# 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.
# !/usr/bin/env python
# -*- encoding: utf-8 -*-
# @File: utils.py
# coding:utf-8
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import os
import sys

import cv2
import numpy as np
import pyclipper
import six
from shapely.geometry import Polygon


class DecodeImage(object):
    """ decode image """

    def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
        self.img_mode = img_mode
        self.channel_first = channel_first

    def __call__(self, data):
        img = data['image']
        if six.PY2:
            assert type(img) is str and len(
                img) > 0, "invalid input 'img' in DecodeImage"
        else:
            assert type(img) is bytes and len(
                img) > 0, "invalid input 'img' in DecodeImage"
        img = np.frombuffer(img, dtype='uint8')
        img = cv2.imdecode(img, 1)
        if img is None:
            return None
        if self.img_mode == 'GRAY':
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        elif self.img_mode == 'RGB':
            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
                img.shape)
            img = img[:, :, ::-1]

        if self.channel_first:
            img = img.transpose((2, 0, 1))

        data['image'] = img
        return data


class NormalizeImage(object):
    """ normalize image such as substract mean, divide std
    """

    def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
        if isinstance(scale, str):
            scale = eval(scale)
        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
        mean = mean if mean is not None else [0.485, 0.456, 0.406]
        std = std if std is not None else [0.229, 0.224, 0.225]

        shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
        self.mean = np.array(mean).reshape(shape).astype('float32')
        self.std = np.array(std).reshape(shape).astype('float32')

    def __call__(self, data):
        img = data['image']
        from PIL import Image
        if isinstance(img, Image.Image):
            img = np.array(img)

        assert isinstance(img,
                          np.ndarray), "invalid input 'img' in NormalizeImage"
        data['image'] = (
            img.astype('float32') * self.scale - self.mean) / self.std
        return data


class ToCHWImage(object):
    """ convert hwc image to chw image
    """

    def __init__(self, **kwargs):
        pass

    def __call__(self, data):
        img = data['image']
        from PIL import Image
        if isinstance(img, Image.Image):
            img = np.array(img)
        data['image'] = img.transpose((2, 0, 1))
        return data


class KeepKeys(object):
    def __init__(self, keep_keys, **kwargs):
        self.keep_keys = keep_keys

    def __call__(self, data):
        data_list = []
        for key in self.keep_keys:
            data_list.append(data[key])
        return data_list


class DetResizeForTest(object):
    def __init__(self, **kwargs):
        super(DetResizeForTest, self).__init__()
        self.resize_type = 0
        if 'image_shape' in kwargs:
            self.image_shape = kwargs['image_shape']
            self.resize_type = 1
        elif 'limit_side_len' in kwargs:
            self.limit_side_len = kwargs['limit_side_len']
            self.limit_type = kwargs.get('limit_type', 'min')

        if 'resize_long' in kwargs:
            self.resize_type = 2
            self.resize_long = kwargs.get('resize_long', 960)
        else:
            self.limit_side_len = 736
            self.limit_type = 'min'

    def __call__(self, data):
        img = data['image']
        src_h, src_w, _ = img.shape

        if self.resize_type == 0:
            # img, shape = self.resize_image_type0(img)
            img, [ratio_h, ratio_w] = self.resize_image_type0(img)
        elif self.resize_type == 2:
            img, [ratio_h, ratio_w] = self.resize_image_type2(img)
        else:
            # img, shape = self.resize_image_type1(img)
            img, [ratio_h, ratio_w] = self.resize_image_type1(img)
        data['image'] = img
        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
        return data

    def resize_image_type1(self, img):
        resize_h, resize_w = self.image_shape
        ori_h, ori_w = img.shape[:2]  # (h, w, c)
        ratio_h = float(resize_h) / ori_h
        ratio_w = float(resize_w) / ori_w
        img = cv2.resize(img, (int(resize_w), int(resize_h)))
        # return img, np.array([ori_h, ori_w])
        return img, [ratio_h, ratio_w]

    def resize_image_type0(self, img):
        """
        resize image to a size multiple of 32 which is required by the network
        args:
            img(array): array with shape [h, w, c]
        return(tuple):
            img, (ratio_h, ratio_w)
        """
        limit_side_len = self.limit_side_len
        h, w, _ = img.shape

        # limit the max side
        if self.limit_type == 'max':
            if max(h, w) > limit_side_len:
                if h > w:
                    ratio = float(limit_side_len) / h
                else:
                    ratio = float(limit_side_len) / w
            else:
                ratio = 1.
        else:
            if min(h, w) < limit_side_len:
                if h < w:
                    ratio = float(limit_side_len) / h
                else:
                    ratio = float(limit_side_len) / w
            else:
                ratio = 1.
        resize_h = int(h * ratio)
        resize_w = int(w * ratio)

        resize_h = int(round(resize_h / 32) * 32)
        resize_w = int(round(resize_w / 32) * 32)

        try:
            if int(resize_w) <= 0 or int(resize_h) <= 0:
                return None, (None, None)
            img = cv2.resize(img, (int(resize_w), int(resize_h)))
        except:
            print(img.shape, resize_w, resize_h)
            sys.exit(0)
        ratio_h = resize_h / float(h)
        ratio_w = resize_w / float(w)
        return img, [ratio_h, ratio_w]

    def resize_image_type2(self, img):
        h, w, _ = img.shape

        resize_w = w
        resize_h = h

        # Fix the longer side
        if resize_h > resize_w:
            ratio = float(self.resize_long) / resize_h
        else:
            ratio = float(self.resize_long) / resize_w

        resize_h = int(resize_h * ratio)
        resize_w = int(resize_w * ratio)

        max_stride = 128
        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
        img = cv2.resize(img, (int(resize_w), int(resize_h)))
        ratio_h = resize_h / float(h)
        ratio_w = resize_w / float(w)

        return img, [ratio_h, ratio_w]


def transform(data, ops=None):
    """ transform """
    if ops is None:
        ops = []
    for op in ops:
        data = op(data)
        if data is None:
            return None
    return data


def check_and_read_gif(img_path):
    if os.path.basename(img_path)[-3:] in ['gif', 'GIF']:
        gif = cv2.VideoCapture(img_path)
        ret, frame = gif.read()
        if not ret:
            print("Cannot read {}. This gif image maybe corrupted.")
            return None, False
        if len(frame.shape) == 2 or frame.shape[-1] == 1:
            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
        imgvalue = frame[:, :, ::-1]
        return imgvalue, True
    return None, False


def create_operators(op_param_list, global_config=None):
    """
    create operators based on the config

    Args:
        params(list): a dict list, used to create some operators
    """
    assert isinstance(
        op_param_list, list), ('operator config should be a list')
    ops = []
    for operator in op_param_list:
        assert isinstance(operator,
                          dict) and len(operator) == 1, "yaml format error"
        op_name = list(operator)[0]
        param = {} if operator[op_name] is None else operator[op_name]
        if global_config is not None:
            param.update(global_config)
        op = eval(op_name)(**param)
        ops.append(op)
    return ops


def draw_text_det_res(dt_boxes, img_path):
    src_im = cv2.imread(img_path)
    for box in dt_boxes:
        box = np.array(box).astype(np.int32).reshape(-1, 2)
        cv2.polylines(src_im, [box], True,
                      color=(255, 255, 0), thickness=2)
    return src_im


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

    def __init__(self,
                 thresh=0.3,
                 box_thresh=0.7,
                 max_candidates=1000,
                 unclip_ratio=2.0,
                 use_dilation=False,
                 **kwargs):
        self.thresh = thresh
        self.box_thresh = box_thresh
        self.max_candidates = max_candidates
        self.unclip_ratio = unclip_ratio
        self.min_size = 3
        self.dilation_kernel = None if not use_dilation else np.array(
            [[1, 1], [1, 1]])

    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 = []
        scores = []
        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)

            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.astype(np.int16))
            scores.append(score)
        return np.array(boxes, dtype=np.int16), scores

    def unclip(self, box):
        unclip_ratio = self.unclip_ratio
        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, pred, shape_list):
        pred = pred[:, 0, :, :]
        segmentation = pred > self.thresh

        boxes_batch = []
        for batch_index in range(pred.shape[0]):
            src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
            if self.dilation_kernel is not None:
                mask = cv2.dilate(
                    np.array(segmentation[batch_index]).astype(np.uint8),
                    self.dilation_kernel)
            else:
                mask = segmentation[batch_index]
            boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
                                                   src_w, src_h)

            boxes_batch.append({'points': boxes})
        return boxes_batch