2

我需要从手部图像中获取轮廓，通常我通过 4 个步骤处理图像：

  1. 从 3 个通道到 1 个通道获取原始 RGB 灰度图像：

    cvtColor(sourceGrayImage, sourceGrayImage, COLOR_BGR2GRAY);

  2. 使用高斯模糊过滤灰度图像：

    GaussianBlur(sourceGrayImage, sourceGrayImage, Size(3,3), 0);

  3. 二值灰度图像，我按高度分割图像，通常我将图像按高度分割为 6 张图像，然后我对每张图像进行阈值处理：

    // we split source picture to binaryImageSectionCount(here it's 8) pieces by its height, 
// then we for every piece, we do threshold, 
// and at last we combine them agin to binaryImage        
const binaryImageSectionCount = 8;
void GetBinaryImage(Mat &grayImage, Mat &binaryImage)
{
    // get every partial gray image's height
    int partImageHeight = grayImage.rows / binaryImageSectionCount;
    for (int i = 0; i < binaryImageSectionCount; i++)
    {
        Mat partialGrayImage;            
        Mat partialBinaryImage;
        Rect partialRect;
        if (i != binaryImageSectionCount - 1)
        {
            // if it's not last piece, Rect's height should be partImageHeight
            partialRect = Rect(0, i * partImageHeight, grayImage.cols, partImageHeight);
        }
        else
        {
            // if it's last piece, Rect's height should be (grayImage.rows - i  * partImageHeight)
            partialRect = Rect(0, i * partImageHeight, grayImage.cols, grayImage.rows - i  * partImageHeight);
        }

        Mat partialResource = grayImage(partialRect);    
        partialResource.copyTo(partialGrayImage);    
        threshold( partialGrayImage, partialBinaryImage, 0, 255, THRESH_OTSU);

        // combin partial binary image to one piece
        partialBinaryImage.copyTo(binaryImage(partialRect));

        ///*stringstream resultStrm;
        //resultStrm << "partial_" << (i + 1);
        //string string = resultStrm.str();

        //imshow(string, partialBinaryImage);
        //waitKey(0);*/
    }
    imshow("result binary image.", binaryImage);
    waitKey(0);
    return;
}

  4. 使用 findcontour 获得最大面积轮廓：

    vector<vector<Point> > contours;        
findContours(binaryImage, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);

通常它工作得很好， 但对于一些低质量的灰度图像，它不起作用，如下所示：

低质量灰色图像1

低质量灰色图像2

完整的代码在这里：

#include <opencv2/imgproc/imgproc.hpp>
#include<opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>

using namespace std;
using namespace cv;


// we split source picture to binaryImageSectionCount(here it's 8) pieces by its height, 
// then we for every piece, we do threshold, 
// and at last we combine them agin to binaryImage        
const binaryImageSectionCount = 8;
void GetBinaryImage(Mat &grayImage, Mat &binaryImage)
{
    // get every partial gray image's height
    int partImageHeight = grayImage.rows / binaryImageSectionCount;
    for (int i = 0; i < binaryImageSectionCount; i++)
    {
        Mat partialGrayImage;            
        Mat partialBinaryImage;
        Rect partialRect;
        if (i != binaryImageSectionCount - 1)
        {
            // if it's not last piece, Rect's height should be partImageHeight
            partialRect = Rect(0, i * partImageHeight, grayImage.cols, partImageHeight);
        }
        else
        {
            // if it's last piece, Rect's height should be (grayImage.rows - i  * partImageHeight)
            partialRect = Rect(0, i * partImageHeight, grayImage.cols, grayImage.rows - i  * partImageHeight);
        }

        Mat partialResource = grayImage(partialRect);    
        partialResource.copyTo(partialGrayImage);    
        threshold( partialGrayImage, partialBinaryImage, 0, 255, THRESH_OTSU);

        // combin partial binary image to one piece
        partialBinaryImage.copyTo(binaryImage(partialRect));

        ///*stringstream resultStrm;
        //resultStrm << "partial_" << (i + 1);
        //string string = resultStrm.str();

        //imshow(string, partialBinaryImage);
        //waitKey(0);*/
    }
    imshow("result binary image.", binaryImage);
    waitKey(0);
    return;
}


int main(int argc, _TCHAR* argv[])
{   
    // get image path
    string imgPath("C:\\Users\\Alfred\\Desktop\\gray.bmp");     

    // read image
    Mat src = imread(imgPath);
    imshow("Source", src);        
    //medianBlur(src, src, 7);  
    cvtColor(src, src, COLOR_BGR2GRAY);     
    imshow("gray", src);    

    // do filter
    GaussianBlur(src, src, Size(3,3), 0);   

    // binary image
    Mat threshold_output(src.rows, src.cols, CV_8UC1, Scalar(0, 0, 0)); 
    GetBinaryImage(src, threshold_output);
    imshow("binaryImage", threshold_output);

    // get biggest contour
    vector<vector<Point> > contours;    
    findContours(threshold_output,contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
    int biggestContourIndex = 0;
    int maxContourArea = -1000;
    for (int i = 0; i < contours.size(); i++)
    {       
        if (contourArea(contours[i]) > maxContourArea)
        {
            maxContourArea = contourArea(contours[i]);
            biggestContourIndex = i;
        }
    }

    // show biggest contour
    Mat biggestContour(threshold_output.rows, threshold_output.cols, CV_8UC1, Scalar(0, 0, 0));
    drawContours(biggestContour, contours, biggestContourIndex, cv::Scalar(255,255,255), 2, 8, vector<Vec4i>(), 0, Point());
    imshow("maxContour", biggestContour);
    waitKey(0);

}

有人可以帮我获得更好的手部轮廓结果吗？谢谢！！！

4

1 回答 1

1

我在 python 中有代码片段，您可以在 C 中遵循相同的方法：

img = cv2.imread(x, 1)
cv2.imshow("img",img)

imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow("gray",imgray)

#Code for histogram equalization
equ = cv2.equalizeHist(imgray)
cv2.imshow('equ', equ)

#Code for contrast limited adaptive histogram equalization
#clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
#cl2 = clahe.apply(imgray)
#cv2.imshow('clahe2', cl2)

这是我得到的结果：

在此处输入图像描述

如果您的图像非常糟糕，您可以尝试我评论的涉及对比度受限自适应直方图均衡的代码。

于 2016-12-16T10:45:29.020 回答