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sample of person ReIDentification.
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samples/dnn/person_reid.cpp
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240
samples/dnn/person_reid.cpp
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//
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// You can download a baseline ReID model and sample input from:
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// https://github.com/ReID-Team/ReID_extra_testdata
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//
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// Authors of samples and Youtu ReID baseline:
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// Xing Sun <winfredsun@tencent.com>
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// Feng Zheng <zhengf@sustech.edu.cn>
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// Xinyang Jiang <sevjiang@tencent.com>
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// Fufu Yu <fufuyu@tencent.com>
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// Enwei Zhang <miyozhang@tencent.com>
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//
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// Copyright (C) 2020-2021, Tencent.
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// Copyright (C) 2020-2021, SUSTech.
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//
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#include <iostream>
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#include <fstream>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/dnn.hpp>
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using namespace cv;
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using namespace cv::dnn;
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const char* keys =
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"{help h | | show help message}"
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"{model m | | network model}"
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"{query_list q | | list of query images}"
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"{gallery_list g | | list of gallery images}"
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"{batch_size | 32 | batch size of each inference}"
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"{resize_h | 256 | resize input to specific height.}"
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"{resize_w | 128 | resize input to specific width.}"
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"{topk k | 5 | number of gallery images showed in visualization}"
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"{output_dir | | path for visualization(it should be existed)}"
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"{backend b | 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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"5: CUDA }"
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"{target t | 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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"6: CUDA ,"
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"7: CUDA fp16 (half-float preprocess) }";
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namespace cv{
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namespace reid{
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static Mat preprocess(const Mat& img)
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{
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const double mean[3] = {0.485, 0.456, 0.406};
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const double std[3] = {0.229, 0.224, 0.225};
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Mat ret = Mat(img.rows, img.cols, CV_32FC3);
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for (int y = 0; y < ret.rows; y ++)
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{
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for (int x = 0; x < ret.cols; x++)
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{
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for (int c = 0; c < 3; c++)
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{
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ret.at<Vec3f>(y,x)[c] = (float)((img.at<Vec3b>(y,x)[c] / 255.0 - mean[2 - c]) / std[2 - c]);
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}
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}
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}
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return ret;
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}
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static std::vector<float> normalization(const std::vector<float>& feature)
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{
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std::vector<float> ret;
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float sum = 0.0;
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for(int i = 0; i < (int)feature.size(); i++)
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{
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sum += feature[i] * feature[i];
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}
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sum = sqrt(sum);
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for(int i = 0; i < (int)feature.size(); i++)
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{
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ret.push_back(feature[i] / sum);
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}
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return ret;
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}
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static void extractFeatures(const std::vector<std::string>& imglist, Net* net, const int& batch_size, const int& resize_h, const int& resize_w, std::vector<std::vector<float>>& features)
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{
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for(int st = 0; st < (int)imglist.size(); st += batch_size)
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{
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std::vector<Mat> batch;
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for(int delta = 0; delta < batch_size && st + delta < (int)imglist.size(); delta++)
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{
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Mat img = imread(imglist[st + delta]);
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batch.push_back(preprocess(img));
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}
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Mat blob = dnn::blobFromImages(batch, 1.0, Size(resize_w, resize_h), Scalar(0.0,0.0,0.0), true, false, CV_32F);
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net->setInput(blob);
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Mat out = net->forward();
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for(int i = 0; i < (int)out.size().height; i++)
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{
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std::vector<float> temp_feature;
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for(int j = 0; j < (int)out.size().width; j++)
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{
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temp_feature.push_back(out.at<float>(i,j));
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}
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features.push_back(normalization(temp_feature));
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}
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}
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return ;
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}
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static void getNames(const std::string& ImageList, std::vector<std::string>& result)
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{
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std::ifstream img_in(ImageList);
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std::string img_name;
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while(img_in >> img_name)
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{
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result.push_back(img_name);
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}
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return ;
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}
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static float similarity(const std::vector<float>& feature1, const std::vector<float>& feature2)
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{
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float result = 0.0;
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for(int i = 0; i < (int)feature1.size(); i++)
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{
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result += feature1[i] * feature2[i];
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}
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return result;
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}
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static void getTopK(const std::vector<std::vector<float>>& queryFeatures, const std::vector<std::vector<float>>& galleryFeatures, const int& topk, std::vector<std::vector<int>>& result)
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{
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for(int i = 0; i < (int)queryFeatures.size(); i++)
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{
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std::vector<float> similarityList;
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std::vector<int> index;
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for(int j = 0; j < (int)galleryFeatures.size(); j++)
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{
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similarityList.push_back(similarity(queryFeatures[i], galleryFeatures[j]));
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index.push_back(j);
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}
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sort(index.begin(), index.end(), [&](int x,int y){return similarityList[x] > similarityList[y];});
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std::vector<int> topk_result;
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for(int j = 0; j < min(topk, (int)index.size()); j++)
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{
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topk_result.push_back(index[j]);
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}
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result.push_back(topk_result);
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}
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return ;
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}
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static void addBorder(const Mat& img, const Scalar& color, Mat& result)
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{
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const int bordersize = 5;
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copyMakeBorder(img, result, bordersize, bordersize, bordersize, bordersize, cv::BORDER_CONSTANT, color);
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return ;
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}
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static void drawRankList(const std::string& queryName, const std::vector<std::string>& galleryImageNames, const std::vector<int>& topk_index, const int& resize_h, const int& resize_w, Mat& result)
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{
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const Size outputSize = Size(resize_w, resize_h);
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Mat q_img = imread(queryName), temp_img;
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resize(q_img, temp_img, outputSize);
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addBorder(temp_img, Scalar(0,0,0), q_img);
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putText(q_img, "Query", Point(10, 30), FONT_HERSHEY_COMPLEX, 1.0, Scalar(0,255,0), 2);
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std::vector<Mat> Images;
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Images.push_back(q_img);
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for(int i = 0; i < (int)topk_index.size(); i++)
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{
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Mat g_img = imread(galleryImageNames[topk_index[i]]);
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resize(g_img, temp_img, outputSize);
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addBorder(temp_img, Scalar(255,255,255), g_img);
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putText(g_img, "G" + std::to_string(i), Point(10, 30), FONT_HERSHEY_COMPLEX, 1.0, Scalar(0,255,0), 2);
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Images.push_back(g_img);
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}
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hconcat(Images, result);
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return ;
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}
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static void visualization(const std::vector<std::vector<int>>& topk, const std::vector<std::string>& queryImageNames, const std::vector<std::string>& galleryImageNames, const std::string& output_dir, const int& resize_h, const int& resize_w)
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{
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for(int i = 0; i < (int)queryImageNames.size(); i++)
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{
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Mat img;
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drawRankList(queryImageNames[i], galleryImageNames, topk[i], resize_h, resize_w, img);
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std::string output_path = output_dir + "/" + queryImageNames[i].substr(queryImageNames[i].rfind("/")+1);
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imwrite(output_path, img);
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}
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return ;
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}
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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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// Parse command line arguments.
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CommandLineParser parser(argc, argv, keys);
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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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parser = CommandLineParser(argc, argv, keys);
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parser.about("Use this script to run ReID networks using OpenCV.");
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const std::string modelPath = parser.get<String>("model");
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const std::string queryImageList = parser.get<String>("query_list");
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const std::string galleryImageList = parser.get<String>("gallery_list");
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const int backend = parser.get<int>("backend");
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const int target = parser.get<int>("target");
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const int batch_size = parser.get<int>("batch_size");
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const int resize_h = parser.get<int>("resize_h");
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const int resize_w = parser.get<int>("resize_w");
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const int topk = parser.get<int>("topk");
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const std::string output_dir= parser.get<String>("output_dir");
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std::vector<std::string> queryImageNames;
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reid::getNames(queryImageList, queryImageNames);
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std::vector<std::string> galleryImageNames;
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reid::getNames(galleryImageList, galleryImageNames);
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dnn::Net net = dnn::readNet(modelPath);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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std::vector<std::vector<float>> queryFeatures;
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reid::extractFeatures(queryImageNames, &net, batch_size, resize_h, resize_w, queryFeatures);
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std::vector<std::vector<float>> galleryFeatures;
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reid::extractFeatures(galleryImageNames, &net, batch_size, resize_h, resize_w, galleryFeatures);
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std::vector<std::vector<int>> topkResult;
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reid::getTopK(queryFeatures, galleryFeatures, topk, topkResult);
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reid::visualization(topkResult, queryImageNames, galleryImageNames, output_dir, resize_h, resize_w);
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return 0;
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}
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samples/dnn/person_reid.py
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236
samples/dnn/person_reid.py
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#!/usr/bin/env python
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'''
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You can download a baseline ReID model and sample input from:
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https://github.com/ReID-Team/ReID_extra_testdata
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Authors of samples and Youtu ReID baseline:
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Xing Sun <winfredsun@tencent.com>
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Feng Zheng <zhengf@sustech.edu.cn>
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Xinyang Jiang <sevjiang@tencent.com>
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Fufu Yu <fufuyu@tencent.com>
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Enwei Zhang <miyozhang@tencent.com>
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Copyright (C) 2020-2021, Tencent.
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Copyright (C) 2020-2021, SUSTech.
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'''
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import argparse
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import os.path
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import numpy as np
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import cv2 as cv
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backends = (cv.dnn.DNN_BACKEND_DEFAULT,
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cv.dnn.DNN_BACKEND_INFERENCE_ENGINE,
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cv.dnn.DNN_BACKEND_OPENCV,
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cv.dnn.DNN_BACKEND_CUDA)
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targets = (cv.dnn.DNN_TARGET_CPU,
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cv.dnn.DNN_TARGET_OPENCL,
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cv.dnn.DNN_TARGET_OPENCL_FP16,
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cv.dnn.DNN_TARGET_MYRIAD,
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cv.dnn.DNN_TARGET_HDDL,
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cv.dnn.DNN_TARGET_CUDA,
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cv.dnn.DNN_TARGET_CUDA_FP16)
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MEAN = (0.485, 0.456, 0.406)
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STD = (0.229, 0.224, 0.225)
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def preprocess(images, height, width):
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"""
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Create 4-dimensional blob from image
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:param image: input image
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:param height: the height of the resized input image
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:param width: the width of the resized input image
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"""
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img_list = []
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for image in images:
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image = cv.resize(image, (width, height))
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img_list.append(image[:, :, ::-1])
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images = np.array(img_list)
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images = (images / 255.0 - MEAN) / STD
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input = cv.dnn.blobFromImages(images.astype(np.float32), ddepth = cv.CV_32F)
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return input
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def extract_feature(img_dir, model_path, batch_size = 32, resize_h = 384, resize_w = 128, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
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"""
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Extract features from images in a target directory
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:param img_dir: the input image directory
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:param model_path: path to ReID model
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:param batch_size: the batch size for each network inference iteration
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:param resize_h: the height of the input image
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:param resize_w: the width of the input image
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:param backend: name of computation backend
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:param target: name of computation target
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"""
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feat_list = []
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path_list = os.listdir(img_dir)
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path_list = [os.path.join(img_dir, img_name) for img_name in path_list]
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count = 0
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for i in range(0, len(path_list), batch_size):
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print('Feature Extraction for images in', img_dir, 'Batch:', count, '/', len(path_list))
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batch = path_list[i : min(i + batch_size, len(path_list))]
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imgs = read_data(batch)
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inputs = preprocess(imgs, resize_h, resize_w)
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feat = run_net(inputs, model_path, backend, target)
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feat_list.append(feat)
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count += batch_size
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feats = np.concatenate(feat_list, axis = 0)
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return feats, path_list
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def run_net(inputs, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
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"""
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Forword propagation for a batch of images.
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:param inputs: input batch of images
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:param model_path: path to ReID model
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:param backend: name of computation backend
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:param target: name of computation target
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"""
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net = cv.dnn.readNet(model_path)
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net.setPreferableBackend(backend)
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net.setPreferableTarget(target)
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net.setInput(inputs)
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out = net.forward()
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out = np.reshape(out, (out.shape[0], out.shape[1]))
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return out
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def read_data(path_list):
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"""
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Read all images from a directory into a list
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:param path_list: the list of image path
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"""
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img_list = []
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for img_path in path_list:
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img = cv.imread(img_path)
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if img is None:
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continue
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img_list.append(img)
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return img_list
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def normalize(nparray, order=2, axis=0):
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"""
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Normalize a N-D numpy array along the specified axis.
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:param nparry: the array of vectors to be normalized
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:param order: order of the norm
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:param axis: the axis of x along which to compute the vector norms
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"""
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norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)
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return nparray / (norm + np.finfo(np.float32).eps)
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def similarity(array1, array2):
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"""
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Compute the euclidean or cosine distance of all pairs.
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:param array1: numpy array with shape [m1, n]
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:param array2: numpy array with shape [m2, n]
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Returns:
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numpy array with shape [m1, m2]
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"""
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array1 = normalize(array1, axis=1)
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array2 = normalize(array2, axis=1)
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dist = np.matmul(array1, array2.T)
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return dist
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def topk(query_feat, gallery_feat, topk = 5):
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"""
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Return the index of top K gallery images most similar to the query images
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:param query_feat: array of feature vectors of query images
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:param gallery_feat: array of feature vectors of gallery images
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:param topk: number of gallery images to return
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"""
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sim = similarity(query_feat, gallery_feat)
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index = np.argsort(-sim, axis = 1)
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return [i[0:int(topk)] for i in index]
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def drawRankList(query_name, gallery_list, output_size = (128, 384)):
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"""
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Draw the rank list
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:param query_name: path of the query image
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:param gallery_name: path of the gallery image
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"param output_size: the output size of each image in the rank list
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"""
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def addBorder(im, color):
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bordersize = 5
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border = cv.copyMakeBorder(
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im,
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top = bordersize,
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bottom = bordersize,
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left = bordersize,
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right = bordersize,
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borderType = cv.BORDER_CONSTANT,
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value = color
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)
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return border
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query_img = cv.imread(query_name)
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query_img = cv.resize(query_img, output_size)
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query_img = addBorder(query_img, [0, 0, 0])
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cv.putText(query_img, 'Query', (10, 30), cv.FONT_HERSHEY_COMPLEX, 1., (0,255,0), 2)
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gallery_img_list = []
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for i, gallery_name in enumerate(gallery_list):
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gallery_img = cv.imread(gallery_name)
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gallery_img = cv.resize(gallery_img, output_size)
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gallery_img = addBorder(gallery_img, [255, 255, 255])
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cv.putText(gallery_img, 'G%02d'%i, (10, 30), cv.FONT_HERSHEY_COMPLEX, 1., (0,255,0), 2)
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gallery_img_list.append(gallery_img)
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ret = np.concatenate([query_img] + gallery_img_list, axis = 1)
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return ret
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def visualization(topk_idx, query_names, gallery_names, output_dir = 'vis'):
|
||||
"""
|
||||
Visualize the retrieval results with the person ReID model
|
||||
:param topk_idx: the index of ranked gallery images for each query image
|
||||
:param query_names: the list of paths of query images
|
||||
:param gallery_names: the list of paths of gallery images
|
||||
:param output_dir: the path to save the visualize results
|
||||
"""
|
||||
if not os.path.exists(output_dir):
|
||||
os.mkdir(output_dir)
|
||||
for i, idx in enumerate(topk_idx):
|
||||
query_name = query_names[i]
|
||||
topk_names = [gallery_names[j] for j in idx]
|
||||
vis_img = drawRankList(query_name, topk_names)
|
||||
output_path = os.path.join(output_dir, '%03d_%s'%(i, os.path.basename(query_name)))
|
||||
cv.imwrite(output_path, vis_img)
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Use this script to run human parsing using JPPNet',
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--query_dir', '-q', required=True, help='Path to query image.')
|
||||
parser.add_argument('--gallery_dir', '-g', required=True, help='Path to gallery directory.')
|
||||
parser.add_argument('--resize_h', default = 256, help='The height of the input for model inference.')
|
||||
parser.add_argument('--resize_w', default = 128, help='The width of the input for model inference')
|
||||
parser.add_argument('--model', '-m', default='reid.onnx', help='Path to pb model.')
|
||||
parser.add_argument('--visualization_dir', default='vis', help='Path for the visualization results')
|
||||
parser.add_argument('--topk', default=10, help='Number of images visualized in the rank list')
|
||||
parser.add_argument('--batchsize', default=32, help='The batch size of each inference')
|
||||
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
|
||||
help="Choose one of computation backends: "
|
||||
"%d: automatically (by default), "
|
||||
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
|
||||
"%d: OpenCV implementation"
|
||||
"%d: CUDA backend"% backends)
|
||||
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
|
||||
help='Choose one of target computation devices: '
|
||||
'%d: CPU target (by default), '
|
||||
'%d: OpenCL, '
|
||||
'%d: OpenCL fp16 (half-float precision), '
|
||||
'%d: NCS2 VPU, '
|
||||
'%d: HDDL VPU'
|
||||
'%d: CUDA,'
|
||||
'%d: CUDA FP16,'
|
||||
% targets)
|
||||
args, _ = parser.parse_known_args()
|
||||
|
||||
if not os.path.isfile(args.model):
|
||||
raise OSError("Model not exist")
|
||||
|
||||
query_feat, query_names = extract_feature(args.query_dir, args.model, args.batchsize, args.resize_h, args.resize_w, args.backend, args.target)
|
||||
gallery_feat, gallery_names = extract_feature(args.gallery_dir, args.model, args.batchsize, args.resize_h, args.resize_w, args.backend, args.target)
|
||||
|
||||
topk_idx = topk(query_feat, gallery_feat, args.topk)
|
||||
visualization(topk_idx, query_names, gallery_names, output_dir = args.visualization_dir)
|
Loading…
Reference in New Issue
Block a user