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CHG: minimize diff
1 parent a0075db commit aded075

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‎examples/annotation_types/basics.ipynb‎

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‎examples/annotation_types/converters.ipynb‎

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@@ -46,73 +46,35 @@
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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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"execution_count": 1,
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"id": "improving-demonstration",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install \"labelbox[data]\""
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]
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"source": "!pip install \"labelbox[data]\""
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"id": "acute-serve",
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"metadata": {},
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"outputs": [],
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"source": [
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"from labelbox.data.annotation_types import (\n",
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" LabelList, \n",
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" Label, \n",
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" Rectangle, \n",
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" Point, \n",
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" ObjectAnnotation, \n",
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" Geometry\n",
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")\n",
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"from labelbox.data.serialization import LBV1Converter, NDJsonConverter\n",
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"from labelbox import Client\n",
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"\n",
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"import IPython\n",
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"import numpy as np\n",
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"from PIL import Image\n",
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"from getpass import getpass\n",
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"import os\n",
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"import cv2"
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]
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"source": "from labelbox.data.annotation_types import (LabelList, Label, Rectangle, Point,\n ObjectAnnotation, Geometry)\nfrom labelbox.data.serialization import LBV1Converter, NDJsonConverter\nfrom labelbox import Client\n\nimport IPython\nimport numpy as np\nfrom PIL import Image\nfrom getpass import getpass\nimport os\nimport cv2"
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"id": "psychological-airfare",
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"metadata": {},
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"outputs": [],
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"source": [
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"# If you don't want to give google access to drive you can skip this cell\n",
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"# and manually set `API_KEY` below.\n",
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"COLAB = \"google.colab\" in str(get_ipython())\n",
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"if COLAB:\n",
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" !pip install colab-env -qU\n",
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" from colab_env import envvar_handler\n",
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" envvar_handler.envload()\n",
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"\n",
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"API_KEY = os.environ.get(\"LABELBOX_API_KEY\")\n",
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"if not os.environ.get(\"LABELBOX_API_KEY\"):\n",
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" API_KEY = getpass(\"Please enter your labelbox api key\")\n",
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" if COLAB:\n",
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" envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)"
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]
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"source": "# If you don't want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\nCOLAB = \"google.colab\" in str(get_ipython())\nif COLAB:\n !pip install colab-env -qU\n from colab_env import envvar_handler\n envvar_handler.envload()\n\nAPI_KEY = os.environ.get(\"LABELBOX_API_KEY\")\nif not os.environ.get(\"LABELBOX_API_KEY\"):\n API_KEY = getpass(\"Please enter your labelbox api key\")\n if COLAB:\n envvar_handler.add_env(\"LABELBOX_API_KEY\", API_KEY)"
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"id": "adult-fleet",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Only update this if you have an on-prem deployment\n",
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"ENDPOINT = \"https://api.labelbox.com/graphql\"\n",
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"client = Client(api_key=API_KEY, endpoint=ENDPOINT)"
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]
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"source": "# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\"\nclient = Client(api_key=API_KEY, endpoint=ENDPOINT)"
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},
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{
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"cell_type": "markdown",
@@ -136,7 +98,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 5,
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"id": "electronic-heart",
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"metadata": {},
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"outputs": [
@@ -381,24 +343,7 @@
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"output_type": "display_data"
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}
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],
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"source": [
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"project = client.get_project(\"ckqcx1d58068c0y619qv7hzgu\")\n",
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"labels = project.video_label_generator()\n",
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"\n",
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"for label in labels:\n",
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" annotation_lookup = label.frame_annotations()\n",
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" for idx, frame in label.data.value:\n",
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" if idx % 30 != 1:\n",
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" continue\n",
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" \n",
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" for annotation in annotation_lookup[idx]:\n",
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" if isinstance(annotation.value, Rectangle):\n",
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" frame = annotation.value.draw(canvas = frame.astype(np.uint8), thickness = 10, color= (255,0,0))\n",
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" \n",
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" im = Image.fromarray(frame)\n",
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" w,h = im.size\n",
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" IPython.display.display(im.resize((w//6, h//6) )) "
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]
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"source": "project = client.get_project(\"ckqcx1d58068c0y619qv7hzgu\")\nlabels = project.video_label_generator()\n\nfor label in labels:\n annotation_lookup = label.frame_annotations()\n for idx, frame in label.data.value:\n if idx % 30 != 1:\n continue\n\n for annotation in annotation_lookup[idx]:\n if isinstance(annotation.value, Rectangle):\n frame = annotation.value.draw(canvas=frame.astype(np.uint8),\n thickness=10,\n color=(255, 0, 0))\n\n im = Image.fromarray(frame)\n w, h = im.size\n IPython.display.display(im.resize((w // 6, h // 6)))"
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},
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{
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"cell_type": "markdown",
@@ -410,18 +355,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"id": "western-lebanon",
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"metadata": {},
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"outputs": [],
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"source": [
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"project = client.get_project(\"ckrdn049u5dia0y3h4l577t1v\")\n",
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"label_list = project.label_generator().as_list()"
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]
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"source": "project = client.get_project(\"ckrdn049u5dia0y3h4l577t1v\")\nlabel_list = project.label_generator().as_list()"
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 7,
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"id": "likely-cleaners",
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"metadata": {},
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"outputs": [
@@ -437,15 +379,11 @@
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"output_type": "execute_result"
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}
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],
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"source": [
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"im_data = label_list[0].data.value\n",
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"h,w = im_data.shape[:2]\n",
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"Image.fromarray(im_data)"
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]
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"source": "im_data = label_list[0].data.value\nh, w = im_data.shape[:2]\nImage.fromarray(im_data)"
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"id": "incredible-storage",
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"metadata": {},
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"outputs": [
@@ -461,17 +399,11 @@
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"output_type": "execute_result"
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}
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],
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"source": [
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"canvas = np.zeros((h, w, 3), dtype = np.uint8)\n",
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"for annotation in label_list[0].annotations:\n",
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" if isinstance(annotation.value, Geometry):\n",
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" canvas = annotation.value.draw(canvas = canvas)\n",
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"Image.fromarray(canvas)"
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]
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"source": "canvas = np.zeros((h, w, 3), dtype=np.uint8)\nfor annotation in label_list[0].annotations:\n if isinstance(annotation.value, Geometry):\n canvas = annotation.value.draw(canvas=canvas)\nImage.fromarray(canvas)"
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"id": "greenhouse-discrimination",
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"metadata": {},
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"outputs": [
@@ -489,18 +421,11 @@
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"output_type": "execute_result"
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}
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],
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"source": [
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"geoms = []\n",
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"for annotation in label_list[0].annotations:\n",
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" if isinstance(annotation.value, Geometry):\n",
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" geoms.append(annotation.value.shapely)\n",
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"from shapely.geometry import MultiPolygon\n",
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"MultiPolygon(geoms)"
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]
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"source": "geoms = []\nfor annotation in label_list[0].annotations:\n if isinstance(annotation.value, Geometry):\n geoms.append(annotation.value.shapely)\nfrom shapely.geometry import MultiPolygon\n\nMultiPolygon(geoms)"
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 10,
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"id": "dried-lightning",
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"metadata": {},
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"outputs": [
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]
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}
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],
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"source": [
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"# We can also serialize back to the original payload:\n",
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"for result in LBV1Converter.serialize(label_list):\n",
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" print(result)"
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]
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"source": "# We can also serialize back to the original payload:\nfor result in LBV1Converter.serialize(label_list):\n print(result)"
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},
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{
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"cell_type": "markdown",
@@ -530,7 +451,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"id": "printable-wagon",
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"metadata": {},
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"outputs": [
@@ -545,16 +466,11 @@
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]
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}
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],
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"source": [
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"ndjson = []\n",
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"for row in NDJsonConverter.serialize(label_list):\n",
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" ndjson.append(row)\n",
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" print(row)"
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]
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"source": "ndjson = []\nfor row in NDJsonConverter.serialize(label_list):\n ndjson.append(row)\n print(row)"
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"id": "operational-project",
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"metadata": {},
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"outputs": [
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Convert back\n",
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"NDJsonConverter.deserialize(ndjson)"
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]
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"source": "# Convert back\nNDJsonConverter.deserialize(ndjson)"
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},
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{
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"cell_type": "markdown",
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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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"execution_count": 13,
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"id": "placed-danger",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": ""
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}
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],
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"metadata": {

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