Formatting codebase with black (#178)

sahi/annotation.py

@@ -111,11 +111,7 @@ def __repr__(self):
 class Mask:
     @classmethod
     def from_float_mask(
-        cls,
-        mask,
-        full_shape=None,
-        mask_threshold: float = 0.5,
-        shift_amount: list = [0, 0],
+        cls, mask, full_shape=None, mask_threshold: float = 0.5, shift_amount: list = [0, 0],
     ):
         """
         Args:
@@ -130,18 +126,11 @@ def from_float_mask(
                 Size of the full image after shifting, should be in the form of [height, width]
         """
         bool_mask = mask > mask_threshold
-        return cls(
-            bool_mask=bool_mask,
-            shift_amount=shift_amount,
-            full_shape=full_shape,
-        )
+        return cls(bool_mask=bool_mask, shift_amount=shift_amount, full_shape=full_shape,)
 
     @classmethod
     def from_coco_segmentation(
-        cls,
-        segmentation,
-        full_shape=None,
-        shift_amount: list = [0, 0],
+        cls, segmentation, full_shape=None, shift_amount: list = [0, 0],
     ):
         """
         Init Mask from coco segmentation representation.
@@ -163,17 +152,10 @@ def from_coco_segmentation(
         assert full_shape is not None, "full_shape must be provided"
 
         bool_mask = get_bool_mask_from_coco_segmentation(segmentation, height=full_shape[0], width=full_shape[1])
-        return cls(
-            bool_mask=bool_mask,
-            shift_amount=shift_amount,
-            full_shape=full_shape,
-        )
+        return cls(bool_mask=bool_mask, shift_amount=shift_amount, full_shape=full_shape,)
 
     def __init__(
-        self,
-        bool_mask=None,
-        full_shape=None,
-        shift_amount: list = [0, 0],
+        self, bool_mask=None, full_shape=None, shift_amount: list = [0, 0],
     ):
         """
         Args:
@@ -234,14 +216,7 @@ def get_shifted_mask(self):
         # Confirm full_shape is specified
         assert (self.full_shape_height is not None) and (self.full_shape_width is not None), "full_shape is None"
         # init full mask
-        mask_fullsized = np.full(
-            (
-                self.full_shape_height,
-                self.full_shape_width,
-            ),
-            0,
-            dtype="float32",
-        )
+        mask_fullsized = np.full((self.full_shape_height, self.full_shape_width,), 0, dtype="float32",)
 
         # arrange starting ending indexes
         starting_pixel = [self.shift_x, self.shift_y]
@@ -255,11 +230,7 @@ def get_shifted_mask(self):
             : ending_pixel[1] - starting_pixel[1], : ending_pixel[0] - starting_pixel[0]
         ]
 
-        return Mask(
-            mask_fullsized,
-            shift_amount=[0, 0],
-            full_shape=self.full_shape,
-        )
+        return Mask(mask_fullsized, shift_amount=[0, 0], full_shape=self.full_shape,)
 
     def to_coco_segmentation(self):
         """
@@ -470,10 +441,7 @@ def from_shapely_annotation(
 
     @classmethod
     def from_imantics_annotation(
-        cls,
-        annotation,
-        shift_amount: Optional[List[int]] = [0, 0],
-        full_shape: Optional[List[int]] = None,
+        cls, annotation, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
     ):
         """
         Creates ObjectAnnotation from imantics.annotation.Annotation
@@ -527,18 +495,11 @@ def __init__(
             self.mask = None
             self.bbox = BoundingBox(bbox, shift_amount)
         else:
-            self.mask = Mask(
-                bool_mask=bool_mask,
-                shift_amount=shift_amount,
-                full_shape=full_shape,
-            )
+            self.mask = Mask(bool_mask=bool_mask, shift_amount=shift_amount, full_shape=full_shape,)
             bbox = get_bbox_from_bool_mask(bool_mask)
             self.bbox = BoundingBox(bbox, shift_amount)
         category_name = category_name if category_name else str(category_id)
-        self.category = Category(
-            id=category_id,
-            name=category_name,
-        )
+        self.category = Category(id=category_id, name=category_name,)
 
         self.merged = None
 
@@ -554,9 +515,7 @@ def to_coco_annotation(self):
             )
         else:
             coco_annotation = CocoAnnotation.from_coco_bbox(
-                bbox=self.bbox.to_coco_bbox(),
-                category_id=self.category.id,
-                category_name=self.category.name,
+                bbox=self.bbox.to_coco_bbox(), category_id=self.category.id, category_name=self.category.name,
             )
         return coco_annotation
 
@@ -573,10 +532,7 @@ def to_coco_prediction(self):
             )
         else:
             coco_prediction = CocoPrediction.from_coco_bbox(
-                bbox=self.bbox.to_coco_bbox(),
-                category_id=self.category.id,
-                category_name=self.category.name,
-                score=1,
+                bbox=self.bbox.to_coco_bbox(), category_id=self.category.id, category_name=self.category.name, score=1,
             )
         return coco_prediction
 
@@ -589,9 +545,7 @@ def to_shapely_annotation(self):
                 segmentation=self.mask.to_coco_segmentation(),
             )
         else:
-            shapely_annotation = ShapelyAnnotation.from_coco_bbox(
-                bbox=self.bbox.to_coco_bbox(),
-            )
+            shapely_annotation = ShapelyAnnotation.from_coco_bbox(bbox=self.bbox.to_coco_bbox(),)
         return shapely_annotation
 
     def to_imantics_annotation(self):

sahi/model.py

@@ -89,9 +89,7 @@ def perform_inference(self, image: np.ndarray, image_size: int = None):
         NotImplementedError()
 
     def _create_object_prediction_list_from_original_predictions(
-        self,
-        shift_amount: Optional[List[int]] = [0, 0],
-        full_shape: Optional[List[int]] = None,
+        self, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
     ):
         """
         This function should be implemented in a way that self._original_predictions should
@@ -119,9 +117,7 @@ def _apply_category_remapping(self):
             object_prediction.category.id = new_category_id_int
 
     def convert_original_predictions(
-        self,
-        shift_amount: Optional[List[int]] = [0, 0],
-        full_shape: Optional[List[int]] = None,
+        self, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
     ):
         """
         Converts original predictions of the detection model to a list of
@@ -134,8 +130,7 @@ def convert_original_predictions(
                 Size of the full image after shifting, should be in the form of [height, width]
         """
         self._create_object_prediction_list_from_original_predictions(
-            shift_amount=shift_amount,
-            full_shape=full_shape,
+            shift_amount=shift_amount, full_shape=full_shape,
         )
         if self.category_remapping:
             self._apply_category_remapping()
@@ -148,9 +143,7 @@ def object_prediction_list(self):
     def original_predictions(self):
         return self._original_predictions
 
-    def _create_predictions_from_object_prediction_list(
-        object_prediction_list: List[ObjectPrediction],
-    ):
+    def _create_predictions_from_object_prediction_list(object_prediction_list: List[ObjectPrediction],):
         """
         This function should be implemented in a way that it converts a list of
         prediction.ObjectPrediction instance to detection model's original prediction format.
@@ -179,11 +172,7 @@ def load_model(self):
         from mmdet.apis import init_detector
 
         # set model
-        model = init_detector(
-            config=self.config_path,
-            checkpoint=self.model_path,
-            device=self.device,
-        )
+        model = init_detector(config=self.config_path, checkpoint=self.model_path, device=self.device,)
         self.model = model
 
         # set category_mapping
@@ -250,9 +239,7 @@ def category_names(self):
             return self.model.CLASSES
 
     def _create_object_prediction_list_from_original_predictions(
-        self,
-        shift_amount: Optional[List[int]] = [0, 0],
-        full_shape: Optional[List[int]] = None,
+        self, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
     ):
         """
         self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
@@ -307,8 +294,7 @@ def _create_object_prediction_list_from_original_predictions(
         self._object_prediction_list = object_prediction_list
 
     def _create_original_predictions_from_object_prediction_list(
-        self,
-        object_prediction_list: List[ObjectPrediction],
+        self, object_prediction_list: List[ObjectPrediction],
     ):
         """
         Converts a list of prediction.ObjectPrediction instance to detection model's original prediction format.
@@ -426,9 +412,7 @@ def category_names(self):
         return self.model.names
 
     def _create_object_prediction_list_from_original_predictions(
-        self,
-        shift_amount: Optional[List[int]] = [0, 0],
-        full_shape: Optional[List[int]] = None,
+        self, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
     ):
         """
         self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
@@ -472,8 +456,7 @@ def _create_object_prediction_list_from_original_predictions(
         self._object_prediction_list = object_prediction_list
 
     def _create_original_predictions_from_object_prediction_list(
-        self,
-        object_prediction_list: List[ObjectPrediction],
+        self, object_prediction_list: List[ObjectPrediction],
     ):
         """
         Converts a list of prediction.ObjectPrediction instance to detection model's original

sahi/postprocess/combine.py

@@ -45,10 +45,7 @@ class PostprocessPredictions:
     """Combines predictions using NMS elimination utilizing provided match metric ('IOU' or 'IOS')"""
 
     def __init__(
-        self,
-        match_threshold: float = 0.5,
-        match_metric: str = "IOU",
-        class_agnostic: bool = True,
+        self, match_threshold: float = 0.5, match_metric: str = "IOU", class_agnostic: bool = True,
     ):
         self.match_threshold = match_threshold
         self.class_agnostic = class_agnostic
@@ -100,8 +97,7 @@ def __call__(self):
 
 class NMSPostprocess(PostprocessPredictions):
     def __call__(
-        self,
-        object_predictions: List[ObjectPrediction],
+        self, object_predictions: List[ObjectPrediction],
     ):
         source_object_predictions: List[ObjectPrediction] = copy.deepcopy(object_predictions)
         selected_object_predictions: List[ObjectPrediction] = []
@@ -124,8 +120,7 @@ def __call__(
 
 class UnionMergePostprocess(PostprocessPredictions):
     def __call__(
-        self,
-        object_predictions: List[ObjectPrediction],
+        self, object_predictions: List[ObjectPrediction],
     ):
         source_object_predictions: List[ObjectPrediction] = copy.deepcopy(object_predictions)
         selected_object_predictions: List[ObjectPrediction] = []
@@ -150,11 +145,7 @@ def __call__(
             selected_object_predictions.append(selected_object_prediction)
         return selected_object_predictions
 
-    def _merge_object_prediction_pair(
-        self,
-        pred1: ObjectPrediction,
-        pred2: ObjectPrediction,
-    ) -> ObjectPrediction:
+    def _merge_object_prediction_pair(self, pred1: ObjectPrediction, pred2: ObjectPrediction,) -> ObjectPrediction:
         shift_amount = pred1.bbox.shift_amount
         merged_bbox: BoundingBox = self._get_merged_bbox(pred1, pred2)
         merged_score: float = self._get_merged_score(pred1, pred2)
@@ -191,10 +182,7 @@ def _get_merged_bbox(pred1: ObjectPrediction, pred2: ObjectPrediction) -> Boundi
         return bbox
 
     @staticmethod
-    def _get_merged_score(
-        pred1: ObjectPrediction,
-        pred2: ObjectPrediction,
-    ) -> float:
+    def _get_merged_score(pred1: ObjectPrediction, pred2: ObjectPrediction,) -> float:
         scores: List[float] = [pred.score.value for pred in (pred1, pred2)]
         return max(scores)
 
@@ -203,8 +191,4 @@ def _get_merged_mask(pred1: ObjectPrediction, pred2: ObjectPrediction) -> Mask:
         mask1 = pred1.mask
         mask2 = pred2.mask
         union_mask = np.logical_or(mask1.bool_mask, mask2.bool_mask)
-        return Mask(
-            bool_mask=union_mask,
-            full_shape=mask1.full_shape,
-            shift_amount=mask1.shift_amount,
-        )
+        return Mask(bool_mask=union_mask, full_shape=mask1.full_shape, shift_amount=mask1.shift_amount,)

sahi/postprocess/legacy/match.py

@@ -46,9 +46,7 @@ class PredictionMatcher:
     """
 
     def __init__(
-        self,
-        threshold: float = 0.5,
-        scorer: Callable[[BoxArray, BoxArray], float] = box_ios,
+        self, threshold: float = 0.5, scorer: Callable[[BoxArray, BoxArray], float] = box_ios,
     ):
         self._threshold = threshold
         self._scorer = scorer

sahi/postprocess/legacy/merge.py

@@ -77,8 +77,7 @@ def merge_batch(
         """
         if merge_type not in ["merge", "ensemble"]:
             raise ValueError(
-                'Unknown merge type. Supported types are ["merge", "ensemble"], got type: ',
-                merge_type,
+                'Unknown merge type. Supported types are ["merge", "ensemble"], got type: ', merge_type,
             )
 
         unions = matcher.find_matched_predictions(predictions, ignore_class_label)
@@ -116,11 +115,7 @@ def _store_merging_info(count, prediction, merge_type):
         else:
             prediction.merged = False
 
-    def _merge_pair(
-        self,
-        pred1: ObjectPrediction,
-        pred2: ObjectPrediction,
-    ) -> ObjectPrediction:
+    def _merge_pair(self, pred1: ObjectPrediction, pred2: ObjectPrediction,) -> ObjectPrediction:
         box1 = extract_box(pred1)
         box2 = extract_box(pred2)
         merged_box = list(self._merge_box(box1, box2))
@@ -159,11 +154,7 @@ def _assert_equal_labels(pred1: ObjectPrediction, pred2: ObjectPrediction):
     def _merge_box(self, box1: BoxArray, box2: BoxArray) -> BoxArray:
         return self._box_merger(box1, box2)
 
-    def _merge_score(
-        self,
-        pred1: ObjectPrediction,
-        pred2: ObjectPrediction,
-    ) -> float:
+    def _merge_score(self, pred1: ObjectPrediction, pred2: ObjectPrediction,) -> float:
         scores = [pred.score.value for pred in (pred1, pred2)]
         policy = self._score_merging_method
         if policy == ScoreMergingPolicy.SMALLER_SCORE:
@@ -185,11 +176,7 @@ def _merge_mask(pred1: ObjectPrediction, pred2: ObjectPrediction) -> Mask:
         mask1 = pred1.mask
         mask2 = pred2.mask
         union_mask = np.logical_or(mask1.bool_mask, mask2.bool_mask)
-        return Mask(
-            bool_mask=union_mask,
-            full_shape=mask1.full_shape,
-            shift_amount=mask1.shift_amount,
-        )
+        return Mask(bool_mask=union_mask, full_shape=mask1.full_shape, shift_amount=mask1.shift_amount,)
 
     def _validate_box_merger(self, box_merger: Callable):
         if box_merger.__name__ not in self.BOX_MERGERS: