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310 lines
11 KiB
C++
310 lines
11 KiB
C++
#include <fstream>
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#include <sstream>
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#include <iostream>
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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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#include "common.hpp"
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using namespace cv;
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using namespace std;
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using namespace dnn;
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const string about =
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"Use this script to run semantic segmentation deep learning networks using OpenCV.\n\n"
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"Firstly, download required models using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.\n"
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"To run:\n"
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"\t ./example_dnn_classification modelName(e.g. u2netp) --input=$OPENCV_SAMPLES_DATA_PATH/butterfly.jpg (or ignore this argument to use device camera)\n"
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"Model path can also be specified using --model argument.";
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const string param_keys =
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"{ help h | | Print help message. }"
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"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
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"{ zoo | ../dnn/models.yml | An optional path to file with preprocessing parameters }"
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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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"{ colors | | Optional path to a text file with colors for an every class. "
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"Every color is represented with three values from 0 to 255 in BGR channels order. }";
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const string backend_keys = format(
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"{ backend | default | Choose one of computation backends: "
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"default: automatically (by default), "
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"openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"opencv: OpenCV implementation, "
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"vkcom: VKCOM, "
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"cuda: CUDA, "
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"webnn: WebNN }");
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const string target_keys = format(
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"{ target | cpu | Choose one of target computation devices: "
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"cpu: CPU target (by default), "
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"opencl: OpenCL, "
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"opencl_fp16: OpenCL fp16 (half-float precision), "
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"vpu: VPU, "
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"vulkan: Vulkan, "
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"cuda: CUDA, "
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"cuda_fp16: CUDA fp16 (half-float preprocess) }");
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string keys = param_keys + backend_keys + target_keys;
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vector<string> labels;
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vector<Vec3b> colors;
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static 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 labels does not match "
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"number of colors (%d != %zu)",
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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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static void showLegend(FontFace fontFace)
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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)labels.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 labels does not match "
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"number of labels (%zu != %zu)",
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colors.size(), labels.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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Rect r = getTextSize(Size(), labels[i], Point(), fontFace, 15, 400);
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r.height += 15; // padding
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r.width += 10; // padding
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rectangle(block, r, Scalar::all(255), FILLED);
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putText(block, labels[i], Point(10, kBlockHeight/2), Scalar(0,0,0), fontFace, 15, 400);
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}
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namedWindow("Legend", WINDOW_AUTOSIZE);
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imshow("Legend", legend);
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}
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}
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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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const string modelName = parser.get<String>("@alias");
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const string zooFile = findFile(parser.get<String>("zoo"));
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keys += genPreprocArguments(modelName, zooFile);
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parser = CommandLineParser(argc, argv, keys);
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parser.about(about);
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if (!parser.has("@alias") || 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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string sha1 = parser.get<String>("sha1");
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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 = findModel(parser.get<String>("model"), sha1);
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const string backend = parser.get<String>("backend");
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const string target = parser.get<String>("target");
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int stdSize = 20;
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int stdWeight = 400;
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int stdImgSize = 512;
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int imgWidth = -1; // Initialization
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int fontSize = 50;
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int fontWeight = 500;
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FontFace fontFace("sans");
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// Open file with labels names.
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if (parser.has("labels"))
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{
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string file = findFile(parser.get<String>("labels"));
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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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string line;
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while (getline(ifs, line))
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{
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labels.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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string file = findFile(parser.get<String>("colors"));
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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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string line;
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while (getline(ifs, line))
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{
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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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EngineType engine = ENGINE_AUTO;
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if (backend != "default" || target != "cpu"){
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engine = ENGINE_CLASSIC;
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}
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Net net = readNetFromONNX(model, engine);
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net.setPreferableBackend(getBackendID(backend));
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net.setPreferableTarget(getTargetID(target));
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//! [Read and initialize network]
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// Create a window
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static const string kWinName = "Deep learning semantic segmentation in OpenCV";
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namedWindow(kWinName, WINDOW_AUTOSIZE);
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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(findFile(parser.get<String>("input")));
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else
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cap.open(parser.get<int>("device"));
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if (!cap.isOpened()) {
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cerr << "Error: Video could not be opened." << endl;
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return -1;
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}
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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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if (imgWidth == -1){
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imgWidth = max(frame.rows, frame.cols);
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fontSize = min(fontSize, (stdSize*imgWidth)/stdImgSize);
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fontWeight = min(fontWeight, (stdWeight*imgWidth)/stdImgSize);
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}
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imshow("Original Image", frame);
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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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//! [Set input blob]
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net.setInput(blob);
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//! [Set input blob]
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if (modelName == "u2netp")
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{
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vector<Mat> output;
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net.forward(output, net.getUnconnectedOutLayersNames());
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Mat pred = output[0].reshape(1, output[0].size[2]);
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pred.convertTo(pred, CV_8U, 255.0);
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Mat mask;
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resize(pred, mask, Size(frame.cols, frame.rows), 0, 0, INTER_AREA);
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// Create overlays for foreground and background
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Mat foreground_overlay;
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// Set foreground (object) to red
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Mat all_zeros = Mat::zeros(frame.size(), CV_8UC1);
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vector<Mat> channels = {all_zeros, all_zeros, mask};
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merge(channels, foreground_overlay);
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// Blend the overlays with the original frame
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addWeighted(frame, 0.25, foreground_overlay, 0.75, 0, frame);
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}
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else
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{
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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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}
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// Put efficiency information.
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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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string label = format("Inference time: %.2f ms", t);
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Rect r = getTextSize(Size(), label, Point(), fontFace, fontSize, fontWeight);
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r.height += fontSize; // padding
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r.width += 10; // padding
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rectangle(frame, r, Scalar::all(255), FILLED);
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putText(frame, label, Point(10, fontSize), Scalar(0,0,0), fontFace, fontSize, fontWeight);
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imshow(kWinName, frame);
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if (!labels.empty())
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showLegend(fontFace);
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}
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return 0;
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}
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