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"""Defines the CharacterModel class."""
from typing import Callable, Dict, Optional, Tuple
import numpy as np
import torch
from torch import nn
from torchvision.transforms import ToTensor
from text_recognizer.datasets.emnist_dataset import load_emnist_mapping
from text_recognizer.models.base import Model
from text_recognizer.networks.mlp import mlp
class CharacterModel(Model):
"""Model for predicting characters from images."""
def __init__(
self,
network_fn: Callable,
network_args: Dict,
data_loader_args: Optional[Dict] = None,
metrics: Optional[Dict] = None,
criterion: Optional[Callable] = None,
criterion_args: Optional[Dict] = None,
optimizer: Optional[Callable] = None,
optimizer_args: Optional[Dict] = None,
lr_scheduler: Optional[Callable] = None,
lr_scheduler_args: Optional[Dict] = None,
device: Optional[str] = None,
) -> None:
"""Initializes the CharacterModel."""
super().__init__(
network_fn,
network_args,
data_loader_args,
metrics,
criterion,
criterion_args,
optimizer,
optimizer_args,
lr_scheduler,
lr_scheduler_args,
device,
)
self.emnist_mapping = self.mapping()
self.eval()
def mapping(self) -> Dict[int, str]:
"""Mapping between integers and classes."""
mapping = load_emnist_mapping()
return mapping
def predict_on_image(self, image: np.ndarray) -> Tuple[str, float]:
"""Character prediction on an image.
Args:
image (np.ndarray): An image containing a character.
Returns:
Tuple[str, float]: The predicted character and the confidence in the prediction.
"""
if image.dtype == np.uint8:
image = (image / 255).astype(np.float32)
# Conver to Pytorch Tensor.
image = ToTensor(image)
prediction = self.network(image)
index = torch.argmax(prediction, dim=1)
confidence_of_prediction = prediction[index]
predicted_character = self.emnist_mapping[index]
return predicted_character, confidence_of_prediction
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