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| # Copyright (C) 2021-2024, Mindee. | |
| # This program is licensed under the Apache License 2.0. | |
| # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details. | |
| from typing import Any, List, Tuple, Union | |
| import numpy as np | |
| import tensorflow as tf | |
| from doctr.models.preprocessor import PreProcessor | |
| from doctr.utils.repr import NestedObject | |
| from ..core import RecognitionModel | |
| from ._utils import remap_preds, split_crops | |
| __all__ = ["RecognitionPredictor"] | |
| class RecognitionPredictor(NestedObject): | |
| """Implements an object able to identify character sequences in images | |
| Args: | |
| ---- | |
| pre_processor: transform inputs for easier batched model inference | |
| model: core detection architecture | |
| split_wide_crops: wether to use crop splitting for high aspect ratio crops | |
| """ | |
| _children_names: List[str] = ["pre_processor", "model"] | |
| def __init__( | |
| self, | |
| pre_processor: PreProcessor, | |
| model: RecognitionModel, | |
| split_wide_crops: bool = True, | |
| ) -> None: | |
| super().__init__() | |
| self.pre_processor = pre_processor | |
| self.model = model | |
| self.split_wide_crops = split_wide_crops | |
| self.critical_ar = 8 # Critical aspect ratio | |
| self.dil_factor = 1.4 # Dilation factor to overlap the crops | |
| self.target_ar = 6 # Target aspect ratio | |
| def __call__( | |
| self, | |
| crops: List[Union[np.ndarray, tf.Tensor]], | |
| **kwargs: Any, | |
| ) -> List[Tuple[str, float]]: | |
| if len(crops) == 0: | |
| return [] | |
| # Dimension check | |
| if any(crop.ndim != 3 for crop in crops): | |
| raise ValueError("incorrect input shape: all crops are expected to be multi-channel 2D images.") | |
| # Split crops that are too wide | |
| remapped = False | |
| if self.split_wide_crops: | |
| new_crops, crop_map, remapped = split_crops(crops, self.critical_ar, self.target_ar, self.dil_factor) | |
| if remapped: | |
| crops = new_crops | |
| # Resize & batch them | |
| processed_batches = self.pre_processor(crops) | |
| # Forward it | |
| raw = [ | |
| self.model(batch, return_preds=True, training=False, **kwargs)["preds"] # type: ignore[operator] | |
| for batch in processed_batches | |
| ] | |
| # Process outputs | |
| out = [charseq for batch in raw for charseq in batch] | |
| # Remap crops | |
| if self.split_wide_crops and remapped: | |
| out = remap_preds(out, crop_map, self.dil_factor) | |
| return out | |