def test_ocrpredictor(mock_pdf, mock_vocab, assume_straight_pages,
straighten_pages):
det_bsize = 4
det_predictor = DetectionPredictor(
PreProcessor(output_size=(512, 512), batch_size=det_bsize),
detection.db_mobilenet_v3_large(
pretrained=False,
pretrained_backbone=False,
assume_straight_pages=assume_straight_pages,
),
)
assert not det_predictor.model.training
reco_bsize = 32
reco_predictor = RecognitionPredictor(
PreProcessor(output_size=(32, 128),
batch_size=reco_bsize,
preserve_aspect_ratio=True),
recognition.crnn_vgg16_bn(pretrained=False,
pretrained_backbone=False,
vocab=mock_vocab),
)
assert not reco_predictor.model.training
doc = DocumentFile.from_pdf(mock_pdf)
predictor = OCRPredictor(
det_predictor,
reco_predictor,
assume_straight_pages=assume_straight_pages,
straighten_pages=straighten_pages,
detect_orientation=True,
detect_language=True,
)
if assume_straight_pages:
assert predictor.crop_orientation_predictor is None
else:
assert isinstance(predictor.crop_orientation_predictor, nn.Module)
out = predictor(doc)
assert isinstance(out, Document)
assert len(out.pages) == 2
# Dimension check
with pytest.raises(ValueError):
input_page = (255 * np.random.rand(1, 256, 512, 3)).astype(np.uint8)
_ = predictor([input_page])
orientation = 0
assert out.pages[0].orientation["value"] == orientation
def test_preprocessor(batch_size, output_size, input_tensor, expected_batches,
expected_value):
processor = PreProcessor(output_size, batch_size)
# Invalid input type
with pytest.raises(TypeError):
processor(42)
# 4D check
with pytest.raises(AssertionError):
processor(np.full((256, 128, 3), 255, dtype=np.uint8))
with pytest.raises(TypeError):
processor(np.full((1, 256, 128, 3), 255, dtype=np.int32))
# 3D check
with pytest.raises(AssertionError):
processor([np.full((3, 256, 128, 3), 255, dtype=np.uint8)])
with pytest.raises(TypeError):
processor([np.full((256, 128, 3), 255, dtype=np.int32)])
out = processor(input_tensor)
assert isinstance(out, list) and len(out) == expected_batches
assert all(isinstance(b, tf.Tensor) for b in out)
assert all(b.dtype == tf.float32 for b in out)
assert all(b.shape[1:3] == output_size for b in out)
assert all(tf.math.reduce_all(b == expected_value) for b in out)
assert len(repr(processor).split("\n")) == 4
def test_recognitionpredictor(mock_pdf, mock_vocab): # noqa: F811
batch_size = 4
predictor = RecognitionPredictor(
PreProcessor(output_size=(32, 128),
batch_size=batch_size,
preserve_aspect_ratio=True),
recognition.crnn_vgg16_bn(vocab=mock_vocab, input_shape=(32, 128, 3)),
)
pages = DocumentFile.from_pdf(mock_pdf).as_images()
# Create bounding boxes
boxes = np.array([[0.5, 0.5, 0.75, 0.75], [0.5, 0.5, 1.0, 1.0]],
dtype=np.float32)
crops = extract_crops(pages[0], boxes)
out = predictor(crops)
# One prediction per crop
assert len(out) == boxes.shape[0]
assert all(
isinstance(val, str) and isinstance(conf, float) for val, conf in out)
# Dimension check
with pytest.raises(ValueError):
input_crop = (255 * np.random.rand(1, 128, 64, 3)).astype(np.uint8)
_ = predictor([input_crop])
return predictor
def test_ocrpredictor(mock_pdf, mock_vocab, assume_straight_pages,
straighten_pages):
det_bsize = 4
det_predictor = DetectionPredictor(
PreProcessor(output_size=(512, 512), batch_size=det_bsize),
detection.db_mobilenet_v3_large(
pretrained=True,
pretrained_backbone=False,
input_shape=(512, 512, 3),
assume_straight_pages=assume_straight_pages,
))
reco_bsize = 16
reco_predictor = RecognitionPredictor(
PreProcessor(output_size=(32, 128),
batch_size=reco_bsize,
preserve_aspect_ratio=True),
recognition.crnn_vgg16_bn(pretrained=False,
pretrained_backbone=False,
vocab=mock_vocab))
doc = DocumentFile.from_pdf(mock_pdf).as_images()
predictor = OCRPredictor(
det_predictor,
reco_predictor,
assume_straight_pages=assume_straight_pages,
straighten_pages=straighten_pages,
)
if assume_straight_pages:
assert predictor.crop_orientation_predictor is None
else:
assert isinstance(predictor.crop_orientation_predictor, NestedObject)
out = predictor(doc)
assert isinstance(out, Document)
assert len(out.pages) == 2
# Dimension check
with pytest.raises(ValueError):
input_page = (255 * np.random.rand(1, 256, 512, 3)).astype(np.uint8)
_ = predictor([input_page])
def _predictor(arch: str, pretrained: bool,
**kwargs: Any) -> RecognitionPredictor:
if arch not in ARCHS:
raise ValueError(f"unknown architecture '{arch}'")
_model = recognition.__dict__[arch](pretrained=pretrained)
kwargs['mean'] = kwargs.get('mean', _model.cfg['mean'])
kwargs['std'] = kwargs.get('std', _model.cfg['std'])
kwargs['batch_size'] = kwargs.get('batch_size', 32)
input_shape = _model.cfg['input_shape'][:2] if is_tf_available(
) else _model.cfg['input_shape'][-2:]
predictor = RecognitionPredictor(
PreProcessor(input_shape, preserve_aspect_ratio=True, **kwargs),
_model)
return predictor
def test_detectionpredictor(mock_pdf): # noqa: F811
batch_size = 4
predictor = DetectionPredictor(
PreProcessor(output_size=(512, 512), batch_size=batch_size),
detection.db_resnet50(input_shape=(512, 512, 3)))
pages = DocumentFile.from_pdf(mock_pdf).as_images()
out = predictor(pages)
# The input PDF has 2 pages
assert len(out) == 2
# Dimension check
with pytest.raises(ValueError):
input_page = (255 * np.random.rand(1, 256, 512, 3)).astype(np.uint8)
_ = predictor([input_page])
return predictor
def _predictor(arch: Any, pretrained: bool,
**kwargs: Any) -> RecognitionPredictor:
if isinstance(arch, str):
if arch not in ARCHS:
raise ValueError(f"unknown architecture '{arch}'")
_model = recognition.__dict__[arch](pretrained=pretrained)
else:
if not isinstance(
arch, (recognition.CRNN, recognition.SAR, recognition.MASTER)):
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", 32)
input_shape = _model.cfg["input_shape"][:2] if is_tf_available(
) else _model.cfg["input_shape"][-2:]
predictor = RecognitionPredictor(
PreProcessor(input_shape, preserve_aspect_ratio=True, **kwargs),
_model)
return predictor
