/* Copyright (c) 2012 Patrick Ruoff
 * Copyright (c) 2015-2017 Stanislaw Halik <sthalik@misaki.pl>
 *
 * Permission to use, copy, modify, and/or distribute this software for any
 * purpose with or without fee is hereby granted, provided that the above
 * copyright notice and this permission notice appear in all copies.
 */

#include "point_extractor.h"
#include "compat/util.hpp"
#include "point_tracker.h"
#include <QDebug>

#include <opencv2/videoio.hpp>

#undef PREVIEW
//#define PREVIEW

#if defined PREVIEW
#   include <opencv2/highgui.hpp>
#endif

#include <cmath>
#include <algorithm>
#include <cinttypes>
#include <memory>

#include <QDebug>

using namespace types;
using namespace pt_impl;

/*
http://en.wikipedia.org/wiki/Mean-shift
In this application the idea, is to eliminate any bias of the point estimate 
which is introduced by the rather arbitrary thresholded area. One must recognize
that the thresholded area can only move in one pixel increments since it is
binary. Thus, its center of mass might make "jumps" as pixels are added/removed
from the thresholded area.
With mean-shift, a moving "window" or kernel is multiplied with the gray-scale
image, and the COM is calculated of the result. This is iterated where the
kernel center is set the previously computed COM. Thus, peaks in the image intensity
distribution "pull" the kernel towards themselves. Eventually it stops moving, i.e.
then the computed COM coincides with the kernel center. We hope that the 
corresponding location is a good candidate for the extracted point.
The idea similar to the window scaling suggested in  Berglund et al. "Fast, bias-free 
algorithm for tracking single particles with variable size and shape." (2008).
*/
static cv::Vec2d MeanShiftIteration(const cv::Mat &frame_gray, const vec2 &current_center, f filter_width)
{
    // Most amazingly this function runs faster with doubles than with floats.
    const f s = 1.0 / filter_width;

    f m = 0;
    vec2 com { 0, 0  };
    for (int i = 0; i < frame_gray.rows; i++)
    {
        auto frame_ptr = (uint8_t const* restrict_ptr)frame_gray.ptr(i);
        for (int j = 0; j < frame_gray.cols; j++)
        {
            f val = frame_ptr[j];
            val = val * val; // taking the square wights brighter parts of the image stronger.
            {
                f dx = (j - current_center[0])*s;
                f dy = (i - current_center[1])*s;
                f f = std::fmax(0, 1 - dx*dx - dy*dy);
                val *= f;
            }
            m += val;
            com[0] += j * val;
            com[1] += i * val;
        }
    }
    if (m > f(.1))
    {
        com *= f(1) / m;
        return com;
    }
    else
        return current_center;
}

PointExtractor::PointExtractor()
{
    blobs.reserve(max_blobs);
}

void PointExtractor::ensure_channel_buffers(const cv::Mat& orig_frame)
{
    if (ch[0].rows != orig_frame.rows || ch[0].cols != orig_frame.cols)
    {
        for (unsigned k = 0; k < 3; k++)
        {
            ch[k] = cv::Mat1b(orig_frame.rows, orig_frame.cols);
            ch_float[k] = cv::Mat1f(orig_frame.rows, orig_frame.cols);
        }
        // extra channel is a scratch buffer
        ch_float[3] = cv::Mat1f(orig_frame.rows, orig_frame.cols);
    }
}

void PointExtractor::ensure_buffers(const cv::Mat& frame)
{
    if (frame_gray.rows != frame.rows || frame_gray.cols != frame.cols)
    {
        frame_gray = cv::Mat1b(frame.rows, frame.cols);
        frame_bin = cv::Mat1b(frame.rows, frame.cols);
        frame_blobs = cv::Mat1b(frame.rows, frame.cols);
    }
}

void PointExtractor::extract_single_channel(const cv::Mat& orig_frame, int idx, cv::Mat& dest)
{
    ensure_channel_buffers(orig_frame);

    const int from_to[] = {
        idx, 0,
    };

    cv::mixChannels(&orig_frame, 1, &dest, 1, from_to, 1);
}

void PointExtractor::extract_channels(const cv::Mat& orig_frame, const int* order, int order_npairs)
{
    ensure_channel_buffers(orig_frame);

    cv::mixChannels(&orig_frame, 1, (cv::Mat*) ch, order_npairs, order, order_npairs);
}

void PointExtractor::extract_all_channels(const cv::Mat& orig_frame)
{
    ensure_channel_buffers(orig_frame);

    cv::split(orig_frame, (cv::Mat*) ch);
}

void PointExtractor::channels_to_float(unsigned num_channels)
{
    for (unsigned k = 0; k < num_channels; k++)
        ch[k].convertTo(ch_float[k], CV_32F);
}

void PointExtractor::color_to_grayscale(const cv::Mat& frame, cv::Mat& output)
{
    switch (s.blob_color)
    {
    case pt_color_floppy_filter:
    {
        // weight for blue color
        static constexpr float B = .667;

        static constexpr int from_to[] = {
            0, 0,
            1, 1
        };

        extract_channels(frame, from_to, 2);
        channels_to_float(2);

        cv::addWeighted(ch_float[0], B, ch_float[1], 1-B, 0, ch_float[2]);
        ch_float[2].convertTo(output, CV_8U);

        break;
    }
    case pt_color_blue_only:
    {
        extract_single_channel(frame, 0, output);
        break;
    }
    case pt_color_red_only:
    {
        extract_single_channel(frame, 2, output);
        break;
    }
    case pt_color_smoothed_average:
    {
        extract_all_channels(frame);
        channels_to_float(3);
        ch_float[3] = (ch_float[0] + ch_float[1] + ch_float[2]) * (1./3);
        ch_float[3].convertTo(ch[0], CV_8U);

        const float diagonal = std::sqrt(frame.rows*frame.rows + frame.cols*frame.cols);
        static constexpr float standard_diagonal = 800; // 640x480 diagonal. sqrt isn't constexpr.

        const unsigned iters = diagonal / standard_diagonal;

        if (iters > 0)
        {
            Timer t;

            int i1 = ~0, i2 = ~0;

            for (unsigned k = 0; k < iters; k++)
            {
                i1 = k&1;
                i2 = 1 - i1;

                cv::GaussianBlur(ch[i1], ch[i2], cv::Size(3, 3), 0, 0, cv::BORDER_REPLICATE);
            }

            ch[i2].copyTo(output);
        }

        break;
    }
    default:
        once_only(qDebug() << "wrong pt_color_type enum value" << int(s.blob_color));
        /*FALLTHROUGH*/
    case pt_color_average:
    {
        extract_all_channels(frame);
        channels_to_float(3);
        ch_float[3] = (ch_float[0] + ch_float[1] + ch_float[2]) * (1./3);
        ch_float[3].convertTo(output, CV_8U);
        break;
    }
    case pt_color_natural:
        cv::cvtColor(frame, output, cv::COLOR_BGR2GRAY);
        break;
    }
}

void PointExtractor::threshold_image(const cv::Mat& frame_gray, cv::Mat& output)
{
    const int threshold_slider_value = s.threshold_slider.to<int>();

    if (!s.auto_threshold)
    {
        cv::threshold(frame_gray, output, threshold_slider_value, 255, cv::THRESH_BINARY);
    }
    else
    {
        int hist_size = 256;
        float ranges_[] = { 0, 256 };
        float const* ranges = (float*) ranges_;

        cv::calcHist(&frame_gray,
                     1,
                     nullptr,
                     cv::noArray(),
                     hist,
                     1,
                     (int const*) &hist_size,
                     &ranges);

        const f radius = (f) threshold_radius_value(frame_gray.cols, frame_gray.rows, threshold_slider_value);

        float const* restrict_ptr ptr = reinterpret_cast<float const* restrict_ptr>(hist.ptr(0));
        const unsigned area = uround(3 * M_PI * radius*radius);
        const unsigned sz = unsigned(hist.cols * hist.rows);
        unsigned thres = 32;
        for (unsigned i = sz-1, cnt = 0; i > 32; i--)
        {
            cnt += ptr[i];
            if (cnt >= area)
            {
                thres = i;
                break;
            }
        }

        cv::threshold(frame_gray, output, thres, 255, CV_THRESH_BINARY);
    }
}

double PointExtractor::threshold_radius_value(int w, int h, int threshold)
{
    double cx = w / 640., cy = h / 480.;

    const double min_radius = 1.75 * cx;
    const double max_radius = 15 * cy;

    const double radius = std::fmax(0., (max_radius-min_radius) * threshold / 255 + min_radius);

    return radius;
}

void PointExtractor::extract_points(const cv::Mat& frame, cv::Mat& preview_frame, std::vector<vec2>& points)
{
    ensure_buffers(frame);

    color_to_grayscale(frame, frame_gray);

#if defined PREVIEW
    cv::imshow("capture", frame_gray);
    cv::waitKey(1);
#endif

    threshold_image(frame_gray, frame_bin);

    blobs.clear();
    frame_bin.copyTo(frame_blobs);

    const f region_size_min = s.min_point_size;
    const f region_size_max = s.max_point_size;

    unsigned idx = 0;
    for (int y=0; y < frame_blobs.rows; y++)
    {
        const unsigned char* ptr_bin = frame_blobs.ptr(y);
        for (int x=0; x < frame_blobs.cols; x++)
        {
            if (ptr_bin[x] != 255)
                continue;
            idx = blobs.size() + 1;

            cv::Rect rect;
            cv::floodFill(frame_blobs,
                          cv::Point(x,y),
                          cv::Scalar(idx),
                          &rect,
                          cv::Scalar(0),
                          cv::Scalar(0),
                          8);

            // these are doubles since m10 and m01 could overflow theoretically
            // log2(255^2 * 640^2 * pi) > 36
            double m10 = 0;
            double m01 = 0;
            // norm can't overflow since there's no 640^2 component
            int norm = 0;
            int cnt = 0;

            for (int i=rect.y; i < (rect.y+rect.height); i++)
            {
                unsigned char* ptr_blobs = frame_blobs.ptr(i);
                const unsigned char* ptr_gray = frame_gray.ptr(i);
                for (int j=rect.x; j < (rect.x+rect.width); j++)
                {
                    if (ptr_blobs[j] != idx)
                        continue;

                    ptr_blobs[j] = 0;

                    // square as a weight gives better results
                    const int val(int(ptr_gray[j]) * int(ptr_gray[j]));

                    norm += val;
                    m01 += i * val;
                    m10 += j * val;
                    cnt++;
                }
            }
            if (norm > 0)
            {
                const double radius = std::sqrt(cnt / M_PI), N = double(norm);
                if (radius > region_size_max || radius < region_size_min)
                    continue;

                blob b(radius, cv::Vec2d(m10 / N, m01 / N), N/std::sqrt(double(cnt)), rect);
                blobs.push_back(b);

                {
                    static const f offx = 10, offy = 7.5;
                    const f cx = preview_frame.cols / f(frame.cols),
                            cy = preview_frame.rows / f(frame.rows),
                            c_ = (cx+cy)/2;

                    static constexpr unsigned fract_bits = 16;
                    static constexpr double c_fract(1 << fract_bits);

                    cv::Point p(iround(b.pos[0] * cx * c_fract), iround(b.pos[1] * cy * c_fract));

                    cv::circle(preview_frame, p, iround((b.radius + 2) * c_ * c_fract), cv::Scalar(255, 255, 0), 1, cv::LINE_AA, fract_bits);

                    char buf[16];
                    std::snprintf(buf, sizeof(buf), "%.2fpx", radius);
                    buf[sizeof(buf)-1] = '\0';

                    cv::putText(preview_frame,
                                buf,
                                cv::Point(iround(b.pos[0]*cx+offx), iround(b.pos[1]*cy+offy)),
                                cv::FONT_HERSHEY_PLAIN,
                                1,
                                cv::Scalar(0, 0, 255),
                                1);
                }

                if (idx >= max_blobs) goto end;
            }
        }
    }
end:

    std::sort(blobs.begin(), blobs.end(), [](const blob& b1, const blob& b2) { return b2.brightness < b1.brightness; });

    const int W = frame.cols;
    const int H = frame.rows;

    for (idx = 0; idx < std::min(PointModel::N_POINTS, unsigned(blobs.size())); ++idx)
    {
        blob &b = blobs[idx];
        cv::Rect rect = b.rect;

        rect.x -= rect.width / 2;
        rect.y -= rect.height / 2;
        rect.width *= 2;
        rect.height *= 2;
        rect &= cv::Rect(0, 0, W, H);  // crop at frame boundaries

        cv::Mat frame_roi = frame_gray(rect);

        // smaller values mean more changes. 1 makes too many changes while 1.5 makes about .1
        // seems values close to 1.3 reduce noise best with about .15->.2 changes
        static constexpr f radius_c = f(1.75);

        const f kernel_radius = b.radius * radius_c;
        vec2 pos(b.pos[0] - rect.x, b.pos[1] - rect.y); // position relative to ROI.

        for (int iter = 0; iter < 10; ++iter)
        {
            vec2 com_new = MeanShiftIteration(frame_roi, pos, kernel_radius);
            vec2 delta = com_new - pos;
            pos = com_new;
            if (delta.dot(delta) < 1e-2)
                break;
        }

        b.pos[0] = pos[0] + rect.x;
        b.pos[1] = pos[1] + rect.y;
    }

    // End of mean shift code. At this point, blob positions are updated with hopefully less noisy less biased values.
    points.reserve(max_blobs);
    points.clear();

    for (const auto& b : blobs)
    {
        // note: H/W is equal to fx/fy

        vec2 p((b.pos[0] - W/2)/W, -(b.pos[1] - H/2)/W);
        points.push_back(p);
    }
}

blob::blob(f radius, const vec2& pos, f brightness, cv::Rect& rect) :
    radius(radius), brightness(brightness), pos(pos), rect(rect)
{
    //qDebug() << "radius" << radius << "pos" << pos[0] << pos[1];
}