Disable page and crop orientation

api/app/schemas.py

@@ -19,6 +19,8 @@ class KIEIn(BaseModel):
     straighten_pages: bool = Field(default=False, examples=[False])
     det_bs: int = Field(default=2, examples=[2])
     reco_bs: int = Field(default=128, examples=[128])
+    disable_page_orientation: bool = Field(default=False, examples=[False])
+    disable_crop_orientation: bool = Field(default=False, examples=[False])
     bin_thresh: float = Field(default=0.1, examples=[0.1])
     box_thresh: float = Field(default=0.1, examples=[0.1])
 

demo/app.py

@@ -72,6 +72,12 @@ def main(det_archs, reco_archs):
     st.sidebar.title("Parameters")
     assume_straight_pages = st.sidebar.checkbox("Assume straight pages", value=True)
     st.sidebar.write("\n")
+    # Disable page orientation detection
+    disable_page_orientation = st.sidebar.checkbox("Disable page orientation detection", value=False)
+    st.sidebar.write("\n")
+    # Disable crop orientation detection
+    disable_crop_orientation = st.sidebar.checkbox("Disable crop orientation detection", value=False)
+    st.sidebar.write("\n")
     # Straighten pages
     straighten_pages = st.sidebar.checkbox("Straighten pages", value=False)
     st.sidebar.write("\n")
@@ -89,7 +95,15 @@ def main(det_archs, reco_archs):
         else:
             with st.spinner("Loading model..."):
                 predictor = load_predictor(
-                    det_arch, reco_arch, assume_straight_pages, straighten_pages, bin_thresh, box_thresh, forward_device
+                    det_arch,
+                    reco_arch,
+                    assume_straight_pages,
+                    straighten_pages,
+                    disable_page_orientation,
+                    disable_crop_orientation,
+                    bin_thresh,
+                    box_thresh,
+                    forward_device,
                 )
 
             with st.spinner("Analyzing..."):

demo/backend/pytorch.py

@@ -37,6 +37,8 @@ def load_predictor(
     reco_arch: str,
     assume_straight_pages: bool,
     straighten_pages: bool,
+    disable_page_orientation: bool,
+    disable_crop_orientation: bool,
     bin_thresh: float,
     box_thresh: float,
     device: torch.device,
@@ -49,6 +51,8 @@ def load_predictor(
         reco_arch: recognition architecture
         assume_straight_pages: whether to assume straight pages or not
         straighten_pages: whether to straighten rotated pages or not
+        disable_page_orientation: whether to disable page orientation or not
+        disable_crop_orientation: whether to disable crop orientation or not
         bin_thresh: binarization threshold for the segmentation map
         box_thresh: minimal objectness score to consider a box
         device: torch.device, the device to load the predictor on
@@ -65,6 +69,8 @@ def load_predictor(
         straighten_pages=straighten_pages,
         export_as_straight_boxes=straighten_pages,
         detect_orientation=not assume_straight_pages,
+        disable_page_orientation=disable_page_orientation,
+        disable_crop_orientation=disable_crop_orientation,
     ).to(device)
     predictor.det_predictor.model.postprocessor.bin_thresh = bin_thresh
     predictor.det_predictor.model.postprocessor.box_thresh = box_thresh

demo/backend/tensorflow.py

@@ -36,6 +36,8 @@ def load_predictor(
     reco_arch: str,
     assume_straight_pages: bool,
     straighten_pages: bool,
+    disable_page_orientation: bool,
+    disable_crop_orientation: bool,
     bin_thresh: float,
     box_thresh: float,
     device: tf.device,
@@ -48,6 +50,8 @@ def load_predictor(
         reco_arch: recognition architecture
         assume_straight_pages: whether to assume straight pages or not
         straighten_pages: whether to straighten rotated pages or not
+        disable_page_orientation: whether to disable page orientation or not
+        disable_crop_orientation: whether to disable crop orientation or not
         bin_thresh: binarization threshold for the segmentation map
         box_thresh: threshold for the detection boxes
         device: tf.device, the device to load the predictor on
@@ -65,6 +69,8 @@ def load_predictor(
             straighten_pages=straighten_pages,
             export_as_straight_boxes=straighten_pages,
             detect_orientation=not assume_straight_pages,
+            disable_page_orientation=disable_page_orientation,
+            disable_crop_orientation=disable_crop_orientation,
         )
         predictor.det_predictor.model.postprocessor.bin_thresh = bin_thresh
         predictor.det_predictor.model.postprocessor.box_thresh = box_thresh

docs/source/using_doctr/using_models.rst

@@ -283,13 +283,16 @@ Those architectures involve one stage of text detection, and one stage of text r
 
 You can pass specific boolean arguments to the predictor:
 
-* `assume_straight_pages`
-* `preserve_aspect_ratio`
-* `symmetric_pad`
+* `assume_straight_pages`: if you work with straight documents only, it will fit straight bounding boxes to the text areas.
+* `preserve_aspect_ratio`: if you want to preserve the aspect ratio of your documents while resizing before sending them to the model.
+* `symmetric_pad`: if you choose to preserve the aspect ratio, it will pad the image symmetrically and not from the bottom-right.
 
 Those 3 are going straight to the detection predictor, as mentioned above (in the detection part).
 
+Additional arguments which can be passed to the `ocr_predictor` are:
+
 * `export_as_straight_boxes`: If you work with rotated and skewed documents but you still want to export straight bounding boxes and not polygons, set it to True.
+* `straighten_pages`: If you want to straighten the pages before sending them to the detection model, set it to True.
 
 For instance, this snippet instantiates an end-to-end ocr_predictor working with rotated documents, which preserves the aspect ratio of the documents, and returns polygons:
 
@@ -298,6 +301,7 @@ For instance, this snippet instantiates an end-to-end ocr_predictor working with
     from doctr.model import ocr_predictor
     model = ocr_predictor('linknet_resnet18', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True)
 
+
 Additionally, you can change the batch size of the underlying detection and recognition predictors to optimize the performance depending on your hardware:
 
 * `det_bs`: batch size for the detection model (default: 2)
@@ -465,6 +469,30 @@ This is useful to detect (possible less) text regions more accurately with a hig
     out = predictor([input_page])
 
 
+* Disable page orientation classification
+
+If you deal with documents which contains only small rotations (~ -45 to 45 degrees), you can disable the page orientation classification to speed up the inference.
+
+This will only have an effect with `assume_straight_pages=False` and/or `straighten_pages=True` and/or `detect_orientation=True`.
+
+.. code:: python3
+
+    from doctr.model import ocr_predictor
+    model = ocr_predictor(pretrained=True, assume_straight_pages=False, disable_page_orientation=True)
+
+
+* Disable crop orientation classification
+
+If you deal with documents which contains only horizontal text, you can disable the crop orientation classification to speed up the inference.
+
+This will only have an effect with `assume_straight_pages=False` and/or `straighten_pages=True`.
+
+.. code:: python3
+
+    from doctr.model import ocr_predictor
+    model = ocr_predictor(pretrained=True, assume_straight_pages=False, disable_crop_orientation=True)
+
+
 * Add a hook to the `ocr_predictor` to manipulate the location predictions before the crops are passed to the recognition model.
 
 .. code:: python3

doctr/models/classification/predictor/pytorch.py

@@ -3,7 +3,7 @@
 # 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
+from typing import List, Optional, Union
 
 import numpy as np
 import torch
@@ -27,12 +27,12 @@ class OrientationPredictor(nn.Module):
 
     def __init__(
         self,
-        pre_processor: PreProcessor,
-        model: nn.Module,
+        pre_processor: Optional[PreProcessor],
+        model: Optional[nn.Module],
     ) -> None:
         super().__init__()
-        self.pre_processor = pre_processor
-        self.model = model.eval()
+        self.pre_processor = pre_processor if isinstance(pre_processor, PreProcessor) else None
+        self.model = model.eval() if isinstance(model, nn.Module) else None
 
     @torch.inference_mode()
     def forward(
@@ -43,12 +43,16 @@ def forward(
         if any(input.ndim != 3 for input in inputs):
             raise ValueError("incorrect input shape: all inputs are expected to be multi-channel 2D images.")
 
+        if self.model is None or self.pre_processor is None:
+            # predictor is disabled
+            return [[0] * len(inputs), [0] * len(inputs), [1.0] * len(inputs)]
+
         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]
+        predicted_batches = [self.model(batch) for batch in processed_batches]  # type: ignore[misc]
         # confidence
         probs = [
             torch.max(torch.softmax(batch, dim=1), dim=1).values.cpu().detach().numpy() for batch in predicted_batches
@@ -57,7 +61,7 @@ def forward(
         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]
+        classes = [int(self.model.cfg["classes"][idx]) for idx in class_idxs]  # type: ignore[union-attr]
         confs = [round(float(p), 2) for prob in probs for p in prob]
 
         return [class_idxs, classes, confs]

doctr/models/classification/predictor/tensorflow.py

@@ -3,7 +3,7 @@
 # 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
+from typing import List, Optional, Union
 
 import numpy as np
 import tensorflow as tf
@@ -29,11 +29,11 @@ class OrientationPredictor(NestedObject):
 
     def __init__(
         self,
-        pre_processor: PreProcessor,
-        model: keras.Model,
+        pre_processor: Optional[PreProcessor],
+        model: Optional[keras.Model],
     ) -> None:
-        self.pre_processor = pre_processor
-        self.model = model
+        self.pre_processor = pre_processor if isinstance(pre_processor, PreProcessor) else None
+        self.model = model if isinstance(model, keras.Model) else None
 
     def __call__(
         self,
@@ -43,6 +43,10 @@ def __call__(
         if any(input.ndim != 3 for input in inputs):
             raise ValueError("incorrect input shape: all inputs are expected to be multi-channel 2D images.")
 
+        if self.model is None or self.pre_processor is None:
+            # predictor is disabled
+            return [[0] * len(inputs), [0] * len(inputs), [1.0] * len(inputs)]
+
         processed_batches = self.pre_processor(inputs)
         predicted_batches = [self.model(batch, training=False) for batch in processed_batches]
 

doctr/models/classification/zoo.py

@@ -34,7 +34,13 @@
 ORIENTATION_ARCHS: List[str] = ["mobilenet_v3_small_crop_orientation", "mobilenet_v3_small_page_orientation"]
 
 
-def _orientation_predictor(arch: Any, pretrained: bool, model_type: str, **kwargs: Any) -> OrientationPredictor:
+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}'")

doctr/models/kie_predictor/base.py

@@ -46,4 +46,8 @@ def __init__(
             assume_straight_pages, straighten_pages, preserve_aspect_ratio, symmetric_pad, detect_orientation, **kwargs
         )
 
+        # Remove the following arguments from kwargs after initialization of the parent class
+        kwargs.pop("disable_page_orientation", None)
+        kwargs.pop("disable_crop_orientation", None)
+
         self.doc_builder: KIEDocumentBuilder = KIEDocumentBuilder(**kwargs)

doctr/models/kie_predictor/pytorch.py

@@ -129,6 +129,7 @@ def forward(
                 dict_loc_preds[class_name],
                 channels_last=channels_last,
                 assume_straight_pages=self.assume_straight_pages,
+                assume_horizontal=self._page_orientation_disabled,
             )
         # Rectify crop orientation
         crop_orientations: Any = {}

doctr/models/kie_predictor/tensorflow.py

@@ -122,7 +122,11 @@ def __call__(
         crops = {}
         for class_name in dict_loc_preds.keys():
             crops[class_name], dict_loc_preds[class_name] = self._prepare_crops(
-                pages, dict_loc_preds[class_name], channels_last=True, assume_straight_pages=self.assume_straight_pages
+                pages,
+                dict_loc_preds[class_name],
+                channels_last=True,
+                assume_straight_pages=self.assume_straight_pages,
+                assume_horizontal=self._page_orientation_disabled,
             )
 
         # Rectify crop orientation

doctr/models/predictor/base.py

@@ -48,9 +48,15 @@ def __init__(
     ) -> None:
         self.assume_straight_pages = assume_straight_pages
         self.straighten_pages = straighten_pages
-        self.crop_orientation_predictor = None if assume_straight_pages else crop_orientation_predictor(pretrained=True)
+        self._page_orientation_disabled = kwargs.pop("disable_page_orientation", False)
+        self._crop_orientation_disabled = kwargs.pop("disable_crop_orientation", False)
+        self.crop_orientation_predictor = (
+            None
+            if assume_straight_pages
+            else crop_orientation_predictor(pretrained=True, disabled=self._crop_orientation_disabled)
+        )
         self.page_orientation_predictor = (
-            page_orientation_predictor(pretrained=True)
+            page_orientation_predictor(pretrained=True, disabled=self._page_orientation_disabled)
             if detect_orientation or straighten_pages or not assume_straight_pages
             else None
         )
@@ -112,13 +118,18 @@ def _generate_crops(
         loc_preds: List[np.ndarray],
         channels_last: bool,
         assume_straight_pages: bool = False,
+        assume_horizontal: bool = False,
     ) -> List[List[np.ndarray]]:
-        extraction_fn = extract_crops if assume_straight_pages else extract_rcrops
-
-        crops = [
-            extraction_fn(page, _boxes[:, :4], channels_last=channels_last)  # type: ignore[operator]
-            for page, _boxes in zip(pages, loc_preds)
-        ]
+        if assume_straight_pages:
+            crops = [
+                extract_crops(page, _boxes[:, :4], channels_last=channels_last)
+                for page, _boxes in zip(pages, loc_preds)
+            ]
+        else:
+            crops = [
+                extract_rcrops(page, _boxes[:, :4], channels_last=channels_last, assume_horizontal=assume_horizontal)
+                for page, _boxes in zip(pages, loc_preds)
+            ]
         return crops
 
     @staticmethod
@@ -127,8 +138,9 @@ def _prepare_crops(
         loc_preds: List[np.ndarray],
         channels_last: bool,
         assume_straight_pages: bool = False,
+        assume_horizontal: bool = False,
     ) -> Tuple[List[List[np.ndarray]], List[np.ndarray]]:
-        crops = _OCRPredictor._generate_crops(pages, loc_preds, channels_last, assume_straight_pages)
+        crops = _OCRPredictor._generate_crops(pages, loc_preds, channels_last, assume_straight_pages, assume_horizontal)
 
         # Avoid sending zero-sized crops
         is_kept = [[all(s > 0 for s in crop.shape) for crop in page_crops] for page_crops in crops]

doctr/models/predictor/pytorch.py

@@ -123,6 +123,7 @@ def forward(
             loc_preds,
             channels_last=channels_last,
             assume_straight_pages=self.assume_straight_pages,
+            assume_horizontal=self._page_orientation_disabled,
         )
         # Rectify crop orientation and get crop orientation predictions
         crop_orientations: Any = []

doctr/models/predictor/tensorflow.py

@@ -116,7 +116,11 @@ def __call__(
 
         # Crop images
         crops, loc_preds = self._prepare_crops(
-            pages, loc_preds, channels_last=True, assume_straight_pages=self.assume_straight_pages
+            pages,
+            loc_preds,
+            channels_last=True,
+            assume_straight_pages=self.assume_straight_pages,
+            assume_horizontal=self._page_orientation_disabled,
         )
         # Rectify crop orientation and get crop orientation predictions
         crop_orientations: Any = []