opencv3.4 发布 dnnFace震撼来袭

在圣诞夜的前夕opencv发布了3.4版本，主要增强了dnn模块，特别是添加了对faster-rcnn的支持，并且带有openCL加速

我主要测试了下其中的resnetface示例，效果还不错，除了速度上有些慢之外，基本上是向MTCNN看齐了

opencv示例向来是繁琐的要死，让人摸不着头脑，还是整个简单点的示例吧

代码如下:

#include <iostream>
#include <cstdlib>
#include "mropencv.h"
using namespace cv;
using namespace cv::dnn;
using namespace std;
const size_t inWidth = 300;
const size_t inHeight = 300;
const double inScaleFactor = 1.0;
const Scalar meanVal(104.0, 177.0, 123.0);

int main(int argc, char** argv)
{
float min_confidence = 0.5;
String modelConfiguration = "face_detector/deploy.prototxt";
String modelBinary = "face_detector/res10_300x300_ssd_iter_140000.caffemodel";
//! [Initialize network]
dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary);
//! [Initialize network]
if (net.empty())
{
cerr << "Can't load network by using the following files: " << endl;
cerr << "prototxt: " << modelConfiguration << endl;
cerr << "caffemodel: " << modelBinary << endl;
cerr << "Models are available here:" << endl;
cerr << "<OPENCV_SRC_DIR>/samples/dnn/face_detector" << endl;
cerr << "or here:" << endl;
cerr << "https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector" << endl;
exit(-1);
}

VideoCapture cap(0);
if (!cap.isOpened())
{
cout << "Couldn't open camera : " << endl;
return -1;
}
for (;;)
{
Mat frame;
cap >> frame; // get a new frame from camera/video or read image

if (frame.empty())
{
waitKey();
break;
}

if (frame.channels() == 4)
cvtColor(frame, frame, COLOR_BGRA2BGR);

//! [Prepare blob]
Mat inputBlob = blobFromImage(frame, inScaleFactor,
Size(inWidth, inHeight), meanVal, false, false); //Convert Mat to batch of images
//! [Prepare blob]

//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]

//! [Make forward pass]
Mat detection = net.forward("detection_out"); //compute output
//! [Make forward pass]

vector<double> layersTimings;
double freq = getTickFrequency() / 1000;
double time = net.getPerfProfile(layersTimings) / freq;

Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());

ostringstream ss;
ss << "FPS: " << 1000 / time << " ; time: " << time << " ms";
putText(frame, ss.str(), Point(20, 20), 0, 0.5, Scalar(0, 0, 255));

float confidenceThreshold = min_confidence;
for (int i = 0; i < detectionMat.rows; i++)
{
float confidence = detectionMat.at<float>(i, 2);

if (confidence > confidenceThreshold)
{
int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);

Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));

rectangle(frame, object, Scalar(0, 255, 0));

ss.str("");
ss << confidence;
String conf(ss.str());
String label = "Face: " + conf;
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
Size(labelSize.width, labelSize.height + baseLine)),
Scalar(255, 255, 255), CV_FILLED);
putText(frame, label, Point(xLeftBottom, yLeftBottom),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
}
}
cv::imshow("detections", frame);
if (waitKey(1) >= 0) break;
}
return 0;
}