实时美颜摄像并生成H264视频流

为什么美颜摄像这么简单的功能，OpenCV这个开源项目网上很少有代码呢？对于在windows平台下，生成h264视频流也比价麻烦，没有现成的api可以使用，需要借助MinGw编译libx264，或者ffmpeg才能使用。

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要推送直播视频流，现在先做到下面几个步骤：

1.OpenCV捕捉摄像头的图像

2.进行识别需要美颜的部分（人脸识别，肤色识别）

3.进行美颜（提升亮度，直方图均衡，滤波）

4.生成YUV视频 5.生成h264

主要功能代码：

/** Global variables */

//-- Note, either copy these two files from opencv/data/haarscascades to your current folder, or change these locations

string face_cascade_name = "haarcascade_frontalface_alt.xml";

CascadeClassifier face_cascade;

CascadeClassifier eyes_cascade;

string window_name_onlyface = "Capture - only Face";

string window_name_face = "Capture - Face ";

/**

* @function detectAndDisplay

void detectAndenhance( Mat &frame )

{

std::vector<Rect> faces;

Mat frame_gray;

Mat hatAlpha;

//hatAlpha = imread("2.png",-1);//圣诞帽的图片

cvtColor( frame, frame_gray, COLOR_BGR2GRAY );

//equalizeHist( frame_gray, frame_gray );

//-- Detect faces

face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );

for( size_t i = 0; i < faces.size(); i++ )

{

Rect face(faces[i].x,faces[i].y,faces[i].x + faces[i].width,faces[i].y + faces[i].height);

cvSetImageROI(&IplImage(frame),face);

// Do the porcess

blur(frame,frame,Size(7,7),Point(-1,-1));

//////////////////////////////////////////////

cvResetImageROI( &IplImage(frame) );

Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );

ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 0, 0, 360, Scalar( 255, 0, 255 ), 2, 8, 0 );

// line(frame,Point(faces[i].x,faces[i].y),center,Scalar(255,0,0),5);

Mat faceROI = frame_gray( faces[i] );

std::vector<Rect> eyes;

imshow( window_name_onlyface, faceROI );

}

//-- Show what you got

imshow( window_name_face, frame );

//imwrite("merry christmas.jpg",frame);

}

/** @函数 detectAndDisplay */

void detectAndDisplay( Mat frame )

{

std::vector<Rect> faces;

Mat frame_gray;

cvtColor( frame, frame_gray, CV_BGR2GRAY );

equalizeHist( frame_gray, frame_gray );

//-- 多尺寸检测人脸

face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );

for( int i = 0; i < faces.size(); i++ )

{

Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );

ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );

Mat faceROI = frame_gray( faces[i] );

std::vector<Rect> eyes;

//-- 在每张人脸上检测双眼

eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );

for( int j = 0; j < eyes.size(); j++ )

{

Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );

int radius = cvRound( (eyes[j].width + eyes[i].height)*0.25 );

circle( frame, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );

}

//-- 显示结果图像

imshow( window_name_face, frame );

}

// add by shiter 2016/3/3

Mat equalizeChannelHist(const Mat & inputImage)

{

if( inputImage.channels() >= 3 )

{

vector<Mat> channels;

split(inputImage, channels);

Mat B,G,R;

equalizeHist( channels[0], B );

equalizeHist( channels[1], G );

equalizeHist( channels[2], R );

vector<Mat> combined;

combined.push_back(B);

combined.push_back(G);

combined.push_back(R);

Mat result;

merge(combined, result);

return result;

}

else{return inputImage;}

return inputImage;

}

Mat equalizeIntensityHist(const Mat & inputImage)

{

if(inputImage.channels() >= 3)

{

Mat ycrcb;

cvtColor(inputImage, ycrcb, COLOR_BGR2YCrCb);

vector<Mat> channels;

split(ycrcb, channels);

equalizeHist(channels[0], channels[0]);

Mat result;

merge(channels,ycrcb);

cvtColor(ycrcb, result, COLOR_YCrCb2BGR);

return result;

}

return Mat();

}

//皮肤检测,并针对皮肤进行增强，模糊

void MySkinEnhance(Mat &frame)

{

Mat input_image =frame;

Mat output_mask;

Mat output_image;

Mat mask;

//肤色椭圆

/*椭圆皮肤模型*/

Mat skinCrCbHist = Mat::zeros(Size(256, 256), CV_8UC1);

ellipse(skinCrCbHist, Point(113, 155.6), Size(23.4, 15.2), 43.0, 0.0, 360.0, Scalar(255, 255, 255), -1);

Mat element = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1) );

if(input_image.empty())

return ;

Mat ycrcb_image;

output_mask = Mat::zeros(input_image.size(), CV_8UC1);

cvtColor(input_image, ycrcb_image, CV_BGR2YCrCb); //首先转换成到YCrCb空间

for(int i = 0; i < input_image.rows; i++) //利用椭圆皮肤模型进行皮肤检测

{

uchar* p = (uchar*)output_mask.ptr<uchar>(i);

Vec3b* ycrcb = (Vec3b*)ycrcb_image.ptr<Vec3b>(i);

for(int j = 0; j < input_image.cols; j++)

{

if(skinCrCbHist.at<uchar>(ycrcb[j][1], ycrcb[j][2]) > 0)

p[j] = 255;

}

//morphologyEx(output_mask,output_mask,MORPH_CLOSE,element);

//output_mask.setTo(0);

dilate(output_mask,output_mask,Mat(32,32,CV_8U),Point(-1,-1),2);

//imwrite("dilate.jpg",dst);

// output_image.setTo(0);

input_image.copyTo(output_image, output_mask);

Mat enhance = output_image;

medianBlur(output_image,enhance,11);

//blur(enhance,enhance,Size(4,4),Point(-1,-1),4);

imshow("blur face",enhance);

for(int i = 0; i < output_image.rows; i++) //

{

uchar* p = (uchar*)output_mask.ptr<uchar>(i);

for(int j = 0; j < output_image.cols; j++)

{

if((enhance.at<Vec3b>(i,j)[0] < 50) && (enhance.at<Vec3b>(i,j)[1] < 50)&& (enhance.at<Vec3b>(i,j)[2] < 50) )

{

//不是纯黑的

}

else

{

frame.at<Vec3b>(i,j)[0] = enhance.at<Vec3b>(i,j)[0];

frame.at<Vec3b>(i,j)[1] = enhance.at<Vec3b>(i,j)[1];

frame.at<Vec3b>(i,j)[2] = enhance.at<Vec3b>(i,j)[2];

}

// 图像融合

//addWeighted(input_image, 0.95, enhance, 0.05, 0.0, input_image);

imshow("ouput image",frame);

}

//提高亮度对比度

void highlight(Mat &frame)

{

Mat src,dst;

double alpha =1.5;

double beta = 20;

src = frame;

if(!src.data)

{

cout<<"Failed to load image!"<<endl;

return ;

}

//dst = Mat::zeros(src.size(),src.type());

for (int i = 0;i<src.rows;++i)

{

//uchar* inData=src.ptr<uchar>(i);

for(int j= 0;j<src.cols;++j)

{

/*src.at<Vec3b>(i,j)[0] = saturate_cast<uchar>(src.at<Vec3b>(i,j)[0]*alpha+beta);

src.at<Vec3b>(i,j)[1] = saturate_cast<uchar>(src.at<Vec3b>(i,j)[1]*alpha+beta);

src.at<Vec3b>(i,j)[2] = saturate_cast<uchar>(src.at<Vec3b>(i,j)[2]*alpha+beta); */

//上面的效率低，下面的有越界

src.at<Vec3b>(i,j)[0] = (src.at<Vec3b>(i,j)[0]*alpha+beta);

src.at<Vec3b>(i,j)[1] = (src.at<Vec3b>(i,j)[1]*alpha+beta);

src.at<Vec3b>(i,j)[2] = (src.at<Vec3b>(i,j)[2]*alpha+beta);

}

namedWindow("Handled Image");

imshow("Handled Image",src);

//waitKey();

}

来源：oschina

链接：https://my.oschina.net/u/4203900/blog/4314217

标签

Amp

Cascade