#include <fstream>
#include <utility>
#include <vector>
#include <iostream>

#include <tensorflow/cc/ops/const_op.h>
#include <tensorflow/cc/ops/image_ops.h>
#include <tensorflow/cc/ops/standard_ops.h>
#include <tensorflow/core/framework/graph.pb.h>
#include <tensorflow/core/framework/tensor.h>
#include <tensorflow/core/graph/default_device.h>
#include <tensorflow/core/graph/graph_def_builder.h>
#include <tensorflow/core/lib/core/errors.h>
#include <tensorflow/core/lib/core/stringpiece.h>
#include <tensorflow/core/lib/core/threadpool.h>
#include <tensorflow/core/lib/io/path.h>
#include <tensorflow/core/lib/strings/stringprintf.h>
#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/platform/init_main.h>
#include <tensorflow/core/platform/logging.h>
#include <tensorflow/core/platform/types.h>
#include <tensorflow/core/public/session.h>
#include <tensorflow/core/util/command_line_flags.h>

// These are all common classes it's handy to reference with no namespace.
using tensorflow::Flag;
using tensorflow::Tensor;
using tensorflow::Status;
using tensorflow::string;
using tensorflow::int32;

using namespace std;

namespace{

static Status ReadEntireFile(tensorflow::Env* env, const string& filename,
                             Tensor* output) {
    tensorflow::uint64 file_size = 0;
    TF_RETURN_IF_ERROR(env->GetFileSize(filename, &file_size));

    string contents;
    contents.resize(file_size);

    std::unique_ptr<tensorflow::RandomAccessFile> file;
    TF_RETURN_IF_ERROR(env->NewRandomAccessFile(filename, &file));

    tensorflow::StringPiece data;
    TF_RETURN_IF_ERROR(file->Read(0, file_size, &data, &(contents)[0]));
    if (data.size() != file_size) {
        return tensorflow::errors::DataLoss("Truncated read of '", filename,
                                            "' expected ", file_size, " got ",
                                            data.size());
    }
    output->scalar<string>()() = data.ToString();
    return Status::OK();
}

// Given an image file name, read in the data, try to decode it as an image,
// resize it to the requested size, and then scale the values as desired.
Status ReadTensorFromImageFile(const string& file_name,
                               std::vector<Tensor>* out_tensors) {
    auto root = tensorflow::Scope::NewRootScope();
    using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)

    // read file_name into a tensor named input
    Tensor input(tensorflow::DT_STRING, tensorflow::TensorShape());
    TF_RETURN_IF_ERROR(
                ReadEntireFile(tensorflow::Env::Default(), file_name, &input));

    // use a placeholder to read input data
    auto file_reader =
            Placeholder(root.WithOpName("input"), tensorflow::DataType::DT_STRING);

    std::vector<std::pair<string, tensorflow::Tensor>> inputs = {
        {"input", input},
    };

    // Now try to figure out what kind of file it is and decode it.
    const int wanted_channels = 3;
    tensorflow::Output image_reader;
    if (tensorflow::StringPiece(file_name).ends_with(".png")) {
        image_reader = DecodePng(root.WithOpName("png_reader"), file_reader,
                                 DecodePng::Channels(wanted_channels));
    } else if (tensorflow::StringPiece(file_name).ends_with(".gif")) {
        // gif decoder returns 4-D tensor, remove the first dim
        image_reader =
                Squeeze(root.WithOpName("squeeze_first_dim"),
                        DecodeGif(root.WithOpName("gif_reader"), file_reader));
    } else {
        // Assume if it's neither a PNG nor a GIF then it must be a JPEG.
        image_reader = DecodeJpeg(root.WithOpName("jpeg_reader"), file_reader,
                                  DecodeJpeg::Channels(wanted_channels));
    }
    // Now cast the image data to float so we can do normal math on it.
    // auto float_caster =
    //     Cast(root.WithOpName("float_caster"), image_reader, tensorflow::DT_FLOAT);

    auto uint8_caster =  Cast(root.WithOpName("uint8_caster"), image_reader, tensorflow::DT_UINT8);

    // The convention for image ops in TensorFlow is that all images are expected
    // to be in batches, so that they're four-dimensional arrays with indices of
    // [batch, height, width, channel]. Because we only have a single image, we
    // have to add a batch dimension of 1 to the start with ExpandDims().
    auto dims_expander = ExpandDims(root.WithOpName("dim"), uint8_caster, 0);

    // Bilinearly resize the image to fit the required dimensions.
    // auto resized = ResizeBilinear(
    //     root, dims_expander,
    //     Const(root.WithOpName("size"), {input_height, input_width}));


    // Subtract the mean and divide by the scale.
    // auto div =  Div(root.WithOpName(output_name), Sub(root, dims_expander, {input_mean}),
    //     {input_std});


    //cast to int
    //auto uint8_caster =  Cast(root.WithOpName("uint8_caster"), div, tensorflow::DT_UINT8);

    // This runs the GraphDef network definition that we've just constructed, and
    // returns the results in the output tensor.
    tensorflow::GraphDef graph;
    TF_RETURN_IF_ERROR(root.ToGraphDef(&graph));

    std::unique_ptr<tensorflow::Session> session(
                tensorflow::NewSession(tensorflow::SessionOptions()));
    TF_RETURN_IF_ERROR(session->Create(graph));
    TF_RETURN_IF_ERROR(session->Run({inputs}, {"dim"}, {}, out_tensors));
    return Status::OK();
}

// Reads a model graph definition from disk, and creates a session object you
// can use to run it.
Status LoadGraph(const string& graph_file_name,
                 std::unique_ptr<tensorflow::Session>* session) {
    tensorflow::GraphDef graph_def;
    Status load_graph_status =
            ReadBinaryProto(tensorflow::Env::Default(), graph_file_name, &graph_def);
    if(!load_graph_status.ok()) {
        return tensorflow::errors::NotFound("Failed to load compute graph at '",
                                            graph_file_name, "'");
    }
    session->reset(tensorflow::NewSession(tensorflow::SessionOptions()));
    Status session_create_status = (*session)->Create(graph_def);
    if (!session_create_status.ok()) {
        return session_create_status;
    }
    return Status::OK();
}

}

int tensorflow_example(int argc, char* argv[]) {
    // These are the command-line flags the program can understand.
    // They define where the graph and input data is located, and what kind of
    // input the model expects. If you train your own model, or use something
    // other than inception_v3, then you'll need to update these.
    string const image(argv[1]);
    std::string const model_folder("/home/ramsus/Qt/computer_vision_model/tensorflow/object_detection/");
    string const graph = model_folder + "ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb";
    int32 input_width = 300;
    int32 input_height = 300;
    float input_mean = 0;
    float input_std = 255;
    string input_layer = "image_tensor:0";
    vector<string> output_layer ={ "detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0" };

    // First we load and initialize the model.
    std::unique_ptr<tensorflow::Session> session;
    string graph_path = graph;
    std::cout<<"graph path:"<<graph_path<<std::endl;

    LOG(ERROR) << "graph_path:" << graph_path;
    Status load_graph_status = LoadGraph(graph_path, &session);
    if (!load_graph_status.ok()) {
        LOG(ERROR) << "LoadGraph ERROR!!!!"<< load_graph_status;
        return -1;
    }

    std::cout<<"load graph success"<<std::endl;
    // Get the image from disk as a float array of numbers, resized and normalized
    // to the specifications the main graph expects.
    std::vector<Tensor> resized_tensors;
    string const image_path = image;
    Status read_tensor_status = ReadTensorFromImageFile(image_path, &resized_tensors);
    if (!read_tensor_status.ok()) {
        LOG(ERROR) << read_tensor_status;
        return -1;
    }
    const Tensor& resized_tensor = resized_tensors[0];

    LOG(ERROR) <<"image shape:" << resized_tensor.shape().DebugString()<< ",len:" << resized_tensors.size() << ",tensor type:"<< resized_tensor.dtype();
    // << ",data:" << resized_tensor.flat<tensorflow::uint8>();
    // Actually run the image through the model.
    std::vector<Tensor> outputs;
    Status run_status = session->Run({{input_layer, resized_tensor}},
                                     output_layer, {}, &outputs);
    if (!run_status.ok()) {
        LOG(ERROR) << "Running model failed: " << run_status;
        return -1;
    }

    int image_width = resized_tensor.dims();
    int image_height = 0;
    //int image_height = resized_tensor.shape()[1];

    LOG(ERROR) << "size:" << outputs.size() << ",image_width:" << image_width << ",image_height:" << image_height << endl;

    //tensorflow::TTypes<float>::Flat iNum = outputs[0].flat<float>();
    tensorflow::TTypes<float>::Flat scores = outputs[1].flat<float>();

    tensorflow::TTypes<float>::Flat classes = outputs[2].flat<float>();
    tensorflow::TTypes<float>::Flat num_detections = outputs[3].flat<float>();
    auto boxes = outputs[0].flat_outer_dims<float,3>();

    LOG(ERROR) << "num_detections:" << num_detections(0) << "," << outputs[0].shape().DebugString();

    for(size_t i = 0; i < num_detections(0) && i < 20;++i){
        if(scores(i) > 0.5){
            LOG(ERROR) << i << ",score:" << scores(i) << ",class:" << classes(i)<< ",box:" << "," << boxes(0,i,0) << "," << boxes(0,i,1) << "," << boxes(0,i,2)<< "," << boxes(0,i,3);
        }
    }
}