OpenCV - 如何从低质量灰度图像中获得更好的手部轮廓？

I need to get contour from hand image, usually I process image with 4 steps:

我需要从手部图像获取轮廓，通常我用4个步骤处理图像：

get raw RGB gray image from 3 channels to 1 channel:

从3个通道到1个通道获取原始RGB灰度图像：
```
cvtColor(sourceGrayImage, sourceGrayImage, COLOR_BGR2GRAY);
```
use Gaussian blur to filter gray image:

使用高斯模糊过滤灰度图像：
```
GaussianBlur(sourceGrayImage, sourceGrayImage, Size(3,3), 0);
```

binary gray image, I split image by height, normally I split image to 6 images by its height, then each one I do threshold process:

二进制灰度图像，我按高度分割图像，通常我将图像按其高度分割为6个图像，然后每个我做阈值处理：

// 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;
}

use findcontour to get biggest area contour:

使用findcontour获得最大的区域轮廓：

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

normally it works well, But for some low quality gray image, it doesn't work,like below:

通常它运作良好，但对于一些低质量的灰色图像，它不起作用，如下所示：

the complete code is here:

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);

}

could anybody please help me to get a better hand contour result? thanks!!!

请问有谁能帮助我获得更好的手部轮廓效果？谢谢！！！

1 个解决方案

#1

I have the code snippet in python, you can follow the same approach in C:

我在python中有代码片段，你可以在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)

This is the result I obtained:

这是我获得的结果：

If you're image is horribly bad you could try the code that I commented involving contrast limited adaptive histogram equalization.

如果您的图像非常糟糕，您可以尝试我评论的代码，包括对比度限制自适应直方图均衡。

#1