examples/Keypoints.ipynb

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    "<!-- Autogenerated by `scripts/make_examples.py` -->\n",
    "<table align=\"left\">\n",
    "    <td>\n",
    "        <a target=\"_blank\" href=\"https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/Keypoints.ipynb\">\n",
    "            <img src=\"https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png\" height=\"32\" width=\"32\">\n",
    "            Try in Google Colab\n",
    "        </a>\n",
    "    </td>\n",
    "    <td>\n",
    "        <a target=\"_blank\" href=\"https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/Keypoints.ipynb\">\n",
    "            <img src=\"https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png\" height=\"32\" width=\"32\">\n",
    "            Share via nbviewer\n",
    "        </a>\n",
    "    </td>\n",
    "    <td>\n",
    "        <a target=\"_blank\" href=\"https://github.com/voxel51/fiftyone-examples/blob/master/examples/Keypoints.ipynb\">\n",
    "            <img src=\"https://user-images.githubusercontent.com/25985824/104791633-6efa1d80-5769-11eb-8ee3-4b2123fe4b66.png\" height=\"32\" width=\"32\">\n",
    "            View on GitHub\n",
    "        </a>\n",
    "    </td>\n",
    "    <td>\n",
    "        <a href=\"https://github.com/voxel51/fiftyone-examples/raw/master/examples/Keypoints.ipynb\" download>\n",
    "            <img src=\"https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png\" height=\"32\" width=\"32\">\n",
    "            Download notebook\n",
    "        </a>\n",
    "    </td>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color: orange\">**FiftyOne Keypoint Skeletons**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already downloaded\n",
      "Loading 'quickstart'\n",
      " 100% |█████████████████| 200/200 [2.4s elapsed, 0s remaining, 82.8 samples/s]          \n",
      "Dataset 'skeletons' created\n"
     ]
    },
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       "</div>\n",
       "\n",
       "<script type=\"text/javascript\">\n",
       "   (function() {\n",
       "     var container = document.getElementById(\"focontainer-a1a740f7-43b2-47d1-b8a8-0532a8c93658\");\n",
       "     var overlay = document.getElementById(\"fooverlay-a1a740f7-43b2-47d1-b8a8-0532a8c93658\");\n",
       "     fetch(`http://localhost:5151/fiftyone`)\n",
       "     .then(() => {\n",
       "        overlay.addEventListener(\"click\", () => {\n",
       "          fetch(`http://localhost:5151/event`, {\n",
       "            method: \"POST\",\n",
       "            body: JSON.stringify({\n",
       "              event: \"reactivate_notebook_cell\",\n",
       "              data: { subscription: \"a1a740f7-43b2-47d1-b8a8-0532a8c93658\" },\n",
       "              subscription: \"a1a740f7-43b2-47d1-b8a8-0532a8c93658\"\n",
       "            })\n",
       "          })\n",
       "        });\n",
       "        container.addEventListener(\"mouseenter\", () => overlay.style.display = \"block\");\n",
       "        container.addEventListener(\"mouseleave\", () => overlay.style.display = \"none\");\n",
       "     });\n",
       "   })();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Could not connect session, trying again in 10 seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import fiftyone as fo\n",
    "import fiftyone.zoo as foz\n",
    "\n",
    "\n",
    "dataset = foz.load_zoo_dataset(\n",
    "   \"quickstart\",\n",
    "   dataset_name=\"skeletons\",\n",
    ")\n",
    "\n",
    "\n",
    "session = fo.launch_app(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define your skeleton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.skeletons = {\n",
    "    \"points\": fo.KeypointSkeleton(\n",
    "        labels=[\n",
    "            \"left hand\" \"left shoulder\", \"right shoulder\", \"right hand\",\n",
    "            \"left eye\", \"right eye\", \"mouth\",\n",
    "        ],\n",
    "        edges=[[0, 1, 2, 3], [4, 5, 6]],\n",
    "    )\n",
    "}\n",
    "\n",
    "dataset.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!export FIFTYONE_CVAT_USERNAME=\"\"\n",
    "!export FIFTYONE_CVAT_PASSWORD=\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotate using one of FiftyOne's annotation integrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset:     skeletons\n",
       "Media type:  image\n",
       "Num samples: 1\n",
       "Sample fields:\n",
       "    id:           fiftyone.core.fields.ObjectIdField\n",
       "    filepath:     fiftyone.core.fields.StringField\n",
       "    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
       "    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)\n",
       "    ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
       "    uniqueness:   fiftyone.core.fields.FloatField\n",
       "    predictions:  fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
       "View stages:\n",
       "    1. MatchTags(tags=['annotate'], bool=True, all=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann_view = dataset.match_tags(\"annotate\")\n",
    "ann_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing metadata...\n",
      " 100% |█████████████████████| 1/1 [155.0ms elapsed, 0s remaining, 6.5 samples/s] \n",
      "Uploading samples to CVAT...\n",
      "Launching editor at 'http://localhost:8080/tasks/18/jobs/18'...\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# A unique identifer for this run\n",
    "anno_key = \"skeletons51\"\n",
    "\n",
    "# Upload the sample and launch CVAT\n",
    "anno_results = ann_view.annotate(\n",
    "    anno_key,\n",
    "    label_field=\"points\",\n",
    "    label_type=\"keypoints\",\n",
    "    classes=[\"person\"],\n",
    "    launch_editor=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'points': {18: {'name': 'FiftyOne_skeletons',\n",
       "   'status': 'completed',\n",
       "   'assignee': None,\n",
       "   'last_updated': '2023-09-15T15:34:40.809777Z',\n",
       "   'jobs': {18: {'url': 'http://localhost:8080/api/jobs/18',\n",
       "     'id': 18,\n",
       "     'task_id': 18,\n",
       "     'project_id': None,\n",
       "     'assignee': None,\n",
       "     'guide_id': None,\n",
       "     'dimension': '2d',\n",
       "     'bug_tracker': None,\n",
       "     'status': 'completed',\n",
       "     'stage': 'acceptance',\n",
       "     'state': 'completed',\n",
       "     'mode': 'annotation',\n",
       "     'frame_count': 1,\n",
       "     'start_frame': 0,\n",
       "     'stop_frame': 0,\n",
       "     'data_chunk_size': 72,\n",
       "     'data_compressed_chunk_type': 'imageset',\n",
       "     'created_date': '2023-09-15T15:33:36.292099Z',\n",
       "     'updated_date': '2023-09-15T15:34:40.792068Z',\n",
       "     'issues': {'url': 'http://localhost:8080/api/issues?job_id=18',\n",
       "      'count': 0},\n",
       "     'labels': {'url': 'http://localhost:8080/api/labels?job_id=18',\n",
       "      'count': 1},\n",
       "     'type': 'annotation',\n",
       "     'organization': None}}}}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anno_results.get_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'points': {18: {'name': 'FiftyOne_skeletons',\n",
       "   'status': 'completed',\n",
       "   'assignee': None,\n",
       "   'last_updated': '2023-09-15T15:34:40.809777Z',\n",
       "   'jobs': {18: {'url': 'http://localhost:8080/api/jobs/18',\n",
       "     'id': 18,\n",
       "     'task_id': 18,\n",
       "     'project_id': None,\n",
       "     'assignee': None,\n",
       "     'guide_id': None,\n",
       "     'dimension': '2d',\n",
       "     'bug_tracker': None,\n",
       "     'status': 'completed',\n",
       "     'stage': 'acceptance',\n",
       "     'state': 'completed',\n",
       "     'mode': 'annotation',\n",
       "     'frame_count': 1,\n",
       "     'start_frame': 0,\n",
       "     'stop_frame': 0,\n",
       "     'data_chunk_size': 72,\n",
       "     'data_compressed_chunk_type': 'imageset',\n",
       "     'created_date': '2023-09-15T15:33:36.292099Z',\n",
       "     'updated_date': '2023-09-15T15:34:40.792068Z',\n",
       "     'issues': {'url': 'http://localhost:8080/api/issues?job_id=18',\n",
       "      'count': 0},\n",
       "     'labels': {'url': 'http://localhost:8080/api/labels?job_id=18',\n",
       "      'count': 1},\n",
       "     'type': 'annotation',\n",
       "     'organization': None}}}}}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = ann_view.load_annotation_results(anno_key)\n",
    "result.get_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 100% |█████████████████████| 1/1 [8.2ms elapsed, 0s remaining, 122.0 samples/s] \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'name': 'skeletons',\n",
       " 'version': '0.21.6',\n",
       " 'media_type': 'image',\n",
       " 'sample_fields': {'id': 'fiftyone.core.fields.ObjectIdField',\n",
       "  'filepath': 'fiftyone.core.fields.StringField',\n",
       "  'tags': 'fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)',\n",
       "  'metadata': 'fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)',\n",
       "  'ground_truth': 'fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)',\n",
       "  'uniqueness': 'fiftyone.core.fields.FloatField',\n",
       "  'predictions': 'fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)'},\n",
       " 'info': {},\n",
       " 'skeletons': {'points': SON([('labels', ['left handleft shoulder', 'right shoulder', 'right hand', 'left eye', 'right eye', 'mouth']), ('edges', [[0, 1, 2, 3], [4, 5, 6]])])},\n",
       " 'stages': [{'_cls': 'fiftyone.core.stages.MatchTags',\n",
       "   'kwargs': [['tags', ['annotate']], ['bool', True], ['all', False]]}],\n",
       " 'samples': [{'filepath': '/home/dan/fiftyone/quickstart/data/004126.jpg',\n",
       "   'tags': ['annotate'],\n",
       "   'metadata': {'_cls': 'ImageMetadata',\n",
       "    'size_bytes': 76725,\n",
       "    'mime_type': 'image/jpeg',\n",
       "    'width': 640,\n",
       "    'height': 367,\n",
       "    'num_channels': 3},\n",
       "   'ground_truth': {'_cls': 'Detections',\n",
       "    'detections': [{'_id': {'$oid': '5f452486ef00e6374aad1f3b'},\n",
       "      '_cls': 'Detection',\n",
       "      'attributes': {},\n",
       "      'tags': [],\n",
       "      'label': 'person',\n",
       "      'bounding_box': [0.40128125,\n",
       "       0.10084468664850135,\n",
       "       0.4025625,\n",
       "       0.7338147138964578],\n",
       "      'area': 25741.941399999996,\n",
       "      'iscrowd': 0.0},\n",
       "     {'_id': {'$oid': '5f452486ef00e6374aad1f3c'},\n",
       "      '_cls': 'Detection',\n",
       "      'attributes': {},\n",
       "      'tags': [],\n",
       "      'label': 'skateboard',\n",
       "      'bounding_box': [0.42212500000000003,\n",
       "       0.6819891008174387,\n",
       "       0.369765625,\n",
       "       0.2647956403269755],\n",
       "      'area': 6746.244799999998,\n",
       "      'iscrowd': 0.0}]},\n",
       "   'uniqueness': 0.5988490085951711,\n",
       "   'predictions': {'_cls': 'Detections',\n",
       "    'detections': [{'_id': {'$oid': '5f452c62ef00e6374aadad97'},\n",
       "      '_cls': 'Detection',\n",
       "      'attributes': {},\n",
       "      'tags': [],\n",
       "      'label': 'person',\n",
       "      'bounding_box': [0.4162792682647705,\n",
       "       0.10708199274962214,\n",
       "       0.38528170585632326,\n",
       "       0.7485176107214322],\n",
       "      'confidence': 0.9996997117996216},\n",
       "     {'_id': {'$oid': '5f452c62ef00e6374aadad98'},\n",
       "      '_cls': 'Detection',\n",
       "      'attributes': {},\n",
       "      'tags': [],\n",
       "      'label': 'skateboard',\n",
       "      'bounding_box': [0.42113075256347654,\n",
       "       0.7071953963193971,\n",
       "       0.35188612937927244,\n",
       "       0.2210996040531335],\n",
       "      'confidence': 0.9966714382171631},\n",
       "     {'_id': {'$oid': '5f452c62ef00e6374aadad99'},\n",
       "      '_cls': 'Detection',\n",
       "      'attributes': {},\n",
       "      'tags': [],\n",
       "      'label': 'skateboard',\n",
       "      'bounding_box': [0.42644128799438474,\n",
       "       0.7756693577571526,\n",
       "       0.25977129936218263,\n",
       "       0.1626938656175494],\n",
       "      'confidence': 0.26288551092147827},\n",
       "     {'_id': {'$oid': '5f452c62ef00e6374aadad9a'},\n",
       "      '_cls': 'Detection',\n",
       "      'attributes': {},\n",
       "      'tags': [],\n",
       "      'label': 'backpack',\n",
       "      'bounding_box': [0.4955601692199707,\n",
       "       0.2537781302220815,\n",
       "       0.1733004093170166,\n",
       "       0.1738174189014071],\n",
       "      'confidence': 0.09162653982639313},\n",
       "     {'_id': {'$oid': '5f452c62ef00e6374aadad9b'},\n",
       "      '_cls': 'Detection',\n",
       "      'attributes': {},\n",
       "      'tags': [],\n",
       "      'label': 'skateboard',\n",
       "      'bounding_box': [0.5207001686096191,\n",
       "       0.697685137756514,\n",
       "       0.2611091613769531,\n",
       "       0.16240049772756301],\n",
       "      'confidence': 0.07061836868524551}]}}]}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann_view.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the skeleton back in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading labels from CVAT...\n",
      "Download complete\n",
      "Loading labels for field 'points'...\n",
      " 100% |█████████████████████| 1/1 [5.3ms elapsed, 0s remaining, 189.9 samples/s] \n",
      "Deleting tasks...\n",
      " 100% |█████████████████████| 1/1 [108.0ms elapsed, 0s remaining, 9.3 samples/s] \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800\"\n",
       "            src=\"http://localhost:5151/?notebook=True&subscription=234419e5-41dd-4c0b-9402-5b6a61fea878\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fc89165dfc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ann_view.load_annotations(\"skeletons51\", cleanup=True)\n",
    "\n",
    "session.view = ann_view"
   ]
  }
 ],
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