PaddleClas/deploy/cpp/src/main.cpp

// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <opencv2/core/utils/filesystem.hpp>
#include <ostream>
#include <vector>

#include <algorithm>
#include <cstring>
#include <fstream>
#include <numeric>

#include <auto_log/autolog.h>
#include <gflags/gflags.h>
#include <include/cls.h>
#include <include/cls_config.h>

using namespace std;
using namespace cv;
using namespace PaddleClas;

DEFINE_string(config,
"", "Path of yaml file");
DEFINE_string(c,
"", "Path of yaml file");

int main(int argc, char **argv) {
    google::ParseCommandLineFlags(&argc, &argv, true);
    std::string yaml_path = "";
    if (FLAGS_config == "" && FLAGS_c == "") {
        std::cerr << "[ERROR] usage: " << std::endl
                  << argv[0] << " -c $yaml_path" << std::endl
                  << "or:" << std::endl
                  << argv[0] << " -config $yaml_path" << std::endl;
        exit(1);
    } else if (FLAGS_config != "") {
        yaml_path = FLAGS_config;
    } else {
        yaml_path = FLAGS_c;
    }
    ClsConfig config(yaml_path);
    config.PrintConfigInfo();

    std::string path(config.infer_imgs);

    std::vector <std::string> img_files_list;
    if (cv::utils::fs::isDirectory(path)) {
        std::vector <cv::String> filenames;
        cv::glob(path, filenames);
        for (auto f : filenames) {
            img_files_list.push_back(f);
        }
    } else {
        img_files_list.push_back(path);
    }

    std::cout << "img_file_list length: " << img_files_list.size() << std::endl;

    Classifier classifier(config);

    std::vector<double> cls_times = {0, 0, 0};
    std::vector<double> cls_times_total = {0, 0, 0};
    double infer_time;
    std::vector<float> out_data;
    std::vector<int> result_index;
    int warmup_iter = 5;
    bool label_output_equal_flag = true;
    for (int idx = 0; idx < img_files_list.size(); ++idx) {
        std::string img_path = img_files_list[idx];
        cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
        if (!srcimg.data) {
            std::cerr << "[ERROR] image read failed! image path: " << img_path
                      << "\n";
            exit(-1);
        }

        cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
        classifier.Run(srcimg, out_data, result_index, cls_times);
        if (label_output_equal_flag and out_data.size() != config.id_map.size()) {
            std::cout << "Warning: the label size is not equal to output size!"
                      << std::endl;
            label_output_equal_flag = false;
        }

        int max_len = std::min(config.topk, int(out_data.size()));
        std::cout << "Current image path: " << img_path << std::endl;
        infer_time = cls_times[0] + cls_times[1] + cls_times[2];
        std::cout << "Current total inferen time cost: " << infer_time << " ms."
                  << std::endl;
        for (int i = 0; i < max_len; ++i) {
            printf("\tTop%d: class_id: %d, score: %.4f, ", i + 1, result_index[i],
                   out_data[result_index[i]]);
            if (label_output_equal_flag)
                printf("label: %s\n", config.id_map[result_index[i]].c_str());
        }
        if (idx >= warmup_iter) {
            for (int i = 0; i < cls_times.size(); ++i)
                cls_times_total[i] += cls_times[i];
        }
    }
    if (img_files_list.size() > warmup_iter) {

        infer_time = cls_times_total[0] + cls_times_total[1] + cls_times_total[2];
        std::cout << "average time cost in all: "
                  << infer_time / (img_files_list.size() - warmup_iter) << " ms."
                  << std::endl;
    }

    std::string presion = "fp32";
    if (config.use_fp16)
        presion = "fp16";
    if (config.benchmark) {
        AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
                           config.use_mkldnn, config.cpu_threads, 1,
                           "1, 3, 224, 224", presion, cls_times_total,
                           img_files_list.size());
        autolog.report();
    }
    return 0;
}