如何使用OpenCV在图像上找到每个单独轮廓的高度和宽度

Question

在上述图像中，如果整个宽度已经指定为30'5"。如何使用opencv计算该图像上每个轮廓的高度和宽度。

- user1027130

Answer 1

要获取轮廓的高度和宽度，您可以使用cv2.boundingRect。该函数以x，y，w，h的形式返回轮廓信息。特定轮廓的高度将为h，宽度将为w。下面是在图像上绘制以像素为单位的w的结果。

import cv2

# Load image, convert to grayscale, Otsu's threshold
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Find contours, obtain bounding rect, and draw width
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:
    x,y,w,h = cv2.boundingRect(c)
    cv2.putText(image, str(w), (x,y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2)
    cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 1)

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

- nathancy

Answer 2

My approach is using minAreaRect:

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>

using namespace cv;
using namespace std;


int main()
{

    Mat src; Mat src_gray;
    int thresh = 100;
    RNG rng(12345);
    /// Load source image and convert it to gray
    src = imread( "/ur/img/directory/image.jpg", 1 );
    Mat original = src.clone();
    /// Convert image to gray and blur it
    cvtColor( src, src_gray, CV_BGR2GRAY );

    Mat threshold_output;
    vector<vector<Point> > contours;
    vector<Vec4i> hierarchy;

    /// Detect edges using Threshold
    threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
    /// Find contours
    findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );

    /// Find the rotated rectangles for each contour
    vector<RotatedRect> minRect( contours.size() );

    for( int i = 0; i < contours.size(); i++ )
        minRect[i] = minAreaRect( Mat(contours[i]) );

    /// Draw contours + rotated rects
    Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );
    Mat result_zero = Mat::zeros( threshold_output.size(), CV_8UC3 );

    for( int i = 0; i< contours.size(); i++ )
    {
        Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
        // detect contours
        drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
        // detect rectangle for each contour
        Point2f rect_points[4]; minRect[i].points( rect_points );

        double length_1 = cv::norm(cv::Mat(rect_points[0]),cv::Mat(rect_points[1]));
        double length_2 = cv::norm(cv::Mat(rect_points[1]),cv::Mat(rect_points[2]));


        for( int j = 0; j < 4; j++ )
        {
            int temp1 = (int)length_1;
            int temp2 = (int)length_2;

            if(length_1>length_2)
                putText(original,to_string(temp1),rect_points[0],FONT_HERSHEY_SIMPLEX,1.0,Scalar(0,255,255),2);
            else
                putText(original,to_string(temp2),rect_points[0],FONT_HERSHEY_SIMPLEX,1.0,Scalar(0,255,255),2);

            line( result_zero, rect_points[j], rect_points[(j+1)%4], color, 1, 8 );
        }

    }

    /// Show in windows
    imshow("First",original);
    imshow( "Contours", drawing );
    waitKey(0);
    return(0);
}

源图像：

每行检测到的矩形：

每行的像素长度：

- Yunus Temurlenk

---

很棒的解决方案！但是下次怎么样考虑使用Python呢，因为它更容易进行测试。大多数人可能没有设置C++环境！ - Raviteja Narra

将其转换成Python并不困难。没有什么难度可以转换它。我的环境是基于C ++的，这就是为什么我用C ++回答的原因。 - Yunus Temurlenk

Answer 3

std::vector<std::vector<cv::Point2i>> vecContours;
cv::Mat mat = cv::imread("[path to image]", cv::IMREAD_GRAYSCALE);
cv::threshold(mat, mat, 200, 255, cv::THRESH_BINARY);
cv::findContours(mat, vecContours, cv::RetrievalModes::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE);
float inchPerPixel = 30.5f / mat.cols;
for (const std::vector<cv::Point2i>& vecContour : vecContours) {
    cv::Rect2i contourRect = cv::boundingRect(vecContour);
    printf("Contour width pixels : %d, width inches %f\n", contourRect.width, inchPerPixel*contourRect.width);
}

您可以通过以下方式实现：

1. 使用阈值方法创建二进制图像
2. 使用findContours方法查找图像中矩形的轮廓
3. 使用boundingRect方法获取矩形轮廓的大小
4. 将轮廓的宽度乘以计算出的每像素英寸因子

- Black0ut