import time
import cv2
import numpy as np
import math
from utils.logging import get_logger
from utils.utils import build_post_process, create_predictor

logger = get_logger()
class TextRecognizer(object):
    def __init__(self, args):
        self.rec_image_shape = [int(v) for v in args['rec_image_shape'].split(",")]
        self.character_type = args['rec_char_type']
        self.rec_batch_num = args['rec_batch_num']
        self.rec_algorithm = args['name']

        self.postprocess_op = build_post_process(args['postprocess'], global_config={'character_type':self.character_type})
        self.predictor, self.input_tensor, self.output_tensors = create_predictor(args, 'rec', logger)

    def resize_norm_img(self, img, max_wh_ratio):
        imgC, imgH, imgW = self.rec_image_shape
        assert imgC == img.shape[2]
        if self.character_type == "ch":
            imgW = int((32 * max_wh_ratio))
        h, w = img.shape[:2]
        ratio = w / float(h)
        if math.ceil(imgH * ratio) > imgW:
            resized_w = imgW
        else:
            resized_w = int(math.ceil(imgH * ratio))
        resized_image = cv2.resize(img, (resized_w, imgH))
        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((imgC, imgH, imgW), dtype=np.float32)
        padding_im[:, :, 0:resized_w] = resized_image
        return padding_im

    def __call__(self, img_list):
        img_num = len(img_list)
        # Calculate the aspect ratio of all text bars
        width_list = []
        for img in img_list:
            width_list.append(img.shape[1] / float(img.shape[0]))
        # Sorting can speed up the recognition process
        indices = np.argsort(np.array(width_list))

        # rec_res = []
        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)
            norm_img_batch = []
            max_wh_ratio = 0
            for ino in range(beg_img_no, end_img_no):
                # h, w = img_list[ino].shape[0:2]
                h, w = img_list[indices[ino]].shape[0:2]
                wh_ratio = w * 1.0 / h
                max_wh_ratio = max(max_wh_ratio, wh_ratio)
            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 = norm_img[np.newaxis, :]
                norm_img_batch.append(norm_img)
  
            norm_img_batch = np.concatenate(norm_img_batch)
            norm_img_batch = norm_img_batch.copy()


            starttime = time.time()
            self.input_tensor.copy_from_cpu(norm_img_batch)
            self.predictor.run()

            outputs = []
            for output_tensor in self.output_tensors:
                output = output_tensor.copy_to_cpu()
                outputs.append(output)
            preds = outputs[0]

            rec_result = self.postprocess_op(preds)
            for rno in range(len(rec_result)):
                rec_res[indices[beg_img_no + rno]] = rec_result[rno]
            elapse += time.time() - starttime
        return rec_res, elapse