rename RasterData -> ImageData

Author: Matt Sokoloff

CHANGELOG.md

@@ -2,7 +2,7 @@
 
 # Version 3.0.0-rc2 (2021-08-09)
 ## Updates
-* Rename `data` property of TextData, RasterData, and VideoData types to `value`.
+* Rename `data` property of TextData, ImageData, and VideoData types to `value`.
 * Decrease wait time between updates for `BulkImportRequest.wait_until_done()`
 * Organization is no longer used to create the LFO in `Project.setup()`
 

examples/annotation_types/basics.ipynb

@@ -55,7 +55,7 @@
    "source": [
     "from labelbox.data.annotation_types import (\n",
     "    Label,\n",
-    "    RasterData,\n",
+    "    ImageData,\n",
     "    LabelList,\n",
     "    TextData,\n",
     "    VideoData,\n",
@@ -176,7 +176,7 @@
    "outputs": [],
    "source": [
     "labels = [Label(\n",
-    "    data = RasterData(url = \"http://my-img.jpg\"),\n",
+    "    data = ImageData(url = \"http://my-img.jpg\"),\n",
     "    annotations = [\n",
     "        ObjectAnnotation(\n",
     "            value = Point(x = 10, y = 10),\n",
@@ -215,7 +215,7 @@
     "* There are three classes to represent data\n",
     "    * TextData\n",
     "    * VideoData\n",
-    "    * RasterData\n",
+    "    * ImageData\n",
     "* The data objects can be constructed from various representations\n",
     "    - remote (url, uri)\n",
     "    - disk ( file path)\n",
@@ -229,7 +229,7 @@
    "id": "associate-cutting",
    "metadata": {},
    "source": [
-    "### RasterData"
+    "### ImageData"
    ]
   },
   {
@@ -240,11 +240,11 @@
    "outputs": [],
    "source": [
     "# Data can be instantiated with any of the following\n",
-    "image = RasterData(im_bytes = b'bytes')\n",
-    "image = RasterData(arr = np.zeros((10,10,3)).astype(np.uint8))\n",
-    "image = RasterData(file_path = '/tmp/img.jpg')\n",
+    "image = ImageData(im_bytes = b'bytes')\n",
+    "image = ImageData(arr = np.zeros((10,10,3)).astype(np.uint8))\n",
+    "image = ImageData(file_path = '/tmp/img.jpg')\n",
     "image_url = \"https://picsum.photos/id/1003/200/300\"\n",
-    "image = RasterData(url = image_url)\n",
+    "image = ImageData(url = image_url)\n",
     "# All of these can access the numpy representation the same way:\n",
     "np_data = image.value\n",
     "\n",
@@ -342,7 +342,7 @@
    "outputs": [],
    "source": [
     "td = TextData(text = \"some text\", uid = \"ckrey6o07000008l50uk2gcr3\", external_id = \"my_text_data_row\")\n",
-    "rd = RasterData(url = image_url, uid = \"ckrey7te2000108l5hl8564dr\", external_id = \"my_raster_data_row\")\n",
+    "rd = ImageData(url = image_url, uid = \"ckrey7te2000108l5hl8564dr\", external_id = \"my_raster_data_row\")\n",
     "vd = VideoData(url = video_url, uid = \"ckrey7xef000208l57hwfgi3v\", external_id = \"my_video_data_row\")"
    ]
   },
@@ -539,7 +539,7 @@
    "metadata": {},
    "source": [
     "##### Mask\n",
-    "* The mask can be any RasterData object.\n"
+    "* The mask can be any ImageData object.\n"
    ]
   },
   {
@@ -551,12 +551,12 @@
    "source": [
     "\n",
     "mask_annotation = ObjectAnnotation(\n",
-    "        value = Mask(mask =  RasterData(arr = np.zeros((32,32,3), dtype = np.uint8)), color = (255,255,255)),\n",
+    "        value = Mask(mask =  ImageData(arr = np.zeros((32,32,3), dtype = np.uint8)), color = (255,255,255)),\n",
     "            name = \"mask class name\"\n",
     ")\n",
     "\n",
     "mask_annotation = ObjectAnnotation(\n",
-    "        value = Mask(mask =  RasterData(arr = np.zeros((32,32,3), dtype = np.uint8)), color = (255,255,255)),\n",
+    "        value = Mask(mask =  ImageData(arr = np.zeros((32,32,3), dtype = np.uint8)), color = (255,255,255)),\n",
     "            schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
     ")"
    ]
@@ -577,7 +577,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "raster_data = RasterData(arr = np.zeros((32,32,3), dtype = np.uint8))\n",
+    "raster_data = ImageData(arr = np.zeros((32,32,3), dtype = np.uint8))\n",
     "mask_annotation = ObjectAnnotation(\n",
     "        value = Mask(mask  = raster_data, color = [255,255,255]),\n",
     "            name = \"eyes\"\n",
@@ -604,7 +604,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "raster_data = RasterData(arr = np.zeros((32,32, 3), dtype = np.uint8))\n",
+    "raster_data = ImageData(arr = np.zeros((32,32, 3), dtype = np.uint8))\n",
     "mask_annotation = ObjectAnnotation(\n",
     "        value = Mask(mask  = raster_data, color = (128,255,255)),\n",
     "            name = \"eye\"\n",
@@ -734,7 +734,7 @@
    "outputs": [],
    "source": [
     "mask_annotation = Mask(\n",
-    "    mask = RasterData(arr = np_mask),\n",
+    "    mask = ImageData(arr = np_mask),\n",
     "    color = color \n",
     ")\n",
     "\n",
@@ -790,7 +790,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mask_data = RasterData(arr = np_seg_mask)\n",
+    "mask_data = ImageData(arr = np_seg_mask)\n",
     "eye_mask = Mask(mask = mask_data, color = eye_color)\n",
     "nose_mask = Mask(mask = mask_data, color = nose_color)\n"
    ]
@@ -946,15 +946,15 @@
    "outputs": [],
    "source": [
     "label = Label(\n",
-    "    data = RasterData(url = image_url),\n",
+    "    data = ImageData(url = image_url),\n",
     "    annotations = [\n",
     "        ObjectAnnotation(\n",
     "            value = Polygon(points = [Point(x = x, y = y) for x,y in xy_poly]),\n",
     "            name = \"deer\"\n",
     "        ),\n",
     "        ObjectAnnotation(\n",
     "            name = \"deer_eyes\",\n",
-    "            value = Mask(mask = RasterData(arr = np_mask), color = color)\n",
+    "            value = Mask(mask = ImageData(arr = np_mask), color = color)\n",
     "        )\n",
     "    ]\n",
     ")"
@@ -994,7 +994,7 @@
    "outputs": [],
    "source": [
     "# Manually set just by adding a value:\n",
-    "image = RasterData(arr = np_data)\n",
+    "image = ImageData(arr = np_data)\n",
     "image.url = \"http://myurl\""
    ]
   },
@@ -1048,7 +1048,7 @@
    "source": [
     "\n",
     "label = Label(\n",
-    "    data = RasterData(arr = np_data),\n",
+    "    data = ImageData(arr = np_data),\n",
     "    annotations = [\n",
     "         ObjectAnnotation(\n",
     "            name = \"deer_nose\",\n",

examples/annotation_types/label_containers.ipynb

@@ -36,7 +36,7 @@
     "from labelbox import LabelingFrontend\n",
     "from labelbox.data.annotation_types import (\n",
     "    Label, \n",
-    "    RasterData, \n",
+    "    ImageData, \n",
     "    Mask, \n",
     "    Point, \n",
     "    Polygon, \n",
@@ -136,9 +136,9 @@
     "        Point(x = 99, y = 181).raster(im_h, im_w, thickness = 3),\n",
     "    ]\n",
     "    mask_arr = np.max([*eye_masks,nose_mask] , axis = 0)\n",
-    "    mask = RasterData(arr = mask_arr)\n",
+    "    mask = ImageData(arr = mask_arr)\n",
     "    return [Label(\n",
-    "        data = RasterData(im_bytes = requests.get(image_url).content),\n",
+    "        data = ImageData(im_bytes = requests.get(image_url).content),\n",
     "        annotations = [\n",
     "            ObjectAnnotation(value = get_polygon(),name = \"deer\"),\n",
     "            ObjectAnnotation(name = \"deer_eyes\", value = Mask(mask = mask, color = eye_color)),  \n",

examples/annotation_types/mal_using_annotation_types.ipynb

@@ -66,7 +66,7 @@
     "\n",
     "from labelbox.data.annotation_types import (\n",
     "    LabelList,\n",
-    "    RasterData,\n",
+    "    ImageData,\n",
     "    Rectangle,\n",
     "    ObjectAnnotation,\n",
     "    ClassificationAnnotation,\n",
@@ -170,7 +170,7 @@
     "                start = Point(x = box[1], y = box[0]), end = Point(x = box[3], y = box[2])\n",
     "            )\n",
     "        elif name == 'car':\n",
-    "            value = Mask(mask = RasterData.from_2D_arr(arr = seg), color = (1,1,1))\n",
+    "            value = Mask(mask = ImageData.from_2D_arr(arr = seg), color = (1,1,1))\n",
     "        if value is not None:\n",
     "            annotations.append(\n",
     "                ObjectAnnotation(\n",
@@ -202,7 +202,7 @@
     "\n",
     "labellist = LabelList()\n",
     "for image_url in image_urls:\n",
-    "    image_data = RasterData(url = image_url)\n",
+    "    image_data = ImageData(url = image_url)\n",
     "    height, width = image_data.value.shape[:2]\n",
     "    prediction = predict(np.array([image_data.im_bytes]), min_score=0.5, height=height, width = width)\n",
     "    annotations = get_annotations(prediction['boxes'], prediction['class_indices'], prediction['seg_masks'])\n",

examples/model_diagnostics/model_diagnostics_demo.ipynb

@@ -135,7 +135,7 @@
     "    ScalarMetric, \n",
     "    LabelList, \n",
     "    Label, \n",
-    "    RasterData, \n",
+    "    ImageData, \n",
     "    Mask, \n",
     "    Polygon,\n",
     "    Point, \n",
@@ -253,7 +253,7 @@
    "source": [
     "predictions = LabelList()\n",
     "for (image_url, external_id) in notebook.tqdm(image_data):\n",
-    "    image = RasterData(url = image_url, external_id = external_id)\n",
+    "    image = ImageData(url = image_url, external_id = external_id)\n",
     "    height, width = image.value.shape[:2]\n",
     "    prediction = predict(np.array([image.im_bytes]), min_score=0.5, height=height, width = width)\n",
     "    boxes, classes, seg_masks = prediction[\"boxes\"], prediction[\"class_indices\"], prediction[\"seg_masks\"]\n",
@@ -270,7 +270,7 @@
     "            elif class_info['kind'] == Tool.Type.POINT:\n",
     "                value = Point(x=(box[1] + box[3]) / 2., y = (box[0] + box[2]) / 2.)\n",
     "            elif class_info['kind'] == Tool.Type.SEGMENTATION:\n",
-    "                value = Mask(mask = RasterData.from_2D_arr(seg * class_info['color']), color = (class_info['color'],)* 3)\n",
+    "                value = Mask(mask = ImageData.from_2D_arr(seg * class_info['color']), color = (class_info['color'],)* 3)\n",
     "            else:\n",
     "                raise ValueError(f\"Unsupported kind found. {class_info['kind']}\")\n",
     "            annotations.append(ObjectAnnotation(name = class_info['name'], value = value))\n",

examples/model_diagnostics/model_diagnostics_guide.ipynb

@@ -137,7 +137,7 @@
     "    ScalarMetric, \n",
     "    LabelList, \n",
     "    Label, \n",
-    "    RasterData, \n",
+    "    ImageData, \n",
     "    Mask,\n",
     "    Polygon,\n",
     "    Line,\n",
@@ -271,7 +271,7 @@
     "        value = Rectangle(start = Point(x = xmin, y = ymin), end = Point(x=xmax, y=ymax))\n",
     "        value = Point(x=x, y =y)\n",
     "        value = Line(points = [Point(x = x, y = y) for x,y in instance])\n",
-    "        value = Mask(mask = RasterData.from_2D_arr(seg * grayscale_color), color = (grayscale_color,)* 3)\n",
+    "        value = Mask(mask = ImageData.from_2D_arr(seg * grayscale_color), color = (grayscale_color,)* 3)\n",
     "        \n",
     "        annotations.append(ObjectAnnotation(name =class_name, value = value))\n",
     "            \n",

labelbox/data/annotation_types/__init__.py

@@ -18,7 +18,7 @@
 from .classification import Radio
 from .classification import Text
 
-from .data import RasterData
+from .data import ImageData
 from .data import TextData
 from .data import VideoData
 

labelbox/data/annotation_types/collection.py

@@ -77,7 +77,7 @@ def add_to_dataset(self,
     def add_url_to_masks(self, signer, max_concurrency=20) -> "LabelList":
         """
         Creates signed urls for all masks in the LabelList.
-        Multiple masks can reference the same RasterData mask so this makes sure we only upload that url once.
+        Multiple masks can reference the same ImageData mask so this makes sure we only upload that url once.
         Only uploads url if one doesn't already exist.
 
         Args:
@@ -235,7 +235,7 @@ def add_url_to_masks(self, signer: Callable[[bytes],
                                                 str]) -> "LabelGenerator":
         """
         Creates signed urls for all masks in the LabelGenerator.
-        Multiple masks can reference the same RasterData mask so this makes sure we only upload that url once.
+        Multiple masks can reference the same ImageData mask so this makes sure we only upload that url once.
         Only uploads url if one doesn't already exist.
 
         Args:

labelbox/data/annotation_types/data/__init__.py

@@ -1,3 +1,3 @@
-from .raster import RasterData
+from .raster import ImageData
 from .text import TextData
 from .video import VideoData

labelbox/data/annotation_types/data/raster.py

@@ -12,7 +12,7 @@
 from ..types import TypedArray
 
 
-class RasterData(BaseData):
+class ImageData(BaseData):
     """
     Represents an image or segmentation mask.
     """
@@ -50,7 +50,7 @@ def np_to_bytes(self, arr: np.ndarray) -> bytes:
         """
         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")
+                             "Use ImageData.from_2D_arr to construct from 2D")
         if arr.dtype != np.uint8:
             raise TypeError(f"image data type must be uint8. Found {arr.dtype}")
 
@@ -140,12 +140,12 @@ def validate_args(cls, values):
             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")
+                    "Use ImageData.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)}," \
+        return  f"ImageData(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)})"