389 lines
15 KiB
C++
389 lines
15 KiB
C++
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <math.h>
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#include <stdarg.h>
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#include <sys/stat.h>
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#include <sys/types.h>
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#include <algorithm>
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#include <iostream>
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#include <numeric>
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#include <string>
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#include <vector>
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#include "include/config_parser.h"
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#include "include/keypoint_detector.h"
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#include "include/object_detector.h"
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#include "include/preprocess_op.h"
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#include "json/json.h"
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Json::Value RT_Config;
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void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
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std::cout << "----------------------- Config info -----------------------"
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<< std::endl;
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std::cout << "num_threads: " << RT_Config["cpu_threads"].as<int>()
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<< std::endl;
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std::cout << "----------------------- Data info -----------------------"
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<< std::endl;
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std::cout << "batch_size_det: " << RT_Config["batch_size_det"].as<int>()
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<< std::endl;
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std::cout << "----------------------- Model info -----------------------"
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<< std::endl;
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RT_Config["model_dir_det"].as<std::string>().erase(
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RT_Config["model_dir_det"].as<std::string>().find_last_not_of("/") + 1);
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std::cout << "detection model_name: "
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<< RT_Config["model_dir_det"].as<std::string>() << std::endl;
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std::cout << "----------------------- Perf info ------------------------"
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<< std::endl;
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std::cout << "Total number of predicted data: " << img_num
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<< " and total time spent(ms): "
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<< std::accumulate(det_time.begin(), det_time.end(), 0.)
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<< std::endl;
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img_num = std::max(1, img_num);
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std::cout << "preproce_time(ms): " << det_time[0] / img_num
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<< ", inference_time(ms): " << det_time[1] / img_num
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<< ", postprocess_time(ms): " << det_time[2] / img_num << std::endl;
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}
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void PrintKptsBenchmarkLog(std::vector<double> det_time, int img_num) {
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std::cout << "----------------------- Data info -----------------------"
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<< std::endl;
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std::cout << "batch_size_keypoint: "
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<< RT_Config["batch_size_keypoint"].as<int>() << std::endl;
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std::cout << "----------------------- Model info -----------------------"
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<< std::endl;
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RT_Config["model_dir_keypoint"].as<std::string>().erase(
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RT_Config["model_dir_keypoint"].as<std::string>().find_last_not_of("/") +
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1);
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std::cout << "keypoint model_name: "
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<< RT_Config["model_dir_keypoint"].as<std::string>() << std::endl;
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std::cout << "----------------------- Perf info ------------------------"
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<< std::endl;
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std::cout << "Total number of predicted data: " << img_num
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<< " and total time spent(ms): "
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<< std::accumulate(det_time.begin(), det_time.end(), 0.)
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<< std::endl;
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img_num = std::max(1, img_num);
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std::cout << "Average time cost per person:" << std::endl
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<< "preproce_time(ms): " << det_time[0] / img_num
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<< ", inference_time(ms): " << det_time[1] / img_num
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<< ", postprocess_time(ms): " << det_time[2] / img_num << std::endl;
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}
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void PrintTotalIimeLog(double det_time,
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double keypoint_time,
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double crop_time) {
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std::cout << "----------------------- Time info ------------------------"
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<< std::endl;
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std::cout << "Total Pipeline time(ms) per image: "
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<< det_time + keypoint_time + crop_time << std::endl;
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std::cout << "Average det time(ms) per image: " << det_time
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<< ", average keypoint time(ms) per image: " << keypoint_time
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<< ", average crop time(ms) per image: " << crop_time << std::endl;
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}
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static std::string DirName(const std::string& filepath) {
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auto pos = filepath.rfind(OS_PATH_SEP);
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if (pos == std::string::npos) {
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return "";
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}
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return filepath.substr(0, pos);
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}
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static bool PathExists(const std::string& path) {
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struct stat buffer;
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return (stat(path.c_str(), &buffer) == 0);
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}
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static void MkDir(const std::string& path) {
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if (PathExists(path)) return;
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int ret = 0;
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ret = mkdir(path.c_str(), 0755);
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if (ret != 0) {
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std::string path_error(path);
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path_error += " mkdir failed!";
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throw std::runtime_error(path_error);
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}
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}
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static void MkDirs(const std::string& path) {
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if (path.empty()) return;
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if (PathExists(path)) return;
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MkDirs(DirName(path));
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MkDir(path);
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}
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void PredictImage(const std::vector<std::string> all_img_paths,
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const int batch_size_det,
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const double threshold_det,
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const bool run_benchmark,
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PaddleDetection::ObjectDetector* det,
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PaddleDetection::KeyPointDetector* keypoint,
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const std::string& output_dir = "output") {
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std::vector<double> det_t = {0, 0, 0};
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int steps = ceil(static_cast<float>(all_img_paths.size()) / batch_size_det);
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int kpts_imgs = 0;
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std::vector<double> keypoint_t = {0, 0, 0};
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double midtimecost = 0;
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for (int idx = 0; idx < steps; idx++) {
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std::vector<cv::Mat> batch_imgs;
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int left_image_cnt = all_img_paths.size() - idx * batch_size_det;
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if (left_image_cnt > batch_size_det) {
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left_image_cnt = batch_size_det;
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}
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for (int bs = 0; bs < left_image_cnt; bs++) {
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std::string image_file_path = all_img_paths.at(idx * batch_size_det + bs);
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cv::Mat im = cv::imread(image_file_path, 1);
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batch_imgs.insert(batch_imgs.end(), im);
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}
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// Store all detected result
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std::vector<PaddleDetection::ObjectResult> result;
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std::vector<int> bbox_num;
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std::vector<double> det_times;
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// Store keypoint results
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std::vector<PaddleDetection::KeyPointResult> result_kpts;
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std::vector<cv::Mat> imgs_kpts;
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std::vector<std::vector<float>> center_bs;
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std::vector<std::vector<float>> scale_bs;
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std::vector<int> colormap_kpts = PaddleDetection::GenerateColorMap(20);
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bool is_rbox = false;
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if (run_benchmark) {
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det->Predict(
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batch_imgs, threshold_det, 50, 50, &result, &bbox_num, &det_times);
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} else {
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det->Predict(
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batch_imgs, threshold_det, 0, 1, &result, &bbox_num, &det_times);
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}
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// get labels and colormap
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auto labels = det->GetLabelList();
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auto colormap = PaddleDetection::GenerateColorMap(labels.size());
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int item_start_idx = 0;
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for (int i = 0; i < left_image_cnt; i++) {
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cv::Mat im = batch_imgs[i];
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std::vector<PaddleDetection::ObjectResult> im_result;
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int detect_num = 0;
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for (int j = 0; j < bbox_num[i]; j++) {
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PaddleDetection::ObjectResult item = result[item_start_idx + j];
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if (item.confidence < threshold_det || item.class_id == -1) {
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continue;
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}
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detect_num += 1;
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im_result.push_back(item);
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if (item.rect.size() > 6) {
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is_rbox = true;
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printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
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item.class_id,
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item.confidence,
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item.rect[0],
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item.rect[1],
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item.rect[2],
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item.rect[3],
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item.rect[4],
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item.rect[5],
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item.rect[6],
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item.rect[7]);
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} else {
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printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
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item.class_id,
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item.confidence,
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item.rect[0],
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item.rect[1],
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item.rect[2],
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item.rect[3]);
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}
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}
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std::cout << all_img_paths.at(idx * batch_size_det + i)
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<< " The number of detected box: " << detect_num << std::endl;
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item_start_idx = item_start_idx + bbox_num[i];
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std::vector<int> compression_params;
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compression_params.push_back(cv::IMWRITE_JPEG_QUALITY);
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compression_params.push_back(95);
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std::string output_path(output_dir);
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if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
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output_path += OS_PATH_SEP;
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}
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std::string image_file_path = all_img_paths.at(idx * batch_size_det + i);
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if (keypoint) {
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int imsize = im_result.size();
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for (int i = 0; i < imsize; i++) {
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auto keypoint_start_time = std::chrono::steady_clock::now();
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auto item = im_result[i];
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cv::Mat crop_img;
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std::vector<double> keypoint_times;
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std::vector<int> rect = {
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item.rect[0], item.rect[1], item.rect[2], item.rect[3]};
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std::vector<float> center;
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std::vector<float> scale;
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if (item.class_id == 0) {
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PaddleDetection::CropImg(im, crop_img, rect, center, scale);
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center_bs.emplace_back(center);
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scale_bs.emplace_back(scale);
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imgs_kpts.emplace_back(crop_img);
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kpts_imgs += 1;
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}
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auto keypoint_crop_time = std::chrono::steady_clock::now();
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std::chrono::duration<float> midtimediff =
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keypoint_crop_time - keypoint_start_time;
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midtimecost += static_cast<double>(midtimediff.count() * 1000);
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if (imgs_kpts.size() == RT_Config["batch_size_keypoint"].as<int>() ||
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((i == imsize - 1) && !imgs_kpts.empty())) {
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if (run_benchmark) {
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keypoint->Predict(imgs_kpts,
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center_bs,
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scale_bs,
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10,
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10,
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&result_kpts,
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&keypoint_times);
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} else {
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keypoint->Predict(imgs_kpts,
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center_bs,
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scale_bs,
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0,
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1,
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&result_kpts,
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&keypoint_times);
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}
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imgs_kpts.clear();
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center_bs.clear();
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scale_bs.clear();
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keypoint_t[0] += keypoint_times[0];
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keypoint_t[1] += keypoint_times[1];
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keypoint_t[2] += keypoint_times[2];
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}
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}
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std::string kpts_savepath =
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output_path + "keypoint_" +
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image_file_path.substr(image_file_path.find_last_of('/') + 1);
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cv::Mat kpts_vis_img = VisualizeKptsResult(
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im, result_kpts, colormap_kpts, keypoint->get_threshold());
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cv::imwrite(kpts_savepath, kpts_vis_img, compression_params);
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printf("Visualized output saved as %s\n", kpts_savepath.c_str());
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} else {
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// Visualization result
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cv::Mat vis_img = PaddleDetection::VisualizeResult(
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im, im_result, labels, colormap, is_rbox);
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std::string det_savepath =
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output_path + "result_" +
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image_file_path.substr(image_file_path.find_last_of('/') + 1);
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cv::imwrite(det_savepath, vis_img, compression_params);
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printf("Visualized output saved as %s\n", det_savepath.c_str());
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}
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}
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det_t[0] += det_times[0];
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det_t[1] += det_times[1];
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det_t[2] += det_times[2];
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}
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PrintBenchmarkLog(det_t, all_img_paths.size());
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if (keypoint) {
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PrintKptsBenchmarkLog(keypoint_t, kpts_imgs);
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PrintTotalIimeLog(
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(det_t[0] + det_t[1] + det_t[2]) / all_img_paths.size(),
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(keypoint_t[0] + keypoint_t[1] + keypoint_t[2]) / all_img_paths.size(),
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midtimecost / all_img_paths.size());
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}
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}
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int main(int argc, char** argv) {
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std::cout << "Usage: " << argv[0] << " [config_path] [image_dir](option)\n";
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if (argc < 2) {
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std::cout << "Usage: ./main det_runtime_config.json" << std::endl;
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return -1;
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}
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std::string config_path = argv[1];
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std::string img_path = "";
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if (argc >= 3) {
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img_path = argv[2];
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}
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// Parsing command-line
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PaddleDetection::load_jsonf(config_path, RT_Config);
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if (RT_Config["model_dir_det"].as<std::string>().empty()) {
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std::cout << "Please set [model_det_dir] in " << config_path << std::endl;
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return -1;
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}
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if (RT_Config["image_file"].as<std::string>().empty() &&
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RT_Config["image_dir"].as<std::string>().empty() && img_path.empty()) {
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std::cout << "Please set [image_file] or [image_dir] in " << config_path
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<< " Or use command: <" << argv[0] << " [image_dir]>"
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<< std::endl;
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return -1;
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}
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if (!img_path.empty()) {
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std::cout << "Use image_dir in command line overide the path in config file"
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<< std::endl;
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RT_Config["image_dir"] = img_path;
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RT_Config["image_file"] = "";
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}
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// Load model and create a object detector
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PaddleDetection::ObjectDetector det(
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RT_Config["model_dir_det"].as<std::string>(),
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RT_Config["cpu_threads"].as<int>(),
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RT_Config["batch_size_det"].as<int>());
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PaddleDetection::KeyPointDetector* keypoint = nullptr;
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if (!RT_Config["model_dir_keypoint"].as<std::string>().empty()) {
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keypoint = new PaddleDetection::KeyPointDetector(
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RT_Config["model_dir_keypoint"].as<std::string>(),
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RT_Config["cpu_threads"].as<int>(),
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RT_Config["batch_size_keypoint"].as<int>(),
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RT_Config["use_dark_decode"].as<bool>());
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RT_Config["batch_size_det"] = 1;
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printf(
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"batchsize of detection forced to be 1 while keypoint model is not "
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"empty()");
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}
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// Do inference on input image
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if (!RT_Config["image_file"].as<std::string>().empty() ||
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!RT_Config["image_dir"].as<std::string>().empty()) {
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if (!PathExists(RT_Config["output_dir"].as<std::string>())) {
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MkDirs(RT_Config["output_dir"].as<std::string>());
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}
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std::vector<std::string> all_img_paths;
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std::vector<cv::String> cv_all_img_paths;
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if (!RT_Config["image_file"].as<std::string>().empty()) {
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all_img_paths.push_back(RT_Config["image_file"].as<std::string>());
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if (RT_Config["batch_size_det"].as<int>() > 1) {
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std::cout << "batch_size_det should be 1, when set `image_file`."
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<< std::endl;
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return -1;
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}
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} else {
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cv::glob(RT_Config["image_dir"].as<std::string>(), cv_all_img_paths);
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for (const auto& img_path : cv_all_img_paths) {
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all_img_paths.push_back(img_path);
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}
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}
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PredictImage(all_img_paths,
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RT_Config["batch_size_det"].as<int>(),
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RT_Config["threshold_det"].as<float>(),
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RT_Config["run_benchmark"].as<bool>(),
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&det,
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keypoint,
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RT_Config["output_dir"].as<std::string>());
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}
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delete keypoint;
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keypoint = nullptr;
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return 0;
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}
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