void expectNoFallbacks(Net& net)
{
// Check if all the layers are supported with current backend and target.
// Some layers might be fused so their timings equal to zero.
std::vector<double> timings;
net.getPerfProfile(timings);
std::vector<String> names = net.getLayerNames();
CV_Assert(names.size() == timings.size());
for (int i = 0; i < names.size(); ++i)
{
Ptr<dnn::Layer> l = net.getLayer(net.getLayerId(names[i]));
bool fused = !timings[i];
if ((!l->supportBackend(backend) || l->preferableTarget != target) && !fused)
CV_Error(Error::StsNotImplemented, "Layer [" + l->name + "] of type [" +
l->type + "] is expected to has backend implementation");
}
}
int main(int argc, char** argv)
{
CommandLineParser parser(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");
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");
// 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.
CV_Assert(parser.has("model"));
Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
net.setPreferableBackend(parser.get<int>("backend"));
net.setPreferableTarget(parser.get<int>("target"));
// 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(0);
// Process frames.
Mat frame, blob;
while (waitKey(1) < 0)
{
cap >> frame;
if (frame.empty())
{
waitKey();
break;
}
// Create a 4D blob from a frame.
Size inpSize(inpWidth > 0 ? inpWidth : frame.cols,
inpHeight > 0 ? inpHeight : frame.rows);
blobFromImage(frame, blob, scale, inpSize, mean, swapRB, false);
// Run a model.
net.setInput(blob);
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");
}
std::vector<Mat> outs;
net.forward(outs, getOutputsNames(net));
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);
}
return 0;
}
