#include <QDebug>
#include <QThread>
#include <iostream>
#include <vector>
#include "aiengineinferencencnn.h"


const int target_size      = 640;
const float prob_threshold = 0.25f;
const float nms_threshold  = 0.45f;
const int num_labels       = 10; // 80 object labels in COCO, 10 in Azaion
const int MAX_STRIDE       = 32;


char* getCharPointerCopy(const QString& modelPath) {
    QByteArray byteArray = modelPath.toUtf8();
    char* cString = new char[byteArray.size() + 1]; // Allocate memory
    std::strcpy(cString, byteArray.constData()); // Copy the data
    return cString; // Remember to delete[] this when done!
}


AiEngineInferencevNcnn::AiEngineInferencevNcnn(QString modelPath, QObject *parent) :
    AiEngineInference{modelPath, parent}
{
    qDebug() << "TUOMAS AiEngineInferencevNcnn() mModelPath=" << mModelPath;

    yolov8.opt.num_threads        = 4;
    yolov8.opt.use_vulkan_compute = false;

    QString paramPath = modelPath.chopped(3).append("param");
    char *model = getCharPointerCopy(modelPath);
    char *param = getCharPointerCopy(paramPath);

    qDebug() << "model:" << model;
    qDebug() << "param:" << param;

    yolov8.load_param(param);
    yolov8.load_model(model);
}


static inline float intersection_area(const Object& a, const Object& b)
{
    cv::Rect_<float> inter = a.rect & b.rect;
    return inter.area();
}


static void qsort_descent_inplace(std::vector<Object>& objects, int left, int right)
{
    int i = left;
    int j = right;
    float p = objects[(left + right) / 2].prob;

    while (i <= j)
    {
        while (objects[i].prob > p)
            i++;

        while (objects[j].prob < p)
            j--;

        if (i <= j)
        {
            // swap
            std::swap(objects[i], objects[j]);

            i++;
            j--;
        }
    }

#pragma omp parallel sections
    {
#pragma omp section
        {
            if (left < j) qsort_descent_inplace(objects, left, j);
        }
#pragma omp section
        {
            if (i < right) qsort_descent_inplace(objects, i, right);
        }
    }
}


static void qsort_descent_inplace(std::vector<Object>& objects)
{
    if (objects.empty())
        return;

    qsort_descent_inplace(objects, 0, objects.size() - 1);
}


static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold, bool agnostic = false)
{
    picked.clear();

    const int n = faceobjects.size();

    std::vector<float> areas(n);
    for (int i = 0; i < n; i++)
    {
        areas[i] = faceobjects[i].rect.area();
    }

    for (int i = 0; i < n; i++)
    {
        const Object& a = faceobjects[i];

        int keep = 1;
        for (int j = 0; j < (int)picked.size(); j++)
        {
            const Object& b = faceobjects[picked[j]];

            if (!agnostic && a.label != b.label)
                continue;

            // intersection over union
            float inter_area = intersection_area(a, b);
            float union_area = areas[i] + areas[picked[j]] - inter_area;
            // float IoU = inter_area / union_area
            if (inter_area / union_area > nms_threshold)
                keep = 0;
        }

        if (keep)
            picked.push_back(i);
    }
}


static inline float sigmoid(float x)
{
    return static_cast<float>(1.f / (1.f + exp(-x)));
}


static inline float clampf(float d, float min, float max)
{
    const float t = d < min ? min : d;
    return t > max ? max : t;
}


static void parse_yolov8_detections(
    float* inputs, float confidence_threshold,
    int num_channels, int num_anchors, int num_labels,
    int infer_img_width, int infer_img_height,
    std::vector<Object>& objects)
{
    std::vector<Object> detections;
    cv::Mat output = cv::Mat((int)num_channels, (int)num_anchors, CV_32F, inputs).t();

    for (int i = 0; i < num_anchors; i++)
    {
        const float* row_ptr = output.row(i).ptr<float>();
        const float* bboxes_ptr = row_ptr;
        const float* scores_ptr = row_ptr + 4;
        const float* max_s_ptr = std::max_element(scores_ptr, scores_ptr + num_labels);
        float score = *max_s_ptr;
        if (score > confidence_threshold)
        {
            float x = *bboxes_ptr++;
            float y = *bboxes_ptr++;
            float w = *bboxes_ptr++;
            float h = *bboxes_ptr;

            float x0 = clampf((x - 0.5f * w), 0.f, (float)infer_img_width);
            float y0 = clampf((y - 0.5f * h), 0.f, (float)infer_img_height);
            float x1 = clampf((x + 0.5f * w), 0.f, (float)infer_img_width);
            float y1 = clampf((y + 0.5f * h), 0.f, (float)infer_img_height);

            cv::Rect_<float> bbox;
            bbox.x = x0;
            bbox.y = y0;
            bbox.width = x1 - x0;
            bbox.height = y1 - y0;
            Object object;
            object.label = max_s_ptr - scores_ptr;
            object.prob = score;
            object.rect = bbox;
            detections.push_back(object);
        }
    }
    objects = detections;
}


int AiEngineInferencevNcnn::detect_yolov8(const cv::Mat& bgr, std::vector<Object>& objects)
{
    /*
    int img_w = bgr.cols;
    int img_h = bgr.rows;

    // letterbox pad to multiple of MAX_STRIDE
    int w = img_w;
    int h = img_h;
    float scale = 1.f;
    if (w > h)
    {
        scale = (float)target_size / w;
        w = target_size;
        h = h * scale;
    }
    else
    {
        scale = (float)target_size / h;
        h = target_size;
        w = w * scale;
    }
    */
    int w       = 640;
    int h       = 640;
    int img_w   = 640;
    int img_h   = 640;
    float scale = 1.f;


    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h);

    int wpad = (target_size + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - w;
    int hpad = (target_size + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - h;
    ncnn::Mat in_pad;
    ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);

    const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
    in_pad.substract_mean_normalize(0, norm_vals);

    auto start = std::chrono::high_resolution_clock::now();

    ncnn::Extractor ex = yolov8.create_extractor();

    ex.input("in0", in_pad);

    std::vector<Object> proposals;

    // stride 32
    {
        ncnn::Mat out;
        ex.extract("out0", out);

        std::vector<Object> objects32;
        parse_yolov8_detections(
            (float*)out.data, prob_threshold,
            out.h, out.w, num_labels,
            in_pad.w, in_pad.h,
            objects32);
        proposals.insert(proposals.end(), objects32.begin(), objects32.end());
    }

    // sort all proposals by score from highest to lowest
    qsort_descent_inplace(proposals);

    // apply nms with nms_threshold
    std::vector<int> picked;
    nms_sorted_bboxes(proposals, picked, nms_threshold);

    int count = picked.size();

    objects.resize(count);
    for (int i = 0; i < count; i++)
    {
        objects[i] = proposals[picked[i]];

        // adjust offset to original unpadded
        float x0 = (objects[i].rect.x - (wpad / 2)) / scale;
        float y0 = (objects[i].rect.y - (hpad / 2)) / scale;
        float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale;
        float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale;

        // clip
        x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
        y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
        x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
        y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);

        objects[i].rect.x = x0;
        objects[i].rect.y = y0;
        objects[i].rect.width = x1 - x0;
        objects[i].rect.height = y1 - y0;
    }

    auto end = std::chrono::high_resolution_clock::now();
    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
    std::cout << "Time taken: " << duration.count() << " milliseconds" << std::endl;
    return 0;
}


static cv::Mat draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
    static const char* class_names[] = {
        "Armoured vehicles",
        "Truck",
        "Passenger car",
        "Artillery",
        "Shadow of the vehicle",
        "Trenches",
        "Military",
        "Ramps",
        "Tank with additional protection",
        "Smoke"
    };

    static const unsigned char colors[19][3] = {
        {54, 67, 244},
        {99, 30, 233},
        {176, 39, 156},
        {183, 58, 103},
        {181, 81, 63},
        {243, 150, 33},
        {244, 169, 3},
        {212, 188, 0},
        {136, 150, 0},
        {80, 175, 76},
        {74, 195, 139},
        {57, 220, 205},
        {59, 235, 255},
        {7, 193, 255},
        {0, 152, 255},
        {34, 87, 255},
        {72, 85, 121},
        {158, 158, 158},
        {139, 125, 96}
    };

    int color_index = 0;

    cv::Mat image = bgr.clone();

    for (size_t i = 0; i < objects.size(); i++)
    {
        const Object& obj = objects[i];

        const unsigned char* color = colors[color_index % 19];
        color_index++;

        cv::Scalar cc(color[0], color[1], color[2]);

        fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
                obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);

        cv::rectangle(image, obj.rect, cc, 2);

        char text[256];
        sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);

        int baseLine = 0;
        cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

        int x = obj.rect.x;
        int y = obj.rect.y - label_size.height - baseLine;
        if (y < 0)
            y = 0;
        if (x + label_size.width > image.cols)
            x = image.cols - label_size.width;

        cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
                      cc, -1);

        cv::putText(image, text, cv::Point(x, y + label_size.height),
                    cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(255, 255, 255));
    }

    return image;
}


void AiEngineInferencevNcnn::performInferenceSlot(cv::Mat frame)
{
    mActive = true;

    cv::Mat scaledImage = resizeAndPad(frame);

    std::vector<Object> objects;
    detect_yolov8(scaledImage, objects);

    if (objects.empty() == false) {
        AiEngineInferenceResult result;
        result.frame = draw_objects(scaledImage, objects);

        for (uint i = 0; i < objects.size(); i++) {
            const Object &detection = objects[i];
            AiEngineObject object;
            object.classId          = detection.label;
            object.classStr         = mClassNames[detection.label];
            object.propability      = detection.prob;
            object.rectangle.top    = detection.rect.y;
            object.rectangle.left   = detection.rect.x;
            object.rectangle.bottom = detection.rect.y + detection.rect.height;
            object.rectangle.right  = detection.rect.x + detection.rect.width;
            result.objects.append(object);
        }

        emit resultsReady(result);
    }

    mActive = false;
}


void AiEngineInferencevNcnn::initialize(int number)
{
    (void)number;
}