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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 List, Union
import numpy as np
import torch
from torch import nn
from doctr.models.preprocessor import PreProcessor
from doctr.models.utils import set_device_and_dtype
__all__ = ["OrientationPredictor"]
class OrientationPredictor(nn.Module):
"""Implements an object able to detect the reading direction of a text box or a page.
4 possible orientations: 0, 90, 180, 270 (-90) degrees counter clockwise.
Args:
----
pre_processor: transform inputs for easier batched model inference
model: core classification architecture (backbone + classification head)
"""
def __init__(
self,
pre_processor: PreProcessor,
model: nn.Module,
) -> None:
super().__init__()
self.pre_processor = pre_processor
self.model = model.eval()
@torch.inference_mode()
def forward(
self,
inputs: List[Union[np.ndarray, torch.Tensor]],
) -> List[Union[List[int], List[float]]]:
# Dimension check
if any(input.ndim != 3 for input in inputs):
raise ValueError("incorrect input shape: all inputs are expected to be multi-channel 2D images.")
processed_batches = self.pre_processor(inputs)
_params = next(self.model.parameters())
self.model, processed_batches = set_device_and_dtype(
self.model, processed_batches, _params.device, _params.dtype
)
predicted_batches = [self.model(batch) for batch in processed_batches]
# confidence
probs = [
torch.max(torch.softmax(batch, dim=1), dim=1).values.cpu().detach().numpy() for batch in predicted_batches
]
# Postprocess predictions
predicted_batches = [out_batch.argmax(dim=1).cpu().detach().numpy() for out_batch in predicted_batches]
class_idxs = [int(pred) for batch in predicted_batches for pred in batch]
classes = [int(self.model.cfg["classes"][idx]) for idx in class_idxs]
confs = [round(float(p), 2) for prob in probs for p in prob]
return [class_idxs, classes, confs]
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