OpenCV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。

最近有个小需求，要推送直播视频流，我在网上查了一下有live555或者用librtmp来推送，但是前者还需要修改源代码，也挺麻烦的，现在先做到了下面几个步骤：

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

现在用librtmp时候，出现了

ERROR:RTMP_Connect0,failed to connect socket,10061(unknow error)

不知道是咋回事了，怀疑是1935端口被禁，但是一时半会儿不知道咋弄。

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();

}