Merge pull request #456 from Labelbox/develop

SDK Update 13.5

Author: jtsodapop

CHANGELOG.md

@@ -1,8 +1,20 @@
 # Changelog
+# Version 3.15.0 (2022-02-28) 
+## Added 
+* Extras folder which contains useful applications using the sdk
+* Addition of ResourceTag at the Organization and Project level
+* Updates to the example notebooks 
+
+## Fix
+* EPSGTransformer now properly transforms Polygon to Polygon
+* VideoData string representation now properly shows VideoData
+
+
 # Version 3.14.0 (2022-02-10) 
 ## Added 
 * Updated metrics for classifications to be per-answer 
 
+
 # Version 3.13.0 (2022-02-07) 
 ## Added
 * Added `from_shapely` method to create annotation types from Shapely objects

examples/README.md

@@ -82,3 +82,9 @@ Learn more about annotation types in the [docs](https://docs.labelbox.com/docs/a
 | Model Diagnostics Demo  | [Github](model_diagnostics/model_diagnostics_demo.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/model_diagnostics/model_diagnostics_demo.ipynb) |
 | Custom Metrics Basics  | [Github](model_diagnostics/custom_metrics_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/model_diagnostics/custom_metrics_basics.ipynb) | [Docs](https://docs.labelbox.com/docs/model-diagnostics) |
 | Custom Metrics Demo  | [Github](model_diagnostics/custom_metrics_demo.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/model_diagnostics/custom_metrics_demo.ipynb) |
+
+
+## [Extras](extras)
+| Notebook                        | Github                                         | Google Colab | Learn more |
+|---------------------------------|------------------------------------------------| ------------ | ---------- |
+| Classification Confusion Matrix | [Github](extras/classification-confusion-matrix.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/extras/classification-confusion-matrix.ipynb) | |

examples/annotation_types/basics.ipynb

@@ -66,23 +66,20 @@
    "id": "indie-bracket",
    "metadata": {},
    "outputs": [],
-   "source": "!pip install labelbox"
+   "source": [
+    "!pip install labelbox"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "everyday-street",
    "metadata": {},
    "outputs": [],
-   "source": "from labelbox import Project, Dataset, Client\nfrom getpass import getpass\nimport os"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "crude-receiver",
-   "metadata": {},
-   "outputs": [],
-   "source": "# If you don't want to give google access to drive you can skip this cell\n# and manually set `API_KEY` below.\n\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)"
+   "source": [
+    "from labelbox import Project, Dataset, Client\n",
+    "import os"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -109,7 +106,14 @@
    "id": "instructional-reply",
    "metadata": {},
    "outputs": [],
-   "source": "PROJECT_ID = \"ckk4q1viuc0w20704eh69u28h\"\nDATASET_ID = \"ckk4q1vjznyhu087203wlghfr\"\nPROJECT_NAME = \"Sample Project\"\nDATASET_NAME = \"Example Jellyfish Dataset\"\n# Only update this if you have an on-prem deployment\nENDPOINT = \"https://api.labelbox.com/graphql\""
+   "source": [
+    "PROJECT_ID = \"ckk4q1viuc0w20704eh69u28h\"\n",
+    "DATASET_ID = \"ckk4q1vjznyhu087203wlghfr\"\n",
+    "PROJECT_NAME = \"Sample Project\"\n",
+    "DATASET_NAME = \"Example Jellyfish Dataset\"\n",
+    "# Only update this if you have an on-prem deployment\n",
+    "ENDPOINT = \"https://api.labelbox.com/graphql\""
+   ]
   },
   {
    "cell_type": "markdown",
@@ -120,21 +124,38 @@
     "* Starting point for all db interactions"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "485451ad",
+   "metadata": {},
+   "source": [
+    "# API Key and Client\n",
+    "Provide a valid api key below in order to properly connect to the Labelbox Client."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
    "id": "thick-gasoline",
    "metadata": {},
    "outputs": [],
-   "source": "client = Client(api_key=API_KEY, endpoint=ENDPOINT)"
+   "source": [
+    "# Add your api key\n",
+    "API_KEY = None\n",
+    "client = Client(api_key=API_KEY)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "id": "victorian-consumer",
    "metadata": {},
    "outputs": [],
-   "source": "#Client can be used to fetch by id:\nproject = client.get_project(PROJECT_ID)\ndataset = client.get_dataset(DATASET_ID)"
+   "source": [
+    "#Client can be used to fetch by id:\n",
+    "project = client.get_project(PROJECT_ID)\n",
+    "dataset = client.get_dataset(DATASET_ID)"
+   ]
   },
   {
    "cell_type": "code",
@@ -153,7 +174,9 @@
      "output_type": "execute_result"
     }
    ],
-   "source": "project"
+   "source": [
+    "project"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -181,7 +204,11 @@
      ]
     }
    ],
-   "source": "print(project.name)\nprint(project.description)\nprint(dataset.name)"
+   "source": [
+    "print(project.name)\n",
+    "print(project.description)\n",
+    "print(dataset.name)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -197,7 +224,10 @@
    "id": "according-subdivision",
    "metadata": {},
    "outputs": [],
-   "source": "project.update(description=\"new description field\")\nprint(project.description)"
+   "source": [
+    "project.update(description=\"new description field\")\n",
+    "print(project.description)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -226,15 +256,23 @@
      "output_type": "execute_result"
     }
    ],
-   "source": "labels_paginated_collection = project.labels()\nlabels_paginated_collection"
+   "source": [
+    "labels_paginated_collection = project.labels()\n",
+    "labels_paginated_collection"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "id": "convinced-force",
    "metadata": {},
    "outputs": [],
-   "source": "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n# Iterate over them to get the items out.\nnext(labels_paginated_collection)\n# list(paginated...) should be avoided for queries that could return more than a dozen results"
+   "source": [
+    "# Note that if you selected a `project_id` without any labels this will raise `StopIteration`\n",
+    "# Iterate over them to get the items out.\n",
+    "next(labels_paginated_collection)\n",
+    "# list(paginated...) should be avoided for queries that could return more than a dozen results"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -263,7 +301,21 @@
      ]
     }
    ],
-   "source": "datasets = client.get_datasets(where=Dataset.name == DATASET_NAME)\n\nprojects = client.get_projects(\n    where=((Project.name == PROJECT_NAME) &\n           (Project.description == \"new description field\")))\n\n# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n# So even if there is one element returned it is in a paginatedCollection.\nprint(projects)\nprint(next(projects, None))\nprint(next(projects, None))\nprint(next(projects, None))\n# We can see there is only one."
+   "source": [
+    "datasets = client.get_datasets(where=Dataset.name == DATASET_NAME)\n",
+    "\n",
+    "projects = client.get_projects(\n",
+    "    where=((Project.name == PROJECT_NAME) &\n",
+    "           (Project.description == \"new description field\")))\n",
+    "\n",
+    "# The above two queries return PaginatedCollections because the filter parameters aren't guaranteed to be unique.\n",
+    "# So even if there is one element returned it is in a paginatedCollection.\n",
+    "print(projects)\n",
+    "print(next(projects, None))\n",
+    "print(next(projects, None))\n",
+    "print(next(projects, None))\n",
+    "# We can see there is only one."
+   ]
   },
   {
    "cell_type": "markdown",
@@ -305,7 +357,11 @@
      "output_type": "execute_result"
     }
    ],
-   "source": "# Dataset has a Relationship to a Project so we can use the following\nlist(dataset.projects())\n# This will return all projects that are attached to this dataset"
+   "source": [
+    "# Dataset has a Relationship to a Project so we can use the following\n",
+    "list(dataset.projects())\n",
+    "# This will return all projects that are attached to this dataset"
+   ]
   },
   {
    "cell_type": "code",
@@ -324,7 +380,10 @@
      "output_type": "execute_result"
     }
    ],
-   "source": "sample_project_datasets = project.datasets()\nlist(sample_project_datasets)"
+   "source": [
+    "sample_project_datasets = project.datasets()\n",
+    "list(sample_project_datasets)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -341,7 +400,12 @@
    "id": "persistent-briefs",
    "metadata": {},
    "outputs": [],
-   "source": "#Eg.\n##### project.delete()\n##### dataset.delete()\n##### data_row.delete()"
+   "source": [
+    "#Eg.\n",
+    "##### project.delete()\n",
+    "##### dataset.delete()\n",
+    "##### data_row.delete()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -351,14 +415,6 @@
     "* We reccomend using bulk operations where possible.\n",
     "* You can find specific deletion instructions in tutorials on each object."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "thirty-interval",
-   "metadata": {},
-   "outputs": [],
-   "source": ""
   }
  ],
  "metadata": {
@@ -382,4 +438,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}