format fix bug

Author: Matt Sokoloff

labelbox/data/serialization/coco/converter.py

@@ -9,7 +9,7 @@
 def create_path_if_not_exists(path: str):
     if not os.path.exists(path):
         os.makedirs(path)
-    elif os.path.listdir(path):
+    elif os.listdir(path):
         raise ValueError(f"Directory `{path}`` must be empty.")
 
 
@@ -45,8 +45,10 @@ def serialize_instances(labels: LabelCollection,
         return CocoInstanceDataset.from_common(labels=labels,
                                                image_root=image_root).dict()
 
-    def serialize_panoptic(labels: LabelCollection, image_root: str,
-                           mask_root: str, all_stuff: bool = False) -> Dict[str, Any]:
+    def serialize_panoptic(labels: LabelCollection,
+                           image_root: str,
+                           mask_root: str,
+                           all_stuff: bool = False) -> Dict[str, Any]:
         """
         Convert a Labelbox LabelCollection into an mscoco dataset.
         This function will only convert masks, polygons, and rectangles.
@@ -66,7 +68,8 @@ def serialize_panoptic(labels: LabelCollection, image_root: str,
         create_path_if_not_exists(mask_root)
         return CocoPanopticDataset.from_common(labels=labels,
                                                image_root=image_root,
-                                               mask_root=mask_root, all_stuff=all_stuff).dict()
+                                               mask_root=mask_root,
+                                               all_stuff=all_stuff).dict()
 
     @classmethod
     def deserialize_panoptic(cls, json_data: Dict[str, Any], image_root: str,

labelbox/data/serialization/coco/instance_dataset.py

@@ -82,7 +82,10 @@ def segmentations_to_common(class_annotations, class_name):
     return annotations
 
 
-def process_label(label: Label, idx, image_root, max_annotations_per_image = 10000):
+def process_label(label: Label,
+                  idx,
+                  image_root,
+                  max_annotations_per_image=10000):
     annot_idx = idx * max_annotations_per_image
     image_id = get_image_id(label, idx)
     image = get_image(label, image_root, image_id)
@@ -133,14 +136,16 @@ def from_common(cls, labels: LabelCollection, image_root):
                 all_coco_annotations.extend(annotations)
                 coco_categories.update(categories)
 
-        category_mapping = {category_id : idx + 1 for idx, category_id in enumerate(coco_categories.values())}
-        categories=[
-                Categories(id=category_mapping[idx],
-                            name=name,
-                            supercategory='all',
-       isthing=1)
-                                       for name, idx in coco_categories.items()
-                                   ]
+        category_mapping = {
+            category_id: idx + 1
+            for idx, category_id in enumerate(coco_categories.values())
+        }
+        categories = [
+            Categories(id=category_mapping[idx],
+                       name=name,
+                       supercategory='all',
+                       isthing=1) for name, idx in coco_categories.items()
+        ]
         for annot in all_coco_annotations:
             annot.category_id = category_mapping[annot.category_id]
 
@@ -149,7 +154,6 @@ def from_common(cls, labels: LabelCollection, image_root):
                                    annotations=all_coco_annotations,
                                    categories=categories)
 
-
     def to_common(self, image_root):
         category_lookup = {
             category.id: category for category in self.categories

labelbox/data/serialization/coco/panoptic_dataset.py

@@ -33,9 +33,10 @@ def vector_to_coco_segment_info(canvas: np.ndarray,
                        bbox=[xmin, ymin, xmax - xmin, ymax - ymin]), canvas
 
 
-def mask_to_coco_segment_info(canvas: np.ndarray, annotation, annotation_idx: int, category_id):
+def mask_to_coco_segment_info(canvas: np.ndarray, annotation,
+                              annotation_idx: int, category_id):
     color = id_to_rgb(annotation_idx)
-    mask = annotation.value.draw(color = color)
+    mask = annotation.value.draw(color=color)
     shapely = annotation.value.shapely
     xmin, ymin, xmax, ymax = shapely.bounds
     canvas = np.where(canvas == (0, 0, 0), mask, canvas)
@@ -45,7 +46,11 @@ def mask_to_coco_segment_info(canvas: np.ndarray, annotation, annotation_idx: in
                        bbox=[xmin, ymin, xmax - xmin, ymax - ymin]), canvas
 
 
-def process_label(label: Label, idx: Union[int, str], image_root, mask_root, all_stuff = False):
+def process_label(label: Label,
+                  idx: Union[int, str],
+                  image_root,
+                  mask_root,
+                  all_stuff=False):
     """
     Masks become stuff
     Polygon and rectangle become thing
@@ -64,17 +69,19 @@ def process_label(label: Label, idx: Union[int, str], image_root, mask_root, all
             categories[annotation.name] = hash_category_name(annotation.name)
             if isinstance(annotation.value, Mask):
                 segment, canvas = (mask_to_coco_segment_info(
-                    canvas, annotation, class_idx + 1 ,categories[annotation.name]))
+                    canvas, annotation, class_idx + 1,
+                    categories[annotation.name]))
                 segments.append(segment)
                 is_thing[annotation.name] = 0
 
             elif isinstance(annotation.value, (Polygon, Rectangle)):
                 segment, canvas = vector_to_coco_segment_info(
-                        canvas,
-                        annotation,
-                        annotation_idx=(class_idx if all_stuff else annotation_idx) + 1,
-                        image=image,
-                        category_id=categories[annotation.name])
+                    canvas,
+                    annotation,
+                    annotation_idx=(class_idx if all_stuff else annotation_idx)
+                    + 1,
+                    image=image,
+                    category_id=categories[annotation.name])
                 segments.append(segment)
                 is_thing[annotation.name] = 1 - int(all_stuff)
 
@@ -94,15 +101,16 @@ class CocoPanopticDataset(BaseModel):
     categories: List[Categories]
 
     @classmethod
-    def from_common(cls, labels: LabelCollection, image_root, mask_root, all_stuff):
+    def from_common(cls, labels: LabelCollection, image_root, mask_root,
+                    all_stuff):
         all_coco_annotations = []
         coco_categories = {}
         coco_things = {}
         images = []
         with ProcessPoolExecutor(max_workers=8) as exc:
             futures = [
-                exc.submit(process_label, label, idx, image_root, mask_root, all_stuff)
-                for idx, label in enumerate(labels)
+                exc.submit(process_label, label, idx, image_root, mask_root,
+                           all_stuff) for idx, label in enumerate(labels)
             ]
             for future in tqdm(as_completed(futures)):
                 image, annotation, categories, things = future.result()
@@ -111,16 +119,17 @@ def from_common(cls, labels: LabelCollection, image_root, mask_root, all_stuff):
                 coco_categories.update(categories)
                 coco_things.update(things)
 
-
-        category_mapping = {category_id : idx + 1 for idx, category_id in enumerate(coco_categories.values())}
-        categories=[
-                                       Categories(id=category_mapping[idx],
-                                                  name=name,
-                                                  supercategory='all',
-                                                  isthing=coco_things.get(
-                                                      name, 1))
-                                       for name, idx in coco_categories.items()
-                                   ]
+        category_mapping = {
+            category_id: idx + 1
+            for idx, category_id in enumerate(coco_categories.values())
+        }
+        categories = [
+            Categories(id=category_mapping[idx],
+                       name=name,
+                       supercategory='all',
+                       isthing=coco_things.get(name, 1))
+            for name, idx in coco_categories.items()
+        ]
 
         for annot in all_coco_annotations:
             for segment in annot.segments_info:

labelbox/data/serialization/ndjson/objects.py

@@ -166,7 +166,9 @@ def from_common(cls, text_entity: TextEntity,
                    uuid=extra.get('uuid'),
                    classifications=classifications)
 
+
 class NDObject:
+
     @staticmethod
     def to_common(annotation: "NDObjectType") -> ObjectAnnotation:
         common_annotation = annotation.to_common()