Release 3.56.0 (#1291)

Author: kopreschko

CHANGELOG.md

@@ -1,5 +1,14 @@
 # Changelog
 
+# Version 3.56.0 (2023-11-21)
+## Added
+* Support for importing raster video masks from image bytes as a source
+* Add new ExportTask class to handle streaming of exports
+## Fixed
+* Check for empty fields during webhook creation
+## Notebooks
+* Updates to use bytes array for masks (video, image), and add examples of multiple notations per frame (video)
+
 # Version 3.55.0 (2023-11-06)
 ## Fixed
 * Fix the instantiation of `failed_data_row_ids` in Batch. This fix will address the issue with the `create_batch` method for more than 1,000 data rows.

Makefile

@@ -1,6 +1,6 @@
 
 build-image:
-	docker build -t local/labelbox-python:test .
+	docker build --quiet -t local/labelbox-python:test .
 
 test-local: build-image
 

README.md

@@ -18,7 +18,7 @@ The Labelbox Python API offers a simple, user-friendly way to interact with the
 
 ## Requirements
 
-- Use Python 3.7, 3.8 or 3.9
+- Use Python 3.7, 3.8, 3.9 and 3.10
 - [Create an account](http://app.labelbox.com/)
 - [Generate an API key](https://docs.labelbox.com/docs/create-an-api-key)
 

docs/source/conf.py

@@ -21,7 +21,7 @@
 copyright = '2021, Labelbox'
 author = 'Labelbox'
 
-release = '3.55.0'
+release = '3.56.0'
 
 # -- General configuration ---------------------------------------------------
 

docs/source/index.rst

@@ -222,3 +222,10 @@ QualityMode
 .. automodule:: labelbox.schema.quality_mode
    :members:
    :show-inheritance:
+
+ExportTask
+---------------------------
+
+.. automodule:: labelbox.schema.export_task
+   :members:
+   :show-inheritance:

examples/annotation_import/image.ipynb

@@ -85,6 +85,8 @@
       "metadata": {},
       "source": [
         "import uuid\n",
+        "import requests\n",
+        "import base64\n",
         "import numpy as np\n",
         "import labelbox as lb\n",
         "import labelbox.types as lb_types"
@@ -297,7 +299,7 @@
         "bbox_annotation = lb_types.ObjectAnnotation(\n",
         "    name=\"bounding_box\",  # must match your ontology feature\"s name\n",
         "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top \n",
+        "        start=lb_types.Point(x=1690, y=977),  #  x = left, y = top\n",
         "        end=lb_types.Point(x=1915, y=1307),  # x= left + width , y = top + height\n",
         "    ))\n",
         "\n",
@@ -330,7 +332,7 @@
         "bbox_with_radio_subclass_annotation = lb_types.ObjectAnnotation(\n",
         "    name=\"bbox_with_radio_subclass\",\n",
         "    value=lb_types.Rectangle(\n",
-        "        start=lb_types.Point(x=541, y=933),  #  x = left, y = top \n",
+        "        start=lb_types.Point(x=541, y=933),  #  x = left, y = top\n",
         "        end=lb_types.Point(x=871, y=1124),  # x= left + width , y = top + height\n",
         "    ),\n",
         "    classifications=[\n",
@@ -373,7 +375,7 @@
       "source": [
         "# Python annotation\n",
         "polygon_annotation = lb_types.ObjectAnnotation(\n",
-        "    name=\"polygon\",  # must match your ontology feature\"s name \n",
+        "    name=\"polygon\",  # must match your ontology feature\"s name\n",
         "    value=lb_types.Polygon(  # Coordinates for the vertices of your polygon\n",
         "        points=[\n",
         "            lb_types.Point(x=1489.581, y=183.934),\n",
@@ -430,23 +432,25 @@
     {
       "metadata": {},
       "source": [
-        "# Identifying what values in the numpy array correspond to the mask annotation\n",
-        "color = (255, 255, 255)\n",
-        "mask_data = lb_types.MaskData(url=\"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\")\n",
+        "### Raster Segmentation (Byte string array)\n",
+        "url = \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\"\n",
+        "response = requests.get(url)\n",
         "\n",
-        "# Python annotation\n",
+        "mask_data = lb.types.MaskData(im_bytes=response.content) # You can also use \"url\" instead of img_bytes to pass the PNG mask url.\n",
         "mask_annotation = lb_types.ObjectAnnotation(\n",
-        "  name = \"mask\", # must match your ontology feature\"s name\n",
-        "  value=lb_types.Mask(mask=mask_data, color=color),\n",
+        "    name=\"mask\",\n",
+        "    value=lb_types.Mask(\n",
+        "        mask=mask_data,\n",
+        "        color=(255, 255, 255))\n",
         ")\n",
         "\n",
-        "# NDJSON\n",
+        "# NDJSON using instanceURI, bytes array is not fully supported.\n",
         "mask_annotation_ndjson = {\n",
         "  \"name\": \"mask\",\n",
         "  \"classifications\": [],\n",
-        "  \"mask\": {\"instanceURI\": \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\",\n",
+        "  \"mask\": {\"instanceURI\": url,\n",
         "  \"colorRGB\": (255, 255, 255)}\n",
-        "}"
+        "}\n"
       ],
       "cell_type": "code",
       "outputs": [],
@@ -591,7 +595,7 @@
         "        type=lb_types.Relationship.Type.UNIDIRECTIONAL,\n",
         "    ))\n",
         "\n",
-        "## Only supported for MAL imports \n",
+        "## Only supported for MAL imports\n",
         "uuid_source = str(uuid.uuid4())\n",
         "uuid_target = str(uuid.uuid4())\n",
         "\n",
@@ -855,7 +859,7 @@
     {
       "metadata": {},
       "source": [
-        "ndjson_label = []\n",
+        "label_ndjson = []\n",
         "annotations = [\n",
         "    radio_annotation_ndjson,\n",
         "    nested_radio_annotation_ndjson,\n",
@@ -870,15 +874,15 @@
         "    polyline_annotation_ndjson,\n",
         "    bbox_source_ndjson,\n",
         "    bbox_target_ndjson,\n",
-        "    relationship_ndjson,  ## Only supported for MAL imports \n",
+        "    relationship_ndjson,  ## Only supported for MAL imports\n",
         "]\n",
         "for annotation in annotations:\n",
         "    annotation.update({\n",
         "        \"dataRow\": {\n",
         "            \"globalKey\": global_key\n",
-        "        },\n",
+        "        }\n",
         "    })\n",
-        "    ndjson_label.append(annotation)"
+        "    label_ndjson.append(annotation)"
       ],
       "cell_type": "code",
       "outputs": [],
@@ -911,7 +915,7 @@
         ")\n",
         "upload_job.wait_until_done()\n",
         "\n",
-        "print(f\"Errors: {upload_job.errors}\", )\n",
+        "print(f\"Errors: {upload_job.errors}\")\n",
         "print(f\"Status of uploads: {upload_job.statuses}\")"
       ],
       "cell_type": "code",
@@ -928,14 +932,14 @@
     {
       "metadata": {},
       "source": [
-        "# Uncomment if relationships are not being imported. \n",
-        "# Relationships will be supported for label import in the near future. \n",
+        "# Relationships are not supported with LabelImport\n",
+        "# For this demo either run MAL or Ground Truth, not both\n",
         "\n",
         "# Upload label for this data row in project\n",
         "# upload_job = lb.LabelImport.create_from_objects(\n",
-        "#     client = client, \n",
-        "#     project_id = project.uid, \n",
-        "#     name=\"label_import_job\"+str(uuid.uuid4()),  \n",
+        "#     client = client,\n",
+        "#     project_id = project.uid,\n",
+        "#     name=\"label_import_job\"+str(uuid.uuid4()),\n",
         "#     labels=label)\n",
         "\n",
         "# print(\"Errors:\", upload_job.errors)\n",
@@ -954,6 +958,13 @@
       "cell_type": "code",
       "outputs": [],
       "execution_count": null
+    },
+    {
+      "metadata": {},
+      "source": [],
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
     }
   ]
 }