// 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 <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);

    double elapsed_time = 0.0;
    std::vector<double> cls_times;
    int warmup_iter = img_files_list.size() > 5 ? 5 : 0;
    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);

        double run_time = classifier.Run(srcimg, &cls_times);
        if (idx >= warmup_iter) {
            elapsed_time += run_time;
            std::cout << "Current image path: " << img_path << std::endl;
            std::cout << "Current time cost: " << run_time << " s, "
                      << "average time cost in all: "
                      << elapsed_time / (idx + 1 - warmup_iter) << " s." << std::endl;
        } else {
            std::cout << "Current time cost: " << run_time << " s." << 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,
                           img_files_list.size());
        autolog.report();
    }
    return 0;
}