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Update tutorials. A new cv::dnn::readNet function

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This commit is contained in:
Dmitry Kurtaev
2018-03-03 19:29:37 +03:00
parent 8e4fe30db6
commit f2440ceae6
10 changed files with 149 additions and 155 deletions
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samples/dnn/object_detection.cpp
+12 -28
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@@ -2,8 +2,9 @@
#include <iostream>
#include <sstream>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
const char* keys =
"{ help h | | Print help message. }"
@@ -35,8 +36,6 @@ using namespace dnn;
float confThreshold;
std::vector<std::string> classes;
Net readNet(const std::string& model, const std::string& config = "", const std::string& framework = "");
void postprocess(Mat& frame, const Mat& out, Net& net);
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);
@@ -95,7 +94,7 @@ int main(int argc, char** argv)
// Create a window
static const std::string kWinName = "Deep learning object detection in OpenCV";
namedWindow(kWinName, WINDOW_NORMAL);
int initialConf = confThreshold * 100;
int initialConf = (int)(confThreshold * 100);
createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);
// Open a video file or an image file or a camera stream.
@@ -135,8 +134,9 @@ int main(int argc, char** argv)
// Put efficiency information.
std::vector<double> layersTimes;
double t = net.getPerfProfile(layersTimes);
std::string label = format("Inference time: %.2f", t * 1000 / getTickFrequency());
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);
@@ -160,10 +160,10 @@ void postprocess(Mat& frame, const Mat& out, Net& net)
float confidence = data[i + 2];
if (confidence > confThreshold)
{
int left = data[i + 3];
int top = data[i + 4];
int right = data[i + 5];
int bottom = data[i + 6];
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 classId = (int)(data[i + 1]) - 1; // Skip 0th background class id.
drawPred(classId, confidence, left, top, right, bottom, frame);
}
@@ -208,7 +208,7 @@ void postprocess(Mat& frame, const Mat& out, Net& net)
int height = (int)(data[3] * frame.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;
drawPred(classId, confidence, left, top, left + width, top + height, frame);
drawPred(classId, (float)confidence, left, top, left + width, top + height, frame);
}
}
}
@@ -238,21 +238,5 @@ void drawPred(int classId, float conf, int left, int top, int right, int bottom,
void callback(int pos, void*)
{
confThreshold = pos * 0.01;
}
Net readNet(const std::string& model, const std::string& config, const std::string& framework)
{
std::string modelExt = model.substr(model.rfind('.'));
if (framework == "caffe" || modelExt == ".caffemodel")
return readNetFromCaffe(config, model);
else if (framework == "tensorflow" || modelExt == ".pb")
return readNetFromTensorflow(model, config);
else if (framework == "torch" || modelExt == ".t7" || modelExt == ".net")
return readNetFromTorch(model);
else if (framework == "darknet" || modelExt == ".weights")
return readNetFromDarknet(config, model);
else
CV_Error(Error::StsError, "Cannot determine an origin framework of model from file " + model);
return Net();
confThreshold = pos * 0.01f;
}