# Copyright (C) 2021-2025, 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
from doctr.file_utils import is_tf_available, is_torch_available
from .. import classification
from ..preprocessor import PreProcessor
from .predictor import OrientationPredictor
__all__ = ["crop_orientation_predictor", "page_orientation_predictor"]
ARCHS: list[str] = [
"magc_resnet31",
"mobilenet_v3_small",
"mobilenet_v3_small_r",
"mobilenet_v3_large",
"mobilenet_v3_large_r",
"resnet18",
"resnet31",
"resnet34",
"resnet50",
"resnet34_wide",
"textnet_tiny",
"textnet_small",
"textnet_base",
"vgg16_bn_r",
"vit_s",
"vit_b",
]
if is_torch_available():
ARCHS.extend(["vip_tiny", "vip_base"])
ORIENTATION_ARCHS: list[str] = ["mobilenet_v3_small_crop_orientation", "mobilenet_v3_small_page_orientation"]
def _orientation_predictor(
arch: Any, pretrained: bool, model_type: str, disabled: bool = False, **kwargs: Any
) -> OrientationPredictor:
if disabled:
# Case where the orientation predictor is disabled
return OrientationPredictor(None, None)
if isinstance(arch, str):
if arch not in ORIENTATION_ARCHS:
raise ValueError(f"unknown architecture '{arch}'")
# Load directly classifier from backbone
_model = classification.__dict__[arch](pretrained=pretrained)
else:
allowed_archs = [classification.MobileNetV3]
if is_torch_available():
# Adding the type for torch compiled models to the allowed architectures
from doctr.models.utils import _CompiledModule
allowed_archs.append(_CompiledModule)
if not isinstance(arch, tuple(allowed_archs)):
raise ValueError(f"unknown architecture: {type(arch)}")
_model = arch
kwargs["mean"] = kwargs.get("mean", _model.cfg["mean"])
kwargs["std"] = kwargs.get("std", _model.cfg["std"])
kwargs["batch_size"] = kwargs.get("batch_size", 128 if model_type == "crop" else 4)
input_shape = _model.cfg["input_shape"][:-1] if is_tf_available() else _model.cfg["input_shape"][1:]
predictor = OrientationPredictor(
PreProcessor(input_shape, preserve_aspect_ratio=True, symmetric_pad=True, **kwargs), _model
)
return predictor
[docs]
def crop_orientation_predictor(
arch: Any = "mobilenet_v3_small_crop_orientation", pretrained: bool = False, batch_size: int = 128, **kwargs: Any
) -> OrientationPredictor:
"""Crop orientation classification architecture.
>>> import numpy as np
>>> from doctr.models import crop_orientation_predictor
>>> model = crop_orientation_predictor(arch='mobilenet_v3_small_crop_orientation', pretrained=True)
>>> input_crop = (255 * np.random.rand(256, 256, 3)).astype(np.uint8)
>>> out = model([input_crop])
Args:
arch: name of the architecture to use (e.g. 'mobilenet_v3_small_crop_orientation')
pretrained: If True, returns a model pre-trained on our recognition crops dataset
batch_size: number of samples the model processes in parallel
**kwargs: keyword arguments to be passed to the OrientationPredictor
Returns:
OrientationPredictor
"""
return _orientation_predictor(arch=arch, pretrained=pretrained, batch_size=batch_size, model_type="crop", **kwargs)
[docs]
def page_orientation_predictor(
arch: Any = "mobilenet_v3_small_page_orientation", pretrained: bool = False, batch_size: int = 4, **kwargs: Any
) -> OrientationPredictor:
"""Page orientation classification architecture.
>>> import numpy as np
>>> from doctr.models import page_orientation_predictor
>>> model = page_orientation_predictor(arch='mobilenet_v3_small_page_orientation', pretrained=True)
>>> input_page = (255 * np.random.rand(512, 512, 3)).astype(np.uint8)
>>> out = model([input_page])
Args:
arch: name of the architecture to use (e.g. 'mobilenet_v3_small_page_orientation')
pretrained: If True, returns a model pre-trained on our recognition crops dataset
batch_size: number of samples the model processes in parallel
**kwargs: keyword arguments to be passed to the OrientationPredictor
Returns:
OrientationPredictor
"""
return _orientation_predictor(arch=arch, pretrained=pretrained, batch_size=batch_size, model_type="page", **kwargs)