# 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.

import utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
import cv2
from ppocr.data.det.east_process import EASTProcessTest
from ppocr.data.det.db_process import DBProcessTest
from ppocr.postprocess.db_postprocess import DBPostProcess
from ppocr.postprocess.east_postprocess import EASTPostPocess
from ppocr.utils.utility import get_image_file_list
from tools.infer.utility import draw_ocr
import copy
import numpy as np
import math
import time
import sys
import os


class TextDetector(object):
    def __init__(self, args):
        max_side_len = args.det_max_side_len
        self.det_algorithm = args.det_algorithm
        preprocess_params = {'max_side_len': max_side_len}
        postprocess_params = {}
        if self.det_algorithm == "DB":
            self.preprocess_op = DBProcessTest(preprocess_params)
            postprocess_params["thresh"] = args.det_db_thresh
            postprocess_params["box_thresh"] = args.det_db_box_thresh
            postprocess_params["max_candidates"] = 1000
            self.postprocess_op = DBPostProcess(postprocess_params)
        elif self.det_algorithm == "EAST":
            self.preprocess_op = EASTProcessTest(preprocess_params)
            postprocess_params["score_thresh"] = args.det_east_score_thresh
            postprocess_params["cover_thresh"] = args.det_east_cover_thresh
            postprocess_params["nms_thresh"] = args.det_east_nms_thresh
            self.postprocess_op = EASTPostPocess(postprocess_params)
        else:
            logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
            sys.exit(0)

        self.predictor, self.input_tensor, self.output_tensors =\
            utility.create_predictor(args, mode="det")

    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 expand_det_res(self, points, bbox_height, bbox_width, img_height,
                       img_width):
        if bbox_height * 1.0 / bbox_width >= 2.0:
            expand_w = bbox_width * 0.20
            expand_h = bbox_width * 0.20
        elif bbox_width * 1.0 / bbox_height >= 3.0:
            expand_w = bbox_height * 0.20
            expand_h = bbox_height * 0.20
        else:
            expand_w = bbox_height * 0.1
            expand_h = bbox_height * 0.1

        points[0, 0] = int(max((points[0, 0] - expand_w), 0))
        points[1, 0] = int(min((points[1, 0] + expand_w), img_width))
        points[3, 0] = int(max((points[3, 0] - expand_w), 0))
        points[2, 0] = int(min((points[2, 0] + expand_w), img_width))

        points[0, 1] = int(max((points[0, 1] - expand_h), 0))
        points[1, 1] = int(max((points[1, 1] - expand_h), 0))
        points[3, 1] = int(min((points[3, 1] + expand_h), img_height))
        points[2, 1] = int(min((points[2, 1] + expand_h), img_height))
        return points

    def filter_tag_det_res(self, dt_boxes, image_shape):
        img_height, img_width = image_shape[0:2]
        dt_boxes_new = []
        for box in dt_boxes:
            box = self.order_points_clockwise(box)
            left = int(np.min(box[:, 0]))
            right = int(np.max(box[:, 0]))
            top = int(np.min(box[:, 1]))
            bottom = int(np.max(box[:, 1]))
            bbox_height = bottom - top
            bbox_width = right - left
            diffh = math.fabs(box[0, 1] - box[1, 1])
            diffw = math.fabs(box[0, 0] - box[3, 0])
            rect_width = int(np.linalg.norm(box[0] - box[1]))
            rect_height = int(np.linalg.norm(box[0] - box[3]))
            if rect_width <= 10 or rect_height <= 10:
                continue
            # if diffh <= 10 and diffw <= 10:
            #     box = self.expand_det_res(
            #         copy.deepcopy(box), bbox_height, bbox_width, img_height,
            #         img_width)
            dt_boxes_new.append(box)
        dt_boxes = np.array(dt_boxes_new)
        return dt_boxes

    def __call__(self, img):
        ori_im = img.copy()
        im, ratio_list = self.preprocess_op(img)
        if im is None:
            return None, 0
        im = im.copy()
        starttime = time.time()
        self.input_tensor.copy_from_cpu(im)
        self.predictor.zero_copy_run()
        outputs = []
        for output_tensor in self.output_tensors:
            output = output_tensor.copy_to_cpu()
            outputs.append(output)
        outs_dict = {}
        if self.det_algorithm == "EAST":
            outs_dict['f_score'] = outputs[0]
            outs_dict['f_geo'] = outputs[1]
        else:
            outs_dict['maps'] = outputs[0]
        dt_boxes_list = self.postprocess_op(outs_dict, [ratio_list])
        dt_boxes = dt_boxes_list[0]
        dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
        elapse = time.time() - starttime
        return dt_boxes, elapse


if __name__ == "__main__":
    args = utility.parse_args()
    image_file_list = get_image_file_list(args.image_dir)
    text_detector = TextDetector(args)
    count = 0
    total_time = 0
    for image_file in image_file_list:
        img = cv2.imread(image_file)
        if img is None:
            logger.info("error in loading image:{}".format(image_file))
            continue
        dt_boxes, elapse = text_detector(img)
        if count > 0:
            total_time += elapse
        count += 1
        print("Predict time of %s:" % image_file, elapse)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        draw_img = draw_ocr(img, dt_boxes, None, None, False)
        draw_img_save = "./inference_results/"
        if not os.path.exists(draw_img_save):
            os.makedirs(draw_img_save)
        cv2.imwrite(
            os.path.join(draw_img_save, os.path.basename(image_file)),
            draw_img[:, :, ::-1])
        print("The visualized image saved in {}".format(
            os.path.join(draw_img_save, os.path.basename(image_file))))
    print("Avg Time:", total_time / (count - 1))
