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# **************************************************************************
# Modified from github (https://github.com/WenmuZhou/DBNet.pytorch)
# Copyright (c) WenmuZhou
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# https://github.com/WenmuZhou/DBNet.pytorch/blob/master/LICENSE.md
# **************************************************************************
"""Segmentation Detector Representer."""
import cv2
import numpy as np
import pyclipper
from shapely.geometry import Polygon


def get_post_processing(config):
    """Run post processing."""
    try:
        cls = globals()[config['type']](**config['args'])
        return cls
    except Exception:
        return None


class SegDetectorRepresenter():
    """SegDetector Representer class."""

    def __init__(self, thresh=0.3, box_thresh=0.7, max_candidates=1000, unclip_ratio=1.5):
        """Initialize.

        Args:
            thresh (float): The threshold for binarization, which is used in generating an approximate binary map.
            box_thresh (float): The bounding box threshold. If the effective area is lower than this threshold, the
                                prediction will be ignored, which means no text is detected.
            max_candidates (int): The maximum candidate output.
            unclip_ratio (float): The unclip ratio using the Vatti clipping algorithm in the probability map.
        """
        self.min_size = 3
        self.thresh = thresh
        self.box_thresh = box_thresh
        self.max_candidates = max_candidates
        self.unclip_ratio = unclip_ratio

    def __call__(self, batch, pred, is_output_polygon=False):
        """Generate final bbox and score.

        Args:
            batch (dict): Produced by dataloaders. It is a dict contains:
                - image, a tensor of shape (N, C, H, W).
                - polygons, the polygons of objective regions, tensor of shape (N, K, 4, 2)
                - ignore_tags: tensor of shape (N, K), indicates whether a region is ignorable or not.
                - shape: the original shape of images.
                - filename: the original filenames of images.
            pred (list): Prediction result.
        """
        pred = pred[:, 0, :, :]
        segmentation = self.binarize(pred)
        boxes_batch = []
        scores_batch = []
        for batch_index in range(pred.size(0)):
            height = batch['img'].shape[2]
            width = batch['img'].shape[3]
            if is_output_polygon:
                boxes, scores = self.polygons_from_bitmap(pred[batch_index], segmentation[batch_index], width, height)
            else:
                boxes, scores = self.boxes_from_bitmap(pred[batch_index], segmentation[batch_index], width, height)
            boxes_batch.append(boxes)
            scores_batch.append(scores)
        return boxes_batch, scores_batch

    def binarize(self, pred):
        """Binarize."""
        return pred > self.thresh

    def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
        """Generate the polygon according to the approximate binary map."""
        assert len(_bitmap.shape) == 2
        bitmap = _bitmap.cpu().numpy()
        pred = pred.cpu().detach().numpy()
        height, width = bitmap.shape
        boxes = []
        scores = []

        contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

        for contour in contours[:self.max_candidates]:
            epsilon = 0.0001 * cv2.arcLength(contour, True)
            approx = cv2.approxPolyDP(contour, epsilon, True)
            points = approx.reshape((-1, 2))
            if points.shape[0] < 4:
                continue
            score = self.box_score_fast(pred, contour.squeeze(1))
            if self.box_thresh > score:
                continue

            if points.shape[0] > 2:
                box = self.unclip(points, unclip_ratio=self.unclip_ratio)
                if len(box) > 1:
                    continue
            else:
                continue
            box = box.reshape(-1, 2)
            _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2)))
            if sside < self.min_size + 2:
                continue

            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.append(box)
            scores.append(score)
        return boxes, scores

    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
        """Generate the bbox according to the approximate binary map."""
        assert len(_bitmap.shape) == 2
        bitmap = _bitmap.cpu().numpy()
        pred = pred.cpu().detach().numpy()
        height, width = bitmap.shape
        # Find contours from the approximate binary map
        contours, _ = cv2.findContours(
            (bitmap * 255).astype(np.uint8),
            cv2.RETR_LIST,
            cv2.CHAIN_APPROX_SIMPLE)
        # keep contours less then max candidates
        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].squeeze(1)
            points, sside = self.get_mini_boxes(contour)
            # Ignore the bbox which is too small
            if sside < self.min_size:
                continue
            points = np.array(points)
            # Calculate the score
            score = self.box_score_fast(pred, points)
            if self.box_thresh > score:
                continue

            box = self.unclip(points, unclip_ratio=self.unclip_ratio).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=1.5):
        """Expand the bbox."""
        poly = Polygon(box)
        distance = poly.area * unclip_ratio / poly.length
        offset = pyclipper.PyclipperOffset()  # pylint: disable=I1101
        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)  # pylint: disable=I1101
        expanded = np.array(offset.Execute(distance))
        return expanded

    def get_mini_boxes(self, contour):
        """Generate the bbox with minimum area, get its coordinates and the short size of height/width."""
        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):
        """Calculate the bbox score according to bbox's coordinate and bitmap."""
        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]
