Vb/remove UUID notebooks al 4862

examples/annotation_import/conversational.ipynb

@@ -383,7 +383,6 @@
         "                    checklist_annotation,\n",
         "                    radio_annotation]:\n",
         "  annotations.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {\n",
         "          'id': data_row.uid\n",
         "      }\n",
@@ -407,23 +406,19 @@
               "[{'name': 'ner',\n",
               "  'location': {'start': 0, 'end': 8},\n",
               "  'messageId': '4',\n",
-              "  'uuid': 'ce5805b9-1353-432e-9f7a-38cdfa901d5d',\n",
               "  'dataRow': {'id': 'clc0ygvde029307yn96gv2byu'}},\n",
               " {'name': 'text_convo',\n",
               "  'answer': 'the answer to the text questions right here',\n",
               "  'messageId': '0',\n",
-              "  'uuid': '2852bb2d-9355-42df-bb95-3db48247cbf7',\n",
               "  'dataRow': {'id': 'clc0ygvde029307yn96gv2byu'}},\n",
               " {'name': 'checklist_convo',\n",
               "  'answers': [{'name': 'first_checklist_answer'},\n",
               "   {'name': 'second_checklist_answer'}],\n",
               "  'messageId': '2',\n",
-              "  'uuid': '623fa806-166e-436b-8d1b-dbdc30f23ee5',\n",
               "  'dataRow': {'id': 'clc0ygvde029307yn96gv2byu'}},\n",
               " {'name': 'radio_convo',\n",
               "  'answer': {'name': 'first_radio_answer'},\n",
               "  'messageId': '0',\n",
-              "  'uuid': 'a256e84a-5012-4fd4-833f-637935a22fd4',\n",
               "  'dataRow': {'id': 'clc0ygvde029307yn96gv2byu'}}]"
             ]
           },

examples/annotation_import/image.ipynb

@@ -772,7 +772,6 @@
         "\n",
         "              ]:\n",
         "  annotation.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  label_ndjson_method2.append(annotation)\n"

examples/annotation_import/pdf.ipynb

@@ -208,7 +208,6 @@
       "metadata": {},
       "source": [
         "ANNOTATION = {\n",
-        "    \"uuid\": str(uuid.uuid4()),\n",
         "    \"name\" : tool_name,\n",
         "    \"dataRow\": {\"id\": data_row_id.uid},\n",
         "    \"textSelections\": [\n",

examples/annotation_import/text.ipynb

@@ -507,7 +507,6 @@
         "                   checklist_annotation_ndjson,\n",
         "                   text_annotation_ndjson] :\n",
         "  annotations.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {\n",
         "          'id': data_row.uid\n",
         "      }\n",

examples/annotation_import/tiled.ipynb

@@ -997,7 +997,6 @@
         "                    polygon_annotation_two_ndjson\n",
         "                    ]:\n",
         "  annotations.update({\n",
-        "      'uuid' : str(uuid.uuid4()),\n",
         "      'dataRow': {\n",
         "          'id':  next(dataset.export_data_rows()).uid\n",
         "      }\n",

examples/annotation_import/video.ipynb

@@ -675,7 +675,6 @@
         "                    frame_bbox_with_checklist_subclass_ndjson\n",
         "                    ]:      \n",
         "  annotations.update({\n",
-        "      'uuid' : str(uuid.uuid4()),\n",
         "      'dataRow': {\n",
         "          'id':  next(dataset.export_data_rows()).uid\n",
         "      }\n",
@@ -699,7 +698,6 @@
               "[{'name': 'point_video',\n",
               "  'segments': [{'keyframes': [{'frame': 17,\n",
               "      'point': {'x': 660.134, 'y': 407.926}}]}],\n",
-              "  'uuid': '9316e8dc-3b3f-4ce7-b1f3-d06e1caeb83c',\n",
               "  'dataRow': {'id': 'cldxj6yxi0a4o07yc8qkf0bvm'}},\n",
               " {'name': 'bbox_video',\n",
               "  'segments': [{'keyframes': [{'frame': 13,\n",
@@ -711,7 +709,6 @@
               "       'left': 98.0,\n",
               "       'height': 382.0,\n",
               "       'width': 341.0}}]}],\n",
-              "  'uuid': '920af9d6-9ad5-4ae2-b2d4-6c34a0d63be5',\n",
               "  'dataRow': {'id': 'cldxj6yxi0a4o07yc8qkf0bvm'}},\n",
               " {'name': 'line_video_frame',\n",
               "  'segments': [{'keyframes': [{'frame': 5,\n",
@@ -729,30 +726,25 @@
               "   {'keyframes': [{'frame': 24,\n",
               "      'line': [{'x': 300, 'y': 310}, {'x': 330, 'y': 430}]},\n",
               "     {'frame': 45, 'line': [{'x': 600, 'y': 810}, {'x': 900, 'y': 930}]}]}],\n",
-              "  'uuid': '9be4e3b9-c0e9-4a71-95a7-2aad35d4cc4c',\n",
               "  'dataRow': {'id': 'cldxj6yxi0a4o07yc8qkf0bvm'}},\n",
               " {'name': 'checklist_class',\n",
               "  'answer': [{'name': 'first_checklist_answer',\n",
               "    'frames': [{'start': 29, 'end': 35}, {'start': 48, 'end': 65}]},\n",
               "   {'name': 'second_checklist_answer',\n",
               "    'frames': [{'start': 29, 'end': 35}, {'start': 48, 'end': 65}]}],\n",
-              "  'uuid': '2be8bf17-94c1-4638-929b-41bd7f86e4bd',\n",
               "  'dataRow': {'id': 'cldxj6yxi0a4o07yc8qkf0bvm'}},\n",
               " {'name': 'radio_class_global',\n",
               "  'answer': {'name': 'first_radio_answer'},\n",
-              "  'uuid': 'df230cdc-dbb7-4142-a53a-8c662f32b713',\n",
               "  'dataRow': {'id': 'cldxj6yxi0a4o07yc8qkf0bvm'}},\n",
               " {'name': 'radio_question_nested',\n",
               "  'answer': {'name': 'first_radio_question'},\n",
               "  'classifications': [{'name': 'sub_question_radio',\n",
               "    'answer': {'name': 'sub_answer'}}],\n",
-              "  'uuid': 'f7b24fee-005c-4a22-a52a-b2c6fcc94db4',\n",
               "  'dataRow': {'id': 'cldxj6yxi0a4o07yc8qkf0bvm'}},\n",
               " {'name': 'nested_checklist_question',\n",
               "  'answer': [{'name': 'first_checklist_answer',\n",
               "    'classifications': [{'name': 'sub_checklist_question',\n",
               "      'answer': {'name': 'first_sub_checklist_answer'}}]}],\n",
-              "  'uuid': '691792f8-0627-4a37-8ef4-81691d3f1d78',\n",
               "  'dataRow': {'id': 'cldxj6yxi0a4o07yc8qkf0bvm'}},\n",
               " {'name': 'bbox_class',\n",
               "  'segments': [{'keyframes': [{'frame': 10,\n",
@@ -767,7 +759,6 @@
               "      'bbox': {'top': 146.0, 'left': 98.0, 'height': 382.0, 'width': 341.0},\n",
               "      'classifications': [{'schemaId': 'cldxj6zy80cw307vdgapmcu4e',\n",
               "        'answer': {'schemaId': 'cldxj6zy80cw407vd1ynfckwg'}}]}]}],\n",
-              "  'uuid': '1d445aa2-e348-4b49-bb23-2cae52293af8',\n",
               "  'dataRow': {'id': 'cldxj6yxi0a4o07yc8qkf0bvm'}}]"
             ]
           },

examples/integrations/detectron2/coco_object.ipynb

@@ -116,6 +116,7 @@
         "    feature_miou_metric, \n",
         "    feature_confusion_matrix_metric\n",
         ")\n",
+        "from labelbox.data.metrics.group import get_label_pairs\n",
         "\n",
         "with open('./coco_utils.py', 'w' ) as file:\n",
         "    helper = requests.get(\"https://raw.githubusercontent.com/Labelbox/labelbox-python/master/examples/integrations/detectron2/coco_utils.py\").text\n",

examples/integrations/detectron2/coco_panoptic.ipynb

@@ -1014,7 +1014,7 @@
         "    for future in tqdm(as_completed(futures)):\n",
         "        labels.append(future.result())\n",
         "\n",
-	"labels_mea = LabelGenerator(labels)\n",
+	"labels_mea = lb_types.LabelGenerator(labels)\n",
         "labels_mea.add_url_to_masks(signer) \\\n",
         "      .add_url_to_data(signer) "
       ],

examples/integrations/tlt/labelbox_upload.ipynb

@@ -285,7 +285,6 @@
         "for datarow in datarows:\n",
         "    label = get_labels(datarow)['coords'][0]\n",
         "    row = {\n",
-        "        'uuid' : str(uuid.uuid4()),\n",
         "        'name' : 'animal',\n",
         "        'dataRow' : {'id' : datarow.uid},\n",
         "        'bbox' : {\n",

examples/prediction_upload/geospatial_predictions.ipynb

@@ -675,7 +675,7 @@
         "\n",
         "Create the annotations payload using the snippets in the **Supported Predictions Section**. \n",
         "\n",
-        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
       ],
       "cell_type": "markdown"
     },
@@ -808,7 +808,6 @@
         "    polygon_prediction_two_ndjson\n",
         "]:\n",
         "  prediction.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  label_ndjson_method_2.append(prediction)"

examples/prediction_upload/html_predictions.ipynb

@@ -415,7 +415,7 @@
         "\n",
         "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
         "\n",
-        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
       ],
       "cell_type": "markdown"
     },
@@ -460,7 +460,6 @@
         "    nested_checklist_prediction_ndjson\n",
         "]:\n",
         "  annot.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  ndjson_prediction_method2.append(annot)"
@@ -641,7 +640,6 @@
         "    text_annotation_ndjson\n",
         "  ]:\n",
         "  annot.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  ndjson_annotation.append(annot)"

examples/prediction_upload/image_predictions.ipynb

@@ -678,7 +678,7 @@
         "\n",
         "Create the prediction payload using the snippets of code in ***Supported Predictions*** section. \n",
         "\n",
-        "The resulting label_ndjson should have exactly the same content for predictions that are supported by both (with exception of the uuid strings that are generated)"
+        "The resulting label_ndjson should have exactly the same content for predictions that are supported by both"
       ],
       "cell_type": "markdown"
     },
@@ -734,7 +734,6 @@
         "    nested_checklist_prediction_ndjson\n",
         "]:\n",
         "  annot.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  ndjson_prediction_method2.append(annot)"
@@ -1019,7 +1018,6 @@
         "    text_annotation_ndjson\n",
         "]:\n",
         "  annot.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  ndjson_annotation.append(annot) \n",

examples/prediction_upload/text_predictions.ipynb

@@ -440,7 +440,7 @@
         "\n",
         "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below to compose your annotations into Labels attached to the data rows.\n",
         "\n",
-        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)"
+        "The resulting label_ndjson should have exactly the same content for annotations that are supported by both"
       ],
       "cell_type": "markdown"
     },
@@ -485,7 +485,6 @@
         "    nested_checklist_prediction_ndjson\n",
         "  ]:\n",
         "  annot.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  ndjson_prediction_method2.append(annot)"
@@ -682,7 +681,6 @@
         "    nested_radio_annotation_ndjson   \n",
         "  ]:\n",
         "  annot.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  ndjson_annotation.append(annot)"

examples/prediction_upload/video_predictions.ipynb

@@ -712,7 +712,6 @@
         "    nested_checklist_prediction_ndjson\n",
         "]:\n",
         "  prediction.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  label_ndjson.append(prediction)"
@@ -1147,7 +1146,6 @@
         "    bbox_with_radio_subclass_annotation_ndjson\n",
         "]:\n",
         "  annot.update({\n",
-        "      'uuid': str(uuid.uuid4()),\n",
         "      'dataRow': {'id': data_row.uid},\n",
         "  })\n",
         "  ndjson_annotation.append(annot) "