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#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/ocl.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <cstdlib>
using namespace std;
static const string fcnType = "fcn8s";
static vector<cv::Vec3b> readColors(const string &filename = "pascal-classes.txt")
{
vector<cv::Vec3b> colors;
ifstream fp(filename.c_str());
if (!fp.is_open())
{
cerr << "File with colors not found: " << filename << endl;
exit(-1);
}
string line;
while (!fp.eof())
{
getline(fp, line);
if (line.length())
{
stringstream ss(line);
string name; ss >> name;
int temp;
cv::Vec3b color;
ss >> temp; color[0] = temp;
ss >> temp; color[1] = temp;
ss >> temp; color[2] = temp;
colors.push_back(color);
}
}
fp.close();
return colors;
}
static void colorizeSegmentation(dnn::Blob &score, const vector<cv::Vec3b> &colors, cv::Mat &segm)
{
const int rows = score.rows();
const int cols = score.cols();
const int chns = score.channels();
cv::Mat maxCl(rows, cols, CV_8UC1);
cv::Mat maxVal(rows, cols, CV_32FC1);
for (int ch = 0; ch < chns; ch++)
{
for (int row = 0; row < rows; row++)
{
const float *ptrScore = score.ptrf(0, ch, row);
uchar *ptrMaxCl = maxCl.ptr<uchar>(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] = ch;
}
}
}
}
segm.create(rows, cols, CV_8UC3);
for (int row = 0; row < rows; row++)
{
const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
cv::Vec3b *ptrSegm = segm.ptr<cv::Vec3b>(row);
for (int col = 0; col < cols; col++)
{
ptrSegm[col] = colors[ptrMaxCl[col]];
}
}
}
int main(int argc, char **argv)
{
cv::dnn::initModule(); //Required if OpenCV is built as static libs
cv::ocl::setUseOpenCL(false); //OpenCL switcher
String modelTxt = fcnType + "-heavy-pascal.prototxt";
String modelBin = fcnType + "-heavy-pascal.caffemodel";
String imageFile = (argc > 1) ? argv[1] : "rgb.jpg";
vector<cv::Vec3b> colors = readColors();
//! [Create the importer of Caffe model]
Ptr<dnn::Importer> importer;
try //Try to import Caffe GoogleNet model
{
importer = dnn::createCaffeImporter(modelTxt, modelBin);
}
catch (const cv::Exception &err) //Importer can throw errors, we will catch them
{
cerr << err.msg << endl;
}
//! [Create the importer of Caffe model]
if (!importer)
{
cerr << "Can't load network by using the following files: " << endl;
cerr << "prototxt: " << modelTxt << endl;
cerr << "caffemodel: " << modelBin << endl;
cerr << fcnType << "-heavy-pascal.caffemodel can be downloaded here:" << endl;
cerr << "http://dl.caffe.berkeleyvision.org/" << fcnType << "-heavy-pascal.caffemodel" << endl;
exit(-1);
}
//! [Initialize network]
dnn::Net net;
importer->populateNet(net);
importer.release(); //We don't need importer anymore
//! [Initialize network]
//! [Prepare blob]
Mat img = imread(imageFile);
if (img.empty())
{
cerr << "Can't read image from the file: " << imageFile << endl;
exit(-1);
}
resize(img, img, Size(500, 500)); //FCN accepts 500x500 RGB-images
dnn::Blob inputBlob = dnn::Blob::fromImages(img); //Convert Mat to dnn::Blob batch of images
//! [Prepare blob]
//! [Set input blob]
net.setBlob(".data", inputBlob); //set the network input
//! [Set input blob]
//! [Make forward pass]
double t = (double)cv::getTickCount();
net.forward(); //compute output
t = (double)cv::getTickCount() - t;
printf("processing time: %.1fms\n", t*1000./getTickFrequency());
//! [Make forward pass]
//! [Gather output]
dnn::Blob score = net.getBlob("score");
cv::Mat colorize;
colorizeSegmentation(score, colors, colorize);
cv::Mat show;
cv::addWeighted(img, 0.4, colorize, 0.6, 0.0, show);
cv::imshow("show", show);
cv::waitKey(0);
return 0;
} //main
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