95 lines
2.9 KiB
C++
95 lines
2.9 KiB
C++
// Copyright (c) 2020 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 "opencv2/core.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/imgproc.hpp"
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#include <chrono>
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#include <iomanip>
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#include <iostream>
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#include <opencv2/core/utils/filesystem.hpp>
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#include <ostream>
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#include <vector>
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#include <cstring>
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#include <fstream>
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#include <numeric>
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#include <include/cls.h>
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#include <include/cls_config.h>
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using namespace std;
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using namespace cv;
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using namespace PaddleClas;
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int main(int argc, char **argv) {
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if (argc < 3) {
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std::cerr << "[ERROR] usage: " << argv[0]
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<< " configure_filepath image_path\n";
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exit(1);
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}
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ClsConfig config(argv[1]);
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config.PrintConfigInfo();
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std::string path(argv[2]);
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std::vector<std::string> img_files_list;
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if (cv::utils::fs::isDirectory(path)) {
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std::vector<cv::String> filenames;
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cv::glob(path, filenames);
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for (auto f : filenames) {
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img_files_list.push_back(f);
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}
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} else {
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img_files_list.push_back(path);
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}
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std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
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Classifier classifier(config.cls_model_path, config.cls_params_path,
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config.use_gpu, config.gpu_id, config.gpu_mem,
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config.cpu_math_library_num_threads, config.use_mkldnn,
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config.use_tensorrt, config.use_fp16,
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config.resize_short_size, config.crop_size);
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double elapsed_time = 0.0;
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int warmup_iter = img_files_list.size() > 5 ? 5 : 0;
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for (int idx = 0; idx < img_files_list.size(); ++idx) {
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std::string img_path = img_files_list[idx];
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cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
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if (!srcimg.data) {
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std::cerr << "[ERROR] image read failed! image path: " << img_path
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<< "\n";
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exit(-1);
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}
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cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
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double run_time = classifier.Run(srcimg);
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if (idx >= warmup_iter) {
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elapsed_time += run_time;
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std::cout << "Current image path: " << img_path << std::endl;
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std::cout << "Current time cost: " << run_time << " s, "
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<< "average time cost in all: "
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<< elapsed_time / (idx + 1 - warmup_iter) << " s." << std::endl;
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} else {
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std::cout << "Current time cost: " << run_time << " s." << std::endl;
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}
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}
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return 0;
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}
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