opencv-049-二值图像分析(轮廓外接矩形)

知识点

对图像二值图像的每个轮廓,OpenCV都提供了API可以求取轮廓的外接矩形.

求取轮廓外接矩形有两种方式,一种是可以基于像素的最大外接轮廓矩形,API解释如下:

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Rect cv::boundingRect(
InputArray points
)

输入参数points可以一系列点的集合,对轮廓来说就是该轮廓的点集
返回结果是一个矩形,x, y, w, h

另一种是可以基于像素的最大外接轮廓矩形,API解释如下:

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RotatedRect cv::minAreaRect(
InputArray points
)

输入参数points可以一系列点的集合,对轮廓来说就是该轮廓的点集
返回结果是一个旋转矩形,包含下面的信息:
-矩形中心位置
-矩形的宽高
-旋转角度

代码(c++,python)

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#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main() {
Mat src = imread("../images/stuff.jpg");
if (src.empty()) {
cout << "could not load image.." << endl;
}
imshow("input", src);

// 提取边缘
Mat binary;
Canny(src, binary, 80, 160);
imshow("binary", binary);

// 膨胀
Mat k = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
dilate(binary, binary, k);

// 轮廓发现于绘制
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(binary, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point());
for (size_t t = 0; t < contours.size(); ++t) {
// 最大外接矩形
Rect rect = boundingRect(contours[t]);
rectangle(src, rect, Scalar(0, 0, 255));

// 最小外接矩形
RotatedRect rrt = minAreaRect(contours[t]);
Point2f pts[4];
rrt.points(pts);

// 绘制旋转矩形与中心位置
for (int i = 0; i < 4; ++i) {
line(src, pts[i % 4], pts[(i + 1) % 4], Scalar(0, 255, 0), 2);
}
Point2f cpt = rrt.center;
circle(src, cpt, 2, Scalar(255, 0, 0), 2);
}

imshow("contours", src);

waitKey(0);
return 0;
}
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import cv2 as cv
import numpy as np


def canny_demo(image):
t = 80
canny_output = cv.Canny(image, t, t * 2)
cv.imshow("canny_output", canny_output)
cv.imwrite("D:/canny_output.png", canny_output)
return canny_output


src = cv.imread("D:/images/stuff.jpg")
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
cv.imshow("input", src)
binary = canny_demo(src)
k = np.ones((3, 3), dtype=np.uint8)
binary = cv.morphologyEx(binary, cv.MORPH_DILATE, k)

# 轮廓发现
out, contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
for c in range(len(contours)):
x, y, w, h = cv.boundingRect(contours[c]);
cv.drawContours(src, contours, c, (0, 0, 255), 2, 8)
cv.rectangle(src, (x, y), (x+w, y+h), (0, 0, 255), 1, 8, 0);
rect = cv.minAreaRect(contours[c])
cx, cy = rect[0]
box = cv.boxPoints(rect)
box = np.int0(box)
cv.drawContours(src,[box],0,(0,0,255),2)
cv.circle(src, (np.int32(cx), np.int32(cy)), 2, (255, 0, 0), 2, 8, 0)


# 显示
cv.imshow("contours_analysis", src)
cv.imwrite("D:/contours_analysis.png", src)
cv.waitKey(0)
cv.destroyAllWindows()

结果

代码地址

github