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Unite deep learning image classification samples

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This commit is contained in:
Dmitry Kurtaev
2018-03-03 16:43:21 +03:00
parent e8d94ea87c
commit 8e4fe30db6
21 changed files with 1292 additions and 9444 deletions
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samples/dnn/object_detection.cpp
+16 -13
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@@ -1,8 +1,10 @@
#include <opencv2/opencv.hpp>
#include <fstream>
#include <iostream>
#include <sstream>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
const char* keys =
"{ help h | | Print help message. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
@@ -19,7 +21,13 @@ const char* keys =
"{ height | -1 | Preprocess input image by resizing to a specific height. }"
"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
"{ thr | .5 | Confidence threshold. }"
"{ opencl | | Enable OpenCL }";
"{ backend | 0 | Choose one of computation backends: "
"0: default C++ backend, "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default),"
"1: OpenCL }";
using namespace cv;
using namespace dnn;
@@ -27,8 +35,6 @@ using namespace dnn;
float confThreshold;
std::vector<std::string> classes;
void loadClasses(const std::string& file);
Net readNet(const std::string& model, const std::string& config = "", const std::string& framework = "");
void postprocess(Mat& frame, const Mat& out, Net& net);
@@ -74,7 +80,7 @@ int main(int argc, char** argv)
if (!ifs.is_open())
CV_Error(Error::StsError, "File " + file + " not found");
std::string line;
while (ifs >> line)
while (std::getline(ifs, line))
{
classes.push_back(line);
}
@@ -83,17 +89,14 @@ int main(int argc, char** argv)
// Load a model.
CV_Assert(parser.has("model"));
Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
if (parser.get<bool>("opencl"))
{
net.setPreferableTarget(DNN_TARGET_OPENCL);
}
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 = confThreshold * 100;
createTrackbar("Confidence threshold", kWinName, &initialConf, 99, callback);
createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);
// Open a video file or an image file or a camera stream.
VideoCapture cap;
@@ -134,7 +137,7 @@ int main(int argc, char** argv)
std::vector<double> layersTimes;
double t = net.getPerfProfile(layersTimes);
std::string label = format("Inference time: %.2f", t * 1000 / getTickFrequency());
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
imshow(kWinName, frame);
}
@@ -240,7 +243,7 @@ void callback(int pos, void*)
Net readNet(const std::string& model, const std::string& config, const std::string& framework)
{
std::string modelExt = model.substr(model.find('.'));
std::string modelExt = model.substr(model.rfind('.'));
if (framework == "caffe" || modelExt == ".caffemodel")
return readNetFromCaffe(config, model);
else if (framework == "tensorflow" || modelExt == ".pb")