# 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 argparse
import os
import sys
import cv2
import numpy as np
import paddle
import PIL
from PIL import Image, ImageDraw, ImageFont
import math
from paddle import inference
import random
import yaml
from ppocr.utils.logging import get_logger


def str2bool(v):
    return v.lower() in ("true", "yes", "t", "y", "1")


def str2int_tuple(v):
    return tuple([int(i.strip()) for i in v.split(",")])


def init_args():
    parser = argparse.ArgumentParser()
    # params for prediction engine
    parser.add_argument("--use_gpu", type=str2bool, default=True)
    parser.add_argument("--use_xpu", type=str2bool, default=False)
    parser.add_argument("--use_npu", type=str2bool, default=False)
    parser.add_argument("--use_mlu", type=str2bool, default=False)
    parser.add_argument("--use_metax_gpu", type=str2bool, default=False)
    parser.add_argument(
        "--use_gcu",
        type=str2bool,
        default=False,
        help="Use Enflame GCU(General Compute Unit)",
    )
    parser.add_argument("--ir_optim", type=str2bool, default=True)
    parser.add_argument("--use_tensorrt", type=str2bool, default=False)
    parser.add_argument("--min_subgraph_size", type=int, default=15)
    parser.add_argument("--precision", type=str, default="fp32")
    parser.add_argument("--gpu_mem", type=int, default=500)
    parser.add_argument("--gpu_id", type=int, default=0)

    # params for text detector
    parser.add_argument("--image_dir", type=str)
    parser.add_argument("--page_num", type=int, default=0)
    parser.add_argument("--det_algorithm", type=str, default="DB")
    parser.add_argument("--det_model_dir", type=str)
    parser.add_argument("--det_limit_side_len", type=float, default=960)
    parser.add_argument("--det_limit_type", type=str, default="max")
    parser.add_argument("--det_box_type", type=str, default="quad")

    # DB params
    parser.add_argument("--det_db_thresh", type=float, default=0.3)
    parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
    parser.add_argument("--max_batch_size", type=int, default=10)
    parser.add_argument("--use_dilation", type=str2bool, default=False)
    parser.add_argument("--det_db_score_mode", type=str, default="fast")

    # EAST params
    parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
    parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
    parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)

    # SAST params
    parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
    parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)

    # PSE params
    parser.add_argument("--det_pse_thresh", type=float, default=0)
    parser.add_argument("--det_pse_box_thresh", type=float, default=0.85)
    parser.add_argument("--det_pse_min_area", type=float, default=16)
    parser.add_argument("--det_pse_scale", type=int, default=1)

    # FCE params
    parser.add_argument("--scales", type=list, default=[8, 16, 32])
    parser.add_argument("--alpha", type=float, default=1.0)
    parser.add_argument("--beta", type=float, default=1.0)
    parser.add_argument("--fourier_degree", type=int, default=5)

    # params for text recognizer
    parser.add_argument("--rec_algorithm", type=str, default="SVTR_LCNet")
    parser.add_argument("--rec_model_dir", type=str)
    parser.add_argument("--rec_image_inverse", type=str2bool, default=True)
    parser.add_argument("--rec_image_shape", type=str, default="3, 48, 320")
    parser.add_argument("--rec_batch_num", type=int, default=6)
    parser.add_argument("--max_text_length", type=int, default=25)
    parser.add_argument(
        "--rec_char_dict_path", type=str, default="./ppocr/utils/ppocr_keys_v1.txt"
    )
    parser.add_argument("--use_space_char", type=str2bool, default=True)
    parser.add_argument("--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
    parser.add_argument("--drop_score", type=float, default=0.5)

    # params for e2e
    parser.add_argument("--e2e_algorithm", type=str, default="PGNet")
    parser.add_argument("--e2e_model_dir", type=str)
    parser.add_argument("--e2e_limit_side_len", type=float, default=768)
    parser.add_argument("--e2e_limit_type", type=str, default="max")

    # PGNet params
    parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
    parser.add_argument(
        "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt"
    )
    parser.add_argument("--e2e_pgnet_valid_set", type=str, default="totaltext")
    parser.add_argument("--e2e_pgnet_mode", type=str, default="fast")

    # params for text classifier
    parser.add_argument("--use_angle_cls", type=str2bool, default=False)
    parser.add_argument("--cls_model_dir", type=str)
    parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
    parser.add_argument("--label_list", type=list, default=["0", "180"])
    parser.add_argument("--cls_batch_num", type=int, default=6)
    parser.add_argument("--cls_thresh", type=float, default=0.9)

    parser.add_argument("--enable_mkldnn", type=str2bool, default=None)
    parser.add_argument("--cpu_threads", type=int, default=10)
    parser.add_argument("--use_pdserving", type=str2bool, default=False)
    parser.add_argument("--warmup", type=str2bool, default=False)

    # SR params
    parser.add_argument("--sr_model_dir", type=str)
    parser.add_argument("--sr_image_shape", type=str, default="3, 32, 128")
    parser.add_argument("--sr_batch_num", type=int, default=1)

    #
    parser.add_argument("--draw_img_save_dir", type=str, default="./inference_results")
    parser.add_argument("--save_crop_res", type=str2bool, default=False)
    parser.add_argument("--crop_res_save_dir", type=str, default="./output")

    # multi-process
    parser.add_argument("--use_mp", type=str2bool, default=False)
    parser.add_argument("--total_process_num", type=int, default=1)
    parser.add_argument("--process_id", type=int, default=0)

    parser.add_argument("--benchmark", type=str2bool, default=False)
    parser.add_argument("--save_log_path", type=str, default="./log_output/")

    parser.add_argument("--show_log", type=str2bool, default=True)
    parser.add_argument("--use_onnx", type=str2bool, default=False)
    parser.add_argument("--onnx_providers", nargs="+", type=str, default=False)
    parser.add_argument("--onnx_sess_options", type=list, default=False)

    # extended function
    parser.add_argument(
        "--return_word_box",
        type=str2bool,
        default=False,
        help="Whether return the bbox of each word (split by space) or chinese character. Only used in ppstructure for layout recovery",
    )

    return parser


def parse_args():
    parser = init_args()
    return parser.parse_args()


def create_predictor(args, mode, logger):
    if mode == "det":
        model_dir = args.det_model_dir
    elif mode == "cls":
        model_dir = args.cls_model_dir
    elif mode == "rec":
        model_dir = args.rec_model_dir
    elif mode == "table":
        model_dir = args.table_model_dir
    elif mode == "ser":
        model_dir = args.ser_model_dir
    elif mode == "re":
        model_dir = args.re_model_dir
    elif mode == "sr":
        model_dir = args.sr_model_dir
    elif mode == "layout":
        model_dir = args.layout_model_dir
    else:
        model_dir = args.e2e_model_dir

    if model_dir is None:
        logger.info("not find {} model file path {}".format(mode, model_dir))
        sys.exit(0)
    if args.use_onnx:
        import onnxruntime as ort

        model_file_path = model_dir
        if not os.path.exists(model_file_path):
            raise ValueError("not find model file path {}".format(model_file_path))

        sess_options = args.onnx_sess_options or None

        if args.onnx_providers and len(args.onnx_providers) > 0:
            sess = ort.InferenceSession(
                model_file_path,
                providers=args.onnx_providers,
                sess_options=sess_options,
            )
        elif args.use_gpu:
            sess = ort.InferenceSession(
                model_file_path,
                providers=[
                    (
                        "CUDAExecutionProvider",
                        {
                            "device_id": args.gpu_id,
                            "cudnn_conv_algo_search": "HEURISTIC",
                        },
                    )
                ],
                sess_options=sess_options,
            )
        else:
            sess = ort.InferenceSession(
                model_file_path,
                providers=["CPUExecutionProvider"],
                sess_options=sess_options,
            )
        inputs = sess.get_inputs()
        return (
            sess,
            inputs[0] if len(inputs) == 1 else [vo.name for vo in inputs],
            None,
            None,
        )

    else:
        file_names = ["model", "inference"]
        for file_name in file_names:
            params_file_path = f"{model_dir}/{file_name}.pdiparams"
            if os.path.exists(params_file_path):
                break

        if not os.path.exists(params_file_path):
            raise ValueError(f"not find {file_name}.pdiparams in {model_dir}")

        if not (
            os.path.exists(f"{model_dir}/{file_name}.pdmodel")
            or os.path.exists(f"{model_dir}/{file_name}.json")
        ):
            raise ValueError(
                f"neither {file_name}.json nor {file_name}.pdmodel was found in {model_dir}."
            )

        if os.path.exists(f"{model_dir}/{file_name}.json"):
            model_file_path = f"{model_dir}/{file_name}.json"
        else:
            model_file_path = f"{model_dir}/{file_name}.pdmodel"

        config = inference.Config(model_file_path, params_file_path)

        if hasattr(args, "precision"):
            if args.precision == "fp16" and args.use_tensorrt:
                precision = inference.PrecisionType.Half
            elif args.precision == "int8":
                precision = inference.PrecisionType.Int8
            else:
                precision = inference.PrecisionType.Float32
        else:
            precision = inference.PrecisionType.Float32

        if args.use_gpu:
            gpu_id = get_infer_gpuid()
            if gpu_id is None:
                logger.warning(
                    "GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jetson."
                )
            config.enable_use_gpu(args.gpu_mem, args.gpu_id)
            if args.use_tensorrt:
                if ".json" in model_file_path:
                    trt_dynamic_shapes = {}
                    trt_dynamic_shape_input_data = {}
                    if os.path.exists(f"{model_dir}/inference.yml"):
                        model_config = load_config(f"{model_dir}/inference.yml")
                        trt_dynamic_shapes = (
                            model_config.get("Hpi", {})
                            .get("backend_configs", {})
                            .get("paddle_infer", {})
                            .get("trt_dynamic_shapes", {})
                        )
                        trt_dynamic_shape_input_data = (
                            model_config.get("Hpi", {})
                            .get("backend_configs", {})
                            .get("paddle_infer", {})
                            .get("trt_dynamic_shapes_input_data", {})
                        )

                    if not trt_dynamic_shapes:
                        raise RuntimeError(
                            "Configuration Error: 'trt_dynamic_shapes' must be defined in 'inference.yml' for Paddle Inference TensorRT."
                        )

                    trt_save_path = f"{model_dir}/.cache/trt/{file_name}"
                    trt_model_file_path = trt_save_path + ".json"
                    trt_params_file_path = trt_save_path + ".pdiparams"
                    if not os.path.exists(trt_model_file_path) or not os.path.exists(
                        trt_params_file_path
                    ):
                        _convert_trt(
                            {},
                            model_file_path,
                            params_file_path,
                            trt_save_path,
                            args.gpu_id,
                            trt_dynamic_shapes,
                            trt_dynamic_shape_input_data,
                        )
                    config = inference.Config(model_file_path, params_file_path)
                    config.exp_disable_mixed_precision_ops({"feed", "fetch"})
                    config.enable_use_gpu(args.gpu_mem, args.gpu_id)
                else:
                    config.enable_tensorrt_engine(
                        workspace_size=1 << 30,
                        precision_mode=precision,
                        max_batch_size=args.max_batch_size,
                        min_subgraph_size=args.min_subgraph_size,  # skip the minimum trt subgraph
                        use_calib_mode=False,
                    )

                    # collect shape
                    trt_shape_f = os.path.join(
                        model_dir, f"{mode}_trt_dynamic_shape.txt"
                    )

                    if not os.path.exists(trt_shape_f):
                        config.collect_shape_range_info(trt_shape_f)
                        logger.info(f"collect dynamic shape info into : {trt_shape_f}")
                    try:
                        config.enable_tuned_tensorrt_dynamic_shape(trt_shape_f, True)
                    except Exception as E:
                        logger.info(E)
                        logger.info("Please keep your paddlepaddle-gpu >= 2.3.0!")

        elif args.use_npu:
            config.enable_custom_device("npu")
        elif args.use_mlu:
            config.enable_custom_device("mlu")
        elif args.use_metax_gpu:
            if args.precision == "fp16":
                config.enable_custom_device(
                    "metax_gpu", 0, paddle.inference.PrecisionType.Half
                )

            else:
                config.enable_custom_device("metax_gpu")
        elif args.use_xpu:
            config.enable_xpu(10 * 1024 * 1024)
        elif args.use_gcu:  # for Enflame GCU(General Compute Unit)
            assert paddle.device.is_compiled_with_custom_device("gcu"), (
                "Args use_gcu cannot be set as True while your paddle "
                "is not compiled with gcu! \nPlease try: \n"
                "\t1. Install paddle-custom-gcu to run model on GCU. \n"
                "\t2. Set use_gcu as False in args to run model on CPU."
            )
            import paddle_custom_device.gcu.passes as gcu_passes

            gcu_passes.setUp()
            if args.precision == "fp16":
                config.enable_custom_device(
                    "gcu", 0, paddle.inference.PrecisionType.Half
                )
                gcu_passes.set_exp_enable_mixed_precision_ops(config)
            else:
                config.enable_custom_device("gcu")

            if paddle.framework.use_pir_api():
                config.enable_new_ir(True)
                config.enable_new_executor(True)
            else:
                pass_builder = config.pass_builder()
                gcu_passes.append_passes_for_legacy_ir(pass_builder, "PaddleOCR")
        else:
            config.disable_gpu()
            if args.enable_mkldnn is not None:
                if args.enable_mkldnn:
                    # cache 10 different shapes for mkldnn to avoid memory leak
                    config.set_mkldnn_cache_capacity(10)
                    config.enable_mkldnn()
                    if args.precision == "fp16":
                        config.enable_mkldnn_bfloat16()
                else:
                    if hasattr(config, "disable_mkldnn"):
                        config.disable_mkldnn()

            if hasattr(args, "cpu_threads"):
                config.set_cpu_math_library_num_threads(args.cpu_threads)
            else:
                # default cpu threads as 10
                config.set_cpu_math_library_num_threads(10)

            if hasattr(config, "enable_new_ir"):
                config.enable_new_ir()
            if hasattr(config, "enable_new_executor"):
                config.enable_new_executor()

        # enable memory optim
        config.enable_memory_optim()
        config.disable_glog_info()
        if not args.use_gcu:  # for Enflame GCU(General Compute Unit)
            config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
        config.delete_pass("matmul_transpose_reshape_fuse_pass")
        if mode == "rec" and args.rec_algorithm == "SRN":
            config.delete_pass("gpu_cpu_map_matmul_v2_to_matmul_pass")
        if mode == "re":
            config.delete_pass("simplify_with_basic_ops_pass")
        if mode == "table":
            config.delete_pass("fc_fuse_pass")  # not supported for table
        config.switch_use_feed_fetch_ops(False)
        config.switch_ir_optim(True)

        # create predictor
        predictor = inference.create_predictor(config)
        input_names = predictor.get_input_names()
        if mode in ["ser", "re"]:
            input_tensor = []
            for name in input_names:
                input_tensor.append(predictor.get_input_handle(name))
        else:
            for name in input_names:
                input_tensor = predictor.get_input_handle(name)
        output_tensors = get_output_tensors(args, mode, predictor)
        return predictor, input_tensor, output_tensors, config


def _convert_trt(
    trt_cfg_setting,
    pp_model_file,
    pp_params_file,
    trt_save_path,
    device_id,
    dynamic_shapes,
    dynamic_shape_input_data,
):
    from paddle.tensorrt.export import Input, TensorRTConfig, convert

    def _set_trt_config():
        for attr_name in trt_cfg_setting:
            assert hasattr(
                trt_config, attr_name
            ), f"The `{type(trt_config)}` don't have the attribute `{attr_name}`!"
            setattr(trt_config, attr_name, trt_cfg_setting[attr_name])

    def _get_predictor(model_file, params_file):
        # HACK
        config = inference.Config(str(model_file), str(params_file))
        config.enable_use_gpu(100, device_id)
        # NOTE: Disable oneDNN to circumvent a bug in Paddle Inference
        config.disable_mkldnn()
        config.disable_glog_info()
        return inference.create_predictor(config)

    dynamic_shape_input_data = dynamic_shape_input_data or {}

    predictor = _get_predictor(pp_model_file, pp_params_file)
    input_names = predictor.get_input_names()
    for name in dynamic_shapes:
        if name not in input_names:
            raise ValueError(
                f"Invalid input name {repr(name)} found in `dynamic_shapes`"
            )
    for name in input_names:
        if name not in dynamic_shapes:
            raise ValueError(f"Input name {repr(name)} not found in `dynamic_shapes`")
    for name in dynamic_shape_input_data:
        if name not in input_names:
            raise ValueError(
                f"Invalid input name {repr(name)} found in `dynamic_shape_input_data`"
            )

    trt_inputs = []
    for name, candidate_shapes in dynamic_shapes.items():
        # XXX: Currently we have no way to get the data type of the tensor
        # without creating an input handle.
        handle = predictor.get_input_handle(name)
        dtype = _pd_dtype_to_np_dtype(handle.type())
        min_shape, opt_shape, max_shape = candidate_shapes
        if name in dynamic_shape_input_data:
            min_arr = np.array(dynamic_shape_input_data[name][0], dtype=dtype).reshape(
                min_shape
            )
            opt_arr = np.array(dynamic_shape_input_data[name][1], dtype=dtype).reshape(
                opt_shape
            )
            max_arr = np.array(dynamic_shape_input_data[name][2], dtype=dtype).reshape(
                max_shape
            )
        else:
            min_arr = np.ones(min_shape, dtype=dtype)
            opt_arr = np.ones(opt_shape, dtype=dtype)
            max_arr = np.ones(max_shape, dtype=dtype)

        # refer to: https://github.com/PolaKuma/Paddle/blob/3347f225bc09f2ec09802a2090432dd5cb5b6739/test/tensorrt/test_converter_model_resnet50.py
        trt_input = Input((min_arr, opt_arr, max_arr))
        trt_inputs.append(trt_input)

    # Create TensorRTConfig
    trt_config = TensorRTConfig(inputs=trt_inputs)
    _set_trt_config()
    trt_config.save_model_dir = trt_save_path
    pp_model_path = pp_model_file.split(".")[0]
    convert(pp_model_path, trt_config)


def _pd_dtype_to_np_dtype(pd_dtype):
    if pd_dtype == inference.DataType.FLOAT64:
        return np.float64
    elif pd_dtype == inference.DataType.FLOAT32:
        return np.float32
    elif pd_dtype == inference.DataType.INT64:
        return np.int64
    elif pd_dtype == inference.DataType.INT32:
        return np.int32
    elif pd_dtype == inference.DataType.UINT8:
        return np.uint8
    elif pd_dtype == inference.DataType.INT8:
        return np.int8
    else:
        raise TypeError(f"Unsupported data type: {pd_dtype}")


def load_config(file_path):
    _, ext = os.path.splitext(file_path)
    if ext not in [".yml", ".yaml"]:
        raise ValueError(f"only support yaml files for now, got {file_path}")
    with open(file_path, "rb") as file:
        config = yaml.load(file, Loader=yaml.SafeLoader)
    return config


def get_output_tensors(args, mode, predictor):
    output_names = predictor.get_output_names()
    output_tensors = []
    if mode == "rec" and args.rec_algorithm in ["CRNN", "SVTR_LCNet", "SVTR_HGNet"]:
        output_name = "softmax_0.tmp_0"
        if output_name in output_names:
            return [predictor.get_output_handle(output_name)]
        else:
            for output_name in output_names:
                output_tensor = predictor.get_output_handle(output_name)
                output_tensors.append(output_tensor)
    else:
        for output_name in output_names:
            output_tensor = predictor.get_output_handle(output_name)
            output_tensors.append(output_tensor)
    return output_tensors


def get_infer_gpuid():
    """
    Get the GPU ID to be used for inference.

    Returns:
        int: The GPU ID to be used for inference.
    """
    logger = get_logger()
    if not paddle.device.is_compiled_with_rocm:
        gpu_id_str = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
    else:
        gpu_id_str = os.environ.get("HIP_VISIBLE_DEVICES", "0")

    gpu_ids = gpu_id_str.split(",")
    logger.warning(
        "The first GPU is used for inference by default, GPU ID: {}".format(gpu_ids[0])
    )
    return int(gpu_ids[0])


def draw_e2e_res(dt_boxes, strs, img_path):
    src_im = cv2.imread(img_path)
    for box, str in zip(dt_boxes, strs):
        box = box.astype(np.int32).reshape((-1, 1, 2))
        cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
        cv2.putText(
            src_im,
            str,
            org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
            fontFace=cv2.FONT_HERSHEY_COMPLEX,
            fontScale=0.7,
            color=(0, 255, 0),
            thickness=1,
        )
    return src_im


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


def resize_img(img, input_size=600):
    """
    resize img and limit the longest side of the image to input_size
    """
    img = np.array(img)
    im_shape = img.shape
    im_size_max = np.max(im_shape[0:2])
    im_scale = float(input_size) / float(im_size_max)
    img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
    return img


def draw_ocr(
    image,
    boxes,
    txts=None,
    scores=None,
    drop_score=0.5,
    font_path="./doc/fonts/simfang.ttf",
):
    """
    Visualize the results of OCR detection and recognition
    args:
        image(Image|array): RGB image
        boxes(list): boxes with shape(N, 4, 2)
        txts(list): the texts
        scores(list): txxs corresponding scores
        drop_score(float): only scores greater than drop_threshold will be visualized
        font_path: the path of font which is used to draw text
    return(array):
        the visualized img
    """
    if scores is None:
        scores = [1] * len(boxes)
    box_num = len(boxes)
    for i in range(box_num):
        if scores is not None and (scores[i] < drop_score or math.isnan(scores[i])):
            continue
        box = np.reshape(np.array(boxes[i]), [-1, 1, 2]).astype(np.int64)
        image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
    if txts is not None:
        img = np.array(resize_img(image, input_size=600))
        txt_img = text_visual(
            txts,
            scores,
            img_h=img.shape[0],
            img_w=600,
            threshold=drop_score,
            font_path=font_path,
        )
        img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
        return img
    return image


def draw_ocr_box_txt(
    image,
    boxes,
    txts=None,
    scores=None,
    drop_score=0.5,
    font_path="./doc/fonts/simfang.ttf",
):
    h, w = image.height, image.width
    img_left = image.copy()
    img_right = np.ones((h, w, 3), dtype=np.uint8) * 255
    random.seed(0)

    draw_left = ImageDraw.Draw(img_left)
    if txts is None or len(txts) != len(boxes):
        txts = [None] * len(boxes)
    for idx, (box, txt) in enumerate(zip(boxes, txts)):
        if scores is not None and scores[idx] < drop_score:
            continue
        color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
        draw_left.polygon(box, fill=color)
        img_right_text = draw_box_txt_fine((w, h), box, txt, font_path)
        pts = np.array(box, np.int32).reshape((-1, 1, 2))
        cv2.polylines(img_right_text, [pts], True, color, 1)
        img_right = cv2.bitwise_and(img_right, img_right_text)
    img_left = Image.blend(image, img_left, 0.5)
    img_show = Image.new("RGB", (w * 2, h), (255, 255, 255))
    img_show.paste(img_left, (0, 0, w, h))
    img_show.paste(Image.fromarray(img_right), (w, 0, w * 2, h))
    return np.array(img_show)


def draw_box_txt_fine(img_size, box, txt, font_path="./doc/fonts/simfang.ttf"):
    box_height = int(
        math.sqrt((box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][1]) ** 2)
    )
    box_width = int(
        math.sqrt((box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][1]) ** 2)
    )

    if box_height > 2 * box_width and box_height > 30:
        img_text = Image.new("RGB", (box_height, box_width), (255, 255, 255))
        draw_text = ImageDraw.Draw(img_text)
        if txt:
            font = create_font(txt, (box_height, box_width), font_path)
            draw_text.text([0, 0], txt, fill=(0, 0, 0), font=font)
        img_text = img_text.transpose(Image.ROTATE_270)
    else:
        img_text = Image.new("RGB", (box_width, box_height), (255, 255, 255))
        draw_text = ImageDraw.Draw(img_text)
        if txt:
            font = create_font(txt, (box_width, box_height), font_path)
            draw_text.text([0, 0], txt, fill=(0, 0, 0), font=font)

    pts1 = np.float32(
        [[0, 0], [box_width, 0], [box_width, box_height], [0, box_height]]
    )
    pts2 = np.array(box, dtype=np.float32)
    M = cv2.getPerspectiveTransform(pts1, pts2)

    img_text = np.array(img_text, dtype=np.uint8)
    img_right_text = cv2.warpPerspective(
        img_text,
        M,
        img_size,
        flags=cv2.INTER_NEAREST,
        borderMode=cv2.BORDER_CONSTANT,
        borderValue=(255, 255, 255),
    )
    return img_right_text


def create_font(txt, sz, font_path="./doc/fonts/simfang.ttf"):
    font_size = int(sz[1] * 0.99)
    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
    if int(PIL.__version__.split(".")[0]) < 10:
        length = font.getsize(txt)[0]
    else:
        length = font.getlength(txt)

    if length > sz[0]:
        font_size = int(font_size * sz[0] / length)
        font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
    return font


def str_count(s):
    """
    Count the number of Chinese characters,
    a single English character and a single number
    equal to half the length of Chinese characters.
    args:
        s(string): the input of string
    return(int):
        the number of Chinese characters
    """
    import string

    count_zh = count_pu = 0
    s_len = len(s)
    en_dg_count = 0
    for c in s:
        if c in string.ascii_letters or c.isdigit() or c.isspace():
            en_dg_count += 1
        elif c.isalpha():
            count_zh += 1
        else:
            count_pu += 1
    return s_len - math.ceil(en_dg_count / 2)


def text_visual(
    texts, scores, img_h=400, img_w=600, threshold=0.0, font_path="./doc/simfang.ttf"
):
    """
    create new blank img and draw txt on it
    args:
        texts(list): the text will be draw
        scores(list|None): corresponding score of each txt
        img_h(int): the height of blank img
        img_w(int): the width of blank img
        font_path: the path of font which is used to draw text
    return(array):
    """
    if scores is not None:
        assert len(texts) == len(
            scores
        ), "The number of txts and corresponding scores must match"

    def create_blank_img():
        blank_img = np.ones(shape=[img_h, img_w], dtype=np.uint8) * 255
        blank_img[:, img_w - 1 :] = 0
        blank_img = Image.fromarray(blank_img).convert("RGB")
        draw_txt = ImageDraw.Draw(blank_img)
        return blank_img, draw_txt

    blank_img, draw_txt = create_blank_img()

    font_size = 20
    txt_color = (0, 0, 0)
    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")

    gap = font_size + 5
    txt_img_list = []
    count, index = 1, 0
    for idx, txt in enumerate(texts):
        index += 1
        if scores[idx] < threshold or math.isnan(scores[idx]):
            index -= 1
            continue
        first_line = True
        while str_count(txt) >= img_w // font_size - 4:
            tmp = txt
            txt = tmp[: img_w // font_size - 4]
            if first_line:
                new_txt = str(index) + ": " + txt
                first_line = False
            else:
                new_txt = "    " + txt
            draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
            txt = tmp[img_w // font_size - 4 :]
            if count >= img_h // gap - 1:
                txt_img_list.append(np.array(blank_img))
                blank_img, draw_txt = create_blank_img()
                count = 0
            count += 1
        if first_line:
            new_txt = str(index) + ": " + txt + "   " + "%.3f" % (scores[idx])
        else:
            new_txt = "  " + txt + "  " + "%.3f" % (scores[idx])
        draw_txt.text((0, gap * count), new_txt, txt_color, font=font)
        # whether add new blank img or not
        if count >= img_h // gap - 1 and idx + 1 < len(texts):
            txt_img_list.append(np.array(blank_img))
            blank_img, draw_txt = create_blank_img()
            count = 0
        count += 1
    txt_img_list.append(np.array(blank_img))
    if len(txt_img_list) == 1:
        blank_img = np.array(txt_img_list[0])
    else:
        blank_img = np.concatenate(txt_img_list, axis=1)
    return np.array(blank_img)


def base64_to_cv2(b64str):
    import base64

    data = base64.b64decode(b64str.encode("utf8"))
    data = np.frombuffer(data, np.uint8)
    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
    return data


def draw_boxes(image, boxes, scores=None, drop_score=0.5):
    if scores is None:
        scores = [1] * len(boxes)
    for box, score in zip(boxes, scores):
        if score < drop_score:
            continue
        box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
        image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
    return image


def get_rotate_crop_image(img, points):
    """
    img_height, img_width = img.shape[0:2]
    left = int(np.min(points[:, 0]))
    right = int(np.max(points[:, 0]))
    top = int(np.min(points[:, 1]))
    bottom = int(np.max(points[:, 1]))
    img_crop = img[top:bottom, left:right, :].copy()
    points[:, 0] = points[:, 0] - left
    points[:, 1] = points[:, 1] - top
    """
    assert len(points) == 4, "shape of points must be 4*2"
    img_crop_width = int(
        max(
            np.linalg.norm(points[0] - points[1]), np.linalg.norm(points[2] - points[3])
        )
    )
    img_crop_height = int(
        max(
            np.linalg.norm(points[0] - points[3]), np.linalg.norm(points[1] - points[2])
        )
    )
    pts_std = np.float32(
        [
            [0, 0],
            [img_crop_width, 0],
            [img_crop_width, img_crop_height],
            [0, img_crop_height],
        ]
    )
    M = cv2.getPerspectiveTransform(points, pts_std)
    dst_img = cv2.warpPerspective(
        img,
        M,
        (img_crop_width, img_crop_height),
        borderMode=cv2.BORDER_REPLICATE,
        flags=cv2.INTER_CUBIC,
    )
    dst_img_height, dst_img_width = dst_img.shape[0:2]
    if dst_img_height * 1.0 / dst_img_width >= 1.5:
        dst_img = np.rot90(dst_img)
    return dst_img


def get_minarea_rect_crop(img, points):
    bounding_box = cv2.minAreaRect(np.array(points).astype(np.int32))
    points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])

    index_a, index_b, index_c, index_d = 0, 1, 2, 3
    if points[1][1] > points[0][1]:
        index_a = 0
        index_d = 1
    else:
        index_a = 1
        index_d = 0
    if points[3][1] > points[2][1]:
        index_b = 2
        index_c = 3
    else:
        index_b = 3
        index_c = 2

    box = [points[index_a], points[index_b], points[index_c], points[index_d]]
    crop_img = get_rotate_crop_image(img, np.array(box))
    return crop_img


def slice_generator(image, horizontal_stride, vertical_stride, maximum_slices=500):
    if not isinstance(image, np.ndarray):
        image = np.array(image)

    image_h, image_w = image.shape[:2]
    vertical_num_slices = (image_h + vertical_stride - 1) // vertical_stride
    horizontal_num_slices = (image_w + horizontal_stride - 1) // horizontal_stride

    assert (
        vertical_num_slices > 0
    ), f"Invalid number ({vertical_num_slices}) of vertical slices"

    assert (
        horizontal_num_slices > 0
    ), f"Invalid number ({horizontal_num_slices}) of horizontal slices"

    if vertical_num_slices >= maximum_slices:
        recommended_vertical_stride = max(1, image_h // maximum_slices) + 1
        assert (
            False
        ), f"Too computationally expensive with {vertical_num_slices} slices, try a higher vertical stride (recommended minimum: {recommended_vertical_stride})"

    if horizontal_num_slices >= maximum_slices:
        recommended_horizontal_stride = max(1, image_w // maximum_slices) + 1
        assert (
            False
        ), f"Too computationally expensive with {horizontal_num_slices} slices, try a higher horizontal stride (recommended minimum: {recommended_horizontal_stride})"

    for v_slice_idx in range(vertical_num_slices):
        v_start = max(0, (v_slice_idx * vertical_stride))
        v_end = min(((v_slice_idx + 1) * vertical_stride), image_h)
        vertical_slice = image[v_start:v_end, :]
        for h_slice_idx in range(horizontal_num_slices):
            h_start = max(0, (h_slice_idx * horizontal_stride))
            h_end = min(((h_slice_idx + 1) * horizontal_stride), image_w)
            horizontal_slice = vertical_slice[:, h_start:h_end]

            yield (horizontal_slice, v_start, h_start)


def calculate_box_extents(box):
    min_x = box[0][0]
    max_x = box[1][0]
    min_y = box[0][1]
    max_y = box[2][1]
    return min_x, max_x, min_y, max_y


def merge_boxes(box1, box2, x_threshold, y_threshold):
    min_x1, max_x1, min_y1, max_y1 = calculate_box_extents(box1)
    min_x2, max_x2, min_y2, max_y2 = calculate_box_extents(box2)

    if (
        abs(min_y1 - min_y2) <= y_threshold
        and abs(max_y1 - max_y2) <= y_threshold
        and abs(max_x1 - min_x2) <= x_threshold
    ):
        new_xmin = min(min_x1, min_x2)
        new_xmax = max(max_x1, max_x2)
        new_ymin = min(min_y1, min_y2)
        new_ymax = max(max_y1, max_y2)
        return [
            [new_xmin, new_ymin],
            [new_xmax, new_ymin],
            [new_xmax, new_ymax],
            [new_xmin, new_ymax],
        ]
    else:
        return None


def merge_fragmented(boxes, x_threshold=10, y_threshold=10):
    merged_boxes = []
    visited = set()

    for i, box1 in enumerate(boxes):
        if i in visited:
            continue

        merged_box = [point[:] for point in box1]

        for j, box2 in enumerate(boxes[i + 1 :], start=i + 1):
            if j not in visited:
                merged_result = merge_boxes(
                    merged_box, box2, x_threshold=x_threshold, y_threshold=y_threshold
                )
                if merged_result:
                    merged_box = merged_result
                    visited.add(j)

        merged_boxes.append(merged_box)

    if len(merged_boxes) == len(boxes):
        return np.array(merged_boxes)
    else:
        return merge_fragmented(merged_boxes, x_threshold, y_threshold)


def check_gpu(use_gpu):
    if use_gpu and (
        not paddle.is_compiled_with_cuda() or paddle.device.get_device() == "cpu"
    ):
        use_gpu = False
    return use_gpu


if __name__ == "__main__":
    pass
