import numpy as np
import time
import cv2
import torch
from torch.autograd import Variable
import lib.utils.utils as utils
import lib.models.crnn as crnn
import lib.config.alphabets as alphabets
import yaml
from easydict import EasyDict as edict
import argparse

def parse_arg():
    parser = argparse.ArgumentParser(description="demo")

    parser.add_argument('--cfg', help='experiment configuration filename', type=str, default='lib/config/360CC_config.yaml')
    parser.add_argument('--image_path', type=str, default='images/test.png', help='the path to your image')
    parser.add_argument('--checkpoint', type=str, default='output/checkpoints/mixed_second_finetune_acc_97P7.pth',
                        help='the path to your checkpoints')

    args = parser.parse_args()

    with open(args.cfg, 'r') as f:
        config = yaml.load(f)
        config = edict(config)

    config.DATASET.ALPHABETS = alphabets.alphabet
    config.MODEL.NUM_CLASSES = len(config.DATASET.ALPHABETS)

    return config, args

def recognition(config, img, model, converter, device):

    # ratio resize
    w_cur = int(img.shape[1] / (config.MODEL.IMAGE_SIZE.OW / config.MODEL.IMAGE_SIZE.W))
    h, w = img.shape
    img = cv2.resize(img, (0, 0), fx=w_cur / w, fy=config.MODEL.IMAGE_SIZE.H / h, interpolation=cv2.INTER_CUBIC)
    img = np.reshape(img, (config.MODEL.IMAGE_SIZE.H, w_cur, 1))

    # normalize
    img = img.astype(np.float32)
    img = (img / 255. - config.DATASET.MEAN) / config.DATASET.STD
    img = img.transpose([2, 0, 1])
    img = torch.from_numpy(img)

    img = img.to(device)
    img = img.view(1, *img.size())
    model.eval()
    preds = model(img)

    _, preds = preds.max(2)
    preds = preds.transpose(1, 0).contiguous().view(-1)

    preds_size = Variable(torch.IntTensor([preds.size(0)]))
    sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
    print('results: {0}'.format(sim_pred))


if __name__ == '__main__':

    config, args = parse_arg()
    device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')

    model = crnn.get_crnn(config).to(device)
    print('loading pretrained model from {0}'.format(args.checkpoint))
    model.load_state_dict(torch.load(args.checkpoint))

    started = time.time()

    img = cv2.imread(args.image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    converter = utils.strLabelConverter(config.DATASET.ALPHABETS)

    recognition(config, img, model, converter, device)

    finished = time.time()
    print('elapsed time: {0}'.format(finished - started))