From 343bb4ce1e736162e9d413083b3e0fd43c7407bf Mon Sep 17 00:00:00 2001
From: Andrea Ovalle <74880762+ovalle15@users.noreply.github.com>
Date: Thu, 22 Dec 2022 16:12:50 -0500
Subject: [PATCH 1/7] New NDJSON format

---
 examples/annotation_import/tiled.ipynb | 1159 +++++++++++++++---------
 1 file changed, 748 insertions(+), 411 deletions(-)

diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 546729994..81856667b 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -38,34 +38,52 @@
       },
       "source": [
         "# Tiled Imagery Annotation Import\n",
-        "* This notebook will provide examples of each supported annotation type for tiled imagery assets. It will cover the following:\n",
-        "    * Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
-        "    * Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing."
+        "* This notebook will provide examples of each supported annotation type for tiled imagery assets, and also cover MAL and Label Import methods:\n",
+        "\n",
+        "Supported annotations that can be uploaded through the SDK: \n",
+        "  * Point \n",
+        "  * Polygon\n",
+        "  * Bounding Box \n",
+        "  * Classification radio \n",
+        "  * Classification checklist \n",
+        "  * Classification free-form text\n",
+        "\n",
+        "**Not** supported:\n",
+        "  * Segmentation mask\n",
+        "\n",
+        "\n",
+        "MAL and Label Import: \n",
+        "\n",
+        "* Model-assisted labeling - used to provide pre-annotated data for your labelers. This will enable a reduction in the total amount of time to properly label your assets. Model-assisted labeling does not submit the labels automatically, and will need to be reviewed by a labeler for submission.\n",
+        "* Label Import - used to provide ground truth labels. These can in turn be used and compared against prediction labels, or used as benchmarks to see how your labelers are doing.\n",
+        "\n",
+        "For information on what types of annotations are supported per data type, refer to this documentation:\n",
+        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
       ]
     },
     {
       "cell_type": "markdown",
-      "id": "XxggLNJ76vDw",
+      "id": "QRizVjhb6vlH",
       "metadata": {
-        "id": "XxggLNJ76vDw"
+        "id": "QRizVjhb6vlH"
       },
       "source": [
-        "* For information on what types of annotations are supported per data type, refer to this documentation:\n",
-        "    * https://docs.labelbox.com/docs/model-assisted-labeling#option-1-import-via-python-annotation-types-recommended"
+        "Notes:\n",
+        " * This notebook uses the Slippy Maps format\n",
+        " * If you are importing more than 1,000 annotations at a time, consider submitting separate jobs, as they can take longer than other annotation types to import.\n",
+        " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly.\n",
+        " * You may need to refresh your browser in order to see the results of the import job."
       ]
     },
     {
       "cell_type": "markdown",
-      "id": "QRizVjhb6vlH",
+      "source": [
+        "### Setup"
+      ],
       "metadata": {
-        "id": "QRizVjhb6vlH"
+        "id": "_RXNjZ8dplBk"
       },
-      "source": [
-        "* Notes:\n",
-        " * This notebook uses the Slippy Maps format\n",
-        " * If you are importing more than 1,000 mask annotations at a time, consider submitting separate jobs, as they can take longer than other annotation types to import.\n",
-        " * Wait until the import job is complete before opening the Editor to make sure all annotations are imported properly."
-      ]
+      "id": "_RXNjZ8dplBk"
     },
     {
       "cell_type": "code",
@@ -81,7 +99,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "id": "committed-richards",
       "metadata": {
         "id": "committed-richards"
@@ -95,16 +113,18 @@
         "from PIL import Image\n",
         "import cv2\n",
         "import ndjson\n",
+        "import gdal\n",
         "\n",
         "from labelbox.data.annotation_types.data.tiled_image import TiledBounds, TiledImageData, TileLayer, EPSG, EPSGTransformer\n",
         "from labelbox.data.serialization.ndjson.converter import NDJsonConverter\n",
+        "from labelbox.schema.queue_mode import QueueMode\n",
+        "from labelbox.schema.media_type import MediaType\n",
         "from labelbox.data.annotation_types import (\n",
         "    Label, ImageData, ObjectAnnotation, MaskData,\n",
         "    Rectangle, Point, Line, Mask, Polygon,\n",
         "    Radio, Checklist, Text,\n",
         "    ClassificationAnnotation, ClassificationAnswer\n",
         ")\n",
-        "from labelbox.schema.media_type import MediaType\n",
         "from labelbox.schema.ontology import OntologyBuilder, Tool, Classification, Option\n",
         "from labelbox import Client, LabelingFrontend, LabelImport, MALPredictionImport"
       ]
@@ -116,47 +136,21 @@
         "id": "xdFJ1Qgj68Q5"
       },
       "source": [
-        "# API Key and Client\n",
-        "Provide a valid api key below in order to properly connect to the Labelbox Client."
+        "### Replace with your API key\n",
+        "Guides on [Create an API key](https://docs.labelbox.com/docs/create-an-api-key)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "id": "affecting-myanmar",
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "affecting-myanmar",
-        "outputId": "2f693cfe-bada-48d1-e260-5694bfa0a43f"
+        "id": "affecting-myanmar"
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "INFO:labelbox.client:Initializing Labelbox client at 'https://api.labelbox.com/graphql'\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "API_KEY = None\n",
-        "client = Client(api_key=API_KEY)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "V8LAOSucAIoc",
-      "metadata": {
-        "id": "V8LAOSucAIoc"
-      },
-      "source": [
-        "---- \n",
-        "### Steps\n",
-        "1. Make sure project is setup\n",
-        "2. Collect annotations\n",
-        "3. Upload"
+        "client = Client(API_KEY)"
       ]
     },
     {
@@ -166,7 +160,7 @@
         "id": "blessed-venture"
       },
       "source": [
-        "### Project setup"
+        "## Supported annotations for tiled imagery"
       ]
     },
     {
@@ -176,541 +170,884 @@
         "id": "AmKgfZ8ZALT_"
       },
       "source": [
-        "We will be creating two projects, one for model-assisted labeling, and one for label imports"
+        "### Supported Python annotation types and NDJSON "
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
-      "id": "suburban-crowd",
+      "source": [
+        "####### Point #######\n",
+        "\n",
+        "# Python Annotation\n",
+        "point_annotation = ObjectAnnotation(\n",
+        "  name = \"point_geo\",\n",
+        "  value = Point(x=-99.21052827588443, y=19.405662413477728),\n",
+        ")\n",
+        "\n",
+        "# NDJSON\n",
+        "point_annotation_ndjson = {\n",
+        "    \"name\": \"point_geo\",\n",
+        "    \"point\": {\n",
+        "         \"x\": -99.21052827588443,\n",
+        "         \"y\": 19.405662413477728\n",
+        "     }\n",
+        "}"
+      ],
       "metadata": {
-        "id": "suburban-crowd"
+        "id": "3mQh-yQ2TLmd"
       },
-      "outputs": [],
-      "source": [
-        "ontology_builder = OntologyBuilder(\n",
-        "    tools=[\n",
-        "        Tool(tool=Tool.Type.BBOX, name=\"box\"),\n",
-        "        Tool(tool=Tool.Type.LINE, name=\"line\"),\n",
-        "        Tool(tool=Tool.Type.POINT, name=\"point\"),\n",
-        "        Tool(tool=Tool.Type.POLYGON, name=\"polygon\"),    \n",
-        "        Tool(tool=Tool.Type.SEGMENTATION, name=\"mask\")],\n",
-        "    classifications=[\n",
-        "        Classification(class_type=Classification.Type.TEXT, instructions=\"text\"),\n",
-        "        Classification(class_type=Classification.Type.CHECKLIST, instructions=\"checklist\", options=[\n",
-        "            Option(value=\"first_checklist_answer\"),\n",
-        "            Option(value=\"second_checklist_answer\")            \n",
-        "        ]),\n",
-        "        Classification(class_type=Classification.Type.RADIO, instructions=\"radio\", options=[\n",
-        "            Option(value=\"first_radio_answer\"),\n",
-        "            Option(value=\"second_radio_answer\")\n",
-        "        ]),                                      \n",
-        "])"
-      ]
+      "id": "3mQh-yQ2TLmd",
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
-      "id": "PhfXaqDB7hRt",
-      "metadata": {
-        "id": "PhfXaqDB7hRt"
-      },
-      "outputs": [],
       "source": [
-        "mal_project = client.create_project(name=\"tiled_imagery_mal_project\", media_type=MediaType.Geospatial_Tile)\n",
-        "li_project = client.create_project(name=\"tiled_imagery_import_project\", media_type=MediaType.Geospatial_Tile)\n",
+        "####### Polyline #######\n",
+        "# Coordinates\n",
+        "coords = [[-99.2257610436396,19.418710777269197],[-99.22610441645385,19.341320919070036],[-99.1464419235419,19.341644802568833],[-99.14781541479901,19.420653145480912],[-99.22439165216781,19.42047504824125],[-99.1989820639114,19.414324129199997]]\n",
+        "line_points = []\n",
+        "line_points_ndjson = []\n",
         "\n",
+        "for sub in coords: \n",
+        "  line_points.append(Point(x=sub[0], y=sub[1]))\n",
+        "  line_points_ndjson.append({\"x\":sub[0], \"y\":sub[1]})\n",
         "\n",
-        "dataset = client.create_dataset(name=\"tiled_imagery_annotation_import_demo_dataset\")\n",
-        "editor = next(client.get_labeling_frontends(where=LabelingFrontend.name == \"Editor\"))\n",
+        "# Python Annotation \n",
+        "polyline_annotation = ObjectAnnotation(\n",
+        "  name = \"polyline_geo\",\n",
+        "  value = Line(points=line_points),\n",
+        ")\n",
         "\n",
-        "mal_project.setup(editor, ontology_builder.asdict())\n",
-        "mal_project.datasets.connect(dataset)\n",
         "\n",
-        "li_project.setup(editor, ontology_builder.asdict())\n",
-        "li_project.datasets.connect(dataset)"
-      ]
+        "# NDJSON \n",
+        "polyline_annotation_ndjson = {\n",
+        "    \"name\": \"polyline_geo\",\n",
+        "    \"line\": line_points_ndjson\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "f3OoBByTTYda"
+      },
+      "id": "f3OoBByTTYda",
+      "execution_count": null,
+      "outputs": []
     },
     {
-      "cell_type": "markdown",
-      "id": "1zAp5kCxB2kp",
+      "cell_type": "code",
+      "source": [
+        "####### Polygon #######\n",
+        "# Coordinates in the desired EPSG coordinate system\n",
+        "coords_polygon = [[-99.2103219219407,19.382085377579806],[-99.21375304694426,19.362328387802638],[-99.19006492831323,19.36297607074792],[-99.19281196088781,19.381437207301936],[-99.2103219219407,19.382085377579806]]\n",
+        "polygon_points = []\n",
+        "polygon_points_ndjson = []\n",
+        "\n",
+        "for sub in coords_polygon: \n",
+        "  polygon_points.append(Point(x=sub[0], y=sub[1]))\n",
+        "  polygon_points_ndjson.append({\"x\":sub[0], \"y\":sub[1]})\n",
+        "\n",
+        "# Python Annotation \n",
+        "polygon_annotation = ObjectAnnotation(\n",
+        "  name = \"polygon_geo\",\n",
+        "  value = Polygon(points=polygon_points),\n",
+        ")\n",
+        "\n",
+        "# NDJSON \n",
+        "polygon_annotation_ndjson = {\n",
+        "    \"name\": \"polygon_geo\",\n",
+        "    \"polygon\": polygon_points_ndjson\n",
+        "}"
+      ],
       "metadata": {
-        "id": "1zAp5kCxB2kp"
+        "id": "BSjRhlFvTdfS"
       },
-      "source": [
-        "### Create Tiled Image Data"
-      ]
+      "id": "BSjRhlFvTdfS",
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
-      "id": "IlwNSdRQB4gC",
+      "source": [
+        "####### Bounding Box #######\n",
+        "coord_object = {\n",
+        "      \"coordinates\": [\n",
+        "        [\n",
+        "            [\n",
+        "                -99.210528,\n",
+        "                19.405662\n",
+        "            ],\n",
+        "            [\n",
+        "                -99.210528,\n",
+        "                19.400499\n",
+        "            ],\n",
+        "            [\n",
+        "                -99.205348,\n",
+        "                19.400499\n",
+        "            ],\n",
+        "            [\n",
+        "                -99.205348,\n",
+        "                19.405662\n",
+        "            ],\n",
+        "            [\n",
+        "                -99.210528,\n",
+        "                19.405662\n",
+        "            ]\n",
+        "          ]\n",
+        "        ]\n",
+        "      \n",
+        "    }\n",
+        "\n",
+        "\n",
+        "bbox_top_left = Point(x=-99.21052827588443, y=19.405662413477728)\n",
+        "bbox_bottom_right = Point(x=-99.20534818927473, y=19.400498983095076)\n",
+        "\n",
+        "# Python Annotation\n",
+        "bbox_annotation = ObjectAnnotation(\n",
+        "  name = \"bbox_geo\",\n",
+        "  value = Rectangle(start=bbox_top_left, end=bbox_bottom_right)\n",
+        ")\n",
+        "\n",
+        "\n",
+        "# NDJSON\n",
+        "bbox_annotation_ndjson = {\n",
+        "    \"name\" : \"bbox_geo\",\n",
+        "    \"bbox\" : {\n",
+        "        'top': coord_object[\"coordinates\"][0][1][1],\n",
+        "        'left': coord_object[\"coordinates\"][0][1][0],\n",
+        "        'height': coord_object[\"coordinates\"][0][3][1] - coord_object[\"coordinates\"][0][1][1],        \n",
+        "        'width': coord_object[\"coordinates\"][0][3][0] - coord_object[\"coordinates\"][0][1][0]\n",
+        "    }\n",
+        "}\n"
+      ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "IlwNSdRQB4gC",
-        "outputId": "2d398791-1aeb-4b30-c017-07749cb25721"
+        "id": "oLX3adDOTmYS"
       },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<Task ID: cl084bxk04ov01085ct4x094q>"
-            ]
-          },
-          "execution_count": 6,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+      "id": "oLX3adDOTmYS",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "####### Classification - radio (single choice) #######\n",
+        "\n",
+        "# Python Annotation \n",
+        "radio_annotation = ClassificationAnnotation(\n",
+        "    name=\"radio_question_geo\", \n",
+        "    value=Radio(answer=ClassificationAnswer(name=\"first_radio_answer\"))\n",
+        ")\n",
+        "\n",
+        "# NDJSON \n",
+        "radio_annotation_ndjson = {\n",
+        "    \"name\": \"radio_question_geo\",\n",
+        "    \"answer\": { \"name\": \"first_radio_answer\"}\n",
+        "}"
       ],
+      "metadata": {
+        "id": "OsWf8Y_mTxLm"
+      },
+      "id": "OsWf8Y_mTxLm",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
       "source": [
-        "# This notebook only uploads data for a single dataRow.\n",
+        "####### Classification - Checklist (multi-choice) #######\n",
         "\n",
-        "# Select the region to label within the image\n",
-        "top_left_bound = Point(x=-122.31764674186705, y=37.87276155898985)\n",
-        "bottom_right_bound = Point(x=-122.31635199317932, y=37.87398109727749)\n",
+        "coord_object_checklist = {\n",
+        "    \"coordinates\": [\n",
+        "        [\n",
+        "          [\n",
+        "              -99.173225,\n",
+        "              19.406409\n",
+        "          ],\n",
+        "          [\n",
+        "              -99.173225,\n",
+        "              19.398962\n",
+        "          ],\n",
+        "          [\n",
+        "              -99.192111,\n",
+        "              19.398962\n",
+        "          ],\n",
+        "          [\n",
+        "              -99.192111,\n",
+        "              19.406409\n",
+        "          ],\n",
+        "          [\n",
+        "              -99.173225,\n",
+        "              19.406409\n",
+        "          ]\n",
+        "      ]\n",
+        "    ]          \n",
+        "}\n",
         "\n",
-        "epsg = EPSG.EPSG4326\n",
-        "bounds = TiledBounds(epsg=epsg, bounds=[top_left_bound, bottom_right_bound])\n",
-        "tile_layer = TileLayer(\n",
-        "    url=\"https://api.mapbox.com/styles/v1/mapbox/satellite-streets-v11/tiles/{z}/{x}/{y}?access_token=pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw\"\n",
+        "# Python Annotation\n",
+        "bbox_with_checklist_subclass = ObjectAnnotation(\n",
+        "    name=\"bbox_checklist_geo\",\n",
+        "    value=Rectangle(\n",
+        "        start=Point(x=-99.17322500305542, y=19.40640857294981), # Top left\n",
+        "        end=Point(x=-99.19211050784055, y=19.398962049791287), # Bottom right\n",
+        "    ),\n",
+        "    classifications=[\n",
+        "        ClassificationAnnotation(\n",
+        "            name=\"checklist_class_name\",\n",
+        "            value=Checklist(\n",
+        "                answer=[ClassificationAnswer(name=\"first_checklist_answer\")]\n",
+        "            )\n",
+        "        )\n",
+        "    ]\n",
         ")\n",
         "\n",
-        "tiled_image_data = TiledImageData(tile_layer=tile_layer,\n",
-        "                                  tile_bounds=bounds,\n",
-        "                                  zoom_levels=[17, 23])\n",
         "\n",
-        "dataset.create_data_rows([tiled_image_data.asdict()])"
-      ]
+        "# NDJSON \n",
+        "bbox_with_checklist_subclass_ndjson = {\n",
+        "    \"name\": \"bbox_checklist_geo\", \n",
+        "    \"classifications\": [{\n",
+        "        \"name\": \"checklist_class_name\",\n",
+        "        \"answer\": [\n",
+        "            { \"name\":\"first_checklist_answer\" }\n",
+        "        ]   \n",
+        "    }],\n",
+        "    \"bbox\": {\n",
+        "        'top': coord_object_checklist[\"coordinates\"][0][1][1],\n",
+        "        'left': coord_object_checklist[\"coordinates\"][0][1][0],\n",
+        "        'height': coord_object_checklist[\"coordinates\"][0][3][1] - coord_object_checklist[\"coordinates\"][0][1][1],        \n",
+        "        'width': coord_object_checklist[\"coordinates\"][0][3][0] - coord_object_checklist[\"coordinates\"][0][1][0]\n",
+        "    }\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "2IZXIWP2UcOJ"
+      },
+      "id": "2IZXIWP2UcOJ",
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
-      "id": "Un4Yz7NKV1SV",
+      "source": [
+        "####### Classification free form text with bbox #######\n",
+        "\n",
+        "coord_object_text ={\n",
+        "    \"coordinates\": [\n",
+        "        [\n",
+        "                [\n",
+        "                    -99.181809,\n",
+        "                    19.349418\n",
+        "                ],\n",
+        "                [\n",
+        "                    -99.181809,\n",
+        "                    19.357514\n",
+        "                ],\n",
+        "                [\n",
+        "                    -99.153996,\n",
+        "                    19.357514\n",
+        "                ],\n",
+        "                [\n",
+        "                    -99.153996,\n",
+        "                    19.349418\n",
+        "                ],\n",
+        "                [\n",
+        "                    -99.181809,\n",
+        "                    19.349418\n",
+        "                ]\n",
+        "            ]\n",
+        "        ]\n",
+        "}\n",
+        "# Python Annotation\n",
+        "bbox_with_free_text_subclass = ObjectAnnotation(\n",
+        "    name=\"bbox_text_geo\",\n",
+        "    value=Rectangle(\n",
+        "        start=Point(x=-99.1818093234123, y=19.34941781372347), # Top left\n",
+        "        end=Point(x=-99.15399612545598, y=19.357514306583965), # Bottom right\n",
+        "    ),\n",
+        "    classifications=[\n",
+        "        ClassificationAnnotation(\n",
+        "            name=\"free_text_geo\",\n",
+        "            value=Text(answer=\"sample text\")\n",
+        "        )\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "# NDJSON \n",
+        "bbox_with_free_text_subclass_ndjson = {\n",
+        "    \"name\":\"bbox_text_geo\",\n",
+        "    \"classifications\": [{\n",
+        "        \"name\": \"free_text_geo\",\n",
+        "        \"answer\": \"sample text\"\n",
+        "    }],\n",
+        "    \"bbox\": {\n",
+        "        'top': coord_object_text[\"coordinates\"][0][1][1],\n",
+        "        'left': coord_object_text[\"coordinates\"][0][1][0],\n",
+        "        'height': coord_object_text[\"coordinates\"][0][3][1] - coord_object_text[\"coordinates\"][0][1][1],        \n",
+        "        'width': coord_object_text[\"coordinates\"][0][3][0] - coord_object_text[\"coordinates\"][0][1][0]\n",
+        "    }\n",
+        "}"
+      ],
       "metadata": {
-        "id": "Un4Yz7NKV1SV"
+        "id": "bnjcIB7FU21R"
       },
-      "outputs": [],
+      "id": "bnjcIB7FU21R",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
       "source": [
-        "from PIL import Image"
-      ]
+        "####### Classification - Checklist (multi-choice) #######\n",
+        "\n",
+        "# Python Annotation\n",
+        "checklist_annotation = ClassificationAnnotation(\n",
+        "    name=\"checklist_question_geo\",\n",
+        "    value=Checklist(answer = [\n",
+        "        ClassificationAnswer(name = \"first_checklist_answer\"),\n",
+        "        ClassificationAnswer(name = \"second_checklist_answer\"),\n",
+        "        ClassificationAnswer(name = \"third_checklist_answer\")\n",
+        "    ])\n",
+        "  )\n",
+        "\n",
+        "\n",
+        "# NDJSON\n",
+        "checklist_annotation_ndjson = {\n",
+        "  'name': 'checklist_question_geo',\n",
+        "  'answer': [\n",
+        "    {'name': 'first_checklist_answer'},\n",
+        "    {'name': 'second_checklist_answer'},\n",
+        "    {'name': 'third_checklist_answer'},\n",
+        "  ]\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "hIkLI1q7glMi"
+      },
+      "id": "hIkLI1q7glMi",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Upload Annotations - putting it all together\n"
+      ],
+      "metadata": {
+        "id": "_DHMCHBhh1j4"
+      },
+      "id": "_DHMCHBhh1j4"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Import data rows into Catalog"
+      ],
+      "metadata": {
+        "id": "BjuYchMdh7ro"
+      },
+      "id": "BjuYchMdh7ro"
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
-      "id": "vxhm6aXtV0o-",
+      "execution_count": null,
+      "id": "IlwNSdRQB4gC",
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 433
+          "base_uri": "https://localhost:8080/"
         },
-        "id": "vxhm6aXtV0o-",
-        "outputId": "cc30af26-775a-43c5-8bc4-2495c18239ae"
+        "id": "IlwNSdRQB4gC",
+        "outputId": "ae16d93c-82e1-462b-b0cf-eee18b104454"
       },
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n"
+            "None\n"
           ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=376x399 at 0x7FC0F7646950>"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
         }
       ],
       "source": [
-        "Image.fromarray(tiled_image_data.value)"
+        "\n",
+        "asset = [{\n",
+        "    \"row_data\": {\n",
+        "    \"tile_layer_url\": \"https://storage.googleapis.com/labelbox-datasets/geospatial-sample-data/sample-geotiff.tif\"\n",
+        "      },\n",
+        "    \"global_key\": str(uuid.uuid4()),\n",
+        "    \"media_type\": \"TMS_GEO\"\n",
+        "}]\n",
+        "\n",
+        "dataset = client.create_dataset(name=\"geo_demo_dataset\")\n",
+        "task = dataset.create_data_rows(asset)\n",
+        "task.wait_till_done()\n",
+        "print(task.errors)"
       ]
     },
     {
       "cell_type": "markdown",
-      "id": "aiA4rz_V74My",
-      "metadata": {
-        "id": "aiA4rz_V74My"
-      },
       "source": [
-        "### Create Label using Annotation Type Objects\n",
-        "* It is recommended to use the Python SDK's annotation types for importing into Labelbox."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "v3HA1bJV76ls",
+        "### Step 2: Create/select an ontology\n",
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name`/`instructions` fields in your annotations to ensure the correct feature schemas are matched.\n",
+        "\n",
+        "For example, when we create the bounding box annotation [above](https://colab.research.google.com/drive/1_sPVTktS1AVvFMCg-GR4q_g2ukQaR4Oq#scrollTo=2IZXIWP2UcOJ&line=2&uniqifier=1), we provided the `name` as `bbox_checklist_geo`. Now, when we setup our ontology, we must ensure that the name of my bounding box tool is also `bbox_checklist_geo`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+      ],
       "metadata": {
-        "id": "v3HA1bJV76ls"
+        "id": "y_tWMvxilTq9"
       },
-      "source": [
-        "### Object Annotations"
-      ]
+      "id": "y_tWMvxilTq9"
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
-      "id": "0WuZ7b_yAett",
+      "execution_count": null,
+      "id": "suburban-crowd",
       "metadata": {
-        "id": "0WuZ7b_yAett"
+        "id": "suburban-crowd"
       },
       "outputs": [],
       "source": [
-        "def create_objects():\n",
-        "  # Point Annotation\n",
-        "  point = Point(x=100,y=100)\n",
-        "  point_annotation = ObjectAnnotation(value=point, name=\"point\")\n",
-        "  # Bounding Box Annotation\n",
-        "  rectangle = Rectangle(start=Point(x=30,y=30), end=Point(x=200,y=200))\n",
-        "  rectangle_annotation = ObjectAnnotation(value=rectangle, name=\"box\")\n",
-        "  # Polyline Annotation\n",
-        "  line = Line(points=[Point(x=60,y=70), Point(x=65,y=100), Point(x=80,y=130), Point(x=40,y=200)])\n",
-        "  line_annotation = ObjectAnnotation(value=line, name=\"line\")\n",
-        "  # Polygon Annotation\n",
-        "  hsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\n",
-        "  mask = cv2.inRange(hsv, (50, 10, 25), (100, 150, 255))\n",
-        "  kernel = np.ones((5, 5), np.uint8)\n",
-        "  mask = cv2.erode(mask, kernel)\n",
-        "  mask = cv2.dilate(mask, kernel)\n",
-        "  mask_annotation = MaskData.from_2D_arr(mask)\n",
-        "  mask_data = Mask(mask=mask_annotation, color=[255, 255, 255])\n",
-        "  h, w, _ = tiled_image_data.value.shape\n",
-        "  pixel_bounds = TiledBounds(epsg=EPSG.SIMPLEPIXEL,\n",
-        "                            bounds=[Point(x=0, y=0),\n",
-        "                                    Point(x=w, y=h)])\n",
-        "  transformer = EPSGTransformer.create_pixel_to_geo_transformer(\n",
-        "      src_epsg=pixel_bounds.epsg,\n",
-        "      pixel_bounds=pixel_bounds,\n",
-        "      geo_bounds=tiled_image_data.tile_bounds,\n",
-        "      zoom=17)\n",
-        "  pixel_polygons = mask_data.shapely.simplify(3)\n",
-        "  geo_polygon = transformer(Polygon.from_shapely(pixel_polygons))\n",
-        "  polygon_annotation = ObjectAnnotation(value=geo_polygon, name=\"polygon\")\n",
-        "  return point_annotation, rectangle_annotation, line_annotation, polygon_annotation"
+        "ontology_builder = OntologyBuilder(\n",
+        "    tools=[\n",
+        "        Tool(tool=Tool.Type.POINT, name=\"point_geo\"),\n",
+        "        Tool(tool=Tool.Type.LINE, name=\"polyline_geo\"),\n",
+        "        Tool(tool=Tool.Type.POLYGON, name=\"polygon_geo\"),\n",
+        "        Tool(tool=Tool.Type.BBOX, name=\"bbox_geo\"), \n",
+        "        Tool( \n",
+        "          tool=Tool.Type.BBOX, \n",
+        "          name=\"bbox_checklist_geo\",\n",
+        "          classifications=[\n",
+        "                Classification(\n",
+        "                    class_type=Classification.Type.CHECKLIST,\n",
+        "                    instructions=\"checklist_class_name\",\n",
+        "                    options=[\n",
+        "                      Option(value=\"first_checklist_answer\")\n",
+        "                    ]\n",
+        "                ),\n",
+        "            ]\n",
+        "          ),\n",
+        "        Tool( \n",
+        "          tool=Tool.Type.BBOX, \n",
+        "          name=\"bbox_text_geo\",\n",
+        "          classifications=[\n",
+        "                Classification(\n",
+        "                    class_type=Classification.Type.TEXT,\n",
+        "                    instructions=\"free_text_geo\"\n",
+        "                ),\n",
+        "            ]\n",
+        "          )    \n",
+        "      ],\n",
+        "      classifications = [\n",
+        "          Classification(\n",
+        "              class_type=Classification.Type.CHECKLIST, \n",
+        "              instructions=\"checklist_question_geo\",\n",
+        "              options=[\n",
+        "                  Option(value=\"first_checklist_answer\"),\n",
+        "                  Option(value=\"second_checklist_answer\"), \n",
+        "                  Option(value=\"third_checklist_answer\")\n",
+        "              ]\n",
+        "          ), \n",
+        "          Classification(\n",
+        "              class_type=Classification.Type.RADIO, \n",
+        "              instructions=\"radio_question_geo\",\n",
+        "              options=[\n",
+        "                  Option(value=\"first_radio_answer\")\n",
+        "              ]\n",
+        "          )\n",
+        "          \n",
+        "      ]\n",
+        ")\n",
+        "\n",
+        "ontology = client.create_ontology(\"Ontology Geospatial Annotations\", ontology_builder.asdict())"
       ]
     },
     {
       "cell_type": "markdown",
-      "id": "JnVJ-PlYATIQ",
+      "source": [
+        "### Step 3: Create a labeling project\n",
+        "Connect the ontology to the labeling project "
+      ],
       "metadata": {
-        "id": "JnVJ-PlYATIQ"
+        "id": "_OeW7Fnrl-Xy"
       },
-      "source": [
-        "### Classification Annotations"
-      ]
+      "id": "_OeW7Fnrl-Xy"
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
-      "id": "iGAgeClVAUfF",
+      "execution_count": null,
+      "id": "PhfXaqDB7hRt",
       "metadata": {
-        "id": "iGAgeClVAUfF"
+        "id": "PhfXaqDB7hRt"
       },
       "outputs": [],
       "source": [
-        "def create_classifications():\n",
-        "  # Text Classification\n",
-        "  text = Text(answer=\"the answer to the text question\")\n",
-        "  text_annotation = ClassificationAnnotation(value=text, name=\"text\")\n",
-        "  # Checklist Classification\n",
-        "  checklist = Checklist(answer=[ClassificationAnswer(name=\"first_checklist_answer\"),ClassificationAnswer(name=\"second_checklist_answer\")])\n",
-        "  checklist_annotation = ClassificationAnnotation(value=checklist, name=\"checklist\")\n",
-        "  # Radio Classification\n",
-        "  radio = Radio(answer = ClassificationAnswer(name = \"second_radio_answer\"))\n",
-        "  radio_annotation = ClassificationAnnotation(value=radio, name=\"radio\")\n",
-        "  return checklist_annotation, radio_annotation, text_annotation"
+        "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n",
+        "# Queue mode will be deprecated once dataset mode is deprecated\n",
+        "\n",
+        "project = client.create_project(name=\"geo_project_demo\",\n",
+        "                                    queue_mode=QueueMode.Batch,\n",
+        "                                    media_type=MediaType.Geospatial_Tile)\n",
+        "\n",
+        "\n",
+        "project.setup_editor(ontology)\n",
+        "\n",
+        "######################### DATASET CONSENSUS OPTION ########################\n",
+        "#Note that dataset base projects will be deprecated in the near future.\n",
+        "\n",
+        "#To use Datasets/Consensus instead of Batches/Benchmarks use the following query: \n",
+        "#In this case, 10% of all data rows need to be annotated by three labelers.\n",
+        "\n",
+        "# dataset_project = client.create_project(name=\"dataset-test-project\",\n",
+        "#                                 description=\"a description\",\n",
+        "#                                 media_type=MediaType.Text,\n",
+        "#                                 auto_audit_percentage=0.1,\n",
+        "#                                 auto_audit_number_of_labels=3,\n",
+        "#                                 queue_mode=QueueMode.Dataset)\n",
+        "\n",
+        "# dataset_project.datasets.connect(dataset)"
       ]
     },
     {
       "cell_type": "markdown",
-      "id": "IjOnzuJXAuOf",
+      "source": [
+        "### Step 4: Send a batch of data rows to the project "
+      ],
       "metadata": {
-        "id": "IjOnzuJXAuOf"
+        "id": "g23ejnMQw1Ix"
       },
-      "source": [
-        "### Create a Label object with all of our annotations"
-      ]
+      "id": "g23ejnMQw1Ix"
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
-      "id": "zFqilM24AxqP",
+      "source": [
+        "# Setup Batches and Ontology\n",
+        "\n",
+        "# Create a batch to send to your MAL project\n",
+        "batch = project.create_batch(\n",
+        "  \"first-batch-geo-demo\", # Each batch in a project must have a unique name\n",
+        "  dataset.export_data_rows(), # Paginated collection of data row objects\n",
+        "  5 # priority between 1(Highest) - 5(lowest)\n",
+        ")\n",
+        "\n",
+        "print(\"Batch: \", batch)"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "zFqilM24AxqP",
-        "outputId": "f0b5b1ea-56ee-4f22-bd70-90903cda36d5"
+        "id": "FHXR6nyKw-8f",
+        "outputId": "e527a8b4-0a93-46c6-97d3-4f9b958e78e3"
       },
+      "id": "FHXR6nyKw-8f",
+      "execution_count": null,
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n",
-            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n"
+            "Batch:  <Batch {\n",
+            "    \"consensus_settings_json\": \"{\\\"numberOfLabels\\\":1,\\\"coveragePercentage\\\":0}\",\n",
+            "    \"created_at\": \"2022-12-22 16:46:54+00:00\",\n",
+            "    \"name\": \"first-batch-geo-demo\",\n",
+            "    \"size\": 1,\n",
+            "    \"uid\": \"3d7f91f0-8218-11ed-85ff-9fd3911c6c0e\",\n",
+            "    \"updated_at\": \"2022-12-22 16:46:54+00:00\"\n",
+            "}>\n"
           ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "{'annotations': [ObjectAnnotation(name='point', feature_schema_id=None, extra={}, value=Point(extra={}, x=100.0, y=100.0), classifications=[]),\n",
-              "  ObjectAnnotation(name='box', feature_schema_id=None, extra={}, value=Rectangle(extra={}, start=Point(extra={}, x=30.0, y=30.0), end=Point(extra={}, x=200.0, y=200.0)), classifications=[]),\n",
-              "  ObjectAnnotation(name='line', feature_schema_id=None, extra={}, value=Line(extra={}, points=[Point(extra={}, x=60.0, y=70.0), Point(extra={}, x=65.0, y=100.0), Point(extra={}, x=80.0, y=130.0), Point(extra={}, x=40.0, y=200.0)]), classifications=[]),\n",
-              "  ObjectAnnotation(name='polygon', feature_schema_id=None, extra={}, value=Polygon(extra={}, points=[Point(extra={}, x=-122.31704253148526, y=37.87398109727749), Point(extra={}, x=-122.3169872893932, y=37.87385883871054), Point(extra={}, x=-122.3169665736087, y=37.87385272577688), Point(extra={}, x=-122.31695621571643, y=37.873809935226944), Point(extra={}, x=-122.31692168940889, y=37.873779370533214), Point(extra={}, x=-122.31691823677814, y=37.87373963641242), Point(extra={}, x=-122.31685954205533, y=37.873684619902065), Point(extra={}, x=-122.31686299468609, y=37.87366322458137), Point(extra={}, x=-122.31682156311703, y=37.873641829254474), Point(extra={}, x=-122.31683192100932, y=37.873608208013955), Point(extra={}, x=-122.3168042999633, y=37.87360209505948), Point(extra={}, x=-122.31679394207103, y=37.87356541732201), Point(extra={}, x=-122.31676632102501, y=37.87356236084304), Point(extra={}, x=-122.31676632102501, y=37.87351651364362), Point(extra={}, x=-122.31674215260972, y=37.87351651364362), Point(extra={}, x=-122.31671107893294, y=37.8734828923459), Point(extra={}, x=-122.31671107893294, y=37.873452327516496), Point(extra={}, x=-122.31667655262542, y=37.87343093212835), Point(extra={}, x=-122.3166558368409, y=37.87336980241369), Point(extra={}, x=-122.31660404737961, y=37.87334840700161), Point(extra={}, x=-122.31660059474883, y=37.873320898605485), Point(extra={}, x=-122.31657642633357, y=37.8733178421164), Point(extra={}, x=-122.31652118424154, y=37.87322920387756), Point(extra={}, x=-122.31653499476452, y=37.87320780842463), Point(extra={}, x=-122.31643486847268, y=37.87312222655072), Point(extra={}, x=-122.3163520053346, y=37.87296328852114), Point(extra={}, x=-122.3163520053346, y=37.87398109727749), Point(extra={}, x=-122.31704253148526, y=37.87398109727749)]), classifications=[]),\n",
-              "  ClassificationAnnotation(name='text', feature_schema_id=None, extra={}, value=Text(answer='the answer to the text question')),\n",
-              "  ClassificationAnnotation(name='checklist', feature_schema_id=None, extra={}, value=Checklist(name='checklist', answer=[ClassificationAnswer(name='first_checklist_answer', feature_schema_id=None, extra={}, keyframe=None), ClassificationAnswer(name='second_checklist_answer', feature_schema_id=None, extra={}, keyframe=None)])),\n",
-              "  ClassificationAnnotation(name='radio', feature_schema_id=None, extra={}, value=Radio(answer=ClassificationAnswer(name='second_radio_answer', feature_schema_id=None, extra={}, keyframe=None)))],\n",
-              " 'data': TextData(file_path=None,text=None,url=None),\n",
-              " 'extra': {},\n",
-              " 'uid': None}"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
         }
-      ],
+      ]
+    },
+    {
+      "cell_type": "markdown",
       "source": [
-        "datarow_id = next(dataset.data_rows()).uid\n",
-        "tiled_image_data.uid = datarow_id\n",
-        "\n",
-        "point_annotation, rectangle_annotation, line_annotation, polygon_annotation = create_objects()\n",
-        "checklist_annotation, radio_annotation, text_annotation = create_classifications()\n",
+        "### Step 5: Create the annotations payload \n",
+        "Create the annotations payload using the snippets of code above\n",
         "\n",
+        "Labelbox support two formats for the annotations payload: NDJSON and Python Annotation types. Both are described below. \n"
+      ],
+      "metadata": {
+        "id": "OGUtGAXjxL5m"
+      },
+      "id": "OGUtGAXjxL5m"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Python annotations\n",
+        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1_sPVTktS1AVvFMCg-GR4q_g2ukQaR4Oq#scrollTo=AmKgfZ8ZALT_)."
+      ],
+      "metadata": {
+        "id": "lVXwyg7ZxVOX"
+      },
+      "id": "lVXwyg7ZxVOX"
+    },
+    {
+      "cell_type": "code",
+      "source": [
         "label = Label(\n",
-        "    data=tiled_image_data,\n",
+        "    data=ImageData(\n",
+        "        uid= next(dataset.export_data_rows()).uid\n",
+        "    ),\n",
         "    annotations = [\n",
-        "        point_annotation, rectangle_annotation, line_annotation, polygon_annotation,\n",
-        "        text_annotation, checklist_annotation, radio_annotation\n",
+        "        point_annotation,\n",
+        "        polyline_annotation,\n",
+        "        polygon_annotation,\n",
+        "        bbox_annotation,\n",
+        "        radio_annotation,\n",
+        "        bbox_with_checklist_subclass,  \n",
+        "        bbox_with_free_text_subclass,\n",
+        "        checklist_annotation\n",
         "    ]\n",
         ")\n",
         "\n",
-        "# Create urls to mask data for upload\n",
         "def signing_function(obj_bytes: bytes) -> str:\n",
         "    url = client.upload_data(content=obj_bytes, sign=True)\n",
         "    return url\n",
         "\n",
-        "\n",
         "label.add_url_to_masks(signing_function)\n",
         "\n",
-        "label.__dict__"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "GHL758kcBC87",
+        "# Convert our label from a Labelbox class object to the underlying NDJSON format required for upload \n",
+        "label_ndjson = list(NDJsonConverter.serialize([label]))\n",
+        "label_ndjson"
+      ],
       "metadata": {
-        "id": "GHL758kcBC87"
+        "id": "3_LZLZY7xS30"
       },
-      "source": [
-        "### Model Assisted Labeling "
-      ]
+      "id": "3_LZLZY7xS30",
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
-      "id": "portable-grenada",
+      "source": [
+        "### NDJSON annotations\n",
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1_sPVTktS1AVvFMCg-GR4q_g2ukQaR4Oq#scrollTo=AmKgfZ8ZALT_)."
+      ],
       "metadata": {
-        "id": "portable-grenada"
+        "id": "LUJtuLa00gZF"
       },
+      "id": "LUJtuLa00gZF"
+    },
+    {
+      "cell_type": "code",
       "source": [
-        "To do model-assisted labeling, we need to convert a Label object into an NDJSON. \n",
-        "\n",
-        "This is easily done with using the NDJSONConverter class\n",
+        "label_ndjson_method2 = []\n",
         "\n",
-        "We will create a Label called mal_label which has the same original structure as the label above\n",
-        "\n",
-        "Notes:\n",
-        "* the NDJsonConverter takes in a list of labels"
-      ]
+        "for annotations in [point_annotation_ndjson,\n",
+        "                    polyline_annotation_ndjson,\n",
+        "                    polygon_annotation_ndjson,\n",
+        "                    bbox_annotation_ndjson,\n",
+        "                    radio_annotation_ndjson,\n",
+        "                    bbox_with_checklist_subclass_ndjson,  \n",
+        "                    bbox_with_free_text_subclass_ndjson,\n",
+        "                    checklist_annotation_ndjson]:\n",
+        "  annotations.update({\n",
+        "      'uuid' : str(uuid.uuid4()),\n",
+        "      'dataRow': {\n",
+        "          'id':  next(dataset.export_data_rows()).uid\n",
+        "      }\n",
+        "  })\n",
+        "  label_ndjson_method2.append(annotations)\n",
+        "  "
+      ],
+      "metadata": {
+        "id": "M88fD66M0f63"
+      },
+      "id": "M88fD66M0f63",
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "id": "TIhOw4b0AwsU",
+      "source": [
+        "label_ndjson_method2"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "TIhOw4b0AwsU",
-        "outputId": "a0c9874e-e9a6-45cb-bac2-918e2d188638"
+        "id": "10pZtHgzfAJS",
+        "outputId": "e3b711e4-721c-43e7-a1ad-1aa2486f77e9"
       },
-      "outputs": [],
-      "source": [
-        "mal_label = Label(\n",
-        "    data=tiled_image_data,\n",
-        "    annotations = [\n",
-        "        point_annotation, rectangle_annotation, line_annotation, polygon_annotation,\n",
-        "        text_annotation, checklist_annotation, radio_annotation\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "\n",
-        "label.add_url_to_masks(signing_function)\n",
-        "\n",
-        "ndjson_labels = list(NDJsonConverter.serialize([mal_label]))\n",
-        "\n",
-        "ndjson_labels"
+      "id": "10pZtHgzfAJS",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[{'name': 'point_geo',\n",
+              "  'point': {'x': -99.21052827588443, 'y': 19.405662413477728},\n",
+              "  'uuid': '2b76f90f-9cd2-4bc8-b5cf-01d8d974f093',\n",
+              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              " {'name': 'polyline_geo',\n",
+              "  'line': [{'x': -99.2257610436396, 'y': 19.418710777269197},\n",
+              "   {'x': -99.22610441645385, 'y': 19.341320919070036},\n",
+              "   {'x': -99.1464419235419, 'y': 19.341644802568833},\n",
+              "   {'x': -99.14781541479901, 'y': 19.420653145480912},\n",
+              "   {'x': -99.22439165216781, 'y': 19.42047504824125},\n",
+              "   {'x': -99.1989820639114, 'y': 19.414324129199997}],\n",
+              "  'uuid': 'fe234598-6e87-4cdb-b530-bd5376d8ee02',\n",
+              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              " {'name': 'polygon_geo',\n",
+              "  'polygon': [{'x': -99.2103219219407, 'y': 19.382085377579806},\n",
+              "   {'x': -99.21375304694426, 'y': 19.362328387802638},\n",
+              "   {'x': -99.19006492831323, 'y': 19.36297607074792},\n",
+              "   {'x': -99.19281196088781, 'y': 19.381437207301936},\n",
+              "   {'x': -99.2103219219407, 'y': 19.382085377579806}],\n",
+              "  'uuid': '0d455b04-12d5-4520-949a-da138e3b0b27',\n",
+              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              " {'name': 'bbox_geo',\n",
+              "  'bbox': {'top': 19.400499,\n",
+              "   'left': -99.210528,\n",
+              "   'height': 0.005162999999999585,\n",
+              "   'width': 0.0051799999999957436},\n",
+              "  'uuid': '3965f9dc-de43-48fc-995e-c9153c7adec3',\n",
+              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              " {'name': 'radio_question_geo',\n",
+              "  'answer': {'name': 'first_radio_answer'},\n",
+              "  'uuid': '8028ad09-fa33-4544-b1ff-33487c3b5666',\n",
+              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              " {'name': 'bbox_checklist_geo',\n",
+              "  'classifications': [{'name': 'checklist_class_name',\n",
+              "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
+              "  'bbox': {'top': 19.398962,\n",
+              "   'left': -99.173225,\n",
+              "   'height': 0.007446999999999093,\n",
+              "   'width': -0.01888599999999485},\n",
+              "  'uuid': '867cc003-3955-4abc-8658-64f0c755b5f0',\n",
+              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              " {'name': 'bbox_text_geo',\n",
+              "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
+              "  'bbox': {'top': 19.357514,\n",
+              "   'left': -99.181809,\n",
+              "   'height': -0.008095999999998327,\n",
+              "   'width': 0.02781299999999476},\n",
+              "  'uuid': '11090695-aed7-40e7-9166-bea96960baaa',\n",
+              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              " {'name': 'checklist_question_geo',\n",
+              "  'answer': [{'name': 'first_checklist_answer'},\n",
+              "   {'name': 'second_checklist_answer'},\n",
+              "   {'name': 'third_checklist_answer'}],\n",
+              "  'uuid': 'f75806a8-1287-42ab-9786-ec27c267f228',\n",
+              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}}]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 113
+        }
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "id": "abstract-fifteen",
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Upload annotations to a project as pre-labels or complete labels\n"
+      ],
       "metadata": {
-        "id": "abstract-fifteen"
+        "id": "hy4ShxCc0P3F"
       },
-      "outputs": [],
+      "id": "hy4ShxCc0P3F"
+    },
+    {
+      "cell_type": "markdown",
       "source": [
-        "upload_job = MALPredictionImport.create_from_objects(\n",
-        "    client = client, \n",
-        "    project_id = mal_project.uid, \n",
-        "    name=\"upload_label_import_job_2\", \n",
-        "    predictions=ndjson_labels)"
-      ]
+        "#### Model-Assisted Labeling (MAL)\n",
+        "For the purpose of this tutorial only run one of the label_ndjosn  annotation type tools at the time (NDJSON or Annotation types). Delete the previous labels before uploading labels that use the 2nd method (ndjson)"
+      ],
+      "metadata": {
+        "id": "y6dL3p17VPQL"
+      },
+      "id": "y6dL3p17VPQL"
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
-      "id": "tr7BcvTNBblq",
+      "source": [
+        "# Upload MAL label for this data row in project\n",
+        "upload_job = MALPredictionImport.create_from_objects(\n",
+        "    client = client, \n",
+        "    project_id = project.uid, \n",
+        "    name=\"mal_import_job\"+str(uuid.uuid4()), \n",
+        "    ### use label_ndjson_method2 if labels were created using NDJSON tools\n",
+        "    predictions=label_ndjson)\n",
+        "\n",
+        "upload_job.wait_until_done();\n",
+        "print(\"Errors:\", upload_job.errors)\n",
+        "print(\"   \")"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "tr7BcvTNBblq",
-        "outputId": "f0613453-4de3-4da2-9ce8-ab987a6b8937"
+        "id": "SrAMnM9L0cAb",
+        "outputId": "7a98df67-ecc6-4d34-89cc-87d70face4fc"
       },
+      "id": "SrAMnM9L0cAb",
+      "execution_count": null,
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
-          "text": [
-            "INFO:labelbox.schema.annotation_import:Sleeping for 10 seconds...\n"
-          ]
-        },
-        {
           "name": "stdout",
-          "output_type": "stream",
           "text": [
-            "Errors: []\n"
+            "Errors: []\n",
+            "   \n"
           ]
         }
-      ],
-      "source": [
-        "# Errors will appear for each annotation that failed.\n",
-        "# Empty list means that there were no errors\n",
-        "# This will provide information only after the upload_job is complete, so we do not need to worry about having to rerun\n",
-        "print(\"Errors:\", upload_job.errors)"
       ]
     },
     {
       "cell_type": "markdown",
-      "id": "3DF2jQbyBb_t",
-      "metadata": {
-        "id": "3DF2jQbyBb_t"
-      },
       "source": [
-        "### Label Import"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "78FvYeXABegu",
+        "#### Label Import"
+      ],
       "metadata": {
-        "id": "78FvYeXABegu"
+        "id": "IXAcQfHl2_38"
       },
-      "source": [
-        "Label import is very similar to model-assisted labeling. We will create a Label called li_label which has the same original structure as the label above"
-      ]
+      "id": "IXAcQfHl2_38"
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "id": "W1AKXEL7Bekm",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "W1AKXEL7Bekm",
-        "outputId": "c072680c-3b27-4d7c-c44c-50a9d0e9e12f"
-      },
-      "outputs": [],
       "source": [
-        "#for the purpose of this notebook, we will need to reset the schema ids of our checklist and radio answers\n",
-        "point_annotation, rectangle_annotation, line_annotation, polygon_annotation = create_objects()\n",
-        "checklist_annotation, radio_annotation, text_annotation = create_classifications()\n",
-        "\n",
-        "li_label = Label(\n",
-        "    data=tiled_image_data,\n",
-        "    annotations = [\n",
-        "        point_annotation, rectangle_annotation, line_annotation, polygon_annotation,\n",
-        "        text_annotation, checklist_annotation, radio_annotation\n",
-        "    ]\n",
-        ")\n",
-        "\n",
-        "ndjson_labels = list(NDJsonConverter.serialize([li_label]))\n",
+        "# Upload label for this data row in project \n",
+        "upload_job = LabelImport.create_from_objects(\n",
+        "    client = client, \n",
+        "    project_id = project.uid, \n",
+        "    name=\"label_geo_import_job\"+str(uuid.uuid4()),  \n",
+        "    # user label_ndjson if labels were created using python annotation tools\n",
+        "    labels=label_ndjson_method2)\n",
         "\n",
-        "ndjson_labels"
-      ]
+        "upload_job.wait_until_done();\n",
+        "print(\"Errors:\", upload_job.errors)"
+      ],
+      "metadata": {
+        "id": "leIutAqA2_Uw"
+      },
+      "id": "leIutAqA2_Uw",
+      "execution_count": null,
+      "outputs": []
     },
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "OMgVLHbyBent",
+      "cell_type": "markdown",
+      "source": [
+        "### Optional deletions for cleanup"
+      ],
       "metadata": {
-        "id": "OMgVLHbyBent"
+        "id": "ZgaBbFA9vajf"
       },
-      "outputs": [],
-      "source": [
-        "upload_job = LabelImport.create_from_objects(\n",
-        "    client = client, \n",
-        "    project_id = li_project.uid, \n",
-        "    name=\"upload_label_import_job\", \n",
-        "    labels=ndjson_labels)"
-      ]
+      "id": "ZgaBbFA9vajf"
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
-      "id": "rFGU6AD2BerR",
+      "source": [
+        "#upload_job\n",
+        "# project.delete()\n",
+        "# dataset.delete()"
+      ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "rFGU6AD2BerR",
-        "outputId": "97c8efa5-2e39-455f-9ba9-78ff2ca3899a"
+        "id": "ZLM9L5ggGqHG"
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "INFO:labelbox.schema.annotation_import:Sleeping for 10 seconds...\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Errors: []\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(\"Errors:\", upload_job.errors)"
-      ]
+      "id": "ZLM9L5ggGqHG",
+      "execution_count": null,
+      "outputs": []
     }
   ],
   "metadata": {
     "colab": {
-      "collapsed_sections": [],
-      "name": "tiled_imagery_mal.ipynb",
       "provenance": []
     },
     "kernelspec": {

From 7215381116ca3e1603179674675429f11d16b853 Mon Sep 17 00:00:00 2001
From: Andrea Ovalle <74880762+ovalle15@users.noreply.github.com>
Date: Tue, 3 Jan 2023 18:26:35 -0500
Subject: [PATCH 2/7] Latest updates

1. Removed unused imports
2. Removed signing function
3. Updated file path
---
 examples/annotation_import/tiled.ipynb | 193 +++++++++++++++++--------
 1 file changed, 136 insertions(+), 57 deletions(-)

diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 81856667b..56b5bb09e 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -99,7 +99,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "id": "committed-richards",
       "metadata": {
         "id": "committed-richards"
@@ -115,13 +115,13 @@
         "import ndjson\n",
         "import gdal\n",
         "\n",
-        "from labelbox.data.annotation_types.data.tiled_image import TiledBounds, TiledImageData, TileLayer, EPSG, EPSGTransformer\n",
+        "\n",
         "from labelbox.data.serialization.ndjson.converter import NDJsonConverter\n",
         "from labelbox.schema.queue_mode import QueueMode\n",
         "from labelbox.schema.media_type import MediaType\n",
         "from labelbox.data.annotation_types import (\n",
-        "    Label, ImageData, ObjectAnnotation, MaskData,\n",
-        "    Rectangle, Point, Line, Mask, Polygon,\n",
+        "    Label, ImageData, ObjectAnnotation, \n",
+        "    Rectangle, Point, Line, Polygon,\n",
         "    Radio, Checklist, Text,\n",
         "    ClassificationAnnotation, ClassificationAnswer\n",
         ")\n",
@@ -142,7 +142,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "id": "affecting-myanmar",
       "metadata": {
         "id": "affecting-myanmar"
@@ -197,7 +197,7 @@
         "id": "3mQh-yQ2TLmd"
       },
       "id": "3mQh-yQ2TLmd",
-      "execution_count": null,
+      "execution_count": 4,
       "outputs": []
     },
     {
@@ -230,7 +230,7 @@
         "id": "f3OoBByTTYda"
       },
       "id": "f3OoBByTTYda",
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": []
     },
     {
@@ -262,7 +262,7 @@
         "id": "BSjRhlFvTdfS"
       },
       "id": "BSjRhlFvTdfS",
-      "execution_count": null,
+      "execution_count": 6,
       "outputs": []
     },
     {
@@ -323,7 +323,7 @@
         "id": "oLX3adDOTmYS"
       },
       "id": "oLX3adDOTmYS",
-      "execution_count": null,
+      "execution_count": 7,
       "outputs": []
     },
     {
@@ -347,7 +347,7 @@
         "id": "OsWf8Y_mTxLm"
       },
       "id": "OsWf8Y_mTxLm",
-      "execution_count": null,
+      "execution_count": 8,
       "outputs": []
     },
     {
@@ -421,7 +421,7 @@
         "id": "2IZXIWP2UcOJ"
       },
       "id": "2IZXIWP2UcOJ",
-      "execution_count": null,
+      "execution_count": 9,
       "outputs": []
     },
     {
@@ -489,7 +489,7 @@
         "id": "bnjcIB7FU21R"
       },
       "id": "bnjcIB7FU21R",
-      "execution_count": null,
+      "execution_count": 10,
       "outputs": []
     },
     {
@@ -522,7 +522,7 @@
         "id": "hIkLI1q7glMi"
       },
       "id": "hIkLI1q7glMi",
-      "execution_count": null,
+      "execution_count": 11,
       "outputs": []
     },
     {
@@ -547,14 +547,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 12,
       "id": "IlwNSdRQB4gC",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "IlwNSdRQB4gC",
-        "outputId": "ae16d93c-82e1-462b-b0cf-eee18b104454"
+        "outputId": "eabd5152-35af-471d-8eaa-d962e5eef8c2"
       },
       "outputs": [
         {
@@ -596,7 +596,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 13,
       "id": "suburban-crowd",
       "metadata": {
         "id": "suburban-crowd"
@@ -670,7 +670,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 22,
       "id": "PhfXaqDB7hRt",
       "metadata": {
         "id": "PhfXaqDB7hRt"
@@ -732,10 +732,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "FHXR6nyKw-8f",
-        "outputId": "e527a8b4-0a93-46c6-97d3-4f9b958e78e3"
+        "outputId": "300646b2-6b7a-4dfd-a02f-5494b9082f15"
       },
       "id": "FHXR6nyKw-8f",
-      "execution_count": null,
+      "execution_count": 23,
       "outputs": [
         {
           "output_type": "stream",
@@ -743,11 +743,11 @@
           "text": [
             "Batch:  <Batch {\n",
             "    \"consensus_settings_json\": \"{\\\"numberOfLabels\\\":1,\\\"coveragePercentage\\\":0}\",\n",
-            "    \"created_at\": \"2022-12-22 16:46:54+00:00\",\n",
+            "    \"created_at\": \"2023-01-03 23:20:49+00:00\",\n",
             "    \"name\": \"first-batch-geo-demo\",\n",
             "    \"size\": 1,\n",
-            "    \"uid\": \"3d7f91f0-8218-11ed-85ff-9fd3911c6c0e\",\n",
-            "    \"updated_at\": \"2022-12-22 16:46:54+00:00\"\n",
+            "    \"uid\": \"421bee80-8bbd-11ed-a6f6-1fd0e0fcd05c\",\n",
+            "    \"updated_at\": \"2023-01-03 23:20:49+00:00\"\n",
             "}>\n"
           ]
         }
@@ -796,22 +796,89 @@
         "    ]\n",
         ")\n",
         "\n",
-        "def signing_function(obj_bytes: bytes) -> str:\n",
-        "    url = client.upload_data(content=obj_bytes, sign=True)\n",
-        "    return url\n",
-        "\n",
-        "label.add_url_to_masks(signing_function)\n",
-        "\n",
         "# Convert our label from a Labelbox class object to the underlying NDJSON format required for upload \n",
         "label_ndjson = list(NDJsonConverter.serialize([label]))\n",
         "label_ndjson"
       ],
       "metadata": {
-        "id": "3_LZLZY7xS30"
+        "id": "3_LZLZY7xS30",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5ad70c40-1643-4329-90fa-52a019038d24"
       },
       "id": "3_LZLZY7xS30",
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[{'uuid': '9dccd23f-7485-4a09-b7ba-b4f6f6e6da31',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
+              "  'name': 'point_geo',\n",
+              "  'classifications': [],\n",
+              "  'point': {'x': -99.21052827588443, 'y': 19.405662413477728}},\n",
+              " {'uuid': '20a8bcfa-5114-461e-981f-2f12f9e5e5d5',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
+              "  'name': 'polyline_geo',\n",
+              "  'classifications': [],\n",
+              "  'line': [{'x': -99.2257610436396, 'y': 19.418710777269197},\n",
+              "   {'x': -99.22610441645385, 'y': 19.341320919070036},\n",
+              "   {'x': -99.1464419235419, 'y': 19.341644802568833},\n",
+              "   {'x': -99.14781541479901, 'y': 19.420653145480912},\n",
+              "   {'x': -99.22439165216781, 'y': 19.42047504824125},\n",
+              "   {'x': -99.1989820639114, 'y': 19.414324129199997}]},\n",
+              " {'uuid': '65b0bde8-d16a-414d-8e95-7f5f193fc986',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
+              "  'name': 'polygon_geo',\n",
+              "  'classifications': [],\n",
+              "  'polygon': [{'x': -99.2103219219407, 'y': 19.382085377579806},\n",
+              "   {'x': -99.21375304694426, 'y': 19.362328387802638},\n",
+              "   {'x': -99.19006492831323, 'y': 19.36297607074792},\n",
+              "   {'x': -99.19281196088781, 'y': 19.381437207301936},\n",
+              "   {'x': -99.2103219219407, 'y': 19.382085377579806}]},\n",
+              " {'uuid': 'df25ed6d-a1f0-48f1-94bb-065d6461ae54',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
+              "  'name': 'bbox_geo',\n",
+              "  'classifications': [],\n",
+              "  'bbox': {'top': 19.405662413477728,\n",
+              "   'left': -99.21052827588443,\n",
+              "   'height': -0.005163430382651768,\n",
+              "   'width': 0.0051800866096982645}},\n",
+              " {'name': 'radio_question_geo',\n",
+              "  'answer': {'name': 'first_radio_answer'},\n",
+              "  'uuid': '55055c07-b15e-4c47-a930-c700b17ede5f',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
+              " {'uuid': '924f7912-a0ff-4098-81dc-fd4c6e750872',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
+              "  'name': 'bbox_checklist_geo',\n",
+              "  'classifications': [{'name': 'checklist_class_name',\n",
+              "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
+              "  'bbox': {'top': 19.40640857294981,\n",
+              "   'left': -99.17322500305542,\n",
+              "   'height': -0.007446523158524343,\n",
+              "   'width': -0.018885504785131957}},\n",
+              " {'uuid': '43d65470-55a8-49ce-903b-3f8ec0574a5a',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
+              "  'name': 'bbox_text_geo',\n",
+              "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
+              "  'bbox': {'top': 19.34941781372347,\n",
+              "   'left': -99.1818093234123,\n",
+              "   'height': 0.00809649286049563,\n",
+              "   'width': 0.02781319795631987}},\n",
+              " {'name': 'checklist_question_geo',\n",
+              "  'uuid': 'd4f783b9-d1a2-4d7d-8283-a4d265cea81d',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
+              "  'answer': [{'name': 'first_checklist_answer'},\n",
+              "   {'name': 'second_checklist_answer'},\n",
+              "   {'name': 'third_checklist_answer'}]}]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -850,7 +917,7 @@
         "id": "M88fD66M0f63"
       },
       "id": "M88fD66M0f63",
-      "execution_count": null,
+      "execution_count": 17,
       "outputs": []
     },
     {
@@ -863,10 +930,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "10pZtHgzfAJS",
-        "outputId": "e3b711e4-721c-43e7-a1ad-1aa2486f77e9"
+        "outputId": "25b1a9b8-5f58-4b8c-f5aa-2da085dc1add"
       },
       "id": "10pZtHgzfAJS",
-      "execution_count": null,
+      "execution_count": 18,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -874,8 +941,8 @@
             "text/plain": [
               "[{'name': 'point_geo',\n",
               "  'point': {'x': -99.21052827588443, 'y': 19.405662413477728},\n",
-              "  'uuid': '2b76f90f-9cd2-4bc8-b5cf-01d8d974f093',\n",
-              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              "  'uuid': '9f3225f9-63bd-4c88-941d-d75ece46945a',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
               " {'name': 'polyline_geo',\n",
               "  'line': [{'x': -99.2257610436396, 'y': 19.418710777269197},\n",
               "   {'x': -99.22610441645385, 'y': 19.341320919070036},\n",
@@ -883,27 +950,27 @@
               "   {'x': -99.14781541479901, 'y': 19.420653145480912},\n",
               "   {'x': -99.22439165216781, 'y': 19.42047504824125},\n",
               "   {'x': -99.1989820639114, 'y': 19.414324129199997}],\n",
-              "  'uuid': 'fe234598-6e87-4cdb-b530-bd5376d8ee02',\n",
-              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              "  'uuid': '22e296af-950d-41eb-b7c2-8f718e480397',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
               " {'name': 'polygon_geo',\n",
               "  'polygon': [{'x': -99.2103219219407, 'y': 19.382085377579806},\n",
               "   {'x': -99.21375304694426, 'y': 19.362328387802638},\n",
               "   {'x': -99.19006492831323, 'y': 19.36297607074792},\n",
               "   {'x': -99.19281196088781, 'y': 19.381437207301936},\n",
               "   {'x': -99.2103219219407, 'y': 19.382085377579806}],\n",
-              "  'uuid': '0d455b04-12d5-4520-949a-da138e3b0b27',\n",
-              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              "  'uuid': 'bd2b0960-d320-4807-8954-dec1afeea653',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
               " {'name': 'bbox_geo',\n",
               "  'bbox': {'top': 19.400499,\n",
               "   'left': -99.210528,\n",
               "   'height': 0.005162999999999585,\n",
               "   'width': 0.0051799999999957436},\n",
-              "  'uuid': '3965f9dc-de43-48fc-995e-c9153c7adec3',\n",
-              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              "  'uuid': '7f8c7827-dd50-4b8d-ae47-33c08da2131b',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
               " {'name': 'radio_question_geo',\n",
               "  'answer': {'name': 'first_radio_answer'},\n",
-              "  'uuid': '8028ad09-fa33-4544-b1ff-33487c3b5666',\n",
-              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              "  'uuid': '57398b93-8d04-48b5-ac72-3b84f90a83eb',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
               " {'name': 'bbox_checklist_geo',\n",
               "  'classifications': [{'name': 'checklist_class_name',\n",
               "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
@@ -911,26 +978,26 @@
               "   'left': -99.173225,\n",
               "   'height': 0.007446999999999093,\n",
               "   'width': -0.01888599999999485},\n",
-              "  'uuid': '867cc003-3955-4abc-8658-64f0c755b5f0',\n",
-              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              "  'uuid': '40cec8ed-34e2-4b05-89d7-3b8f409c0781',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
               " {'name': 'bbox_text_geo',\n",
               "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
               "  'bbox': {'top': 19.357514,\n",
               "   'left': -99.181809,\n",
               "   'height': -0.008095999999998327,\n",
               "   'width': 0.02781299999999476},\n",
-              "  'uuid': '11090695-aed7-40e7-9166-bea96960baaa',\n",
-              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}},\n",
+              "  'uuid': '8dfca523-f9ad-44c0-9346-081a823291dd',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
               " {'name': 'checklist_question_geo',\n",
               "  'answer': [{'name': 'first_checklist_answer'},\n",
               "   {'name': 'second_checklist_answer'},\n",
               "   {'name': 'third_checklist_answer'}],\n",
-              "  'uuid': 'f75806a8-1287-42ab-9786-ec27c267f228',\n",
-              "  'dataRow': {'id': 'clbzbcz21000c097p4clm9td4'}}]"
+              "  'uuid': '06749ae0-5a50-42af-9622-8703bd5e44e2',\n",
+              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}}]"
             ]
           },
           "metadata": {},
-          "execution_count": 113
+          "execution_count": 18
         }
       ]
     },
@@ -975,10 +1042,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "SrAMnM9L0cAb",
-        "outputId": "7a98df67-ecc6-4d34-89cc-87d70face4fc"
+        "outputId": "15932c17-6d3c-4c12-cf96-5ee77a6b99b3"
       },
       "id": "SrAMnM9L0cAb",
-      "execution_count": null,
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "stream",
@@ -1015,11 +1082,23 @@
         "print(\"Errors:\", upload_job.errors)"
       ],
       "metadata": {
-        "id": "leIutAqA2_Uw"
+        "id": "leIutAqA2_Uw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ed882a64-d4e1-43dc-b822-f3e20adc53d6"
       },
       "id": "leIutAqA2_Uw",
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Errors: []\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -1042,7 +1121,7 @@
         "id": "ZLM9L5ggGqHG"
       },
       "id": "ZLM9L5ggGqHG",
-      "execution_count": null,
+      "execution_count": 21,
       "outputs": []
     }
   ],

From d03d7dcb7784e1f48bcbda0e8f92cf19f53e5713 Mon Sep 17 00:00:00 2001
From: Andrea Ovalle <74880762+ovalle15@users.noreply.github.com>
Date: Mon, 30 Jan 2023 10:35:22 -0500
Subject: [PATCH 3/7] Included CV2 and PIL python libraries

Included CV2 and PIL python libraries to generate the polygon annotation
---
 examples/annotation_import/tiled.ipynb | 474 +++++++++++--------------
 1 file changed, 212 insertions(+), 262 deletions(-)

diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 56b5bb09e..68e1a6806 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -87,7 +87,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 120,
       "id": "voluntary-minister",
       "metadata": {
         "id": "voluntary-minister"
@@ -99,7 +99,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 121,
       "id": "committed-richards",
       "metadata": {
         "id": "committed-richards"
@@ -117,12 +117,13 @@
         "\n",
         "\n",
         "from labelbox.data.serialization.ndjson.converter import NDJsonConverter\n",
+        "from labelbox.data.annotation_types.data.tiled_image import TiledBounds, TiledImageData, TileLayer, EPSG, EPSGTransformer\n",
         "from labelbox.schema.queue_mode import QueueMode\n",
         "from labelbox.schema.media_type import MediaType\n",
         "from labelbox.data.annotation_types import (\n",
         "    Label, ImageData, ObjectAnnotation, \n",
         "    Rectangle, Point, Line, Polygon,\n",
-        "    Radio, Checklist, Text,\n",
+        "    Radio, Checklist, Text,MaskData, Mask,\n",
         "    ClassificationAnnotation, ClassificationAnswer\n",
         ")\n",
         "from labelbox.schema.ontology import OntologyBuilder, Tool, Classification, Option\n",
@@ -142,7 +143,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 122,
       "id": "affecting-myanmar",
       "metadata": {
         "id": "affecting-myanmar"
@@ -181,15 +182,15 @@
         "# Python Annotation\n",
         "point_annotation = ObjectAnnotation(\n",
         "  name = \"point_geo\",\n",
-        "  value = Point(x=-99.21052827588443, y=19.405662413477728),\n",
+        "  value = Point(x=-122.31741025134123, y=37.87355669249922),\n",
         ")\n",
         "\n",
         "# NDJSON\n",
         "point_annotation_ndjson = {\n",
         "    \"name\": \"point_geo\",\n",
         "    \"point\": {\n",
-        "         \"x\": -99.21052827588443,\n",
-        "         \"y\": 19.405662413477728\n",
+        "         \"x\": -122.31741025134123,\n",
+        "         \"y\": 37.87355669249922\n",
         "     }\n",
         "}"
       ],
@@ -197,7 +198,7 @@
         "id": "3mQh-yQ2TLmd"
       },
       "id": "3mQh-yQ2TLmd",
-      "execution_count": 4,
+      "execution_count": 123,
       "outputs": []
     },
     {
@@ -205,7 +206,21 @@
       "source": [
         "####### Polyline #######\n",
         "# Coordinates\n",
-        "coords = [[-99.2257610436396,19.418710777269197],[-99.22610441645385,19.341320919070036],[-99.1464419235419,19.341644802568833],[-99.14781541479901,19.420653145480912],[-99.22439165216781,19.42047504824125],[-99.1989820639114,19.414324129199997]]\n",
+        "coords = [ \n",
+        "            [\n",
+        "              -122.31757789012927,\n",
+        "              37.87396317833991\n",
+        "            ],\n",
+        "            [\n",
+        "              -122.31639782443663,\n",
+        "              37.87396741226917\n",
+        "            ],\n",
+        "            [\n",
+        "              -122.31638977853417,\n",
+        "              37.87277872707839\n",
+        "            ]\n",
+        "        ]\n",
+        "\n",
         "line_points = []\n",
         "line_points_ndjson = []\n",
         "\n",
@@ -230,7 +245,7 @@
         "id": "f3OoBByTTYda"
       },
       "id": "f3OoBByTTYda",
-      "execution_count": 5,
+      "execution_count": 124,
       "outputs": []
     },
     {
@@ -238,7 +253,25 @@
       "source": [
         "####### Polygon #######\n",
         "# Coordinates in the desired EPSG coordinate system\n",
-        "coords_polygon = [[-99.2103219219407,19.382085377579806],[-99.21375304694426,19.362328387802638],[-99.19006492831323,19.36297607074792],[-99.19281196088781,19.381437207301936],[-99.2103219219407,19.382085377579806]]\n",
+        "coords_polygon = [\n",
+        "    [\n",
+        "        -122.31691812612837,\n",
+        "        37.873289980495024\n",
+        "    ],\n",
+        "    [\n",
+        "        -122.31710184090099,\n",
+        "        37.87304335144298\n",
+        "    ],\n",
+        "    [\n",
+        "        -122.31680146054286,\n",
+        "        37.87303594197371\n",
+        "    ],\n",
+        "    [\n",
+        "        -122.31691812612837,\n",
+        "        37.873289980495024\n",
+        "    ]\n",
+        "]\n",
+        "\n",
         "polygon_points = []\n",
         "polygon_points_ndjson = []\n",
         "\n",
@@ -262,7 +295,7 @@
         "id": "BSjRhlFvTdfS"
       },
       "id": "BSjRhlFvTdfS",
-      "execution_count": 6,
+      "execution_count": 125,
       "outputs": []
     },
     {
@@ -272,34 +305,33 @@
         "coord_object = {\n",
         "      \"coordinates\": [\n",
         "        [\n",
-        "            [\n",
-        "                -99.210528,\n",
-        "                19.405662\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.210528,\n",
-        "                19.400499\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.205348,\n",
-        "                19.400499\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.205348,\n",
-        "                19.405662\n",
-        "            ],\n",
-        "            [\n",
-        "                -99.210528,\n",
-        "                19.405662\n",
-        "            ]\n",
+        "          [\n",
+        "              -122.31734455895823,\n",
+        "              37.873713376083884\n",
+        "          ],\n",
+        "          [\n",
+        "              -122.31734455895823,\n",
+        "              37.87385944699745\n",
+        "          ],\n",
+        "          [\n",
+        "              -122.31673038840458,\n",
+        "              37.87385944699745\n",
+        "          ],\n",
+        "          [\n",
+        "              -122.31673038840458,\n",
+        "              37.873713376083884\n",
+        "          ],\n",
+        "          [\n",
+        "              -122.31734455895823,\n",
+        "              37.873713376083884\n",
         "          ]\n",
         "        ]\n",
-        "      \n",
+        "      ]  \n",
         "    }\n",
         "\n",
         "\n",
-        "bbox_top_left = Point(x=-99.21052827588443, y=19.405662413477728)\n",
-        "bbox_bottom_right = Point(x=-99.20534818927473, y=19.400498983095076)\n",
+        "bbox_top_left = Point(x=-122.31734455895823, y=37.873713376083884)\n",
+        "bbox_bottom_right = Point(x=-122.31673038840458, y=37.87385944699745)\n",
         "\n",
         "# Python Annotation\n",
         "bbox_annotation = ObjectAnnotation(\n",
@@ -323,7 +355,7 @@
         "id": "oLX3adDOTmYS"
       },
       "id": "oLX3adDOTmYS",
-      "execution_count": 7,
+      "execution_count": 126,
       "outputs": []
     },
     {
@@ -347,7 +379,7 @@
         "id": "OsWf8Y_mTxLm"
       },
       "id": "OsWf8Y_mTxLm",
-      "execution_count": 8,
+      "execution_count": 127,
       "outputs": []
     },
     {
@@ -357,26 +389,26 @@
         "\n",
         "coord_object_checklist = {\n",
         "    \"coordinates\": [\n",
-        "        [\n",
+        "       [\n",
         "          [\n",
-        "              -99.173225,\n",
-        "              19.406409\n",
+        "              -122.31711256877092,\n",
+        "              37.87340218056304\n",
         "          ],\n",
         "          [\n",
-        "              -99.173225,\n",
-        "              19.398962\n",
+        "              -122.31711256877092,\n",
+        "              37.87360752741479\n",
         "          ],\n",
         "          [\n",
-        "              -99.192111,\n",
-        "              19.398962\n",
+        "              -122.31665529331502,\n",
+        "              37.87360752741479\n",
         "          ],\n",
         "          [\n",
-        "              -99.192111,\n",
-        "              19.406409\n",
+        "              -122.31665529331502,\n",
+        "              37.87340218056304\n",
         "          ],\n",
         "          [\n",
-        "              -99.173225,\n",
-        "              19.406409\n",
+        "              -122.31711256877092,\n",
+        "              37.87340218056304\n",
         "          ]\n",
         "      ]\n",
         "    ]          \n",
@@ -386,8 +418,8 @@
         "bbox_with_checklist_subclass = ObjectAnnotation(\n",
         "    name=\"bbox_checklist_geo\",\n",
         "    value=Rectangle(\n",
-        "        start=Point(x=-99.17322500305542, y=19.40640857294981), # Top left\n",
-        "        end=Point(x=-99.19211050784055, y=19.398962049791287), # Bottom right\n",
+        "        start=Point(x=-122.31711256877092, y=37.87340218056304), # Top left\n",
+        "        end=Point(x=-122.31665529331502, y=37.87360752741479), # Bottom right\n",
         "    ),\n",
         "    classifications=[\n",
         "        ClassificationAnnotation(\n",
@@ -421,7 +453,7 @@
         "id": "2IZXIWP2UcOJ"
       },
       "id": "2IZXIWP2UcOJ",
-      "execution_count": 9,
+      "execution_count": 128,
       "outputs": []
     },
     {
@@ -431,36 +463,36 @@
         "\n",
         "coord_object_text ={\n",
         "    \"coordinates\": [\n",
-        "        [\n",
-        "                [\n",
-        "                    -99.181809,\n",
-        "                    19.349418\n",
-        "                ],\n",
-        "                [\n",
-        "                    -99.181809,\n",
-        "                    19.357514\n",
-        "                ],\n",
-        "                [\n",
-        "                    -99.153996,\n",
-        "                    19.357514\n",
-        "                ],\n",
-        "                [\n",
-        "                    -99.153996,\n",
-        "                    19.349418\n",
-        "                ],\n",
-        "                [\n",
-        "                    -99.181809,\n",
-        "                    19.349418\n",
-        "                ]\n",
-        "            ]\n",
-        "        ]\n",
+        "      [\n",
+        "          [\n",
+        "              -122.31750814315438,\n",
+        "              37.87318201423049\n",
+        "          ],\n",
+        "          [\n",
+        "              -122.31750814315438,\n",
+        "              37.87337992476082\n",
+        "          ],\n",
+        "          [\n",
+        "              -122.31710049991725,\n",
+        "              37.87337992476082\n",
+        "          ],\n",
+        "          [\n",
+        "              -122.31710049991725,\n",
+        "              37.87318201423049\n",
+        "          ],\n",
+        "          [\n",
+        "              -122.31750814315438,\n",
+        "              37.87318201423049\n",
+        "          ]\n",
+        "      ]\n",
+        "    ]\n",
         "}\n",
         "# Python Annotation\n",
         "bbox_with_free_text_subclass = ObjectAnnotation(\n",
         "    name=\"bbox_text_geo\",\n",
         "    value=Rectangle(\n",
-        "        start=Point(x=-99.1818093234123, y=19.34941781372347), # Top left\n",
-        "        end=Point(x=-99.15399612545598, y=19.357514306583965), # Bottom right\n",
+        "        start=Point(x=-122.31750814315438, y=37.87318201423049), # Top left\n",
+        "        end=Point(x=-122.31710049991725, y=37.87337992476082), # Bottom right\n",
         "    ),\n",
         "    classifications=[\n",
         "        ClassificationAnnotation(\n",
@@ -489,7 +521,7 @@
         "id": "bnjcIB7FU21R"
       },
       "id": "bnjcIB7FU21R",
-      "execution_count": 10,
+      "execution_count": 129,
       "outputs": []
     },
     {
@@ -522,7 +554,7 @@
         "id": "hIkLI1q7glMi"
       },
       "id": "hIkLI1q7glMi",
-      "execution_count": 11,
+      "execution_count": 130,
       "outputs": []
     },
     {
@@ -547,14 +579,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 131,
       "id": "IlwNSdRQB4gC",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "IlwNSdRQB4gC",
-        "outputId": "eabd5152-35af-471d-8eaa-d962e5eef8c2"
+        "outputId": "e364ca18-621c-4f0a-9eb6-b5ad70a32c13"
       },
       "outputs": [
         {
@@ -567,27 +599,79 @@
       ],
       "source": [
         "\n",
-        "asset = [{\n",
-        "    \"row_data\": {\n",
-        "    \"tile_layer_url\": \"https://storage.googleapis.com/labelbox-datasets/geospatial-sample-data/sample-geotiff.tif\"\n",
-        "      },\n",
-        "    \"global_key\": str(uuid.uuid4()),\n",
-        "    \"media_type\": \"TMS_GEO\"\n",
-        "}]\n",
+        "top_left_bound = Point(x=-122.31764674186705, y=37.87276155898985)\n",
+        "bottom_right_bound = Point(x=-122.31635199317932, y=37.87398109727749)\n",
+        "\n",
+        "epsg = EPSG.EPSG4326\n",
+        "bounds = TiledBounds(epsg=epsg, bounds=[top_left_bound, bottom_right_bound])\n",
         "\n",
-        "dataset = client.create_dataset(name=\"geo_demo_dataset\")\n",
-        "task = dataset.create_data_rows(asset)\n",
+        "tile_layer = TileLayer(\n",
+        "    url=\"https://api.mapbox.com/styles/v1/mapbox/satellite-streets-v11/tiles/{z}/{x}/{y}?access_token=pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw\"\n",
+        ")\n",
+        "\n",
+        "tiled_image_data = TiledImageData(tile_layer=tile_layer,\n",
+        "                                  tile_bounds=bounds,\n",
+        "                                  zoom_levels=[17, 23])\n",
+        "\n",
+        "dataset = client.create_dataset(name=\"geospatial_demo_dataset\")\n",
+        "task = dataset.create_data_rows([tiled_image_data.asdict()])\n",
         "task.wait_till_done()\n",
         "print(task.errors)"
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        " ## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n",
+        "\n",
+        "from PIL import Image\n",
+        "import cv2\n",
+        "\n",
+        "hsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\n",
+        "mask = cv2.inRange(hsv, (50, 10, 25), (100, 150, 255))\n",
+        "kernel = np.ones((5, 5), np.uint8)\n",
+        "mask = cv2.erode(mask, kernel)\n",
+        "mask = cv2.dilate(mask, kernel)\n",
+        "mask_annotation = MaskData.from_2D_arr(mask)\n",
+        "mask_data = Mask(mask=mask_annotation, color=[255, 255, 255])\n",
+        "h, w, _ = tiled_image_data.value.shape\n",
+        "pixel_bounds = TiledBounds(epsg=EPSG.SIMPLEPIXEL,\n",
+        "                          bounds=[Point(x=0, y=0),\n",
+        "                                  Point(x=w, y=h)])\n",
+        "transformer = EPSGTransformer.create_pixel_to_geo_transformer(\n",
+        "    src_epsg=pixel_bounds.epsg,\n",
+        "    pixel_bounds=pixel_bounds,\n",
+        "    geo_bounds=tiled_image_data.tile_bounds,\n",
+        "    zoom=17)\n",
+        "pixel_polygons = mask_data.shapely.simplify(3)\n",
+        "list_of_polygons = [transformer(Polygon.from_shapely(p)) for p in pixel_polygons.geoms]\n",
+        "polygon_annotation_two = ObjectAnnotation(value=list_of_polygons[0], name=\"polygon_geo_2\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dr1dEPzM_tHW",
+        "outputId": "981fb7b9-ec8c-4be0-e08d-59cc348e080b"
+      },
+      "id": "dr1dEPzM_tHW",
+      "execution_count": 132,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n",
+            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n"
+          ]
+        }
+      ]
+    },
     {
       "cell_type": "markdown",
       "source": [
         "### Step 2: Create/select an ontology\n",
-        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name`/`instructions` fields in your annotations to ensure the correct feature schemas are matched.\n",
-        "\n",
-        "For example, when we create the bounding box annotation [above](https://colab.research.google.com/drive/1_sPVTktS1AVvFMCg-GR4q_g2ukQaR4Oq#scrollTo=2IZXIWP2UcOJ&line=2&uniqifier=1), we provided the `name` as `bbox_checklist_geo`. Now, when we setup our ontology, we must ensure that the name of my bounding box tool is also `bbox_checklist_geo`. The same alignment must hold true for the other tools and classifications we create in our ontology."
+        "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the `name`/`instructions` fields in your annotations to ensure the correct feature schemas are matched."
       ],
       "metadata": {
         "id": "y_tWMvxilTq9"
@@ -596,7 +680,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 133,
       "id": "suburban-crowd",
       "metadata": {
         "id": "suburban-crowd"
@@ -608,6 +692,7 @@
         "        Tool(tool=Tool.Type.POINT, name=\"point_geo\"),\n",
         "        Tool(tool=Tool.Type.LINE, name=\"polyline_geo\"),\n",
         "        Tool(tool=Tool.Type.POLYGON, name=\"polygon_geo\"),\n",
+        "        Tool(tool=Tool.Type.POLYGON, name=\"polygon_geo_2\"),\n",
         "        Tool(tool=Tool.Type.BBOX, name=\"bbox_geo\"), \n",
         "        Tool( \n",
         "          tool=Tool.Type.BBOX, \n",
@@ -670,7 +755,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 134,
       "id": "PhfXaqDB7hRt",
       "metadata": {
         "id": "PhfXaqDB7hRt"
@@ -680,7 +765,7 @@
         "# Project defaults to batch mode with benchmark quality settings if this argument is not provided\n",
         "# Queue mode will be deprecated once dataset mode is deprecated\n",
         "\n",
-        "project = client.create_project(name=\"geo_project_demo\",\n",
+        "project = client.create_project(name=\"geospatial_project_demo\",\n",
         "                                    queue_mode=QueueMode.Batch,\n",
         "                                    media_type=MediaType.Geospatial_Tile)\n",
         "\n",
@@ -732,10 +817,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "FHXR6nyKw-8f",
-        "outputId": "300646b2-6b7a-4dfd-a02f-5494b9082f15"
+        "outputId": "c336039e-99e9-47a9-c4ca-23cd4696b226"
       },
       "id": "FHXR6nyKw-8f",
-      "execution_count": 23,
+      "execution_count": 135,
       "outputs": [
         {
           "output_type": "stream",
@@ -743,11 +828,11 @@
           "text": [
             "Batch:  <Batch {\n",
             "    \"consensus_settings_json\": \"{\\\"numberOfLabels\\\":1,\\\"coveragePercentage\\\":0}\",\n",
-            "    \"created_at\": \"2023-01-03 23:20:49+00:00\",\n",
+            "    \"created_at\": \"2023-01-30 15:30:03+00:00\",\n",
             "    \"name\": \"first-batch-geo-demo\",\n",
             "    \"size\": 1,\n",
-            "    \"uid\": \"421bee80-8bbd-11ed-a6f6-1fd0e0fcd05c\",\n",
-            "    \"updated_at\": \"2023-01-03 23:20:49+00:00\"\n",
+            "    \"uid\": \"f79a70b0-a0b2-11ed-a943-f758fb3a8df6\",\n",
+            "    \"updated_at\": \"2023-01-30 15:30:03+00:00\"\n",
             "}>\n"
           ]
         }
@@ -770,7 +855,7 @@
       "cell_type": "markdown",
       "source": [
         "#### Python annotations\n",
-        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1_sPVTktS1AVvFMCg-GR4q_g2ukQaR4Oq#scrollTo=AmKgfZ8ZALT_)."
+        "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created on ***Supported Python annotation types and NDJSON*** section."
       ],
       "metadata": {
         "id": "lVXwyg7ZxVOX"
@@ -780,9 +865,16 @@
     {
       "cell_type": "code",
       "source": [
+        "\n",
+        "tiled_image_data_row_id = str(next(dataset.export_data_rows()).uid)\n",
+        "\n",
+        "\n",
         "label = Label(\n",
-        "    data=ImageData(\n",
-        "        uid= next(dataset.export_data_rows()).uid\n",
+        "    data=TiledImageData(\n",
+        "        uid=next(dataset.export_data_rows()).uid,\n",
+        "        tile_layer=tile_layer,\n",
+        "        tile_bounds=bounds,\n",
+        "        zoom_levels=[17, 23]\n",
         "    ),\n",
         "    annotations = [\n",
         "        point_annotation,\n",
@@ -792,99 +884,26 @@
         "        radio_annotation,\n",
         "        bbox_with_checklist_subclass,  \n",
         "        bbox_with_free_text_subclass,\n",
-        "        checklist_annotation\n",
+        "        checklist_annotation,\n",
+        "        polygon_annotation_two\n",
         "    ]\n",
         ")\n",
-        "\n",
         "# Convert our label from a Labelbox class object to the underlying NDJSON format required for upload \n",
         "label_ndjson = list(NDJsonConverter.serialize([label]))\n",
         "label_ndjson"
       ],
       "metadata": {
-        "id": "3_LZLZY7xS30",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "5ad70c40-1643-4329-90fa-52a019038d24"
+        "id": "3_LZLZY7xS30"
       },
       "id": "3_LZLZY7xS30",
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[{'uuid': '9dccd23f-7485-4a09-b7ba-b4f6f6e6da31',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
-              "  'name': 'point_geo',\n",
-              "  'classifications': [],\n",
-              "  'point': {'x': -99.21052827588443, 'y': 19.405662413477728}},\n",
-              " {'uuid': '20a8bcfa-5114-461e-981f-2f12f9e5e5d5',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
-              "  'name': 'polyline_geo',\n",
-              "  'classifications': [],\n",
-              "  'line': [{'x': -99.2257610436396, 'y': 19.418710777269197},\n",
-              "   {'x': -99.22610441645385, 'y': 19.341320919070036},\n",
-              "   {'x': -99.1464419235419, 'y': 19.341644802568833},\n",
-              "   {'x': -99.14781541479901, 'y': 19.420653145480912},\n",
-              "   {'x': -99.22439165216781, 'y': 19.42047504824125},\n",
-              "   {'x': -99.1989820639114, 'y': 19.414324129199997}]},\n",
-              " {'uuid': '65b0bde8-d16a-414d-8e95-7f5f193fc986',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
-              "  'name': 'polygon_geo',\n",
-              "  'classifications': [],\n",
-              "  'polygon': [{'x': -99.2103219219407, 'y': 19.382085377579806},\n",
-              "   {'x': -99.21375304694426, 'y': 19.362328387802638},\n",
-              "   {'x': -99.19006492831323, 'y': 19.36297607074792},\n",
-              "   {'x': -99.19281196088781, 'y': 19.381437207301936},\n",
-              "   {'x': -99.2103219219407, 'y': 19.382085377579806}]},\n",
-              " {'uuid': 'df25ed6d-a1f0-48f1-94bb-065d6461ae54',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
-              "  'name': 'bbox_geo',\n",
-              "  'classifications': [],\n",
-              "  'bbox': {'top': 19.405662413477728,\n",
-              "   'left': -99.21052827588443,\n",
-              "   'height': -0.005163430382651768,\n",
-              "   'width': 0.0051800866096982645}},\n",
-              " {'name': 'radio_question_geo',\n",
-              "  'answer': {'name': 'first_radio_answer'},\n",
-              "  'uuid': '55055c07-b15e-4c47-a930-c700b17ede5f',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
-              " {'uuid': '924f7912-a0ff-4098-81dc-fd4c6e750872',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
-              "  'name': 'bbox_checklist_geo',\n",
-              "  'classifications': [{'name': 'checklist_class_name',\n",
-              "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
-              "  'bbox': {'top': 19.40640857294981,\n",
-              "   'left': -99.17322500305542,\n",
-              "   'height': -0.007446523158524343,\n",
-              "   'width': -0.018885504785131957}},\n",
-              " {'uuid': '43d65470-55a8-49ce-903b-3f8ec0574a5a',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
-              "  'name': 'bbox_text_geo',\n",
-              "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
-              "  'bbox': {'top': 19.34941781372347,\n",
-              "   'left': -99.1818093234123,\n",
-              "   'height': 0.00809649286049563,\n",
-              "   'width': 0.02781319795631987}},\n",
-              " {'name': 'checklist_question_geo',\n",
-              "  'uuid': 'd4f783b9-d1a2-4d7d-8283-a4d265cea81d',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'},\n",
-              "  'answer': [{'name': 'first_checklist_answer'},\n",
-              "   {'name': 'second_checklist_answer'},\n",
-              "   {'name': 'third_checklist_answer'}]}]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 16
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
       "source": [
         "### NDJSON annotations\n",
-        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created [above](https://colab.research.google.com/drive/1_sPVTktS1AVvFMCg-GR4q_g2ukQaR4Oq#scrollTo=AmKgfZ8ZALT_)."
+        "Here we create the complete label NDJSON payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created on *** Supported Python annotation types and NDJSON *** section."
       ],
       "metadata": {
         "id": "LUJtuLa00gZF"
@@ -917,7 +936,7 @@
         "id": "M88fD66M0f63"
       },
       "id": "M88fD66M0f63",
-      "execution_count": 17,
+      "execution_count": 137,
       "outputs": []
     },
     {
@@ -926,80 +945,11 @@
         "label_ndjson_method2"
       ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "10pZtHgzfAJS",
-        "outputId": "25b1a9b8-5f58-4b8c-f5aa-2da085dc1add"
+        "id": "10pZtHgzfAJS"
       },
       "id": "10pZtHgzfAJS",
-      "execution_count": 18,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[{'name': 'point_geo',\n",
-              "  'point': {'x': -99.21052827588443, 'y': 19.405662413477728},\n",
-              "  'uuid': '9f3225f9-63bd-4c88-941d-d75ece46945a',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
-              " {'name': 'polyline_geo',\n",
-              "  'line': [{'x': -99.2257610436396, 'y': 19.418710777269197},\n",
-              "   {'x': -99.22610441645385, 'y': 19.341320919070036},\n",
-              "   {'x': -99.1464419235419, 'y': 19.341644802568833},\n",
-              "   {'x': -99.14781541479901, 'y': 19.420653145480912},\n",
-              "   {'x': -99.22439165216781, 'y': 19.42047504824125},\n",
-              "   {'x': -99.1989820639114, 'y': 19.414324129199997}],\n",
-              "  'uuid': '22e296af-950d-41eb-b7c2-8f718e480397',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
-              " {'name': 'polygon_geo',\n",
-              "  'polygon': [{'x': -99.2103219219407, 'y': 19.382085377579806},\n",
-              "   {'x': -99.21375304694426, 'y': 19.362328387802638},\n",
-              "   {'x': -99.19006492831323, 'y': 19.36297607074792},\n",
-              "   {'x': -99.19281196088781, 'y': 19.381437207301936},\n",
-              "   {'x': -99.2103219219407, 'y': 19.382085377579806}],\n",
-              "  'uuid': 'bd2b0960-d320-4807-8954-dec1afeea653',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
-              " {'name': 'bbox_geo',\n",
-              "  'bbox': {'top': 19.400499,\n",
-              "   'left': -99.210528,\n",
-              "   'height': 0.005162999999999585,\n",
-              "   'width': 0.0051799999999957436},\n",
-              "  'uuid': '7f8c7827-dd50-4b8d-ae47-33c08da2131b',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
-              " {'name': 'radio_question_geo',\n",
-              "  'answer': {'name': 'first_radio_answer'},\n",
-              "  'uuid': '57398b93-8d04-48b5-ac72-3b84f90a83eb',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
-              " {'name': 'bbox_checklist_geo',\n",
-              "  'classifications': [{'name': 'checklist_class_name',\n",
-              "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
-              "  'bbox': {'top': 19.398962,\n",
-              "   'left': -99.173225,\n",
-              "   'height': 0.007446999999999093,\n",
-              "   'width': -0.01888599999999485},\n",
-              "  'uuid': '40cec8ed-34e2-4b05-89d7-3b8f409c0781',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
-              " {'name': 'bbox_text_geo',\n",
-              "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
-              "  'bbox': {'top': 19.357514,\n",
-              "   'left': -99.181809,\n",
-              "   'height': -0.008095999999998327,\n",
-              "   'width': 0.02781299999999476},\n",
-              "  'uuid': '8dfca523-f9ad-44c0-9346-081a823291dd',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}},\n",
-              " {'name': 'checklist_question_geo',\n",
-              "  'answer': [{'name': 'first_checklist_answer'},\n",
-              "   {'name': 'second_checklist_answer'},\n",
-              "   {'name': 'third_checklist_answer'}],\n",
-              "  'uuid': '06749ae0-5a50-42af-9622-8703bd5e44e2',\n",
-              "  'dataRow': {'id': 'clcgupd6i0c5r077fagkp9vx6'}}]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 18
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -1031,7 +981,7 @@
         "    project_id = project.uid, \n",
         "    name=\"mal_import_job\"+str(uuid.uuid4()), \n",
         "    ### use label_ndjson_method2 if labels were created using NDJSON tools\n",
-        "    predictions=label_ndjson)\n",
+        "    predictions=label_ndjson_method2)\n",
         "\n",
         "upload_job.wait_until_done();\n",
         "print(\"Errors:\", upload_job.errors)\n",
@@ -1042,10 +992,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "SrAMnM9L0cAb",
-        "outputId": "15932c17-6d3c-4c12-cf96-5ee77a6b99b3"
+        "outputId": "31579a61-b273-4302-d769-3070781a961c"
       },
       "id": "SrAMnM9L0cAb",
-      "execution_count": 24,
+      "execution_count": 139,
       "outputs": [
         {
           "output_type": "stream",
@@ -1086,10 +1036,10 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "ed882a64-d4e1-43dc-b822-f3e20adc53d6"
+        "outputId": "a28c8f02-7460-461f-fcde-8e3b75329339"
       },
       "id": "leIutAqA2_Uw",
-      "execution_count": 20,
+      "execution_count": 140,
       "outputs": [
         {
           "output_type": "stream",
@@ -1113,7 +1063,7 @@
     {
       "cell_type": "code",
       "source": [
-        "#upload_job\n",
+        "\n",
         "# project.delete()\n",
         "# dataset.delete()"
       ],
@@ -1121,7 +1071,7 @@
         "id": "ZLM9L5ggGqHG"
       },
       "id": "ZLM9L5ggGqHG",
-      "execution_count": 21,
+      "execution_count": 141,
       "outputs": []
     }
   ],

From eff253d5186292fd325471badc5c32d8a19cd6fe Mon Sep 17 00:00:00 2001
From: Andrea Ovalle <74880762+ovalle15@users.noreply.github.com>
Date: Mon, 30 Jan 2023 10:50:15 -0500
Subject: [PATCH 4/7] Updated colab links

---
 examples/annotation_import/tiled.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 68e1a6806..78acd23a4 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -20,12 +20,12 @@
       },
       "source": [
         "<td>\n",
-        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_assisted_labeling/tiled_imagery_mal.ipynb\" target=\"_blank\"><img\n",
+        "<a href=\"https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img\n",
         "src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
         "</td>\n",
         "\n",
         "<td>\n",
-        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/model_assisted_labeling/tiled_imagery_mal.ipynb\" target=\"_blank\"><img\n",
+        "<a href=\"https://github.com/Labelbox/labelbox-python/tree/develop/examples/annotation_import/tiled.ipynb\" target=\"_blank\"><img\n",
         "src=\"https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white\" alt=\"GitHub\"></a>\n",
         "</td>"
       ]

From a8dc71567ef8e568ba64b20b422d4f3f8c2a82ab Mon Sep 17 00:00:00 2001
From: Andrea Ovalle <74880762+ovalle15@users.noreply.github.com>
Date: Mon, 30 Jan 2023 11:29:53 -0500
Subject: [PATCH 5/7] Updated file with correct notebook

---
 examples/annotation_import/tiled.ipynb | 217 ++++++++++++++++++++-----
 1 file changed, 180 insertions(+), 37 deletions(-)

diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 78acd23a4..d4f46f47d 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -87,7 +87,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 120,
+      "execution_count": 267,
       "id": "voluntary-minister",
       "metadata": {
         "id": "voluntary-minister"
@@ -99,7 +99,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 121,
+      "execution_count": 268,
       "id": "committed-richards",
       "metadata": {
         "id": "committed-richards"
@@ -143,7 +143,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 122,
+      "execution_count": 269,
       "id": "affecting-myanmar",
       "metadata": {
         "id": "affecting-myanmar"
@@ -198,7 +198,7 @@
         "id": "3mQh-yQ2TLmd"
       },
       "id": "3mQh-yQ2TLmd",
-      "execution_count": 123,
+      "execution_count": 270,
       "outputs": []
     },
     {
@@ -245,7 +245,7 @@
         "id": "f3OoBByTTYda"
       },
       "id": "f3OoBByTTYda",
-      "execution_count": 124,
+      "execution_count": 271,
       "outputs": []
     },
     {
@@ -295,7 +295,7 @@
         "id": "BSjRhlFvTdfS"
       },
       "id": "BSjRhlFvTdfS",
-      "execution_count": 125,
+      "execution_count": 272,
       "outputs": []
     },
     {
@@ -355,7 +355,7 @@
         "id": "oLX3adDOTmYS"
       },
       "id": "oLX3adDOTmYS",
-      "execution_count": 126,
+      "execution_count": 273,
       "outputs": []
     },
     {
@@ -379,7 +379,7 @@
         "id": "OsWf8Y_mTxLm"
       },
       "id": "OsWf8Y_mTxLm",
-      "execution_count": 127,
+      "execution_count": 274,
       "outputs": []
     },
     {
@@ -453,7 +453,7 @@
         "id": "2IZXIWP2UcOJ"
       },
       "id": "2IZXIWP2UcOJ",
-      "execution_count": 128,
+      "execution_count": 275,
       "outputs": []
     },
     {
@@ -521,7 +521,7 @@
         "id": "bnjcIB7FU21R"
       },
       "id": "bnjcIB7FU21R",
-      "execution_count": 129,
+      "execution_count": 276,
       "outputs": []
     },
     {
@@ -554,7 +554,7 @@
         "id": "hIkLI1q7glMi"
       },
       "id": "hIkLI1q7glMi",
-      "execution_count": 130,
+      "execution_count": 277,
       "outputs": []
     },
     {
@@ -579,14 +579,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 131,
+      "execution_count": 278,
       "id": "IlwNSdRQB4gC",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "IlwNSdRQB4gC",
-        "outputId": "e364ca18-621c-4f0a-9eb6-b5ad70a32c13"
+        "outputId": "160a95c5-e1a7-48f3-a125-267339a0bc6e"
       },
       "outputs": [
         {
@@ -642,7 +642,7 @@
         "    src_epsg=pixel_bounds.epsg,\n",
         "    pixel_bounds=pixel_bounds,\n",
         "    geo_bounds=tiled_image_data.tile_bounds,\n",
-        "    zoom=17)\n",
+        "    zoom=23)\n",
         "pixel_polygons = mask_data.shapely.simplify(3)\n",
         "list_of_polygons = [transformer(Polygon.from_shapely(p)) for p in pixel_polygons.geoms]\n",
         "polygon_annotation_two = ObjectAnnotation(value=list_of_polygons[0], name=\"polygon_geo_2\")"
@@ -652,10 +652,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "dr1dEPzM_tHW",
-        "outputId": "981fb7b9-ec8c-4be0-e08d-59cc348e080b"
+        "outputId": "c0749279-3871-4c34-8841-13bd94d055df"
       },
       "id": "dr1dEPzM_tHW",
-      "execution_count": 132,
+      "execution_count": 279,
       "outputs": [
         {
           "output_type": "stream",
@@ -680,7 +680,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 133,
+      "execution_count": 280,
       "id": "suburban-crowd",
       "metadata": {
         "id": "suburban-crowd"
@@ -755,7 +755,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 134,
+      "execution_count": 281,
       "id": "PhfXaqDB7hRt",
       "metadata": {
         "id": "PhfXaqDB7hRt"
@@ -817,10 +817,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "FHXR6nyKw-8f",
-        "outputId": "c336039e-99e9-47a9-c4ca-23cd4696b226"
+        "outputId": "e05da8fd-76bf-4667-ae11-353e09d6123b"
       },
       "id": "FHXR6nyKw-8f",
-      "execution_count": 135,
+      "execution_count": 282,
       "outputs": [
         {
           "output_type": "stream",
@@ -828,11 +828,11 @@
           "text": [
             "Batch:  <Batch {\n",
             "    \"consensus_settings_json\": \"{\\\"numberOfLabels\\\":1,\\\"coveragePercentage\\\":0}\",\n",
-            "    \"created_at\": \"2023-01-30 15:30:03+00:00\",\n",
+            "    \"created_at\": \"2023-01-30 16:28:29+00:00\",\n",
             "    \"name\": \"first-batch-geo-demo\",\n",
             "    \"size\": 1,\n",
-            "    \"uid\": \"f79a70b0-a0b2-11ed-a943-f758fb3a8df6\",\n",
-            "    \"updated_at\": \"2023-01-30 15:30:03+00:00\"\n",
+            "    \"uid\": \"20ebdb90-a0bb-11ed-8aef-c799659c348a\",\n",
+            "    \"updated_at\": \"2023-01-30 16:28:29+00:00\"\n",
             "}>\n"
           ]
         }
@@ -893,11 +893,89 @@
         "label_ndjson"
       ],
       "metadata": {
-        "id": "3_LZLZY7xS30"
+        "id": "3_LZLZY7xS30",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2867ef9a-34d8-4f6b-b446-66bb694b9209"
       },
       "id": "3_LZLZY7xS30",
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 283,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[{'uuid': 'c8725378-2578-4e39-bfdb-d556bc1a4486',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'name': 'point_geo',\n",
+              "  'classifications': [],\n",
+              "  'point': {'x': -122.31741025134123, 'y': 37.87355669249922}},\n",
+              " {'uuid': 'bd5136c0-f639-40c4-a391-3a3deb1ffb41',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'name': 'polyline_geo',\n",
+              "  'classifications': [],\n",
+              "  'line': [{'x': -122.31757789012927, 'y': 37.87396317833991},\n",
+              "   {'x': -122.31639782443663, 'y': 37.87396741226917},\n",
+              "   {'x': -122.31638977853417, 'y': 37.87277872707839}]},\n",
+              " {'uuid': 'c5381273-a73f-45ac-8704-1ec7d1cb0e94',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'name': 'polygon_geo',\n",
+              "  'classifications': [],\n",
+              "  'polygon': [{'x': -122.31691812612837, 'y': 37.873289980495024},\n",
+              "   {'x': -122.31710184090099, 'y': 37.87304335144298},\n",
+              "   {'x': -122.31680146054286, 'y': 37.87303594197371},\n",
+              "   {'x': -122.31691812612837, 'y': 37.873289980495024}]},\n",
+              " {'uuid': '221fc6d0-e12b-418b-8613-8eadf202eddd',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'name': 'bbox_geo',\n",
+              "  'classifications': [],\n",
+              "  'bbox': {'top': 37.873713376083884,\n",
+              "   'left': -122.31734455895823,\n",
+              "   'height': 0.0001460709135656657,\n",
+              "   'width': 0.0006141705536464315}},\n",
+              " {'name': 'radio_question_geo',\n",
+              "  'answer': {'name': 'first_radio_answer'},\n",
+              "  'uuid': '20363577-3922-4c67-8199-26ef4e9cfed4',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              " {'uuid': 'be5038eb-a6ad-4eb1-9117-f3b48fad168c',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'name': 'bbox_checklist_geo',\n",
+              "  'classifications': [{'name': 'checklist_class_name',\n",
+              "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
+              "  'bbox': {'top': 37.87340218056304,\n",
+              "   'left': -122.31711256877092,\n",
+              "   'height': 0.00020534685175022105,\n",
+              "   'width': 0.0004572754559006853}},\n",
+              " {'uuid': '91d9f104-2791-48fe-83c0-c0eead7f2b6f',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'name': 'bbox_text_geo',\n",
+              "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
+              "  'bbox': {'top': 37.87318201423049,\n",
+              "   'left': -122.31750814315438,\n",
+              "   'height': 0.00019791053033202388,\n",
+              "   'width': 0.00040764323713915473}},\n",
+              " {'name': 'checklist_question_geo',\n",
+              "  'uuid': 'e292b556-bf54-4dc2-8318-aaa57284d8a0',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'answer': [{'name': 'first_checklist_answer'},\n",
+              "   {'name': 'second_checklist_answer'},\n",
+              "   {'name': 'third_checklist_answer'}]},\n",
+              " {'uuid': '4ba7e936-9100-4d13-8467-8f95b0191066',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'name': 'polygon_geo_2',\n",
+              "  'classifications': [],\n",
+              "  'polygon': [{'x': -122.31636240290715, 'y': 37.87296634502498},\n",
+              "   {'x': -122.31636240290715, 'y': 37.87294800600024},\n",
+              "   {'x': -122.31635551637117, 'y': 37.87294800600024},\n",
+              "   {'x': -122.31635551637117, 'y': 37.87296634502498},\n",
+              "   {'x': -122.31636240290715, 'y': 37.87296634502498}]}]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 283
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -936,7 +1014,7 @@
         "id": "M88fD66M0f63"
       },
       "id": "M88fD66M0f63",
-      "execution_count": 137,
+      "execution_count": 284,
       "outputs": []
     },
     {
@@ -945,11 +1023,76 @@
         "label_ndjson_method2"
       ],
       "metadata": {
-        "id": "10pZtHgzfAJS"
+        "id": "10pZtHgzfAJS",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f26efbfc-71f0-439f-c043-a5d872cc94db"
       },
       "id": "10pZtHgzfAJS",
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 285,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[{'name': 'point_geo',\n",
+              "  'point': {'x': -122.31741025134123, 'y': 37.87355669249922},\n",
+              "  'uuid': 'f566e48a-2c28-4a33-aa4b-2b7b21c67902',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              " {'name': 'polyline_geo',\n",
+              "  'line': [{'x': -122.31757789012927, 'y': 37.87396317833991},\n",
+              "   {'x': -122.31639782443663, 'y': 37.87396741226917},\n",
+              "   {'x': -122.31638977853417, 'y': 37.87277872707839}],\n",
+              "  'uuid': 'ef279517-a7a2-45c0-b9a3-2f696f03b73f',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              " {'name': 'polygon_geo',\n",
+              "  'polygon': [{'x': -122.31691812612837, 'y': 37.873289980495024},\n",
+              "   {'x': -122.31710184090099, 'y': 37.87304335144298},\n",
+              "   {'x': -122.31680146054286, 'y': 37.87303594197371},\n",
+              "   {'x': -122.31691812612837, 'y': 37.873289980495024}],\n",
+              "  'uuid': '1d81e7dc-aede-4e0f-abba-89aa601f2629',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              " {'name': 'bbox_geo',\n",
+              "  'bbox': {'top': 37.87385944699745,\n",
+              "   'left': -122.31734455895823,\n",
+              "   'height': -0.0001460709135656657,\n",
+              "   'width': 0.0006141705536464315},\n",
+              "  'uuid': '9f4dd886-99a2-40fc-a281-4b414637a4c6',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              " {'name': 'radio_question_geo',\n",
+              "  'answer': {'name': 'first_radio_answer'},\n",
+              "  'uuid': '4e445710-58b3-45ac-abd5-8f236e3150b2',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              " {'name': 'bbox_checklist_geo',\n",
+              "  'classifications': [{'name': 'checklist_class_name',\n",
+              "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
+              "  'bbox': {'top': 37.87360752741479,\n",
+              "   'left': -122.31711256877092,\n",
+              "   'height': -0.00020534685175022105,\n",
+              "   'width': 0.0004572754559006853},\n",
+              "  'uuid': '7f858ba1-cbc7-4e54-be14-cde65251ec13',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              " {'name': 'bbox_text_geo',\n",
+              "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
+              "  'bbox': {'top': 37.87337992476082,\n",
+              "   'left': -122.31750814315438,\n",
+              "   'height': -0.00019791053033202388,\n",
+              "   'width': 0.00040764323713915473},\n",
+              "  'uuid': 'af06a5ad-eb7e-47c8-94b1-8ac28d6dc01b',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              " {'name': 'checklist_question_geo',\n",
+              "  'answer': [{'name': 'first_checklist_answer'},\n",
+              "   {'name': 'second_checklist_answer'},\n",
+              "   {'name': 'third_checklist_answer'}],\n",
+              "  'uuid': '99b86050-2118-40bd-bc94-6ce670667828',\n",
+              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}}]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 285
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -981,7 +1124,7 @@
         "    project_id = project.uid, \n",
         "    name=\"mal_import_job\"+str(uuid.uuid4()), \n",
         "    ### use label_ndjson_method2 if labels were created using NDJSON tools\n",
-        "    predictions=label_ndjson_method2)\n",
+        "    predictions=label_ndjson)\n",
         "\n",
         "upload_job.wait_until_done();\n",
         "print(\"Errors:\", upload_job.errors)\n",
@@ -992,10 +1135,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "SrAMnM9L0cAb",
-        "outputId": "31579a61-b273-4302-d769-3070781a961c"
+        "outputId": "6dac0ab6-c7bc-44e2-c3a8-ecff2b27933c"
       },
       "id": "SrAMnM9L0cAb",
-      "execution_count": 139,
+      "execution_count": 286,
       "outputs": [
         {
           "output_type": "stream",
@@ -1026,7 +1169,7 @@
         "    project_id = project.uid, \n",
         "    name=\"label_geo_import_job\"+str(uuid.uuid4()),  \n",
         "    # user label_ndjson if labels were created using python annotation tools\n",
-        "    labels=label_ndjson_method2)\n",
+        "    labels=label_ndjson)\n",
         "\n",
         "upload_job.wait_until_done();\n",
         "print(\"Errors:\", upload_job.errors)"
@@ -1036,10 +1179,10 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "a28c8f02-7460-461f-fcde-8e3b75329339"
+        "outputId": "3f8a2ef0-b8c8-4d6e-f743-191013884172"
       },
       "id": "leIutAqA2_Uw",
-      "execution_count": 140,
+      "execution_count": 287,
       "outputs": [
         {
           "output_type": "stream",
@@ -1071,7 +1214,7 @@
         "id": "ZLM9L5ggGqHG"
       },
       "id": "ZLM9L5ggGqHG",
-      "execution_count": 141,
+      "execution_count": 288,
       "outputs": []
     }
   ],

From 1ff95fce66c555e8d4fbf0c980f01be64a1157df Mon Sep 17 00:00:00 2001
From: Andrea Ovalle <74880762+ovalle15@users.noreply.github.com>
Date: Tue, 31 Jan 2023 14:32:28 -0500
Subject: [PATCH 6/7] Removed / update imports

Modified order of the cell with polygon 2 annotation
---
 examples/annotation_import/tiled.ipynb | 237 ++++++++++++-------------
 1 file changed, 115 insertions(+), 122 deletions(-)

diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index d4f46f47d..5020ca3e2 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -87,7 +87,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 267,
+      "execution_count": 72,
       "id": "voluntary-minister",
       "metadata": {
         "id": "voluntary-minister"
@@ -99,7 +99,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 268,
+      "execution_count": 73,
       "id": "committed-richards",
       "metadata": {
         "id": "committed-richards"
@@ -107,15 +107,13 @@
       "outputs": [],
       "source": [
         "import os\n",
-        "\n",
         "import uuid\n",
         "import numpy as np\n",
         "from PIL import Image\n",
         "import cv2\n",
         "import ndjson\n",
-        "import gdal\n",
-        "\n",
         "\n",
+        "from labelbox import Client, LabelingFrontend, LabelImport, MALPredictionImport, OntologyBuilder, Tool , Classification, Option\n",
         "from labelbox.data.serialization.ndjson.converter import NDJsonConverter\n",
         "from labelbox.data.annotation_types.data.tiled_image import TiledBounds, TiledImageData, TileLayer, EPSG, EPSGTransformer\n",
         "from labelbox.schema.queue_mode import QueueMode\n",
@@ -125,9 +123,7 @@
         "    Rectangle, Point, Line, Polygon,\n",
         "    Radio, Checklist, Text,MaskData, Mask,\n",
         "    ClassificationAnnotation, ClassificationAnswer\n",
-        ")\n",
-        "from labelbox.schema.ontology import OntologyBuilder, Tool, Classification, Option\n",
-        "from labelbox import Client, LabelingFrontend, LabelImport, MALPredictionImport"
+        ")\n"
       ]
     },
     {
@@ -143,7 +139,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 269,
+      "execution_count": 74,
       "id": "affecting-myanmar",
       "metadata": {
         "id": "affecting-myanmar"
@@ -198,7 +194,7 @@
         "id": "3mQh-yQ2TLmd"
       },
       "id": "3mQh-yQ2TLmd",
-      "execution_count": 270,
+      "execution_count": 75,
       "outputs": []
     },
     {
@@ -245,7 +241,7 @@
         "id": "f3OoBByTTYda"
       },
       "id": "f3OoBByTTYda",
-      "execution_count": 271,
+      "execution_count": 76,
       "outputs": []
     },
     {
@@ -295,7 +291,7 @@
         "id": "BSjRhlFvTdfS"
       },
       "id": "BSjRhlFvTdfS",
-      "execution_count": 272,
+      "execution_count": 77,
       "outputs": []
     },
     {
@@ -355,7 +351,7 @@
         "id": "oLX3adDOTmYS"
       },
       "id": "oLX3adDOTmYS",
-      "execution_count": 273,
+      "execution_count": 78,
       "outputs": []
     },
     {
@@ -379,7 +375,7 @@
         "id": "OsWf8Y_mTxLm"
       },
       "id": "OsWf8Y_mTxLm",
-      "execution_count": 274,
+      "execution_count": 79,
       "outputs": []
     },
     {
@@ -453,7 +449,7 @@
         "id": "2IZXIWP2UcOJ"
       },
       "id": "2IZXIWP2UcOJ",
-      "execution_count": 275,
+      "execution_count": 80,
       "outputs": []
     },
     {
@@ -521,7 +517,7 @@
         "id": "bnjcIB7FU21R"
       },
       "id": "bnjcIB7FU21R",
-      "execution_count": 276,
+      "execution_count": 81,
       "outputs": []
     },
     {
@@ -554,7 +550,7 @@
         "id": "hIkLI1q7glMi"
       },
       "id": "hIkLI1q7glMi",
-      "execution_count": 277,
+      "execution_count": 82,
       "outputs": []
     },
     {
@@ -579,14 +575,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 278,
+      "execution_count": 83,
       "id": "IlwNSdRQB4gC",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "IlwNSdRQB4gC",
-        "outputId": "160a95c5-e1a7-48f3-a125-267339a0bc6e"
+        "outputId": "af2d84eb-d88d-410a-9d99-7aa31e313785"
       },
       "outputs": [
         {
@@ -619,54 +615,6 @@
         "print(task.errors)"
       ]
     },
-    {
-      "cell_type": "code",
-      "source": [
-        " ## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n",
-        "\n",
-        "from PIL import Image\n",
-        "import cv2\n",
-        "\n",
-        "hsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\n",
-        "mask = cv2.inRange(hsv, (50, 10, 25), (100, 150, 255))\n",
-        "kernel = np.ones((5, 5), np.uint8)\n",
-        "mask = cv2.erode(mask, kernel)\n",
-        "mask = cv2.dilate(mask, kernel)\n",
-        "mask_annotation = MaskData.from_2D_arr(mask)\n",
-        "mask_data = Mask(mask=mask_annotation, color=[255, 255, 255])\n",
-        "h, w, _ = tiled_image_data.value.shape\n",
-        "pixel_bounds = TiledBounds(epsg=EPSG.SIMPLEPIXEL,\n",
-        "                          bounds=[Point(x=0, y=0),\n",
-        "                                  Point(x=w, y=h)])\n",
-        "transformer = EPSGTransformer.create_pixel_to_geo_transformer(\n",
-        "    src_epsg=pixel_bounds.epsg,\n",
-        "    pixel_bounds=pixel_bounds,\n",
-        "    geo_bounds=tiled_image_data.tile_bounds,\n",
-        "    zoom=23)\n",
-        "pixel_polygons = mask_data.shapely.simplify(3)\n",
-        "list_of_polygons = [transformer(Polygon.from_shapely(p)) for p in pixel_polygons.geoms]\n",
-        "polygon_annotation_two = ObjectAnnotation(value=list_of_polygons[0], name=\"polygon_geo_2\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dr1dEPzM_tHW",
-        "outputId": "c0749279-3871-4c34-8841-13bd94d055df"
-      },
-      "id": "dr1dEPzM_tHW",
-      "execution_count": 279,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n",
-            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n"
-          ]
-        }
-      ]
-    },
     {
       "cell_type": "markdown",
       "source": [
@@ -680,7 +628,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 280,
+      "execution_count": 84,
       "id": "suburban-crowd",
       "metadata": {
         "id": "suburban-crowd"
@@ -755,7 +703,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 281,
+      "execution_count": 85,
       "id": "PhfXaqDB7hRt",
       "metadata": {
         "id": "PhfXaqDB7hRt"
@@ -817,10 +765,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "FHXR6nyKw-8f",
-        "outputId": "e05da8fd-76bf-4667-ae11-353e09d6123b"
+        "outputId": "5b53a7ee-093c-4518-eac8-5ef7936510e2"
       },
       "id": "FHXR6nyKw-8f",
-      "execution_count": 282,
+      "execution_count": 86,
       "outputs": [
         {
           "output_type": "stream",
@@ -828,11 +776,11 @@
           "text": [
             "Batch:  <Batch {\n",
             "    \"consensus_settings_json\": \"{\\\"numberOfLabels\\\":1,\\\"coveragePercentage\\\":0}\",\n",
-            "    \"created_at\": \"2023-01-30 16:28:29+00:00\",\n",
+            "    \"created_at\": \"2023-01-31 19:25:18+00:00\",\n",
             "    \"name\": \"first-batch-geo-demo\",\n",
             "    \"size\": 1,\n",
-            "    \"uid\": \"20ebdb90-a0bb-11ed-8aef-c799659c348a\",\n",
-            "    \"updated_at\": \"2023-01-30 16:28:29+00:00\"\n",
+            "    \"uid\": \"ff079310-a19c-11ed-af26-3dd2d32cafec\",\n",
+            "    \"updated_at\": \"2023-01-31 19:25:18+00:00\"\n",
             "}>\n"
           ]
         }
@@ -865,13 +813,58 @@
     {
       "cell_type": "code",
       "source": [
+        " ## Lets create another polygon annotation with python annotation tools that draws the image using cv2 and PIL python libraries\n",
+        "\n",
         "\n",
-        "tiled_image_data_row_id = str(next(dataset.export_data_rows()).uid)\n",
+        "hsv = cv2.cvtColor(tiled_image_data.value, cv2.COLOR_RGB2HSV)\n",
+        "mask = cv2.inRange(hsv, (50, 10, 25), (100, 150, 255))\n",
+        "kernel = np.ones((5, 5), np.uint8)\n",
+        "mask = cv2.erode(mask, kernel)\n",
+        "mask = cv2.dilate(mask, kernel)\n",
+        "mask_annotation = MaskData.from_2D_arr(mask)\n",
+        "mask_data = Mask(mask=mask_annotation, color=[255, 255, 255])\n",
+        "h, w, _ = tiled_image_data.value.shape\n",
+        "pixel_bounds = TiledBounds(epsg=EPSG.SIMPLEPIXEL,\n",
+        "                          bounds=[Point(x=0, y=0),\n",
+        "                                  Point(x=w, y=h)])\n",
+        "transformer = EPSGTransformer.create_pixel_to_geo_transformer(\n",
+        "    src_epsg=pixel_bounds.epsg,\n",
+        "    pixel_bounds=pixel_bounds,\n",
+        "    geo_bounds=tiled_image_data.tile_bounds,\n",
+        "    zoom=23)\n",
+        "pixel_polygons = mask_data.shapely.simplify(3)\n",
+        "list_of_polygons = [transformer(Polygon.from_shapely(p)) for p in pixel_polygons.geoms]\n",
+        "polygon_annotation_two = ObjectAnnotation(value=list_of_polygons[0], name=\"polygon_geo_2\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dr1dEPzM_tHW",
+        "outputId": "cb9f30c9-0379-429b-fe19-eb84588666cd"
+      },
+      "id": "dr1dEPzM_tHW",
+      "execution_count": 87,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n",
+            "WARNING:labelbox.data.annotation_types.data.tiled_image:Unexpected tile size (512, 512, 3).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
         "\n",
+        "tiled_image_data_row_id = next(dataset.export_data_rows()).uid\n",
         "\n",
         "label = Label(\n",
         "    data=TiledImageData(\n",
-        "        uid=next(dataset.export_data_rows()).uid,\n",
+        "        uid=tiled_image_data_row_id ,\n",
         "        tile_layer=tile_layer,\n",
         "        tile_bounds=bounds,\n",
         "        zoom_levels=[17, 23]\n",
@@ -897,37 +890,37 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "2867ef9a-34d8-4f6b-b446-66bb694b9209"
+        "outputId": "e25c4962-303d-4818-ff45-063e281ff65b"
       },
       "id": "3_LZLZY7xS30",
-      "execution_count": 283,
+      "execution_count": 88,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[{'uuid': 'c8725378-2578-4e39-bfdb-d556bc1a4486',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "[{'uuid': 'c0f5e57b-2039-4b57-9494-1fb702ed0177',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'},\n",
               "  'name': 'point_geo',\n",
               "  'classifications': [],\n",
               "  'point': {'x': -122.31741025134123, 'y': 37.87355669249922}},\n",
-              " {'uuid': 'bd5136c0-f639-40c4-a391-3a3deb1ffb41',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              " {'uuid': '153d8ab2-85e4-446f-8c26-fd9608b859c5',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'},\n",
               "  'name': 'polyline_geo',\n",
               "  'classifications': [],\n",
               "  'line': [{'x': -122.31757789012927, 'y': 37.87396317833991},\n",
               "   {'x': -122.31639782443663, 'y': 37.87396741226917},\n",
               "   {'x': -122.31638977853417, 'y': 37.87277872707839}]},\n",
-              " {'uuid': 'c5381273-a73f-45ac-8704-1ec7d1cb0e94',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              " {'uuid': '64e1b3f3-5efc-4960-80a2-2ed260495027',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'},\n",
               "  'name': 'polygon_geo',\n",
               "  'classifications': [],\n",
               "  'polygon': [{'x': -122.31691812612837, 'y': 37.873289980495024},\n",
               "   {'x': -122.31710184090099, 'y': 37.87304335144298},\n",
               "   {'x': -122.31680146054286, 'y': 37.87303594197371},\n",
               "   {'x': -122.31691812612837, 'y': 37.873289980495024}]},\n",
-              " {'uuid': '221fc6d0-e12b-418b-8613-8eadf202eddd',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              " {'uuid': '2a474383-6986-4172-9651-a31742c671b7',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'},\n",
               "  'name': 'bbox_geo',\n",
               "  'classifications': [],\n",
               "  'bbox': {'top': 37.873713376083884,\n",
@@ -936,10 +929,10 @@
               "   'width': 0.0006141705536464315}},\n",
               " {'name': 'radio_question_geo',\n",
               "  'answer': {'name': 'first_radio_answer'},\n",
-              "  'uuid': '20363577-3922-4c67-8199-26ef4e9cfed4',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
-              " {'uuid': 'be5038eb-a6ad-4eb1-9117-f3b48fad168c',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'uuid': '172f2ffa-da88-456d-b6ab-ecf35c1a1dd0',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}},\n",
+              " {'uuid': 'a1957d02-da90-4a50-9406-de5aa47ef5a8',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'},\n",
               "  'name': 'bbox_checklist_geo',\n",
               "  'classifications': [{'name': 'checklist_class_name',\n",
               "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
@@ -947,8 +940,8 @@
               "   'left': -122.31711256877092,\n",
               "   'height': 0.00020534685175022105,\n",
               "   'width': 0.0004572754559006853}},\n",
-              " {'uuid': '91d9f104-2791-48fe-83c0-c0eead7f2b6f',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              " {'uuid': '27beed41-ca07-4f28-9471-2d2fdfe9eb46',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'},\n",
               "  'name': 'bbox_text_geo',\n",
               "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
               "  'bbox': {'top': 37.87318201423049,\n",
@@ -956,13 +949,13 @@
               "   'height': 0.00019791053033202388,\n",
               "   'width': 0.00040764323713915473}},\n",
               " {'name': 'checklist_question_geo',\n",
-              "  'uuid': 'e292b556-bf54-4dc2-8318-aaa57284d8a0',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              "  'uuid': '6e010615-37b3-4af4-bdcc-01e982ae901d',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'},\n",
               "  'answer': [{'name': 'first_checklist_answer'},\n",
               "   {'name': 'second_checklist_answer'},\n",
               "   {'name': 'third_checklist_answer'}]},\n",
-              " {'uuid': '4ba7e936-9100-4d13-8467-8f95b0191066',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'},\n",
+              " {'uuid': 'f31ed137-9259-4f99-89dc-f6bb840b24b6',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'},\n",
               "  'name': 'polygon_geo_2',\n",
               "  'classifications': [],\n",
               "  'polygon': [{'x': -122.31636240290715, 'y': 37.87296634502498},\n",
@@ -973,7 +966,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 283
+          "execution_count": 88
         }
       ]
     },
@@ -1014,7 +1007,7 @@
         "id": "M88fD66M0f63"
       },
       "id": "M88fD66M0f63",
-      "execution_count": 284,
+      "execution_count": 89,
       "outputs": []
     },
     {
@@ -1027,10 +1020,10 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "f26efbfc-71f0-439f-c043-a5d872cc94db"
+        "outputId": "c8d8b9e7-15e6-48a7-a93e-a26266290c6e"
       },
       "id": "10pZtHgzfAJS",
-      "execution_count": 285,
+      "execution_count": 90,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -1038,32 +1031,32 @@
             "text/plain": [
               "[{'name': 'point_geo',\n",
               "  'point': {'x': -122.31741025134123, 'y': 37.87355669249922},\n",
-              "  'uuid': 'f566e48a-2c28-4a33-aa4b-2b7b21c67902',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              "  'uuid': '491ef98a-70cd-49d2-8a2d-e8753d91bd50',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}},\n",
               " {'name': 'polyline_geo',\n",
               "  'line': [{'x': -122.31757789012927, 'y': 37.87396317833991},\n",
               "   {'x': -122.31639782443663, 'y': 37.87396741226917},\n",
               "   {'x': -122.31638977853417, 'y': 37.87277872707839}],\n",
-              "  'uuid': 'ef279517-a7a2-45c0-b9a3-2f696f03b73f',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              "  'uuid': '8b27ab75-c07a-4afd-a9e6-afd5c059ee9d',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}},\n",
               " {'name': 'polygon_geo',\n",
               "  'polygon': [{'x': -122.31691812612837, 'y': 37.873289980495024},\n",
               "   {'x': -122.31710184090099, 'y': 37.87304335144298},\n",
               "   {'x': -122.31680146054286, 'y': 37.87303594197371},\n",
               "   {'x': -122.31691812612837, 'y': 37.873289980495024}],\n",
-              "  'uuid': '1d81e7dc-aede-4e0f-abba-89aa601f2629',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              "  'uuid': '2aafd451-f09d-4b16-9f27-a4dfbbbd5499',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}},\n",
               " {'name': 'bbox_geo',\n",
               "  'bbox': {'top': 37.87385944699745,\n",
               "   'left': -122.31734455895823,\n",
               "   'height': -0.0001460709135656657,\n",
               "   'width': 0.0006141705536464315},\n",
-              "  'uuid': '9f4dd886-99a2-40fc-a281-4b414637a4c6',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              "  'uuid': 'e55ccd7c-8072-477a-b1ac-e7959d1d32c6',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}},\n",
               " {'name': 'radio_question_geo',\n",
               "  'answer': {'name': 'first_radio_answer'},\n",
-              "  'uuid': '4e445710-58b3-45ac-abd5-8f236e3150b2',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              "  'uuid': '73b080a4-3207-4563-8ef0-984db6e5a39b',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}},\n",
               " {'name': 'bbox_checklist_geo',\n",
               "  'classifications': [{'name': 'checklist_class_name',\n",
               "    'answer': [{'name': 'first_checklist_answer'}]}],\n",
@@ -1071,26 +1064,26 @@
               "   'left': -122.31711256877092,\n",
               "   'height': -0.00020534685175022105,\n",
               "   'width': 0.0004572754559006853},\n",
-              "  'uuid': '7f858ba1-cbc7-4e54-be14-cde65251ec13',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              "  'uuid': '1b6b9f39-da86-4efa-b8e6-f8c4482651d8',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}},\n",
               " {'name': 'bbox_text_geo',\n",
               "  'classifications': [{'name': 'free_text_geo', 'answer': 'sample text'}],\n",
               "  'bbox': {'top': 37.87337992476082,\n",
               "   'left': -122.31750814315438,\n",
               "   'height': -0.00019791053033202388,\n",
               "   'width': 0.00040764323713915473},\n",
-              "  'uuid': 'af06a5ad-eb7e-47c8-94b1-8ac28d6dc01b',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}},\n",
+              "  'uuid': 'c83c5c29-4a50-43fa-909a-ff6a213d1ee4',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}},\n",
               " {'name': 'checklist_question_geo',\n",
               "  'answer': [{'name': 'first_checklist_answer'},\n",
               "   {'name': 'second_checklist_answer'},\n",
               "   {'name': 'third_checklist_answer'}],\n",
-              "  'uuid': '99b86050-2118-40bd-bc94-6ce670667828',\n",
-              "  'dataRow': {'id': 'cldj0ytdq12em075shpataunq'}}]"
+              "  'uuid': '47962318-310d-46ce-a2a1-26c70edf5e6b',\n",
+              "  'dataRow': {'id': 'cldkmpeur008s078t4uz04luf'}}]"
             ]
           },
           "metadata": {},
-          "execution_count": 285
+          "execution_count": 90
         }
       ]
     },
@@ -1135,10 +1128,10 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "SrAMnM9L0cAb",
-        "outputId": "6dac0ab6-c7bc-44e2-c3a8-ecff2b27933c"
+        "outputId": "f033df57-6a04-48f8-dce7-4ccf8c87aed3"
       },
       "id": "SrAMnM9L0cAb",
-      "execution_count": 286,
+      "execution_count": 91,
       "outputs": [
         {
           "output_type": "stream",
@@ -1179,10 +1172,10 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "3f8a2ef0-b8c8-4d6e-f743-191013884172"
+        "outputId": "4b6e3124-1d90-472e-d8af-9ebee1683f80"
       },
       "id": "leIutAqA2_Uw",
-      "execution_count": 287,
+      "execution_count": 92,
       "outputs": [
         {
           "output_type": "stream",
@@ -1214,7 +1207,7 @@
         "id": "ZLM9L5ggGqHG"
       },
       "id": "ZLM9L5ggGqHG",
-      "execution_count": 288,
+      "execution_count": 93,
       "outputs": []
     }
   ],

From 948bf2c372eebf37d3126768c16ebf6d810c3029 Mon Sep 17 00:00:00 2001
From: Andrea Ovalle <74880762+ovalle15@users.noreply.github.com>
Date: Tue, 31 Jan 2023 15:08:15 -0500
Subject: [PATCH 7/7] Move MediaType import to top level

---
 examples/annotation_import/tiled.ipynb | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/examples/annotation_import/tiled.ipynb b/examples/annotation_import/tiled.ipynb
index 5020ca3e2..dcc2e6a4f 100644
--- a/examples/annotation_import/tiled.ipynb
+++ b/examples/annotation_import/tiled.ipynb
@@ -113,11 +113,10 @@
         "import cv2\n",
         "import ndjson\n",
         "\n",
-        "from labelbox import Client, LabelingFrontend, LabelImport, MALPredictionImport, OntologyBuilder, Tool , Classification, Option\n",
+        "from labelbox import Client, LabelingFrontend, LabelImport, MALPredictionImport, OntologyBuilder, Tool , Classification, MediaType, Option\n",
         "from labelbox.data.serialization.ndjson.converter import NDJsonConverter\n",
         "from labelbox.data.annotation_types.data.tiled_image import TiledBounds, TiledImageData, TileLayer, EPSG, EPSGTransformer\n",
         "from labelbox.schema.queue_mode import QueueMode\n",
-        "from labelbox.schema.media_type import MediaType\n",
         "from labelbox.data.annotation_types import (\n",
         "    Label, ImageData, ObjectAnnotation, \n",
         "    Rectangle, Point, Line, Polygon,\n",