mirror of
https://github.com/opencv/opencv.git
synced 2024-11-27 12:40:05 +08:00
472 lines
16 KiB
C++
472 lines
16 KiB
C++
#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, int backend);
|
|
|
|
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 asyncNumReq = 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"));
|
|
int backend = parser.get<int>("backend");
|
|
net.setPreferableBackend(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 (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, backend);
|
|
|
|
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())
|
|
{
|
|
waitKey();
|
|
break;
|
|
}
|
|
|
|
preprocess(frame, net, Size(inpWidth, inpHeight), scale, mean, swapRB);
|
|
|
|
std::vector<Mat> outs;
|
|
net.forward(outs, outNames);
|
|
|
|
postprocess(frame, outs, net, backend);
|
|
|
|
// 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, int backend)
|
|
{
|
|
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);
|
|
|
|
// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for another backends we need NMS in sample
|
|
// or NMS is required if number of outputs > 1
|
|
if (outLayers.size() > 1 || (outLayerType == "Region" && backend != DNN_BACKEND_OPENCV))
|
|
{
|
|
std::map<int, std::vector<size_t> > class2indices;
|
|
for (size_t i = 0; i < classIds.size(); i++)
|
|
{
|
|
if (confidences[i] >= confThreshold)
|
|
{
|
|
class2indices[classIds[i]].push_back(i);
|
|
}
|
|
}
|
|
std::vector<Rect> nmsBoxes;
|
|
std::vector<float> nmsConfidences;
|
|
std::vector<int> nmsClassIds;
|
|
for (std::map<int, std::vector<size_t> >::iterator it = class2indices.begin(); it != class2indices.end(); ++it)
|
|
{
|
|
std::vector<Rect> localBoxes;
|
|
std::vector<float> localConfidences;
|
|
std::vector<size_t> classIndices = it->second;
|
|
for (size_t i = 0; i < classIndices.size(); i++)
|
|
{
|
|
localBoxes.push_back(boxes[classIndices[i]]);
|
|
localConfidences.push_back(confidences[classIndices[i]]);
|
|
}
|
|
std::vector<int> nmsIndices;
|
|
NMSBoxes(localBoxes, localConfidences, confThreshold, nmsThreshold, nmsIndices);
|
|
for (size_t i = 0; i < nmsIndices.size(); i++)
|
|
{
|
|
size_t idx = nmsIndices[i];
|
|
nmsBoxes.push_back(localBoxes[idx]);
|
|
nmsConfidences.push_back(localConfidences[idx]);
|
|
nmsClassIds.push_back(it->first);
|
|
}
|
|
}
|
|
boxes = nmsBoxes;
|
|
classIds = nmsClassIds;
|
|
confidences = nmsConfidences;
|
|
}
|
|
|
|
for (size_t idx = 0; idx < boxes.size(); ++idx)
|
|
{
|
|
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;
|
|
}
|