#include <fstream>
#include <sstream>
#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 }"
"{ 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. }"
"{ 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 }";
using namespace dnn;
std::vector<std::string> classes;
int main(int argc, char** argv)
{
const std::string modelName = parser.get<String>("@alias"); const std::string zooFile = parser.get<String>("zoo"); keys += genPreprocArguments(modelName, zooFile);
parser.about("Use this script to run classification deep learning networks using OpenCV."); if (argc == 1 || parser.has("help")) {
return 0;
}
float scale = parser.get<float>("scale"); bool swapRB = parser.get<bool>("rgb"); int inpWidth = parser.get<int>("width"); int inpHeight = parser.get<int>("height"); int backendId = parser.get<int>("backend"); int targetId = parser.get<int>("target");
if (parser.has("classes")) {
std::string file = parser.get<String>("classes"); std::ifstream ifs(file.c_str());
if (!ifs.is_open())
while (std::getline(ifs, line))
{
classes.push_back(line);
}
}
{
return 1;
}
Net net = readNet(model, config, framework); net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
static const std::string kWinName = "Deep learning image classification in OpenCV";
else
{
cap >> frame;
if (frame.empty())
{
break;
}
net.setInput(blob);
Mat prob = net.forward(); double confidence;
int classId = classIdPoint.x;
std::vector<double> layersTimes;
double t = net.getPerfProfile(layersTimes) / freq;
std::string label = format("Inference time: %.2f ms", t);
label = format("%s: %.4f", (classes.empty() ? format("Class #%d", classId).c_str() :
classes[classId].c_str()),
confidence);
}
return 0;
}