# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import ast
import math
import os
import time

from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
from paddlehub.common.logger import logger
from paddlehub.module.module import moduleinfo, runnable, serving
from PIL import Image
import base64
import cv2
import numpy as np
import paddle.fluid as fluid
import paddlehub as hub


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="chinese_text_detection_db_mobile",
    version="1.0.3",
    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 ChineseTextDetectionDB(hub.Module):
    def _initialize(self, enable_mkldnn=False):
        """
        initialize with the necessary elements
        """
        self.pretrained_model_path = os.path.join(self.directory, 'inference_model')
        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 = os.path.join(self.pretrained_model_path, 'model')
        params_file_path = os.path.join(self.pretrained_model_path, 'params')

        config = AnalysisConfig(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 = create_paddle_predictor(config)
        input_names = self.predictor.get_input_names()
        self.input_tensor = self.predictor.get_input_tensor(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_tensor(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 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 <= 10 or rect_height <= 10:
                continue
            dt_boxes_new.append(box)
        dt_boxes = np.array(dt_boxes_new)
        return dt_boxes

    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 detect_text(self,
                    images=[],
                    paths=[],
                    use_gpu=False,
                    output_dir='detection_result',
                    visualization=False,
                    box_thresh=0.5):
        """
        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
        Returns:
            res (list): The result of text detection box and save path of images.
        """
        self.check_requirements()

        from chinese_text_detection_db_mobile.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': 0.5,
            'max_candidates': 1000,
            'unclip_ratio': 2.0
        })

        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.zero_copy_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]

                # data_out = self.output_tensors[0].copy_to_cpu()
                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

    def save_inference_model(self, dirname, model_filename=None, params_filename=None, combined=True):
        if combined:
            model_filename = "__model__" if not model_filename else model_filename
            params_filename = "__params__" if not params_filename else params_filename
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        model_file_path = os.path.join(self.pretrained_model_path, 'model')
        params_file_path = os.path.join(self.pretrained_model_path, 'params')
        program, feeded_var_names, target_vars = fluid.io.load_inference_model(
            dirname=self.pretrained_model_path,
            model_filename=model_file_path,
            params_filename=params_file_path,
            executor=exe)

        fluid.io.save_inference_model(
            dirname=dirname,
            main_program=program,
            executor=exe,
            feeded_var_names=feeded_var_names,
            target_vars=target_vars,
            model_filename=model_filename,
            params_filename=params_filename)

    @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, 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.")

    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")


if __name__ == '__main__':
    db = ChineseTextDetectionDB()
    image_path = [
        '/mnt/zhangxuefei/PaddleOCR/doc/imgs/2.jpg', '/mnt/zhangxuefei/PaddleOCR/doc/imgs/12.jpg',
        '/mnt/zhangxuefei/PaddleOCR/doc/imgs/test_image.jpg'
    ]
    res = db.detect_text(paths=image_path, visualization=True)
    db.save_inference_model('save')
    print(res)
