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Remove raster seg masks from video prediction (#1097)
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‎examples/prediction_upload/video_predictions.ipynb‎

Lines changed: 89 additions & 68 deletions
Original file line numberDiff line numberDiff line change
@@ -41,13 +41,14 @@
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"- Polyline\n",
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"- Classification - radio\n",
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"- Classification - checklist\n",
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"- Classification - free text\n",
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"- Nested classifications \n",
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"\n",
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"**NOT** supported:\n",
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"- Polygons \n",
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"- Segmentation masks\n",
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"- Free form text classifications\n",
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"\n",
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"Please note that this list of unsupported annotations only refers to limitations for importing annotations. For example, when using the Labelbox editor, segmentation masks can be created and edited on video assets.\n"
48+
"- Polygons [not supported in video editor or model]\n",
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"- Raster segmentation masks [not supported in model] \n",
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"- Vector segmentation masks [not supported in video editor]\n",
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"\n"
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],
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"cell_type": "markdown"
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},
@@ -680,45 +681,6 @@
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
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"##### Raster Segmentation ########\n",
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"\n",
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"instance_uri = \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/mask_example.png\"\n",
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"\n",
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"\n",
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"\n",
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"video_mask_prediction=[\n",
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" lb_types.VideoMaskAnnotation(\n",
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" frames=[\n",
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" lb_types.MaskFrame(index=10, instance_uri=instance_uri)\n",
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" ],\n",
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" instances=[\n",
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" lb_types.MaskInstance(color_rgb=(255,255,255), name=\"video_mask\")\n",
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" ] \n",
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" )\n",
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"]\n",
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"\n",
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"video_mask_prediction_ndjson = {\n",
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" \"masks\": {\n",
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" \"frames\": [{\n",
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" \"index\": 10,\n",
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" \"instanceURI\": instance_uri\n",
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" }],\n",
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" \"instances\": [\n",
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" {\n",
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" \"colorRGB\": (255, 255, 255),\n",
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" \"name\": \"video_mask\",\n",
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" }\n",
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" ]\n",
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" }\n",
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"}"
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],
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"source": [
@@ -763,7 +725,23 @@
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"print(\"Failed data rows: \",task.failed_data_rows)"
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],
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"cell_type": "code",
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"There are errors present. Please look at `task.errors` for more details\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Errors: Duplicate global keys found: sample-video-2.mp4\n",
741+
"Failed data rows: [{'message': 'Duplicate global keys found: sample-video-2.mp4', 'failedDataRows': [{'globalKey': 'sample-video-2.mp4', 'rowData': 'https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4', 'attachmentInputs': []}]}]\n"
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]
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}
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],
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"execution_count": null
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},
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{
@@ -782,7 +760,6 @@
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" lb.Tool(tool=lb.Tool.Type.BBOX, name=\"bbox_video\"),\n",
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" lb.Tool(tool=lb.Tool.Type.POINT, name=\"point_video\"),\n",
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" lb.Tool(tool=lb.Tool.Type.LINE, name=\"line_video_frame\"),\n",
785-
" lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"video_mask\"),\n",
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" lb.Tool(\n",
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" tool=lb.Tool.Type.BBOX, name=\"bbox_class\",\n",
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" classifications=[\n",
@@ -872,7 +849,7 @@
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"\n",
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"ontology = client.create_ontology(\"Ontology Video Annotations\", \n",
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" ontology_builder.asdict(), \n",
875-
" # media_type=lb.MediaType.Video\n",
852+
" media_type=lb.MediaType.Video\n",
876853
" )"
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],
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"cell_type": "code",
@@ -912,7 +889,18 @@
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"model_run.upsert_data_rows(global_keys=[global_key])"
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],
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"cell_type": "code",
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 90,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null
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},
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{
@@ -947,7 +935,6 @@
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" frame_bbox_with_checklist_subclass_prediction,\n",
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" global_radio_prediction,\n",
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" global_checklist_prediction,\n",
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" video_mask_prediction,\n",
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" text_prediction\n",
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" ]\n",
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"\n",
@@ -988,7 +975,6 @@
988975
" frame_bbox_with_checklist_subclass_prediction_ndjson,\n",
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" global_radio_classification_ndjson,\n",
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" global_checklist_classification_ndjson,\n",
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" video_mask_prediction_ndjson,\n",
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" text_prediction_ndjson\n",
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"]: \n",
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" annotation.update({\n",
@@ -1022,7 +1008,16 @@
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"print(\"Status of uploads: \", upload_job_prediction.statuses)"
10231009
],
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"cell_type": "code",
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Errors: []\n",
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"Status of uploads: [{'uuid': 'e3145fa9-42b8-466f-9ac5-0130aeab1060', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '04e035b4-3083-4408-9a67-9cb52cbe027b', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': 'aafaeda7-8ba3-4df1-8e42-a02fe72add94', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '956687f4-091c-4dc0-9c84-0b4e35ae451b', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': 'b7c6e33e-2cc4-46be-8a1f-d920cabf115b', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': 'daff0aff-8834-4e80-97f5-cf2d38684c5c', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': 'a611c275-39d8-47aa-808c-969692eb1698', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '9ed7be94-bf89-432b-99ff-c834f31087f0', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '6ed2ef7c-e83a-48e6-8073-c291779c7497', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '14a4950f-0835-49bb-a968-81c41cda6869', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '5bd23f6c-ab87-44b0-a32b-49c84f72b06c', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}]\n"
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]
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}
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],
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"execution_count": null
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},
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{
@@ -1051,7 +1046,15 @@
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"project.setup_editor(ontology)"
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],
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"cell_type": "code",
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Default createProject behavior will soon be adjusted to prefer batch projects. Pass in `queue_mode` parameter explicitly to opt-out for the time being.\n"
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]
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}
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],
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"execution_count": null
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},
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{
@@ -1071,7 +1074,18 @@
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")"
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],
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"cell_type": "code",
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Batch ID: 28e7da10-f660-11ed-9f2b-f9c234af8129>"
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]
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},
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"execution_count": 95,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null
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},
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{
@@ -1368,18 +1382,6 @@
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"]\n",
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"\n",
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"\n",
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"instance_uri = \"https://storage.googleapis.com/labelbox-datasets/video-sample-data/mask_example.png\"\n",
1372-
"video_mask_annotation=[\n",
1373-
" lb_types.VideoMaskAnnotation(\n",
1374-
" frames=[\n",
1375-
" lb_types.MaskFrame(index=10, instance_uri=instance_uri)\n",
1376-
" ],\n",
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" instances=[\n",
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" lb_types.MaskInstance(color_rgb=(255,255,255), name=\"video_mask\")\n",
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" ] \n",
1380-
" )\n",
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"]\n",
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"\n",
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"text_annotation = [lb_types.ClassificationAnnotation(\n",
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" name=\"free_text\", # must match your ontology feature's name\n",
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" value=lb_types.Text(answer=\"sample text\")\n",
@@ -1416,7 +1418,6 @@
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" polyline_annotation,\n",
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" global_checklist_annotation,\n",
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" global_radio_annotation,\n",
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" video_mask_annotation,\n",
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" nested_checklist_annotation,\n",
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" nested_radio_annotation,\n",
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" text_annotation\n",
@@ -1457,7 +1458,16 @@
14571458
"print(\"Status of uploads: \", upload_job_annotation.statuses)"
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],
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"cell_type": "code",
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Errors: []\n",
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"Status of uploads: [{'uuid': 'c5ddce8f-c672-49d7-bc43-f4cc5afbe0f2', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '9b2bf816-6f22-4b05-818a-d200a2061a94', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': 'f6bf224d-a295-484b-8078-16e49e7583ec', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '0602a136-8383-49c0-be64-36ea1499cd31', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '306df4bd-a22d-48c7-bfee-fb0e8695d965', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '60ec1dfc-64bb-4354-98f0-67aec7794bac', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '6fd8c58d-883d-4fdb-9bdc-598c663e0ad4', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '200c256f-b6cd-4469-987a-e9d773dc5715', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': 'b1c3ccbe-21fd-4c41-aeee-e2df9732cd5f', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '7682c660-147a-4cf8-9b3a-a15859100142', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}, {'uuid': '4e80a895-3722-49e9-8d00-15eded343a60', 'dataRow': {'id': 'clfco73at0080079n5dhm9y3a', 'globalKey': 'sample-video-2.mp4'}, 'status': 'SUCCESS'}]\n"
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]
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}
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],
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"execution_count": null
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},
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{
@@ -1474,7 +1484,18 @@
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"model_run.upsert_labels(project_id=project.uid)"
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],
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"cell_type": "code",
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 99,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": null
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},
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{

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