# 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.
# -*- encoding: utf-8 -*-
# @Author: SWHL
# @Contact: liekkaskono@163.com
import argparse
import time

import cv2
from pathlib import Path
import numpy as np

try:
    from .utils import (DBPostProcess, create_operators,
                        transform, read_yaml, OrtInferSession)
except:
    from utils import (DBPostProcess, create_operators,
                       transform, read_yaml, OrtInferSession)

root_dir = Path(__file__).resolve().parent


class TextDetector():
    def __init__(self, config=str(root_dir / 'config.yaml')):
        if isinstance(config, str):
            config = read_yaml(config)
        config['model_path'] = str(root_dir / config['model_path'])

        self.preprocess_op = create_operators(config['pre_process'])
        self.postprocess_op = DBPostProcess(**config['post_process'])

        session_instance = OrtInferSession(config)
        self.session = session_instance.session
        self.input_name = session_instance.get_input_name()

    def __call__(self, img):
        if img is None:
            raise ValueError('img is None')

        ori_im_shape = img.shape[:2]

        data = {'image': img}
        data = transform(data, self.preprocess_op)
        img, shape_list = data
        if img is None:
            return None, 0

        img = np.expand_dims(img, axis=0).astype(np.float32)
        shape_list = np.expand_dims(shape_list, axis=0)

        starttime = time.time()
        preds = self.session.run(None, {self.input_name: img})

        post_result = self.postprocess_op(preds[0], shape_list)

        dt_boxes = post_result[0]['points']
        dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im_shape)
        elapse = time.time() - starttime
        return dt_boxes, elapse

    def order_points_clockwise(self, pts):
        """
        reference from:
        https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
        sort the points based on their x-coordinates
        """
        xSorted = pts[np.argsort(pts[:, 0]), :]

        # grab the left-most and right-most points from the sorted
        # x-roodinate points
        leftMost = xSorted[:2, :]
        rightMost = xSorted[2:, :]

        # now, sort the left-most coordinates according to their
        # y-coordinates so we can grab the top-left and bottom-left
        # points, respectively
        leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
        (tl, bl) = leftMost

        rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
        (tr, br) = rightMost

        rect = np.array([tl, tr, br, bl], dtype="float32")
        return rect

    def clip_det_res(self, points, img_height, img_width):
        for pno in range(points.shape[0]):
            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
        return points

    def filter_tag_det_res(self, dt_boxes, image_shape):
        img_height, img_width = image_shape[:2]
        dt_boxes_new = []
        for box in dt_boxes:
            box = self.order_points_clockwise(box)
            box = self.clip_det_res(box, img_height, img_width)
            rect_width = int(np.linalg.norm(box[0] - box[1]))
            rect_height = int(np.linalg.norm(box[0] - box[3]))
            if rect_width <= 3 or rect_height <= 3:
                continue
            dt_boxes_new.append(box)
        dt_boxes = np.array(dt_boxes_new)
        return dt_boxes


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--config_path', type=str, default='config.yaml')
    parser.add_argument('--image_path', type=str, default=None)
    args = parser.parse_args()

    config = read_yaml(args.config_path)

    text_detector = TextDetector(config)

    img = cv2.imread(args.image_path)
    dt_boxes, elapse = text_detector(img)

    from utils import draw_text_det_res
    src_im = draw_text_det_res(dt_boxes, args.image_path)
    cv2.imwrite('det_results.jpg', src_im)
    print('The det_results.jpg has been saved in the current directory.')