dnn: move samples

samples/dnn/fcn_semsegm.cpp

@@ -0,0 +1,154 @@
+#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>
+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(const Mat &score, const vector<cv::Vec3b> &colors, cv::Mat &segm)
+{
+    const int rows = score.size[2];
+    const int cols = score.size[3];
+    const int chns = score.size[1];
+
+    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.ptr<float>(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
+
+    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
+    Mat inputBlob = blobFromImage(img);   //Convert Mat to batch of images
+    //! [Prepare blob]
+
+    //! [Set input blob]
+    net.setInput(inputBlob, "data");        //set the network input
+    //! [Set input blob]
+
+    //! [Make forward pass]
+    double t = (double)cv::getTickCount();
+    Mat score = net.forward("score");                          //compute output
+    t = (double)cv::getTickCount() - t;
+    printf("processing time: %.1fms\n", t*1000./getTickFrequency());
+    //! [Make forward pass]
+
+    Mat colorize;
+    colorizeSegmentation(score, colors, colorize);
+    Mat show;
+    addWeighted(img, 0.4, colorize, 0.6, 0.0, show);
+    imshow("show", show);
+    waitKey(0);
+    return 0;
+} //main