def __init__(self, device="cpu"):
self.model = CRNN(**model_parameters)
model_data = torch.load(
dirname(__file__) + '/logs/checkpoints/best_full.pth')
self.model.load_state_dict(model_data['model_state_dict'])
self.transforms = get_transforms_without_augmentations(device)
self.converter = strLabelConverter(alphabet)
def __init__(self, name, state: State, video_reader: VideoReader, model_path):
self.name = name
self.logger = logging.getLogger(self.name)
self.state = state
self.video_reader = video_reader
self.ocr_thread = None
self.time_start = datetime.now()
self.stopped = False
converter = strLabelConverter(CV_CONFIG.get('alphabet'))
self.predictor = Predictor(model_path, converter) #TODO: Your Predictor
self.logger.info("Create OcrStream")
def __init__(
self,
name,
state: State,
video_reader: VideoReader,
model_path='/home/alex/final_version_ocr/project (car numbers)/model/model_crnn.pth'
):
self.name = name
self.logger = logging.getLogger(self.name)
self.state = state
self.video_reader = video_reader
self.ocr_thread = None
self.time_start = datetime.now()
self.stopped = False
converter = strLabelConverter(CV_CONFIG.get('alphabet'))
self.predictor = Predictor(model_path, converter)
self.logger.info("Create OcrStream")
def __init__(self, model_path, transform, device="cuda"):
alphabet = " "
alphabet += string.ascii_uppercase
alphabet += "".join([str(i) for i in range(10)])
MODEL_PARAMS = {
"image_height": 32,
"number_input_channels": 3,
"number_class_symbols": len(alphabet) + 1,
"rnn_size": 64
}
state_dict = torch.load(model_path,
map_location='cpu')['model_state_dict']
self.model = CRNN(**MODEL_PARAMS)
self.model.load_state_dict(state_dict)
self.model = self.model.to(device)
self.model = self.model.eval()
self.device = device
self.converter = strLabelConverter(alphabet)
self.transform = transform
}),
"alphabet":
CV_CONFIG.get('alphabet'),
"loss": {
"reduction": 'mean'
},
"optimizer": ("Adam", {
"lr": 0.001
}),
# CHANGE DEVICE IF YOU USE GPU
"device":
"cpu",
}
if __name__ == "__main__":
converter = strLabelConverter(MODEL_PARAMS['alphabet'])
model_path = EXPERIMENT_DIR / sorted(
os.listdir(EXPERIMENT_DIR))[-1] # Last saved model
#print('Model path is', model_path)
#print('Is it a file?', os.path.isfile(model_path))
predictor = Predictor(model_path,
converter,
CV_CONFIG.get('ocr_image_size'),
device=MODEL_PARAMS['device'])
#print('File name', args.file_name)
#print(os.path.isdir('/workdir/data/CropNumbers'))
#print(os.path.isfile(args.file_name))
if os.path.isdir(args.file_name):
def __init__(self, alphabet, device='cpu'):
super().__init__()
self.converter = strLabelConverter(alphabet)
self.device = device
self.loss = nn.CTCLoss()
def __init__(self, params):
super().__init__(params)
self.converter = strLabelConverter(params["alphabet"])
def __init__(self):
self.alphabet = "ABEKMHOPCTYX" + "".join([str(i)
for i in range(10)]) + "-"
self.encoder = strLabelConverter(self.alphabet)