| OpenCV 3.4.7 Open Source Computer Vision

samples/dnn/object_detection.cpp

Check the corresponding tutorial for more details

#include <fstream>
#include <sstream>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.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:
TickMeter tm;
std::mutex mutex;
};
#endif // CV_CXX11
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv, keys);
const std::string modelName = parser.get<String>("@alias");
const std::string zooFile = parser.get<String>("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 async = parser.get<int>("async");
CV_Assert(parser.has("model"));
std::string modelPath = findFile(parser.get<String>("model"));
std::string configPath = findFile(parser.get<String>("config"));
// Open file with classes names.
if (parser.has("classes"))
{
std::string file = parser.get<String>("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<String>("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";
namedWindow(kWinName, WINDOW_NORMAL);
int initialConf = (int)(confThreshold * 100);
createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);
// Open a video file or an image file or a camera stream.
VideoCapture cap;
if (parser.has("input"))
cap.open(parser.get<String>("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 (async)
{
if (futureOutputs.size() == async)
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 (async)
{
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 (async)
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())
{
waitKey();
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 * height <= 1)
{
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;
}