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/*
Sample of using OpenCV dnn module with Torch ENet model.
*/
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <cstdlib>
#include <sstream>
using namespace std;
const String keys =
"{help h || Sample app for loading ENet Torch model. "
"The model and class names list can be downloaded here: "
"https://www.dropbox.com/sh/dywzk3gyb12hpe5/AAD5YkUa8XgMpHs2gCRgmCVCa }"
"{model m || path to Torch .net model file (model_best.net) }"
"{image i || path to image file }"
"{result r || path to save output blob (optional, binary format, NCHW order) }"
"{show s || whether to show all output channels or not}"
"{o_blob || output blob's name. If empty, last blob's name in net is used}";
static const int kNumClasses = 20;
static const String classes[] = {
"Background", "Road", "Sidewalk", "Building", "Wall", "Fence", "Pole",
"TrafficLight", "TrafficSign", "Vegetation", "Terrain", "Sky", "Person",
"Rider", "Car", "Truck", "Bus", "Train", "Motorcycle", "Bicycle"
};
static const Vec3b colors[] = {
Vec3b(0, 0, 0), Vec3b(244, 126, 205), Vec3b(254, 83, 132), Vec3b(192, 200, 189),
Vec3b(50, 56, 251), Vec3b(65, 199, 228), Vec3b(240, 178, 193), Vec3b(201, 67, 188),
Vec3b(85, 32, 33), Vec3b(116, 25, 18), Vec3b(162, 33, 72), Vec3b(101, 150, 210),
Vec3b(237, 19, 16), Vec3b(149, 197, 72), Vec3b(80, 182, 21), Vec3b(141, 5, 207),
Vec3b(189, 156, 39), Vec3b(235, 170, 186), Vec3b(133, 109, 144), Vec3b(231, 160, 96)
};
static void showLegend();
static void colorizeSegmentation(const Mat &score, Mat &segm);
int main(int argc, char **argv)
{
CommandLineParser parser(argc, argv, keys);
if (parser.has("help") || argc == 1)
{
parser.printMessage();
return 0;
}
String modelFile = parser.get<String>("model");
String imageFile = parser.get<String>("image");
if (!parser.check())
{
parser.printErrors();
return 0;
}
String resultFile = parser.get<String>("result");
//! [Read model and initialize network]
dnn::Net net = dnn::readNetFromTorch(modelFile);
//! [Prepare blob]
Mat img = imread(imageFile), input;
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
exit(-1);
}
Mat inputBlob = blobFromImage(img, 1./255, Size(1024, 512), Scalar(), true, false); //Convert Mat to batch of images
//! [Prepare blob]
//! [Set input blob]
net.setInput(inputBlob); //set the network input
//! [Set input blob]
TickMeter tm;
String oBlob = net.getLayerNames().back();
if (!parser.get<String>("o_blob").empty())
{
oBlob = parser.get<String>("o_blob");
}
//! [Make forward pass]
tm.start();
Mat result = net.forward(oBlob);
tm.stop();
if (!resultFile.empty()) {
CV_Assert(result.isContinuous());
ofstream fout(resultFile.c_str(), ios::out | ios::binary);
fout.write((char*)result.data, result.total() * sizeof(float));
fout.close();
}
std::cout << "Output blob: " << result.size[0] << " x " << result.size[1] << " x " << result.size[2] << " x " << result.size[3] << "\n";
std::cout << "Inference time, ms: " << tm.getTimeMilli() << std::endl;
if (parser.has("show"))
{
Mat segm, show;
colorizeSegmentation(result, segm);
showLegend();
cv::resize(segm, segm, img.size(), 0, 0, cv::INTER_NEAREST);
addWeighted(img, 0.1, segm, 0.9, 0.0, show);
imshow("Result", show);
waitKey();
}
return 0;
} //main
static void showLegend()
{
static const int kBlockHeight = 30;
cv::Mat legend(kBlockHeight * kNumClasses, 200, CV_8UC3);
for(int i = 0; i < kNumClasses; i++)
{
cv::Mat block = legend.rowRange(i * kBlockHeight, (i + 1) * kBlockHeight);
block.setTo(colors[i]);
putText(block, classes[i], Point(0, kBlockHeight / 2), FONT_HERSHEY_SIMPLEX, 0.5, Vec3b(255, 255, 255));
}
imshow("Legend", legend);
}
static void colorizeSegmentation(const Mat &score, Mat &segm)
{
const int rows = score.size[2];
const int cols = score.size[3];
const int chns = score.size[1];
Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
Mat maxVal(rows, cols, CV_32FC1, score.data);
for (int ch = 1; ch < chns; ch++)
{
for (int row = 0; row < rows; row++)
{
const float *ptrScore = score.ptr<float>(0, ch, row);
uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
float *ptrMaxVal = maxVal.ptr<float>(row);
for (int col = 0; col < cols; col++)
{
if (ptrScore[col] > ptrMaxVal[col])
{
ptrMaxVal[col] = ptrScore[col];
ptrMaxCl[col] = (uchar)ch;
}
}
}
}
segm.create(rows, cols, CV_8UC3);
for (int row = 0; row < rows; row++)
{
const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
Vec3b *ptrSegm = segm.ptr<Vec3b>(row);
for (int col = 0; col < cols; col++)
{
ptrSegm[col] = colors[ptrMaxCl[col]];
}
}
}