annotation type mal notebook

Author: Matt Sokoloff

examples/annotation_types/annotation_type_basics.ipynb

@@ -1,3 +1,4 @@
+from labelbox.data.ontology import get_classifications, get_tools
 import logging
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Callable, Generator, Iterable, Union
@@ -110,6 +111,16 @@ def add_url_to_data(self, signer, max_concurrency=20) -> "LabelList":
             ...
         return self
 
+    def get_ontology(self) -> ontology.OntologyBuilder:
+        classifications = []
+        tools = []
+        for label in self._data:
+            tools = get_tools(label.object_annotations(), tools)
+            classifications = get_classifications(
+                label.classification_annotations(), classifications)
+        return ontology.OntologyBuilder(tools=tools,
+                                        classifications=classifications)
+
     def _ensure_unique_external_ids(self) -> None:
         external_ids = set()
         for label in self._data:
@@ -122,6 +133,9 @@ def _ensure_unique_external_ids(self) -> None:
                     )
             external_ids.add(label.data.external_id)
 
+    def append(self, label: Label):
+        self._data.append(label)
+
     def __iter__(self) -> "LabelList":
         self._index = 0
         return self

examples/annotation_types/converters.ipynb

@@ -20,6 +20,15 @@ class RasterData(BaseData):
     url: Optional[str] = None
     arr: Optional[TypedArray[Literal['uint8']]] = None
 
+    @classmethod
+    def from_2D_arr(cls, arr: TypedArray[Literal['uint8']], **kwargs):
+        if len(arr.shape):
+            raise ValueError(
+                f"Found array with shape {arr.shape}. Expected two dimensions ([W,H])"
+            )
+        arr = np.stack((arr,) * 3, axis=-1)
+        return cls(arr=arr, **kwargs)
+
     def bytes_to_np(self, image_bytes: bytes) -> np.ndarray:
         """
         Converts image bytes to a numpy array
@@ -38,9 +47,9 @@ def np_to_bytes(self, arr: np.ndarray) -> bytes:
         Returns:
             png encoded bytes
         """
-        if len(arr.shape) not in [2, 3]:
-            raise ValueError("unsupported image format")
-
+        if len(arr.shape) != 3:
+            raise ValueError("unsupported image format. Must be 3D ([H,W,C])."
+                             "Use RasterData.from_2D_arr to construct from 2D")
         if arr.dtype != np.uint8:
             raise TypeError(f"image data type must be uint8. Found {arr.dtype}")
 
@@ -125,12 +134,19 @@ def validate_args(cls, values):
                 raise TypeError(
                     "Numpy array representing segmentation mask must be np.uint8"
                 )
-            elif len(arr.shape) not in [2, 3]:
-                raise TypeError(
-                    f"Numpy array must have 2 or 3 dims. Found shape {arr.shape}"
-                )
+            elif len(arr.shape) != 3:
+                raise ValueError(
+                    "unsupported image format. Must be 3D ([H,W,C])."
+                    "Use RasterData.from_2D_arr to construct from 2D")
         return values
 
+    def __repr__(self) -> str:
+        symbol_or_none = lambda data: '...' if data is not None else None
+        return  f"RasterData(im_bytes={symbol_or_none(self.im_bytes)}," \
+                f"file_path={self.file_path}," \
+                f"url={self.url}," \
+                f"arr={symbol_or_none(self.arr)})"
+
     class Config:
         # Required for sharing references
         copy_on_model_validation = False

examples/annotation_types/label_containers.ipynb

@@ -82,6 +82,11 @@ def validate_date(cls, values):
                 "One of `file_path`, `text`, `uid`, or `url` required.")
         return values
 
+    def __repr__(self) -> str:
+        return  f"TextData(file_path={self.file_path}," \
+                f"text={self.text[:30] + '...' if self.text is not None else None}," \
+                f"url={self.url})"
+
     class config:
         # Required for discriminating between data types
         extra = 'forbid'

examples/annotation_types/mal_with_annotation_types.ipynb

@@ -156,6 +156,11 @@ def validate_data(cls, values):
                 "One of `file_path`, `frames`, `uid`, or `url` required.")
         return values
 
+    def __repr__(self) -> str:
+        return  f"TextData(file_path={self.file_path}," \
+                f"frames={'...' if self.frames is not None else None}," \
+                f"url={self.url})"
+
     class Config:
         # Required for discriminating between data types
         extra = 'forbid'

labelbox/data/annotation_types/collection.py

@@ -20,9 +20,9 @@ def raster(self,
                height: int,
                width: int,
                thickness=1,
-               color=255) -> np.ndarray:
+               color=(255, 255, 255)) -> np.ndarray:
         """
-        Draw the line onto a 2d mask
+        Draw the line onto a 3d mask
 
         Args:
             height (int): height of the mask
@@ -32,8 +32,7 @@ def raster(self,
         Returns:
             numpy array representing the mask with the line drawn on it.
         """
-
-        canvas = np.zeros((height, width), dtype=np.uint8)
+        canvas = np.zeros((height, width, 3), dtype=np.uint8)
         pts = np.array(self.geometry['coordinates']).astype(np.int32)
         return cv2.polylines(canvas,
                              pts,

labelbox/data/annotation_types/data/raster.py

@@ -14,7 +14,7 @@ class Mask(Geometry):
     # Raster data can be shared across multiple masks... or not
     mask: RasterData
     # RGB or Grayscale
-    color: Union[int, Tuple[int, int, int]]
+    color: Tuple[int, int, int]
 
     @property
     def geometry(self):
@@ -40,17 +40,12 @@ def raster(self,
             np.ndarray representing only this object
         """
         mask = self.mask.data
-        if len(mask.shape) == 2:
-            mask = np.expand_dims(mask, axis=-1)
         mask = np.alltrue(mask == self.color, axis=2).astype(np.uint8)
         if height is not None or width is not None:
             mask = cv2.resize(mask,
                               (width or mask.shape[1], height or mask.shape[0]))
-
         if binary:
             return mask
-        elif isinstance(self.color, int):
-            return mask * self.color
         else:
             color_image = np.zeros((mask.shape[0], mask.shape[1], 3),
                                    dtype=np.uint8)