import argparse
import math
import time
from typing import List
import cv2
import numpy as np
from rapidocr_onnxruntime.utils import OrtInferSession, read_yaml
from .utils import CTCLabelDecode

class TextRecognizer:
    def __init__(self, config):
        self.session = OrtInferSession(config)
        if self.session.have_key():
            self.character_dict_path = self.session.get_character_list()
        else:
            self.character_dict_path = config.get("keys_path", None)
        self.postprocess_op = CTCLabelDecode(self.character_dict_path)
        self.rec_batch_num = config["rec_batch_num"]
        self.rec_image_shape = config["rec_img_shape"]

    def __call__(self, img_list: List[np.ndarray]):
        if isinstance(img_list, np.ndarray):
            img_list = [img_list]

        # Calculate the aspect ratio of all text bars
        width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]

        # Sorting can speed up the recognition process
        indices = np.argsort(np.array(width_list))

        img_num = len(img_list)
        rec_res = [["", 0.0]] * img_num

        batch_num = self.rec_batch_num
        elapse = 0
        for beg_img_no in range(0, img_num, batch_num):
            end_img_no = min(img_num, beg_img_no + batch_num)
            max_wh_ratio = 0
            for ino in range(beg_img_no, end_img_no):
                h, w = img_list[indices[ino]].shape[0:2]
                wh_ratio = w * 1.0 / h
                max_wh_ratio = max(max_wh_ratio, wh_ratio)

            norm_img_batch = []
            for ino in range(beg_img_no, end_img_no):
                norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
                norm_img_batch.append(norm_img[np.newaxis, :])
            norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32)

            starttime = time.time()
            preds = self.session(norm_img_batch)[0]
            rec_result = self.postprocess_op(preds)

            for rno, one_res in enumerate(rec_result):
                rec_res[indices[beg_img_no + rno]] = one_res
            elapse += time.time() - starttime
        return rec_res, elapse

    def resize_norm_img(self, img, max_wh_ratio):
        img_channel, img_height, img_width = self.rec_image_shape
        assert img_channel == img.shape[2]

        img_width = int(img_height * max_wh_ratio)

        h, w = img.shape[:2]
        ratio = w / float(h)
        if math.ceil(img_height * ratio) > img_width:
            resized_w = img_width
        else:
            resized_w = int(math.ceil(img_height * ratio))

        resized_image = cv2.resize(img, (resized_w, img_height))
        resized_image = resized_image.astype("float32")
        resized_image = resized_image.transpose((2, 0, 1)) / 255
        resized_image -= 0.5
        resized_image /= 0.5

        padding_im = np.zeros((img_channel, img_height, img_width), dtype=np.float32)
        padding_im[:, :, 0:resized_w] = resized_image
        return padding_im

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--image_path", type=str, help="image_dir|image_path")
    parser.add_argument("--config_path", type=str, default="config.yaml")
    args = parser.parse_args()

    config = read_yaml(args.config_path)
    text_recognizer = TextRecognizer(config)

    img = cv2.imread(args.image_path)
    rec_res, predict_time = text_recognizer(img)
    print(f"rec result: {rec_res}\t cost: {predict_time}s")
