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247 lines
8.6 KiB
C++
247 lines
8.6 KiB
C++
#include <fstream>
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#include <sstream>
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#include <opencv2/dnn.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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const char* keys =
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"{ help h | | Print help message. }"
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"{ device | 0 | camera device number. }"
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"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
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"{ model m | | Path to a binary file of model contains trained weights. "
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"It could be a file with extensions .caffemodel (Caffe), "
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".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet). }"
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"{ config c | | Path to a text file of model contains network configuration. "
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"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet). }"
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"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
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"{ classes | | Optional path to a text file with names of classes. }"
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"{ colors | | Optional path to a text file with colors for an every class. "
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"An every color is represented with three values from 0 to 255 in BGR channels order. }"
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"{ mean | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
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"{ scale | 1 | Preprocess input image by multiplying on a scale factor. }"
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"{ width | | Preprocess input image by resizing to a specific width. }"
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"{ height | | Preprocess input image by resizing to a specific height. }"
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"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
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"{ backend | 0 | Choose one of computation backends: "
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"0: automatically (by default), "
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"1: Halide language (http://halide-lang.org/), "
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"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"3: OpenCV implementation }"
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"{ target | 0 | Choose one of target computation devices: "
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"0: CPU target (by default), "
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"1: OpenCL, "
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"2: OpenCL fp16 (half-float precision), "
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"3: VPU }";
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using namespace cv;
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using namespace dnn;
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std::vector<std::string> classes;
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std::vector<Vec3b> colors;
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void showLegend();
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void colorizeSegmentation(const Mat &score, Mat &segm);
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int main(int argc, char** argv)
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{
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CommandLineParser parser(argc, argv, keys);
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parser.about("Use this script to run semantic segmentation deep learning networks using OpenCV.");
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if (argc == 1 || parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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float scale = parser.get<float>("scale");
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Scalar mean = parser.get<Scalar>("mean");
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bool swapRB = parser.get<bool>("rgb");
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int inpWidth = parser.get<int>("width");
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int inpHeight = parser.get<int>("height");
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String model = parser.get<String>("model");
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String config = parser.get<String>("config");
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String framework = parser.get<String>("framework");
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int backendId = parser.get<int>("backend");
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int targetId = parser.get<int>("target");
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// Open file with classes names.
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if (parser.has("classes"))
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{
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std::string file = parser.get<String>("classes");
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std::ifstream ifs(file.c_str());
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if (!ifs.is_open())
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CV_Error(Error::StsError, "File " + file + " not found");
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std::string line;
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while (std::getline(ifs, line))
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{
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classes.push_back(line);
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}
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}
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// Open file with colors.
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if (parser.has("colors"))
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{
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std::string file = parser.get<String>("colors");
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std::ifstream ifs(file.c_str());
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if (!ifs.is_open())
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CV_Error(Error::StsError, "File " + file + " not found");
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std::string line;
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while (std::getline(ifs, line))
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{
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std::istringstream colorStr(line.c_str());
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Vec3b color;
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for (int i = 0; i < 3 && !colorStr.eof(); ++i)
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colorStr >> color[i];
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colors.push_back(color);
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}
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}
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if (!parser.check())
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{
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parser.printErrors();
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return 1;
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}
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CV_Assert(!model.empty());
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//! [Read and initialize network]
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Net net = readNet(model, config, framework);
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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//! [Read and initialize network]
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// Create a window
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static const std::string kWinName = "Deep learning semantic segmentation in OpenCV";
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namedWindow(kWinName, WINDOW_NORMAL);
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//! [Open a video file or an image file or a camera stream]
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VideoCapture cap;
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if (parser.has("input"))
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cap.open(parser.get<String>("input"));
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else
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cap.open(parser.get<int>("device"));
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//! [Open a video file or an image file or a camera stream]
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// Process frames.
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Mat frame, blob;
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while (waitKey(1) < 0)
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{
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cap >> frame;
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if (frame.empty())
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{
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waitKey();
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break;
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}
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//! [Create a 4D blob from a frame]
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blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false);
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//! [Create a 4D blob from a frame]
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//! [Set input blob]
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net.setInput(blob);
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//! [Set input blob]
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//! [Make forward pass]
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Mat score = net.forward();
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//! [Make forward pass]
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Mat segm;
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colorizeSegmentation(score, segm);
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resize(segm, segm, frame.size(), 0, 0, INTER_NEAREST);
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addWeighted(frame, 0.1, segm, 0.9, 0.0, frame);
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// Put efficiency information.
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std::vector<double> layersTimes;
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double freq = getTickFrequency() / 1000;
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double t = net.getPerfProfile(layersTimes) / freq;
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std::string label = format("Inference time: %.2f ms", t);
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putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
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imshow(kWinName, frame);
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if (!classes.empty())
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showLegend();
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}
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return 0;
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}
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void colorizeSegmentation(const Mat &score, Mat &segm)
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{
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const int rows = score.size[2];
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const int cols = score.size[3];
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const int chns = score.size[1];
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if (colors.empty())
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{
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// Generate colors.
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colors.push_back(Vec3b());
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for (int i = 1; i < chns; ++i)
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{
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Vec3b color;
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for (int j = 0; j < 3; ++j)
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color[j] = (colors[i - 1][j] + rand() % 256) / 2;
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colors.push_back(color);
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}
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}
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else if (chns != (int)colors.size())
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{
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CV_Error(Error::StsError, format("Number of output classes does not match "
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"number of colors (%d != %d)", chns, colors.size()));
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}
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Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
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Mat maxVal(rows, cols, CV_32FC1, score.data);
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for (int ch = 1; ch < chns; ch++)
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{
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for (int row = 0; row < rows; row++)
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{
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const float *ptrScore = score.ptr<float>(0, ch, row);
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uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
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float *ptrMaxVal = maxVal.ptr<float>(row);
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for (int col = 0; col < cols; col++)
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{
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if (ptrScore[col] > ptrMaxVal[col])
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{
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ptrMaxVal[col] = ptrScore[col];
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ptrMaxCl[col] = (uchar)ch;
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}
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}
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}
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}
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segm.create(rows, cols, CV_8UC3);
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for (int row = 0; row < rows; row++)
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{
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const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
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Vec3b *ptrSegm = segm.ptr<Vec3b>(row);
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for (int col = 0; col < cols; col++)
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{
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ptrSegm[col] = colors[ptrMaxCl[col]];
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}
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}
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}
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void showLegend()
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{
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static const int kBlockHeight = 30;
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static Mat legend;
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if (legend.empty())
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{
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const int numClasses = (int)classes.size();
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if ((int)colors.size() != numClasses)
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{
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CV_Error(Error::StsError, format("Number of output classes does not match "
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"number of labels (%d != %d)", colors.size(), classes.size()));
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}
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legend.create(kBlockHeight * numClasses, 200, CV_8UC3);
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for (int i = 0; i < numClasses; i++)
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{
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Mat block = legend.rowRange(i * kBlockHeight, (i + 1) * kBlockHeight);
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block.setTo(colors[i]);
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putText(block, classes[i], Point(0, kBlockHeight / 2), FONT_HERSHEY_SIMPLEX, 0.5, Vec3b(255, 255, 255));
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}
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namedWindow("Legend", WINDOW_NORMAL);
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imshow("Legend", legend);
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}
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}
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