opencv中的面罩
输入:人脸图像
问题:在应用 Canny 查找轮廓之前对图像进行阈值处理但不返回面罩
Problem: thresholded image before applying Canny to find contours but does not return face mask
期望的输出如果输入不同的面部,它应该生成一个合适的面罩(面部区域白色和背景白色)
Desired output if different face is input,it should generate a proper face mask(face area white and background white)
用苹果图片试过..效果很好
Tried with apple picture..works fine
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
using namespace cv;
using namespace std;
int main(){
Mat right=imread("front.jpg");
Mat img1;
cvtColor(right, img1, CV_RGB2GRAY);
threshold(img1,img1,160,255,cv::THRESH_BINARY);
Canny(img1, img1, 128, 350);
vector< vector<Point> > contours;
findContours(img1, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
Mat mask = Mat::zeros(img1.rows, img1.cols, CV_8UC1);
drawContours(mask, contours, -1, Scalar(255), CV_FILLED);
normalize(mask.clone(), mask, 0.0, 255.0, CV_MINMAX, CV_8UC1);
imshow("original", right);
imshow("thresh",img1);
imshow("mask", mask);
waitKey(0);
return 0;
}
这是我使用的图像
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推荐答案
使用下面的代码,可以完美地创建您在上述命令中提供的示例图像.
Using this below code the mask creation working perfectly for the sample image you provided in the above commants.
这里假设你的背景是任何颜色的,没有其他物体.
Here assumes, your background is of any colour with no other object.
下面的代码就可以了
寻找边缘
Find edge
通过 morphology 操作加强边缘.
Strengthen the edge by morphology operation.
在边缘找到最大的轮廓(总是前景的边界)并通过填充来绘制它.
Find biggest contour in edge( always the boundary of your foreground ) and draw it by filling.
有时您的轮廓可能不会在底部关闭(底部没有边缘),因此填充轮廓将不起作用,因此要使其关闭,代码首先找到最大轮廓(前景)的边界矩形,然后绘制矩形的底部到边缘图像,然后再次找到最大轮廓将为您提供正确的蒙版图像.
Sometimes your contour may not be closed at the bottom(no edges at bottom) so the filling contour wont work, so for make it closed the code first find the bounding rect for biggest contour(foreground) and then draw bottom of rect to the edge image, then find largest contour again will give you the proper mask image.
Rect R;
Mat findLargestContour(Mat thr){
vector< vector <Point> > contours; // Vector for storing contour
vector< Vec4i > hierarchy;
int largest_contour_index=0;
int largest_area=0;
Mat dst(thr.rows,thr.cols,CV_8UC1,Scalar::all(0)); //create destination image
findContours( thr, contours, hierarchy,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE );
for( int i = 0; i< contours.size(); i++ ) // iterate through each contour.
{
double a=contourArea( contours[i],false); // Find the area of contour
if(a>largest_area){
largest_area=a;
largest_contour_index=i; //Store the index of largest contour
}
}
drawContours( dst,contours, largest_contour_index, Scalar(255,255,255),CV_FILLED, 8, hierarchy );
R= boundingRect( contours[largest_contour_index]);
return dst;
}
int main( )
{
Mat right=imread("1.jpg");
// blur(right,right,Size(3,3));
Mat gray;
cvtColor(right, gray, CV_RGB2GRAY);
int borderW=10;
//Mat ROI=gray(Rect(borderW,borderW,img1.cols-2*borderW,img1.rows-2*borderW));
Canny(gray, gray, 30, 255);
Size kernalSize (5,5);
Mat element = getStructuringElement (MORPH_RECT, kernalSize, Point(1,1) );
morphologyEx(gray, gray, MORPH_CLOSE, element );
imshow("canny", gray);
Mat largestCon=findLargestContour(gray.clone());
line(largestCon, Point(R.x,R.y+R.height), Point(R.x+R.width,R.y+R.height), Scalar(255),2,8,0);
Mat mask=findLargestContour(largestCon.clone());
Mat A;
right.copyTo(A,mask);
imshow("original", right);
imshow("dst", A);
imshow("mask", mask);
waitKey(0);
return 0;
}
查看一些示例掩码
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