wip converter notebook

Author: Matt Sokoloff

examples/annotation_types/converters.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "confused-donna",
+   "metadata": {},
+   "source": [
+    "# Converters\n",
+    "* This is where the interfaces get exciting\n",
+    "* The goal is to create a set of converts that convert to and from the labelbox object format\n",
+    "* This includes model formats too\n",
+    "* E.g. \n",
+    "    1. a user has some data in a coco format that they want to use for MAL.\n",
+    "        - They can convert it to labelbox to the labelbox format and upload\n",
+    "    2. a user has labelbox training data\n",
+    "        - They can convert it to the labelbox common format\n",
+    "        - They can convert the common format to a data loader format\n",
+    "    3. A lot more but we are still developing these tools\n",
+    "* Currently we support:\n",
+    "    1. NDJson Converter\n",
+    "        - Convert to and from the prediction import format (mea, mal)\n",
+    "    2. LabelboxV1 Converter\n",
+    "        - Convert to and from the prediction import format (mea, mal)\n",
+    "* Note that tiled imagery is not yet supported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "intelligent-referral",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from labelbox.data.annotation_types import LabelCollection, Label, Rectangle, Point, ObjectAnnotation\n",
+    "from labelbox.data.serialization import LBV1Converter, NDJsonConverter\n",
+    "#from labelbox import Client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "realistic-distance",
+   "metadata": {},
+   "source": [
+    "## Labelbox V1 Converter\n",
+    "* Convert to and from the V1 Format.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "insured-beads",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = Client(api_key = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJja3FjeDFkMDMwNjg0MHk2MWJvd2I1anI1Iiwib3JnYW5pemF0aW9uSWQiOiJja3FjeDFjem4wNjgzMHk2MWdoOXYwMmNzIiwiYXBpS2V5SWQiOiJja3JmMGR0MXc2dTVqMHk5ajRnMWo3ZHh4Iiwic2VjcmV0IjoiMzY3NTBlN2I2MDg0M2RiMTk5MTJhNGRjNmMxMzJhZTciLCJpYXQiOjE2MjY5NjQwNDEsImV4cCI6MjI1ODExNjA0MX0.Dv2c-uYsZXX_4SWYYpuR6ben-6mGn829abgpec1zo_g\")\n",
+    "project = client.get_project(\"ckrdn049u5dia0y3h4l577t1v\")\n",
+    "labels = project.export_labels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "characteristic-simulation",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}