// Copyright (C) 2018 Intel Corporation
//
// SPDX-License-Identifier: Apache-2.0
//

#include <iomanip>
#include <vector>
#include <memory>
#include <string>
#include <cstdlib>

#include <opencv2/opencv.hpp>
#include <inference_engine.hpp>
#include <samples/common.hpp>

using namespace InferenceEngine;

int main(int argc, char *argv[]) {
    try {
        // ------------------------------ Parsing and validation of input args ---------------------------------
        if (argc != 4) {
            std::cout << "Usage : ./hello_autoresize_classification <path_to_model> <path_to_image> <device_name>"
                      << std::endl;
            return EXIT_FAILURE;
        }

        const std::string input_model{argv[1]};
        const std::string input_image_path{argv[2]};
        const std::string device_name{argv[3]};
        // -----------------------------------------------------------------------------------------------------

        // --------------------------- 1. Load Plugin for inference engine -------------------------------------
        InferencePlugin plugin = PluginDispatcher({"../../../lib/intel64", ""}).getPluginByDevice(device_name);
        // -----------------------------------------------------------------------------------------------------

        // --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
        int batchSize = 1;
        CNNNetReader network_reader;
        network_reader.ReadNetwork(input_model);
        network_reader.ReadWeights(input_model.substr(0, input_model.size() - 4) + ".bin");
        network_reader.getNetwork().setBatchSize(batchSize);
        CNNNetwork network = network_reader.getNetwork();
        // -----------------------------------------------------------------------------------------------------

        // --------------------------- 3. Configure input & output ---------------------------------------------
        // --------------------------- Prepare input blobs -----------------------------------------------------
        InputInfo::Ptr input_info = network.getInputsInfo().begin()->second;
        std::string input_name = network.getInputsInfo().begin()->first;

        /* Mark input as resizable by setting of a resize algorithm.
         * In this case we will be able to set an input blob of any shape to an infer request.
         * Resize and layout conversions are executed automatically during inference */
        input_info->getPreProcess().setResizeAlgorithm(RESIZE_BILINEAR);
        input_info->setLayout(Layout::NHWC);
        input_info->setPrecision(Precision::U8);

        // --------------------------- Prepare output blobs ----------------------------------------------------
        DataPtr output_info = network.getOutputsInfo().begin()->second;
        std::string output_name = network.getOutputsInfo().begin()->first;

        output_info->setPrecision(Precision::FP32);
        // -----------------------------------------------------------------------------------------------------

        // --------------------------- 4. Loading model to the plugin ------------------------------------------
        ExecutableNetwork executable_network = plugin.LoadNetwork(network, {});
        // -----------------------------------------------------------------------------------------------------

        // --------------------------- 5. Create infer request -------------------------------------------------
        InferRequest infer_request = executable_network.CreateInferRequest();
        // -----------------------------------------------------------------------------------------------------

        // --------------------------- 6. Prepare input --------------------------------------------------------
        /* Read input image to a blob and set it to an infer request without resize and layout conversions. */
        cv::Mat image = cv::imread(input_image_path);
        Blob::Ptr imgBlob = wrapMat2Blob(image);  // just wrap Mat data by Blob::Ptr without allocating of new memory
        infer_request.SetBlob(input_name, imgBlob);  // infer_request accepts input blob of any size
        // -----------------------------------------------------------------------------------------------------

        // --------------------------- 7. Do inference --------------------------------------------------------
        typedef std::chrono::high_resolution_clock Time;
        typedef std::chrono::duration<double, std::ratio<1, 1000>> ms;

        double total = 0.0;

        /* Running the request synchronously */
        auto t0 = Time::now();
        infer_request.Infer();  // input pre-processing is invoked on this step with resize and layout conversion
        auto t1 = Time::now();
        ms d = std::chrono::duration_cast<ms>(t1 - t0);
        total += d.count();
        // -----------------------------------------------------------------------------------------------------

        // --------------------------- 8. Process output ------------------------------------------------------
        Blob::Ptr output = infer_request.GetBlob(output_name);
        auto output_data = output->buffer().as<PrecisionTrait<Precision::FP32>::value_type*>();

        std::vector<unsigned> results;
        /*  This is to sort output probabilities and put it to results vector */
        TopResults(10, *output, results);

        std::cout << std::endl << "Top 10 results:" << std::endl << std::endl;
        for (size_t id = 0; id < 10; ++id) {
            std::cout.precision(7);
            auto result = output_data[results[id]];
            std::cout << std::left << std::fixed << result << " label #" << results[id] << std::endl;
        }
        // -----------------------------------------------------------------------------------------------------

        std::cout << std::endl << "total inference time: " << total << std::endl;
        std::cout << std::endl << "Throughput: " << 1000 * batchSize / total << " FPS" << std::endl;
        std::cout << std::endl;
    } catch (const std::exception & ex) {
        std::cerr << ex.what() << std::endl;
        return EXIT_FAILURE;
    }
    return EXIT_SUCCESS;
}