cv.drawContours(drawing, contours_poly, i, color)
#    cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
#      (int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
#cv.circle(drawing, (int(centers[i][0]), int(centers[i][1])), int(radius[i]), color, 2)
for i in range(len(boundRect)):
    if boundRect[i][2]/boundRect[i][3] > 2:
        color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
        cv2.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
            (int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
        print(f'Rectangle X:{boundRect[i][0]}, Y:{boundRect[i][1]}, W:{boundRect[i][2]}, H:{boundRect[i][3]}')

但我在图像中找不到矩形的位置，因为@guivi 使用 c++ 得到了其他 4 个矩形坐标，如下图所示

Answer 1

你可以根据自己的想法或多或少做：

1. 加载图片。
2. 转换为灰度。
3. 应用一些阈值。
4. 计算等高线。
5. 查看轮廓并检查形状。
6. 分隔矩形并按纵横比过滤。
7. 你完成了！

我使用 C++ 来实现这一点，但我相信你可以将它转换为 python。

Mat image = imread("st.bmp");

if (image.empty())
    return EXIT_FAILURE;

Mat gray;
cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
vector<vector<Point>> contoursgray;
vector<Vec4i> hierarchygray;
threshold(gray, gray, 200, 255, cv::THRESH_BINARY_INV);
findContours(gray, contoursgray, hierarchygray, RETR_TREE, CHAIN_APPROX_NONE);
// draw contours on the original image
Mat image_contour_gray = image.clone();
//drawContours( image_contour_gray, contoursgray, -1, Scalar(0, 255, 0), 2);
vector<vector<Point>> contoursgray2;
for (auto points : contoursgray) {
    double peri = cv::arcLength(points, true);
    vector<Point> aprox;
    cv::approxPolyDP(points, aprox, 0.04 * peri, true);
    if (aprox.size() == 4)
    {
        cv::Rect rect = cv::boundingRect(aprox);
        if (rect.width / rect.height > 1.5) {
            std::cout << rect.x << "  " << rect.y << " " << rect.width << " " << rect.height << std::endl;
            contoursgray2.push_back(aprox);                
        }
    }
}
if (contoursgray2.size() > 0) {
    drawContours(image_contour_gray, contoursgray2, -1, Scalar(0, 255, 0), 2);
    imshow("Contour detection using gray conversion", image_contour_gray);
}
waitKey(0);
imwrite("gray.jpg", image_contour_gray);
destroyAllWindows();

这给了我以下图像作为输出：

它还在命令prom上输出以下矩形（x，y，w，h）：

我已从opencv 网页上获取教程并更改了以下几行：

contours_poly = [None]*len(contours)
#boundRect = [None]*len(contours)
boundRect = list()
centers = [None]*len(contours)
radius = [None]*len(contours)
for i, c in enumerate(contours):
    contours_poly[i] = cv.approxPolyDP(c, 3, True)
    if len(contours_poly[i]) == 4:
        boundRect.append(cv.boundingRect(contours_poly[i]))
    #boundRect[i] = cv.boundingRect(contours_poly[i])
    #centers[i], radius[i] = cv.minEnclosingCircle(contours_poly[i])


drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)


#for i in range(len(contours)):
#    color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
#    cv.drawContours(drawing, contours_poly, i, color)
#    cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
#      (int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
#cv.circle(drawing, (int(centers[i][0]), int(centers[i][1])), int(radius[i]), color, 2)
for i in range(len(boundRect)):
    if boundRect[i][2]/boundRect[i][3] > 2:
        color = (rng.randint(0,256), rng.randint(0,256), rng.randint(0,256))
        cv.rectangle(drawing, (int(boundRect[i][0]), int(boundRect[i][1])), \
            (int(boundRect[i][0]+boundRect[i][2]), int(boundRect[i][1]+boundRect[i][3])), color, 2)
        print(f'Rectangle X:{boundRect[i][0]}, Y:{boundRect[i][1]}, W:{boundRect[i][2]}, H:{boundRect[i][3]}')