samples/dnn/object_detection.cpp


Check the corresponding tutorial 了解更多细节

#include <fstream>
#include <sstream>
#include < opencv2/dnn.hpp >
#ifdef CV_CXX11
#include <mutex>
#include <thread>
#include <queue>
#endif
#include "common.hpp"
std::string keys =
"{ help h | | Print help message. }"
"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
"{ zoo | models.yml | An optional path to file with preprocessing parameters }"
"{ device | 0 | camera device number. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ classes | | Optional path to a text file with names of classes to label detected objects. }"
"{ thr | .5 | Confidence threshold. }"
"{ nms | .4 | Non-maximum suppression threshold. }"
"{ backend | 0 | Choose one of computation backends: "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }"
"{ async | 0 | Number of asynchronous forwards at the same time. "
"Choose 0 for synchronous mode }" ;
using namespace cv ;
using namespace dnn;
float confThreshold, nmsThreshold;
std::vector<std::string> classes;
inline void preprocess( const Mat & frame, Net& net, Size inpSize, float scale ,
const Scalar & mean , bool swapRB);
void postprocess ( Mat & frame, const std::vector<Mat>& out, Net& net);
void drawPred( int classId, float conf, int left, int top, int right, int bottom, Mat & frame);
void callback( int pos, void * userdata);
#ifdef CV_CXX11
template < typename T>
class QueueFPS : public std::queue<T>
{
public :
QueueFPS() : counter(0) {}
void push( const T& entry)
{
std::lock_guard<std::mutex> lock(mutex);
std::queue<T>::push(entry);
counter += 1;
if (counter == 1)
{
// Start counting from a second frame (warmup).
tm.reset();
tm.start();
}
}
T get ()
{
std::lock_guard<std::mutex> lock(mutex);
T entry = this->front();
this->pop();
return entry;
}
float getFPS()
{
tm.stop();
double fps = counter / tm.getTimeSec();
tm.start();
return static_cast< float > (fps);
}
void clear()
{
std::lock_guard<std::mutex> lock(mutex);
while (!this->empty())
this->pop();
}
unsigned int counter;
private :
std::mutex mutex;
};
#endif // CV_CXX11
int main( int argc, char ** argv)
{
CommandLineParser parser(argc, argv, keys);
const std::string modelName = parser.get< 字符串 >( "@alias" );
const std::string zooFile = parser.get< 字符串 >( "zoo" );
keys += genPreprocArguments(modelName, zooFile);
parser = CommandLineParser (argc, argv, keys);
parser.about( "Use this script to run object detection deep learning networks using OpenCV." );
if (argc == 1 || parser.has( "help" ))
{
parser.printMessage();
return 0;
}
confThreshold = parser.get< float >( "thr" );
nmsThreshold = parser.get< float >( "nms" );
float scale = parser.get< float >( "scale" );
Scalar mean = parser.get< Scalar >( "mean" );
bool swapRB = parser.get< bool >( "rgb" );
int inpWidth = parser.get< int >( "width" );
int inpHeight = parser.get< int >( "height" );
size_t asyncNumReq = parser.get< int >( "async" );
CV_Assert (parser.has( "model" ));
std::string modelPath = findFile (parser.get< 字符串 >( "model" ));
std::string configPath = findFile (parser.get< 字符串 >( "config" ));
// Open file with classes names.
if (parser.has( "classes" ))
{
std::string file = parser.get< 字符串 >( "classes" );
std::ifstream ifs(file.c_str());
if (!ifs.is_open())
CV_Error ( Error::StsError , "File " + file + " not found" );
std::string line ;
while (std::getline(ifs, line))
{
classes.push_back(line);
}
}
// Load a model.
Net net = readNet (modelPath, configPath, parser.get< 字符串 >( "framework" ));
net.setPreferableBackend(parser.get< int >( "backend" ));
net.setPreferableTarget(parser.get< int >( "target" ));
std::vector<String> outNames = net.getUnconnectedOutLayersNames();
// Create a window
static const std::string kWinName = "Deep learning object detection in OpenCV" ;
int initialConf = (int)(confThreshold * 100);
createTrackbar ( "Confidence threshold, %" , kWinName, &initialConf, 99, callback);
// Open a video file or an image file or a camera stream.
if (parser.has( "input" ))
cap. open (parser.get< 字符串 >( "input" ));
else
cap. open (parser.get< int >( "device" ));
#ifdef CV_CXX11
bool process = true ;
// Frames capturing thread
QueueFPS<Mat> framesQueue;
std::thread framesThread([&](){
Mat frame;
while (process)
{
cap >> frame;
if (!frame.empty())
framesQueue.push(frame.clone());
else
break ;
}
});
// Frames processing thread
QueueFPS<Mat> processedFramesQueue;
QueueFPS<std::vector<Mat> > predictionsQueue;
std::thread processingThread([&](){
std::queue<AsyncArray> futureOutputs;
Mat blob;
while (process)
{
// Get a next frame
Mat frame;
{
if (!framesQueue.empty())
{
frame = framesQueue.get();
if (asyncNumReq)
{
if (futureOutputs.size() == asyncNumReq)
frame = Mat ();
}
else
framesQueue.clear(); // Skip the rest of frames
}
}
// Process the frame
if (!frame. empty ())
{
preprocess(frame, net, Size (inpWidth, inpHeight), scale, mean, swapRB);
processedFramesQueue.push(frame);
if (asyncNumReq)
{
futureOutputs.push(net.forwardAsync());
}
else
{
std::vector<Mat> outs;
net.forward(outs, outNames);
predictionsQueue.push(outs);
}
}
while (!futureOutputs.empty() &&
futureOutputs.front().wait_for(std::chrono::seconds(0)))
{
AsyncArray async_out = futureOutputs.front();
futureOutputs.pop();
Mat out;
async_out. get (out);
predictionsQueue.push({out});
}
}
});
// Postprocessing and rendering loop
while ( waitKey (1) < 0)
{
if (predictionsQueue.empty())
continue ;
std::vector<Mat> outs = predictionsQueue.get();
Mat frame = processedFramesQueue.get();
postprocess (frame, outs, net);
if (predictionsQueue.counter > 1)
{
std::string label = format( "Camera: %.2f FPS" , framesQueue.getFPS());
putText (frame, label, Point (0, 15), FONT_HERSHEY_SIMPLEX , 0.5, Scalar (0, 255, 0));
label = format( "Network: %.2f FPS" , predictionsQueue.getFPS());
putText (frame, label, Point (0, 30), FONT_HERSHEY_SIMPLEX , 0.5, Scalar (0, 255, 0));
label = format( "Skipped frames: %d" , framesQueue.counter - predictionsQueue.counter);
putText (frame, label, Point (0, 45), FONT_HERSHEY_SIMPLEX , 0.5, Scalar (0, 255, 0));
}
imshow (kWinName, frame);
}
process = false ;
framesThread.join();
processingThread.join();
#else // CV_CXX11
if (asyncNumReq)
CV_Error ( Error::StsNotImplemented , "Asynchronous forward is supported only with Inference Engine backend." );
// Process frames.
Mat frame, blob;
while ( waitKey (1) < 0)
{
cap >> frame;
if (frame.empty())
{
break ;
}
preprocess(frame, net, Size (inpWidth, inpHeight), scale, mean, swapRB);
std::vector<Mat> outs;
net.forward(outs, outNames);
postprocess (frame, outs, net);
// Put efficiency information.
std::vector<double> layersTimes;
double freq = getTickFrequency () / 1000;
double t = net.getPerfProfile(layersTimes) / freq;
std::string label = format( "Inference time: %.2f ms" , t);
putText (frame, label, Point (0, 15), FONT_HERSHEY_SIMPLEX , 0.5, Scalar (0, 255, 0));
imshow (kWinName, frame);
}
#endif // CV_CXX11
return 0;
}
inline void preprocess( const Mat & frame, Net& net, Size inpSize, float scale,
const Scalar & mean, bool swapRB)
{
static Mat blob;
// Create a 4D blob from a frame.
if (inpSize. width <= 0) inpSize. width = frame. cols ;
if (inpSize. height <= 0) inpSize. height = frame. rows ;
blobFromImage (frame, blob, 1.0, inpSize, Scalar (), swapRB, false , CV_8U );
// Run a model.
net.setInput(blob, "" , scale, mean);
if (net.getLayer(0)->outputNameToIndex( "im_info" ) != -1) // Faster-RCNN or R-FCN
{
resize (frame, frame, inpSize);
Mat imInfo = ( Mat_<float> (1, 3) << inpSize. height , inpSize. width , 1.6f);
net.setInput(imInfo, "im_info" );
}
}
void postprocess ( Mat & frame, const std::vector<Mat>& outs, Net& net)
{
static std::vector<int> outLayers = net.getUnconnectedOutLayers();
static std::string outLayerType = net.getLayer(outLayers[0])->type;
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect> boxes;
if (outLayerType == "DetectionOutput" )
{
// Network produces output blob with a shape 1x1xNx7 where N is a number of
// detections and an every detection is a vector of values
// [batchId, classId, confidence, left, top, right, bottom]
CV_Assert (outs.size() > 0);
for ( size_t k = 0; k < outs.size(); k++)
{
float * data = ( float *)outs[k].data;
for ( size_t i = 0; i < outs[k].total(); i += 7)
{
float confidence = data[i + 2];
if (confidence > confThreshold)
{
int left = (int)data[i + 3];
int top = (int)data[i + 4];
int right = (int)data[i + 5];
int bottom = (int)data[i + 6];
int width = right - left + 1;
int height = bottom - top + 1;
if (width <= 2 || height <= 2)
{
left = (int)(data[i + 3] * frame. cols );
top = (int)(data[i + 4] * frame. rows );
right = (int)(data[i + 5] * frame. cols );
bottom = (int)(data[i + 6] * frame. rows );
width = right - left + 1;
height = bottom - top + 1;
}
classIds.push_back(( int )(data[i + 1]) - 1); // Skip 0th background class id.
boxes.push_back( Rect (left, top, width, height));
confidences.push_back(confidence);
}
}
}
}
else if (outLayerType == "Region" )
{
for ( size_t i = 0; i < outs.size(); ++i)
{
// Network produces output blob with a shape NxC where N is a number of
// detected objects and C is a number of classes + 4 where the first 4
// numbers are [center_x, center_y, width, height]
float * data = ( float *)outs[i].data;
for ( int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
{
Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
Point classIdPoint;
double confidence;
minMaxLoc (scores, 0, &confidence, 0, &classIdPoint);
if (confidence > confThreshold)
{
int centerX = (int)(data[0] * frame. cols );
int centerY = (int)(data[1] * frame. rows );
int width = (int)(data[2] * frame. cols );
int height = (int)(data[3] * frame. rows );
int left = centerX - width / 2;
int top = centerY - height / 2;
classIds.push_back(classIdPoint. x );
confidences.push_back(( float )confidence);
boxes.push_back( Rect (left, top, width, height));
}
}
}
}
else
CV_Error ( Error::StsNotImplemented , "Unknown output layer type: " + outLayerType);
std::vector<int> indices;
NMSBoxes (boxes, confidences, confThreshold, nmsThreshold, indices);
for ( size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect box = boxes[idx];
drawPred(classIds[idx], confidences[idx], box. x , box. y ,
box. x + box. width , box. y + box. height , frame);
}
}
void drawPred( int classId, float conf, int left, int top, int right, int bottom, Mat & frame)
{
rectangle (frame, Point (left, top), Point (right, bottom), Scalar (0, 255, 0));
std::string label = format( "%.2f" , conf);
if (!classes.empty())
{
CV_Assert (classId < ( int )classes.size());
label = classes[classId] + ": " + label;
}
int baseLine;
Size labelSize = getTextSize (label, FONT_HERSHEY_SIMPLEX , 0.5, 1, &baseLine);
top = max (top, labelSize. height );
rectangle (frame, Point (left, top - labelSize. height ),
Point (left + labelSize. width , top + baseLine), Scalar::all (255), FILLED );
putText (frame, label, Point (left, top), FONT_HERSHEY_SIMPLEX , 0.5, Scalar ());
}
void callback( int pos, void *)
{
confThreshold = pos * 0.01f;
}