import argparse
import copy
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 ClsPostProcess

class TextClassifier:
    def __init__(self, config):
        self.cls_image_shape = config["cls_image_shape"]
        self.cls_batch_num = config["cls_batch_num"]
        self.cls_thresh = config["cls_thresh"]
        self.postprocess_op = ClsPostProcess(config["label_list"])
        self.infer = OrtInferSession(config)

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

        img_list = copy.deepcopy(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 cls process
        indices = np.argsort(np.array(width_list))

        img_num = len(img_list)
        cls_res = [["", 0.0]] * img_num
        batch_num = self.cls_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)

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

            starttime = time.time()
            prob_out = self.infer(norm_img_batch)[0]
            cls_result = self.postprocess_op(prob_out)
            elapse += time.time() - starttime

            for rno in range(len(cls_result)):
                label, score = cls_result[rno]
                cls_res[indices[beg_img_no + rno]] = [label, score]
                if "180" in label and score > self.cls_thresh:
                    img_list[indices[beg_img_no + rno]] = cv2.rotate(
                        img_list[indices[beg_img_no + rno]], 1
                    )
        return img_list, cls_res, elapse

    def resize_norm_img(self, img):
        img_c, img_h, img_w = self.cls_image_shape
        h, w = img.shape[:2]
        ratio = w / float(h)
        if math.ceil(img_h * ratio) > img_w:
            resized_w = img_w
        else:
            resized_w = int(math.ceil(img_h * ratio))

        resized_image = cv2.resize(img, (resized_w, img_h))
        resized_image = resized_image.astype("float32")
        if img_c == 1:
            resized_image = resized_image / 255
            resized_image = resized_image[np.newaxis, :]
        else:
            resized_image = resized_image.transpose((2, 0, 1)) / 255

        resized_image -= 0.5
        resized_image /= 0.5
        padding_im = np.zeros((img_c, img_h, img_w), dtype=np.float32)
        padding_im[:, :, :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_classifier = TextClassifier(config)

    img = cv2.imread(args.image_path)
    img_list, cls_res, predict_time = text_classifier(img)
    for ino in range(len(img_list)):
        print(f"cls result:{cls_res[ino]}")
