# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys

from PIL import Image, ImageDraw, ImageFont
from shapely.geometry import Polygon
import cv2
import numpy as np
import pyclipper


class DBPreProcess(object):
    def __init__(self, max_side_len=960):
        self.max_side_len = max_side_len

    def resize_image_type(self, im):
        """
        resize image to a size multiple of 32 which is required by the network
        """
        h, w, _ = im.shape

        resize_w = w
        resize_h = h

        # limit the max side
        if max(resize_h, resize_w) > self.max_side_len:
            if resize_h > resize_w:
                ratio = float(self.max_side_len) / resize_h
            else:
                ratio = float(self.max_side_len) / resize_w
        else:
            ratio = 1.
        resize_h = int(resize_h * ratio)
        resize_w = int(resize_w * ratio)
        if resize_h % 32 == 0:
            resize_h = resize_h
        elif resize_h // 32 <= 1:
            resize_h = 32
        else:
            resize_h = (resize_h // 32 - 1) * 32
        if resize_w % 32 == 0:
            resize_w = resize_w
        elif resize_w // 32 <= 1:
            resize_w = 32
        else:
            resize_w = (resize_w // 32 - 1) * 32
        try:
            if int(resize_w) <= 0 or int(resize_h) <= 0:
                return None, (None, None)
            im = cv2.resize(im, (int(resize_w), int(resize_h)))
        except:
            print(im.shape, resize_w, resize_h)
            sys.exit(0)
        ratio_h = resize_h / float(h)
        ratio_w = resize_w / float(w)
        return im, (ratio_h, ratio_w)

    def normalize(self, im):
        img_mean = [0.485, 0.456, 0.406]
        img_std = [0.229, 0.224, 0.225]
        im = im.astype(np.float32, copy=False)
        im = im / 255
        im -= img_mean
        im /= img_std
        channel_swap = (2, 0, 1)
        im = im.transpose(channel_swap)
        return im

    def __call__(self, im):
        im, (ratio_h, ratio_w) = self.resize_image_type(im)
        im = self.normalize(im)
        im = im[np.newaxis, :]
        return [im, (ratio_h, ratio_w)]


class DBPostProcess(object):
    """
    The post process for Differentiable Binarization (DB).
    """

    def __init__(self, thresh=0.3, box_thresh=0.5, max_candidates=1000):
        self.thresh = thresh
        self.box_thresh = box_thresh
        self.max_candidates = max_candidates
        self.min_size = 3

    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
        '''
        _bitmap: single map with shape (1, H, W),
                whose values are binarized as {0, 1}
        '''

        bitmap = _bitmap
        height, width = bitmap.shape

        outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        if len(outs) == 3:
            img, contours, _ = outs[0], outs[1], outs[2]
        elif len(outs) == 2:
            contours, _ = outs[0], outs[1]

        num_contours = min(len(contours), self.max_candidates)
        boxes = np.zeros((num_contours, 4, 2), dtype=np.int16)
        scores = np.zeros((num_contours, ), dtype=np.float32)

        for index in range(num_contours):
            contour = contours[index]
            points, sside = self.get_mini_boxes(contour)
            if sside < self.min_size:
                continue
            points = np.array(points)
            score = self.box_score_fast(pred, points.reshape(-1, 2))
            if self.box_thresh > score:
                continue

            box = self.unclip(points).reshape(-1, 1, 2)
            box, sside = self.get_mini_boxes(box)
            if sside < self.min_size + 2:
                continue
            box = np.array(box)
            if not isinstance(dest_width, int):
                dest_width = dest_width.item()
                dest_height = dest_height.item()

            box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width)
            box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0, dest_height)
            boxes[index, :, :] = box.astype(np.int16)
            scores[index] = score
        return boxes, scores

    def unclip(self, box, unclip_ratio=2.0):
        poly = Polygon(box)
        distance = poly.area * unclip_ratio / poly.length
        offset = pyclipper.PyclipperOffset()
        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
        expanded = np.array(offset.Execute(distance))
        return expanded

    def get_mini_boxes(self, contour):
        bounding_box = cv2.minAreaRect(contour)
        points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])

        index_1, index_2, index_3, index_4 = 0, 1, 2, 3
        if points[1][1] > points[0][1]:
            index_1 = 0
            index_4 = 1
        else:
            index_1 = 1
            index_4 = 0
        if points[3][1] > points[2][1]:
            index_2 = 2
            index_3 = 3
        else:
            index_2 = 3
            index_3 = 2

        box = [points[index_1], points[index_2], points[index_3], points[index_4]]
        return box, min(bounding_box[1])

    def box_score_fast(self, bitmap, _box):
        h, w = bitmap.shape[:2]
        box = _box.copy()
        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)

        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
        box[:, 0] = box[:, 0] - xmin
        box[:, 1] = box[:, 1] - ymin
        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]

    def __call__(self, predictions, ratio_list):
        pred = predictions[:, 0, :, :]
        segmentation = pred > self.thresh

        boxes_batch = []
        for batch_index in range(pred.shape[0]):
            height, width = pred.shape[-2:]
            tmp_boxes, tmp_scores = self.boxes_from_bitmap(pred[batch_index], segmentation[batch_index], width, height)

            boxes = []
            for k in range(len(tmp_boxes)):
                if tmp_scores[k] > self.box_thresh:
                    boxes.append(tmp_boxes[k])
            if len(boxes) > 0:
                boxes = np.array(boxes)

                ratio_h, ratio_w = ratio_list[batch_index]
                boxes[:, :, 0] = boxes[:, :, 0] / ratio_w
                boxes[:, :, 1] = boxes[:, :, 1] / ratio_h

            boxes_batch.append(boxes)
        return boxes_batch


def draw_boxes(image, boxes, scores=None, drop_score=0.5):
    img = image.copy()
    draw = ImageDraw.Draw(img)
    if scores is None:
        scores = [1] * len(boxes)
    for (box, score) in zip(boxes, scores):
        if score < drop_score:
            continue
        draw.line([(box[0][0], box[0][1]), (box[1][0], box[1][1])], fill='red')
        draw.line([(box[1][0], box[1][1]), (box[2][0], box[2][1])], fill='red')
        draw.line([(box[2][0], box[2][1]), (box[3][0], box[3][1])], fill='red')
        draw.line([(box[3][0], box[3][1]), (box[0][0], box[0][1])], fill='red')
        draw.line([(box[0][0] - 1, box[0][1] + 1), (box[1][0] - 1, box[1][1] + 1)], fill='red')
        draw.line([(box[1][0] - 1, box[1][1] + 1), (box[2][0] - 1, box[2][1] + 1)], fill='red')
        draw.line([(box[2][0] - 1, box[2][1] + 1), (box[3][0] - 1, box[3][1] + 1)], fill='red')
        draw.line([(box[3][0] - 1, box[3][1] + 1), (box[0][0] - 1, box[0][1] + 1)], fill='red')
    return img


def get_image_ext(image):
    if image.shape[2] == 4:
        return ".png"
    return ".jpg"
