// Copyright (c) 2020 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 <iostream>
#include <vector>
#include <math.h>
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "ocr_clipper.hpp"


static void getcontourarea(float **box, float unclip_ratio, float &distance) {
    int pts_num = 4;
    float area = 0.0f;
    float dist = 0.0f;
    for (int i = 0; i < pts_num; i++) {
        area += box[i][0] * box[(i + 1) % pts_num][1] - box[i][1] * box[(i + 1) % pts_num][0];
        dist += sqrtf(
            (box[i][0] - box[(i + 1) % pts_num][0]) * (box[i][0] - box[(i + 1) % pts_num][0]) +
            (box[i][1] - box[(i + 1) % pts_num][1]) * (box[i][1] - box[(i + 1) % pts_num][1]));
    }
    area = fabs(float(area / 2.0));

    distance = area * unclip_ratio / dist;

}

static cv::RotatedRect unclip(float **box) {
    float unclip_ratio = 2.0;
    float distance = 1.0;

    getcontourarea(box, unclip_ratio, distance);

    ClipperLib::ClipperOffset offset;
    ClipperLib::Path p;
    p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
      << ClipperLib::IntPoint(int(box[1][0]), int(box[1][1])) <<
      ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
      << ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
    offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);

    ClipperLib::Paths soln;
    offset.Execute(soln, distance);
    std::vector<cv::Point2f> points;

    for (int j = 0; j < soln.size(); j++) {
        for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
            points.emplace_back(soln[j][i].X, soln[j][i].Y);
        }
    }
    cv::RotatedRect res = cv::minAreaRect(points);

    return res;
}

static float **Mat2Vec(cv::Mat mat) {
    auto **array = new float *[mat.rows];
    for (int i = 0; i < mat.rows; ++i)
        array[i] = new float[mat.cols];
    for (int i = 0; i < mat.rows; ++i) {
        for (int j = 0; j < mat.cols; ++j) {
            array[i][j] = mat.at<float>(i, j);
        }
    }

    return array;
}

static void quickSort(float **s, int l, int r) {
    if (l < r) {
        int i = l, j = r;
        float x = s[l][0];
        float *xp = s[l];
        while (i < j) {
            while (i < j && s[j][0] >= x)
                j--;
            if (i < j)
                std::swap(s[i++], s[j]);
            while (i < j && s[i][0] < x)
                i++;
            if (i < j)
                std::swap(s[j--], s[i]);
        }
        s[i] = xp;
        quickSort(s, l, i - 1);
        quickSort(s, i + 1, r);
    }
}

static void quickSort_vector(std::vector<std::vector<int>> &box, int l, int r, int axis) {
    if (l < r) {
        int i = l, j = r;
        int x = box[l][axis];
        std::vector<int> xp(box[l]);
        while (i < j) {
            while (i < j && box[j][axis] >= x)
                j--;
            if (i < j)
                std::swap(box[i++], box[j]);
            while (i < j && box[i][axis] < x)
                i++;
            if (i < j)
                std::swap(box[j--], box[i]);
        }
        box[i] = xp;
        quickSort_vector(box, l, i - 1, axis);
        quickSort_vector(box, i + 1, r, axis);
    }
}

static std::vector<std::vector<int>> order_points_clockwise(std::vector<std::vector<int>> pts) {
    std::vector<std::vector<int>> box = pts;
    quickSort_vector(box, 0, int(box.size() - 1), 0);
    std::vector<std::vector<int>> leftmost = {box[0], box[1]};
    std::vector<std::vector<int>> rightmost = {box[2], box[3]};

    if (leftmost[0][1] > leftmost[1][1])
        std::swap(leftmost[0], leftmost[1]);

    if (rightmost[0][1] > rightmost[1][1])
        std::swap(rightmost[0], rightmost[1]);

    std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1], leftmost[1]};
    return rect;
}

static float **get_mini_boxes(cv::RotatedRect box, float &ssid) {
    ssid = box.size.width >= box.size.height ? box.size.height : box.size.width;

    cv::Mat points;
    cv::boxPoints(box, points);
    // sorted box points
    auto array = Mat2Vec(points);
    quickSort(array, 0, 3);

    float *idx1 = array[0], *idx2 = array[1], *idx3 = array[2], *idx4 = array[3];
    if (array[3][1] <= array[2][1]) {
        idx2 = array[3];
        idx3 = array[2];
    } else {
        idx2 = array[2];
        idx3 = array[3];
    }
    if (array[1][1] <= array[0][1]) {
        idx1 = array[1];
        idx4 = array[0];
    } else {
        idx1 = array[0];
        idx4 = array[1];
    }

    array[0] = idx1;
    array[1] = idx2;
    array[2] = idx3;
    array[3] = idx4;

    return array;
}

template <class T> T clamp(T x,   T min, T   max) {
    if (x > max){
        return  max;
    }
    if (x < min){
        return min;
    }
    return x;
}

static float clampf(float x, float min, float max) {
    if (x > max)
        return max;
    if (x < min)
        return min;
    return x;
}


float box_score_fast(float **box_array, cv::Mat pred) {
    auto array = box_array;
    int width = pred.cols;
    int height = pred.rows;

    float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
    float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};

    int xmin = clamp(int(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0, width - 1);
    int xmax = clamp(int(std::ceilf(*(std::max_element(box_x, box_x + 4)))), 0, width - 1);
    int ymin = clamp(int(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0, height - 1);
    int ymax = clamp(int(std::ceilf(*(std::max_element(box_y, box_y + 4)))), 0, height - 1);

    cv::Mat mask;
    mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);

    cv::Point root_point[4];
    root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
    root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
    root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
    root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
    const cv::Point *ppt[1] = {root_point};
    int npt[] = {4};
    cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));

    cv::Mat croppedImg;
    pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);

    auto score = cv::mean(croppedImg, mask)[0];
    return score;
}


std::vector<std::vector<std::vector<int>>>
boxes_from_bitmap(const cv::Mat& pred, const cv::Mat& bitmap) {
    const int min_size = 3;
    const int max_candidates = 1000;
    const float box_thresh = 0.5;

    int width = bitmap.cols;
    int height = bitmap.rows;

    std::vector<std::vector<cv::Point>> contours;
    std::vector<cv::Vec4i> hierarchy;

    cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);

    int num_contours = contours.size() >= max_candidates ? max_candidates : contours.size();

    std::vector<std::vector<std::vector<int>>> boxes;

    for (int _i = 0; _i < num_contours; _i++) {
        float ssid;
        cv::RotatedRect box = cv::minAreaRect(contours[_i]);
        auto array = get_mini_boxes(box, ssid);

        auto box_for_unclip = array;
        //end get_mini_box

        if (ssid < min_size) {
            continue;
        }

        float score;
        score = box_score_fast(array, pred);
        //end box_score_fast
        if (score < box_thresh)
            continue;


        // start for unclip
        cv::RotatedRect points = unclip(box_for_unclip);
        // end for unclip

        cv::RotatedRect clipbox = points;
        auto cliparray = get_mini_boxes(clipbox, ssid);

        if (ssid < min_size + 2) continue;

        int dest_width = pred.cols;
        int dest_height = pred.rows;
        std::vector<std::vector<int>> intcliparray;

        for (int num_pt = 0; num_pt < 4; num_pt++) {
            std::vector<int> a{
                int(clampf(roundf(cliparray[num_pt][0] / float(width) * float(dest_width)), 0,
                           float(dest_width))),
                int(clampf(roundf(cliparray[num_pt][1] / float(height) * float(dest_height)), 0,
                           float(dest_height)))};
            intcliparray.push_back(a);
        }
        boxes.push_back(intcliparray);

    }//end for
    return boxes;
}

int _max(int a, int b) {
    return a >= b ? a : b;
}

int _min(int a, int b) {
    return a >= b ? b : a;
}

std::vector<std::vector<std::vector<int>>>
filter_tag_det_res(const std::vector<std::vector<std::vector<int>>>& o_boxes,
                   float ratio_h, float ratio_w,const cv::Mat& srcimg) {
    int oriimg_h = srcimg.rows;
    int oriimg_w = srcimg.cols;
    std::vector<std::vector<std::vector<int>>> boxes{o_boxes};
    std::vector<std::vector<std::vector<int>>> root_points;
    for (int n = 0; n < boxes.size(); n++) {
        boxes[n] = order_points_clockwise(boxes[n]);
        for (int m = 0; m < boxes[0].size(); m++) {
            boxes[n][m][0] /= ratio_w;
            boxes[n][m][1] /= ratio_h;

            boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1));
            boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1));
        }
    }

    for (int n = 0; n < boxes.size(); n++) {
        int rect_width, rect_height;
        rect_width = int(sqrt(
            pow(boxes[n][0][0] - boxes[n][1][0], 2) + pow(boxes[n][0][1] - boxes[n][1][1], 2)));
        rect_height = int(sqrt(
            pow(boxes[n][0][0] - boxes[n][3][0], 2) + pow(boxes[n][0][1] - boxes[n][3][1], 2)));
        if (rect_width <= 10 || rect_height <= 10)
            continue;
        root_points.push_back(boxes[n]);
    }
    return root_points;
}

/*
using namespace std;
// read data from txt file
cv::Mat readtxt2(std::string path, int imgw, int imgh, int imgc) {
  std::cout << "read data file from txt file! " << std::endl;
  ifstream in(path);
  string line;
  int count = 0;
  int i = 0, j = 0;
  std::vector<float> img_mean = {0.485, 0.456, 0.406};
  std::vector<float> img_std = {0.229, 0.224, 0.225};

  float trainData[imgh][imgw*imgc];

  while (getline(in, line)) {
    stringstream ss(line);
    double x;
    while (ss >> x) {
//      trainData[i][j] = float(x) * img_std[j % 3] + img_mean[j % 3];
      trainData[i][j] = float(x);
      j++;
    }
    i++;
    j = 0;
  }

  cv::Mat pred_map(imgh, imgw*imgc, CV_32FC1, (float *) trainData);
  cv::Mat reshape_img = pred_map.reshape(imgc, imgh);
  return reshape_img;
}
 */
//using namespace std;
//
//void writetxt(vector<vector<float>> data, std::string save_path){
//
//  ofstream fout(save_path);
//
//  for (int i = 0; i < data.size(); i++) {
//    for (int j=0; j< data[0].size(); j++){
//      fout << data[i][j] << " ";
//    }
//    fout << endl;
//  }
//  fout << endl;
//  fout.close();
//}
