# -*- coding:utf-8 -*-
# Copyright (c) 2022 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import ast
import base64
import math
import os
import time

import cv2
import numpy as np
import paddle.fluid as fluid
import paddle.inference as paddle_infer
from paddle.fluid.core import AnalysisConfig
from paddle.fluid.core import create_paddle_predictor
from paddle.fluid.core import PaddleTensor
from PIL import Image

import paddlehub as hub
from paddlehub.common.logger import logger
from paddlehub.module.module import moduleinfo
from paddlehub.module.module import runnable
from paddlehub.module.module import serving


def base64_to_cv2(b64str):
    data = base64.b64decode(b64str.encode('utf8'))
    data = np.fromstring(data, np.uint8)
    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
    return data


@moduleinfo(
    name="ch_pp-ocrv3_det",
    version="1.0.0",
    summary=
    "The module aims to detect chinese text position in the image, which is based on differentiable_binarization algorithm.",
    author="paddle-dev",
    author_email="paddle-dev@baidu.com",
    type="cv/text_recognition")
class ChPPOCRv3Det(hub.Module):

    def _initialize(self, enable_mkldnn=False):
        """
        initialize with the necessary elements
        """
        self.pretrained_model_path = os.path.join(self.directory, 'inference_model', 'ppocrv3_det')
        self.enable_mkldnn = enable_mkldnn

        self._set_config()

    def check_requirements(self):
        try:
            import shapely, pyclipper
        except:
            raise ImportError(
                'This module requires the shapely, pyclipper tools. The running environment does not meet the requirements. Please install the two packages.'
            )

    def _set_config(self):
        """
        predictor config setting
        """
        model_file_path = self.pretrained_model_path + '.pdmodel'
        params_file_path = self.pretrained_model_path + '.pdiparams'

        config = paddle_infer.Config(model_file_path, params_file_path)
        try:
            _places = os.environ["CUDA_VISIBLE_DEVICES"]
            int(_places[0])
            use_gpu = True
        except:
            use_gpu = False

        if use_gpu:
            config.enable_use_gpu(8000, 0)
        else:
            config.disable_gpu()
            config.set_cpu_math_library_num_threads(6)
            if self.enable_mkldnn:
                # cache 10 different shapes for mkldnn to avoid memory leak
                config.set_mkldnn_cache_capacity(10)
                config.enable_mkldnn()

        config.disable_glog_info()

        # use zero copy
        config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
        config.switch_use_feed_fetch_ops(False)
        self.predictor = paddle_infer.create_predictor(config)
        input_names = self.predictor.get_input_names()
        self.input_tensor = self.predictor.get_input_handle(input_names[0])
        output_names = self.predictor.get_output_names()
        self.output_tensors = []
        for output_name in output_names:
            output_tensor = self.predictor.get_output_handle(output_name)
            self.output_tensors.append(output_tensor)

    def read_images(self, paths=[]):
        images = []
        for img_path in paths:
            assert os.path.isfile(img_path), "The {} isn't a valid file.".format(img_path)
            img = cv2.imread(img_path)
            if img is None:
                logger.info("error in loading image:{}".format(img_path))
                continue
            images.append(img)
        return images

    def order_points_clockwise(self, pts):
        rect = np.zeros((4, 2), dtype="float32")
        s = pts.sum(axis=1)
        rect[0] = pts[np.argmin(s)]
        rect[2] = pts[np.argmax(s)]
        diff = np.diff(pts, axis=1)
        rect[1] = pts[np.argmin(diff)]
        rect[3] = pts[np.argmax(diff)]
        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[0:2]
        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

    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
        img_height, img_width = image_shape[0:2]
        dt_boxes_new = []
        for box in dt_boxes:
            box = self.clip_det_res(box, img_height, img_width)
            dt_boxes_new.append(box)
        dt_boxes = np.array(dt_boxes_new)
        return dt_boxes

    def detect_text(self,
                    images=[],
                    paths=[],
                    use_gpu=False,
                    output_dir='detection_result',
                    visualization=False,
                    box_thresh=0.5,
                    det_db_unclip_ratio=1.5):
        """
        Get the text box in the predicted images.
        Args:
            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
            paths (list[str]): The paths of images. If paths not images
            use_gpu (bool): Whether to use gpu. Default false.
            output_dir (str): The directory to store output images.
            visualization (bool): Whether to save image or not.
            box_thresh(float): the threshold of the detected text box's confidence
            det_db_unclip_ratio(float): unclip ratio for post processing in DB detection.
        Returns:
            res (list): The result of text detection box and save path of images.
        """
        self.check_requirements()

        from .processor import DBProcessTest, DBPostProcess, draw_boxes, get_image_ext

        if use_gpu:
            try:
                _places = os.environ["CUDA_VISIBLE_DEVICES"]
                int(_places[0])
            except:
                raise RuntimeError(
                    "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
                )

        if images != [] and isinstance(images, list) and paths == []:
            predicted_data = images
        elif images == [] and isinstance(paths, list) and paths != []:
            predicted_data = self.read_images(paths)
        else:
            raise TypeError("The input data is inconsistent with expectations.")

        assert predicted_data != [], "There is not any image to be predicted. Please check the input data."

        preprocessor = DBProcessTest(params={'max_side_len': 960})
        postprocessor = DBPostProcess(params={
            'thresh': 0.3,
            'box_thresh': 0.6,
            'max_candidates': 1000,
            'unclip_ratio': det_db_unclip_ratio
        })

        all_imgs = []
        all_ratios = []
        all_results = []
        for original_image in predicted_data:
            ori_im = original_image.copy()
            im, ratio_list = preprocessor(original_image)
            res = {'save_path': ''}
            if im is None:
                res['data'] = []

            else:
                im = im.copy()
                self.input_tensor.copy_from_cpu(im)
                self.predictor.run()

                outputs = []
                for output_tensor in self.output_tensors:
                    output = output_tensor.copy_to_cpu()
                    outputs.append(output)

                outs_dict = {}
                outs_dict['maps'] = outputs[0]

                dt_boxes_list = postprocessor(outs_dict, [ratio_list])
                dt_boxes = dt_boxes_list[0]
                boxes = self.filter_tag_det_res(dt_boxes_list[0], original_image.shape)
                res['data'] = boxes.astype(np.int).tolist()
                all_imgs.append(im)
                all_ratios.append(ratio_list)
                if visualization:
                    img = Image.fromarray(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))
                    draw_img = draw_boxes(img, boxes)
                    draw_img = np.array(draw_img)
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    ext = get_image_ext(original_image)
                    saved_name = 'ndarray_{}{}'.format(time.time(), ext)
                    cv2.imwrite(os.path.join(output_dir, saved_name), draw_img[:, :, ::-1])
                    res['save_path'] = os.path.join(output_dir, saved_name)

            all_results.append(res)

        return all_results

    @serving
    def serving_method(self, images, **kwargs):
        """
        Run as a service.
        """
        images_decode = [base64_to_cv2(image) for image in images]
        results = self.detect_text(images=images_decode, **kwargs)
        return results

    @runnable
    def run_cmd(self, argvs):
        """
        Run as a command
        """
        self.parser = argparse.ArgumentParser(description="Run the %s module." % self.name,
                                              prog='hub run %s' % self.name,
                                              usage='%(prog)s',
                                              add_help=True)

        self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
        self.arg_config_group = self.parser.add_argument_group(
            title="Config options", description="Run configuration for controlling module behavior, not required.")

        self.add_module_config_arg()
        self.add_module_input_arg()

        args = self.parser.parse_args(argvs)
        results = self.detect_text(paths=[args.input_path],
                                   use_gpu=args.use_gpu,
                                   output_dir=args.output_dir,
                                   det_db_unclip_ratio=args.det_db_unclip_ratio,
                                   visualization=args.visualization)
        return results

    def add_module_config_arg(self):
        """
        Add the command config options
        """
        self.arg_config_group.add_argument('--use_gpu',
                                           type=ast.literal_eval,
                                           default=False,
                                           help="whether use GPU or not")
        self.arg_config_group.add_argument('--output_dir',
                                           type=str,
                                           default='detection_result',
                                           help="The directory to save output images.")
        self.arg_config_group.add_argument('--visualization',
                                           type=ast.literal_eval,
                                           default=False,
                                           help="whether to save output as images.")
        self.arg_config_group.add_argument('--det_db_unclip_ratio',
                                           type=float,
                                           default=1.5,
                                           help="unclip ratio for post processing in DB detection.")

    def add_module_input_arg(self):
        """
        Add the command input options
        """
        self.arg_input_group.add_argument('--input_path', type=str, default=None, help="diretory to image")