270 lines
9.1 KiB
C++
270 lines
9.1 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 <glog/logging.h>
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#include <math.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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#ifdef _WIN32
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#include <direct.h>
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#include <io.h>
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#elif LINUX
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#include <stdarg.h>
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#include <sys/stat.h>
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#endif
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#include <gflags/gflags.h>
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#include <opencv2/opencv.hpp>
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#include "include/jde_detector.h"
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#include "include/object_detector.h"
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DEFINE_string(model_dir, "", "Path of inference model");
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DEFINE_int32(batch_size, 1, "batch_size");
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DEFINE_string(
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video_file,
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"",
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"Path of input video, `video_file` or `camera_id` has a highest priority.");
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DEFINE_int32(camera_id, -1, "Device id of camera to predict");
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DEFINE_bool(
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use_gpu,
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false,
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"Deprecated, please use `--device` to set the device you want to run.");
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DEFINE_string(device,
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"CPU",
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"Choose the device you want to run, it can be: CPU/GPU/XPU, "
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"default is CPU.");
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DEFINE_double(threshold, 0.5, "Threshold of score.");
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DEFINE_string(output_dir, "output", "Directory of output visualization files.");
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DEFINE_string(run_mode,
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"paddle",
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"Mode of running(paddle/trt_fp32/trt_fp16/trt_int8)");
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DEFINE_int32(gpu_id, 0, "Device id of GPU to execute");
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DEFINE_bool(run_benchmark,
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false,
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"Whether to predict a image_file repeatedly for benchmark");
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DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU");
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DEFINE_int32(cpu_threads, 1, "Num of threads with CPU");
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DEFINE_int32(trt_min_shape, 1, "Min shape of TRT DynamicShapeI");
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DEFINE_int32(trt_max_shape, 1280, "Max shape of TRT DynamicShapeI");
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DEFINE_int32(trt_opt_shape, 640, "Opt shape of TRT DynamicShapeI");
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DEFINE_bool(trt_calib_mode,
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false,
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"If the model is produced by TRT offline quantitative calibration, "
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"trt_calib_mode need to set True");
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void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
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LOG(INFO) << "----------------------- Config info -----------------------";
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LOG(INFO) << "runtime_device: " << FLAGS_device;
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LOG(INFO) << "ir_optim: "
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<< "True";
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LOG(INFO) << "enable_memory_optim: "
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<< "True";
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int has_trt = FLAGS_run_mode.find("trt");
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if (has_trt >= 0) {
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LOG(INFO) << "enable_tensorrt: "
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<< "True";
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std::string precision = FLAGS_run_mode.substr(4, 8);
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LOG(INFO) << "precision: " << precision;
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} else {
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LOG(INFO) << "enable_tensorrt: "
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<< "False";
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LOG(INFO) << "precision: "
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<< "fp32";
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}
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LOG(INFO) << "enable_mkldnn: " << (FLAGS_use_mkldnn ? "True" : "False");
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LOG(INFO) << "cpu_math_library_num_threads: " << FLAGS_cpu_threads;
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LOG(INFO) << "----------------------- Data info -----------------------";
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LOG(INFO) << "batch_size: " << FLAGS_batch_size;
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LOG(INFO) << "input_shape: "
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<< "dynamic shape";
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LOG(INFO) << "----------------------- Model info -----------------------";
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FLAGS_model_dir.erase(FLAGS_model_dir.find_last_not_of("/") + 1);
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LOG(INFO) << "model_name: "
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<< FLAGS_model_dir.substr(FLAGS_model_dir.find_last_of('/') + 1);
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LOG(INFO) << "----------------------- Perf info ------------------------";
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LOG(INFO) << "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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LOG(INFO) << "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;
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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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#ifdef _WIN32
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struct _stat buffer;
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return (_stat(path.c_str(), &buffer) == 0);
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#else
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struct stat buffer;
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return (stat(path.c_str(), &buffer) == 0);
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#endif // !_WIN32
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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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#ifdef _WIN32
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ret = _mkdir(path.c_str());
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#else
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ret = mkdir(path.c_str(), 0755);
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#endif // !_WIN32
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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 PredictVideo(const std::string& video_path,
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PaddleDetection::JDEDetector* mot,
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const std::string& output_dir = "output") {
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// Open video
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cv::VideoCapture capture;
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std::string video_out_name = "output.mp4";
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if (FLAGS_camera_id != -1) {
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capture.open(FLAGS_camera_id);
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} else {
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capture.open(video_path.c_str());
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video_out_name =
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video_path.substr(video_path.find_last_of(OS_PATH_SEP) + 1);
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}
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if (!capture.isOpened()) {
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printf("can not open video : %s\n", video_path.c_str());
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return;
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}
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// Get Video info : resolution, fps, frame count
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int video_width = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));
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int video_height = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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int video_fps = static_cast<int>(capture.get(CV_CAP_PROP_FPS));
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int video_frame_count =
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static_cast<int>(capture.get(CV_CAP_PROP_FRAME_COUNT));
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printf("fps: %d, frame_count: %d\n", video_fps, video_frame_count);
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// Create VideoWriter for output
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cv::VideoWriter video_out;
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std::string video_out_path(output_dir);
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if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
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video_out_path += OS_PATH_SEP;
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}
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video_out_path += video_out_name;
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video_out.open(video_out_path.c_str(),
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0x00000021,
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video_fps,
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cv::Size(video_width, video_height),
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true);
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if (!video_out.isOpened()) {
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printf("create video writer failed!\n");
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return;
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}
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PaddleDetection::MOT_Result result;
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std::vector<double> det_times(3);
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double times;
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// Capture all frames and do inference
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cv::Mat frame;
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int frame_id = 1;
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while (capture.read(frame)) {
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if (frame.empty()) {
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break;
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}
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std::vector<cv::Mat> imgs;
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imgs.push_back(frame);
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printf("detect frame: %d\n", frame_id);
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mot->Predict(imgs, FLAGS_threshold, 0, 1, &result, &det_times);
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frame_id += 1;
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times = std::accumulate(det_times.begin(), det_times.end(), 0) / frame_id;
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cv::Mat out_im = PaddleDetection::VisualizeTrackResult(
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frame, result, 1000. / times, frame_id);
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video_out.write(out_im);
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}
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capture.release();
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video_out.release();
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PrintBenchmarkLog(det_times, frame_id);
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printf("Visualized output saved as %s\n", video_out_path.c_str());
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}
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int main(int argc, char** argv) {
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// Parsing command-line
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google::ParseCommandLineFlags(&argc, &argv, true);
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if (FLAGS_model_dir.empty() || FLAGS_video_file.empty()) {
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std::cout << "Usage: ./main --model_dir=/PATH/TO/INFERENCE_MODEL/ "
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<< "--video_file=/PATH/TO/INPUT/VIDEO/" << std::endl;
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return -1;
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}
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if (!(FLAGS_run_mode == "paddle" || FLAGS_run_mode == "trt_fp32" ||
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FLAGS_run_mode == "trt_fp16" || FLAGS_run_mode == "trt_int8")) {
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std::cout
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<< "run_mode should be 'paddle', 'trt_fp32', 'trt_fp16' or 'trt_int8'.";
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return -1;
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}
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transform(FLAGS_device.begin(),
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FLAGS_device.end(),
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FLAGS_device.begin(),
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::toupper);
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if (!(FLAGS_device == "CPU" || FLAGS_device == "GPU" ||
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FLAGS_device == "XPU")) {
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std::cout << "device should be 'CPU', 'GPU' or 'XPU'.";
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return -1;
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}
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if (FLAGS_use_gpu) {
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std::cout << "Deprecated, please use `--device` to set the device you want "
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"to run.";
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return -1;
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}
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// Do inference on input video or image
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PaddleDetection::JDEDetector mot(FLAGS_model_dir,
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FLAGS_device,
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FLAGS_use_mkldnn,
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FLAGS_cpu_threads,
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FLAGS_run_mode,
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FLAGS_batch_size,
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FLAGS_gpu_id,
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FLAGS_trt_min_shape,
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FLAGS_trt_max_shape,
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FLAGS_trt_opt_shape,
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FLAGS_trt_calib_mode);
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if (!PathExists(FLAGS_output_dir)) {
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MkDirs(FLAGS_output_dir);
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}
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PredictVideo(FLAGS_video_file, &mot, FLAGS_output_dir);
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return 0;
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}
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