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Merge pull request #240 from Labelbox/develop
rc3
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‎CHANGELOG.md‎

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# Changelog
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# Version 3.0.0-rc3 (2021-08-11)
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## Updates
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* Geometry.raster now has a consistent interface and improved functionality
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* renamed schema_id to feature_schema_id in the `FeatureSchema` class
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* `Mask` objects now use `MaskData` to represent segmentation masks instead of `ImageData`
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# Version 3.0.0-rc2 (2021-08-09)
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## Updates
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* Rename `data` property of TextData, ImageData, and VideoData types to `value`.

‎examples/README.md‎

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| --------------------------- | --------------------------------- | ------------ |
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| Fundamentals | [Github](basics/basics.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/basics.ipynb) |
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| Data Rows | [Github](basics/data_rows.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_rows.ipynb) |
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| Data Row Metadata | [Github](basics/data_row_metadata.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/data_row_metadata.ipynb) |
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| Datasets | [Github](basics/datasets.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/datasets.ipynb) |
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| Labels | [Github](basics/labels.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/labels.ipynb) |
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| Ontologies | [Github](basics/ontologies.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/basics/ontologies.ipynb) |

‎examples/annotation_types/basics.ipynb‎

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"from labelbox.data.annotation_types import (\n",
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" Label,\n",
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" ImageData,\n",
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" MaskData,\n",
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" LabelList,\n",
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" TextData,\n",
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" VideoData,\n",
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"1. Value\n",
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" - Must be either a Geometry, TextEntity, or Classification\n",
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" - This is the same as a top level tool in labelbox\n",
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"2. name or schema_id\n",
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"2. name or feature_schema_id\n",
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" - This is the id that corresponds to a particular class or just simply the class name\n",
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" - If uploading to labelbox this must match a field in an ontology.\n",
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"3. (Optional) Classifications \n",
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"\n",
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"point_annotation = ObjectAnnotation(\n",
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" value = Point(x = 5, y = 3),\n",
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" schema_id = \"ckrgcgl89000108jtggc9e687\"\n",
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" feature_schema_id = \"ckrgcgl89000108jtggc9e687\"\n",
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")"
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]
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},
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"\n",
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"polygon_annotation = ObjectAnnotation(\n",
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" value = Polygon(points = [Point(x = x, y = y) for x,y in xy_poly]),\n",
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" schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
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" feature_schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
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")\n",
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"\n"
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]
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"\n",
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"line_annotation = ObjectAnnotation(\n",
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" value = Line(points = [Point(x = x, y = y) for x,y in xy_line]),\n",
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" schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
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" feature_schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
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")"
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]
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},
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"\n",
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"rectangle_annotation = ObjectAnnotation(\n",
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" value = Rectangle(start = start, end = end),\n",
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" schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
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" feature_schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
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")"
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]
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},
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"source": [
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"\n",
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"mask_annotation = ObjectAnnotation(\n",
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" value = Mask(mask = ImageData(arr = np.zeros((32,32,3), dtype = np.uint8)), color = (255,255,255)),\n",
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" value = Mask(mask = MaskData(arr = np.zeros((32,32,3), dtype = np.uint8)), color = (255,255,255)),\n",
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" name = \"mask class name\"\n",
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")\n",
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"\n",
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"mask_annotation = ObjectAnnotation(\n",
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" value = Mask(mask = ImageData(arr = np.zeros((32,32,3), dtype = np.uint8)), color = (255,255,255)),\n",
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" schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
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" value = Mask(mask = MaskData(arr = np.zeros((32,32,3), dtype = np.uint8)), color = (255,255,255)),\n",
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" feature_schema_id = \"ckrgcel71000008jtd9mn0szu\"\n",
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")"
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]
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"raster_data = ImageData(arr = np.zeros((32,32,3), dtype = np.uint8))\n",
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"raster_data = MaskData(arr = np.zeros((32,32,3), dtype = np.uint8))\n",
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"mask_annotation = ObjectAnnotation(\n",
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" value = Mask(mask = raster_data, color = [255,255,255]),\n",
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" name = \"eyes\"\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"raster_data = ImageData(arr = np.zeros((32,32, 3), dtype = np.uint8))\n",
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"raster_data = MaskData(arr = np.zeros((32,32, 3), dtype = np.uint8))\n",
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"mask_annotation = ObjectAnnotation(\n",
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" value = Mask(mask = raster_data, color = (128,255,255)),\n",
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" name = \"eye\"\n",
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"\n",
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"entity_annotation = ObjectAnnotation(\n",
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" value = TextEntity(start = 10, end = 12),\n",
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" schema_id = \"ckrgddyli000108mk0c0t9qya\"\n",
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" feature_schema_id = \"ckrgddyli000108mk0c0t9qya\"\n",
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")"
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]
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},
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"outputs": [],
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"source": [
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"mask_annotation = Mask(\n",
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" mask = ImageData(arr = np_mask),\n",
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" mask = MaskData(arr = np_mask),\n",
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" color = color \n",
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")\n",
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"\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"mask_data = ImageData(arr = np_seg_mask)\n",
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"mask_data = MaskData(arr = np_seg_mask)\n",
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"eye_mask = Mask(mask = mask_data, color = eye_color)\n",
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"nose_mask = Mask(mask = mask_data, color = nose_color)\n"
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]
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")\n",
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"\n",
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"text_annotation = ClassificationAnnotation(\n",
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" schema_id = \"my text class\", \n",
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" feature_schema_id = \"my text class\", \n",
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" value = Text(answer = \"some text answer\") \n",
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]
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"\n",
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"\n",
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"radio_annotation = ClassificationAnnotation(\n",
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" schema_id = \"ckresqdg7000001jnb70v4zcc\",\n",
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" value = Radio(answer = ClassificationAnswer(schema_id = \"ckrdy06ia000007ky94h04qlj\")) \n",
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" feature_schema_id = \"ckresqdg7000001jnb70v4zcc\",\n",
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" value = Radio(answer = ClassificationAnswer(feature_schema_id = \"ckrdy06ia000007ky94h04qlj\")) \n",
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")"
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]
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},
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"\n",
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"checklist_annotation = ClassificationAnnotation(\n",
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" schema_id = \"ckrestd5g000101jnhudjf29a\",\n",
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" value = Checklist(answer = [ClassificationAnswer(schema_id = \"ckrdy06ia000007ky94h04qlj\")])\n",
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" feature_schema_id = \"ckrestd5g000101jnhudjf29a\",\n",
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" value = Checklist(answer = [ClassificationAnswer(feature_schema_id = \"ckrdy06ia000007ky94h04qlj\")])\n",
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")\n",
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" \n",
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"checklist_annotation = ClassificationAnnotation(\n",
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" ),\n",
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" ObjectAnnotation(\n",
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" name = \"deer_eyes\",\n",
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" value = Mask(mask = ImageData(arr = np_mask), color = color)\n",
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" value = Mask(mask = MaskData(arr = np_mask), color = color)\n",
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" )\n",
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" ]\n",
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"### Interacting with labelbox:\n",
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"* For this label to be compatible with labelbox we need the following:\n",
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" - all named features must have schema_ids\n",
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" - all named features must have feature_schema_ids\n",
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" - all data must have urls\n",
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" - masks\n",
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" - images\n",
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"metadata": {},
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"source": [
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"### Assigning Schema Ids:\n",
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"* All tools, classifications, and options either have names or schema_ids.\n",
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"* All tools, classifications, and options either have names or feature_schema_ids.\n",
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"* Locally it is convenient to provide a name so that we don't need a labelbox project to use these interfaces.\n",
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"* To use MAL and MEA schema ids are required"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"def show_schema_ids(label):\n",
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"def show_feature_schema_ids(label):\n",
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" for annotation in label.annotations:\n",
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" print(f\"Object : {annotation.name} - {annotation.schema_id}\")\n",
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" print(f\"Object : {annotation.name} - {annotation.feature_schema_id}\")\n",
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" for classification in getattr(annotation, 'classifications', []):\n",
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" print(f\"--- Subclass : {classification.name} - {classification.schema_id}\")\n",
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" print(f\"--- Subclass : {classification.name} - {classification.feature_schema_id}\")\n",
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" option = classification.value\n",
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" print(f\"--- --- Options: {option.answer.name} - {option.answer.schema_id}\")\n",
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" print(f\"--- --- Options: {option.answer.name} - {option.answer.feature_schema_id}\")\n",
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"\n",
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" if isinstance(annotation, ClassificationAnnotation):\n",
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" for option in annotation.value.answer:\n",
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" print(f\"--- Options: {option.name} - {option.schema_id}\")\n",
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"show_schema_ids(label)"
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" print(f\"--- Options: {option.name} - {option.feature_schema_id}\")\n",
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"show_feature_schema_ids(label)"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"label.assign_schema_ids(ontology)\n",
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"show_schema_ids(label)"
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"label.assign_feature_schema_ids(ontology)\n",
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"show_feature_schema_ids(label)"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "progressive-courage",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "asian-album",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎examples/annotation_types/converters.ipynb‎

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