import argparse
import math
import os
import platform

import cv2
import numpy as np
import paddle
from paddle import inference
from PIL import Image, ImageDraw, ImageFont


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


def init_args():
    parser = argparse.ArgumentParser()
    # params for prediction engine
    parser.add_argument("--use_gpu", type=str2bool, default=False)
    parser.add_argument("--use_xpu", type=str2bool, default=False)
    parser.add_argument("--ir_optim", type=str2bool, default=False)
    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)

    # params for text detector
    parser.add_argument("--image_dir", type=str)
    parser.add_argument("--det_algorithm", type=str, default="DB")
    parser.add_argument("--det_model_dir", type=str, default="./ch_PP-OCRv3_det_infer/")
    parser.add_argument("--det_limit_side_len", type=float, default=960)
    parser.add_argument("--det_limit_type", type=str, default="max")

    # DB parmas
    parser.add_argument("--det_db_thresh", type=float, default=0.1)
    parser.add_argument("--det_db_box_thresh", type=float, default=0.1)
    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.7)
    parser.add_argument("--max_batch_size", type=int, default=10)
    parser.add_argument("--use_dilation", type=str2bool, default=True)
    parser.add_argument("--det_db_score_mode", type=str, default="fast")

    # EAST parmas
    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 parmas
    parser.add_argument("--det_sast_score_thresh", type=float, default=0.5)
    parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
    parser.add_argument("--det_sast_polygon", type=str2bool, default=False)

    # PSE parmas
    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_box_type", type=str, default="quad")
    parser.add_argument("--det_pse_scale", type=int, default=1)

    # FCE parmas
    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)
    parser.add_argument("--det_fce_box_type", type=str, default="poly")

    # params for text recognizer
    parser.add_argument("--rec_algorithm", type=str, default="SVTR_LCNet")
    parser.add_argument("--rec_model_dir", type=str, default="./ch_PP-OCRv3_rec_infer/")
    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/ppocr_keys_v1.txt"
    )
    parser.add_argument("--use_space_char", type=str2bool, default=True)
    parser.add_argument("--drop_score", type=float, default=0.5)

    # 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=True)
    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)

    #
    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("--use_onnx", type=str2bool, default=False)
    return parser


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


def create_predictor(args, mode):
    if mode == "det":
        model_dir = args.det_model_dir
    elif mode == "rec":
        model_dir = args.rec_model_dir

    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 = ort.InferenceSession(model_file_path)
        return sess, sess.get_inputs()[0], None, None

    else:
        model_file_path = model_dir + "/inference.pdmodel"
        params_file_path = model_dir + "/inference.pdiparams"
        if not os.path.exists(model_file_path):
            raise ValueError("not find model file path {}".format(model_file_path))
        if not os.path.exists(params_file_path):
            raise ValueError("not find params file path {}".format(params_file_path))

        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()
            config.enable_use_gpu(args.gpu_mem, 0)
            if args.use_tensorrt:
                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 minmum trt subgraph
            use_dynamic_shape = True
            if mode == "det":
                min_input_shape = {
                    "x": [1, 3, 50, 50],
                    "conv2d_92.tmp_0": [1, 120, 20, 20],
                    "conv2d_91.tmp_0": [1, 24, 10, 10],
                    "conv2d_59.tmp_0": [1, 96, 20, 20],
                    "nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
                    "nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
                    "conv2d_124.tmp_0": [1, 256, 20, 20],
                    "nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
                    "nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
                    "nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
                    "elementwise_add_7": [1, 56, 2, 2],
                    "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2],
                }
                max_input_shape = {
                    "x": [1, 3, 1536, 1536],
                    "conv2d_92.tmp_0": [1, 120, 400, 400],
                    "conv2d_91.tmp_0": [1, 24, 200, 200],
                    "conv2d_59.tmp_0": [1, 96, 400, 400],
                    "nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
                    "conv2d_124.tmp_0": [1, 256, 400, 400],
                    "nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
                    "nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
                    "nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
                    "nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
                    "elementwise_add_7": [1, 56, 400, 400],
                    "nearest_interp_v2_0.tmp_0": [1, 256, 400, 400],
                }
                opt_input_shape = {
                    "x": [1, 3, 640, 640],
                    "conv2d_92.tmp_0": [1, 120, 160, 160],
                    "conv2d_91.tmp_0": [1, 24, 80, 80],
                    "conv2d_59.tmp_0": [1, 96, 160, 160],
                    "nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
                    "nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
                    "conv2d_124.tmp_0": [1, 256, 160, 160],
                    "nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
                    "nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
                    "nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
                    "elementwise_add_7": [1, 56, 40, 40],
                    "nearest_interp_v2_0.tmp_0": [1, 256, 40, 40],
                }
                min_pact_shape = {
                    "nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
                    "nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
                    "nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
                    "nearest_interp_v2_29.tmp_0": [1, 64, 20, 20],
                }
                max_pact_shape = {
                    "nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
                    "nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
                    "nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
                    "nearest_interp_v2_29.tmp_0": [1, 64, 400, 400],
                }
                opt_pact_shape = {
                    "nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
                    "nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
                    "nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
                    "nearest_interp_v2_29.tmp_0": [1, 64, 160, 160],
                }
                min_input_shape.update(min_pact_shape)
                max_input_shape.update(max_pact_shape)
                opt_input_shape.update(opt_pact_shape)
            elif mode == "rec":
                if args.rec_algorithm not in ["CRNN", "SVTR_LCNet"]:
                    use_dynamic_shape = False
                imgH = int(args.rec_image_shape.split(",")[-2])
                min_input_shape = {"x": [1, 3, imgH, 10]}
                max_input_shape = {"x": [args.rec_batch_num, 3, imgH, 2304]}
                opt_input_shape = {"x": [args.rec_batch_num, 3, imgH, 320]}
                config.exp_disable_tensorrt_ops(["transpose2"])
            elif mode == "cls":
                min_input_shape = {"x": [1, 3, 48, 10]}
                max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
                opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
            else:
                use_dynamic_shape = False
            if use_dynamic_shape:
                config.set_trt_dynamic_shape_info(
                    min_input_shape, max_input_shape, opt_input_shape
                )

        elif args.use_xpu:
            config.enable_xpu(10 * 1024 * 1024)
        else:
            config.disable_gpu()
            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 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()
        # enable memory optim
        config.enable_memory_optim()
        config.disable_glog_info()
        config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
        config.delete_pass("matmul_transpose_reshape_fuse_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()
        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 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"]:
        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():
    sysstr = platform.system()
    if sysstr == "Windows":
        return 0

    if not paddle.fluid.core.is_compiled_with_rocm():
        cmd = "env | grep CUDA_VISIBLE_DEVICES"
    else:
        cmd = "env | grep HIP_VISIBLE_DEVICES"
    env_cuda = os.popen(cmd).readlines()
    if len(env_cuda) == 0:
        return 0
    else:
        gpu_id = env_cuda[0].strip().split("=")[1]
        return int(gpu_id[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_path):
    src_im = cv2.imread(img_path)
    for box in dt_boxes:
        box = np.array(box).astype(np.int32).reshape(-1, 2)
        cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
    return src_im


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, scores=None, drop_score=0.5, font_path="./doc/simfang.ttf"
):
    h, w = image.height, image.width
    img_left = image.copy()
    img_right = Image.new("RGB", (w, h), (255, 255, 255))

    import random

    random.seed(0)
    draw_left = ImageDraw.Draw(img_left)
    draw_right = ImageDraw.Draw(img_right)
    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)
        draw_right.polygon(
            [
                box[0][0],
                box[0][1],
                box[1][0],
                box[1][1],
                box[2][0],
                box[2][1],
                box[3][0],
                box[3][1],
            ],
            outline=color,
        )
        box_height = math.sqrt(
            (box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][1]) ** 2
        )
        box_width = math.sqrt(
            (box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][1]) ** 2
        )
        if box_height > 2 * box_width:
            font_size = max(int(box_width * 0.9), 10)
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
            cur_y = box[0][1]
            for c in txt:
                char_size = font.getsize(c)
                draw_right.text((box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)
                cur_y += char_size[1]
        else:
            font_size = max(int(box_height * 0.8), 10)
            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
            draw_right.text([box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
    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(img_right, (w, 0, w * 2, h))
    return np.array(img_show)


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.int8) * 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 check_gpu(use_gpu):
    if use_gpu and not paddle.is_compiled_with_cuda():
        use_gpu = False
    return use_gpu