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Merge pull request #375 from Labelbox/gj/video-mal
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‎examples/model_assisted_labeling/image_mal.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install labelbox\n",
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"!pip install requests\n",
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"!pip install ndjson\n",
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"!pip install scikit-image\n",
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"!pip install PILLOW\n",
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"!pip install tensorflow\n",
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"!pip install opencv-python"
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"!pip install -q labelbox requests ndjson scikit-image PILLOW tensorflow opencv-python"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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"version": "3.8.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

‎examples/model_assisted_labeling/video_mal.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "stupid-court",
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"metadata": {},
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"source": [
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"# Video MAL"
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]
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},
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{
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"cell_type": "markdown",
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"id": "intellectual-idaho",
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"metadata": {},
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"source": [
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"* Upload model inferences for video tasks\n",
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"* Support types\n",
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" * bounding box"
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]
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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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"id": "voluntary-minister",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -q labelbox"
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]
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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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"id": "committed-richards",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import uuid\n",
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"from io import BytesIO\n",
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"from typing import Dict, Any, Tuple\n",
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"\n",
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"from labelbox import Client, LabelingFrontend\n",
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"from labelbox.schema.ontology import OntologyBuilder, Tool, Classification, Option"
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]
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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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"id": "thirty-grocery",
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"metadata": {},
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"outputs": [],
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"source": [
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"API_KEY = os.environ.get(\"LABELBOX_API_KEY\")\n",
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"if not API_KEY:\n",
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" raise EnvironmentError(\"Missing API Key\")"
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]
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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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"id": "conservative-marsh",
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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\""
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]
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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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"id": "affecting-myanmar",
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"metadata": {},
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"outputs": [],
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"source": [
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"client = Client(\n",
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" api_key=API_KEY,\n",
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" endpoint=ENDPOINT\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "blessed-venture",
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"metadata": {},
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"source": [
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"### Project Setup"
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]
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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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"id": "suburban-crowd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# We want to try out a few different tools here.\n",
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"ontology_builder = OntologyBuilder(\n",
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" tools=[\n",
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" Tool(tool=Tool.Type.BBOX, name=\"jellyfish\")\n",
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" ]\n",
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")"
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]
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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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"id": "modern-program",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Lets setup a project to label\n",
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"# Note see Ontology, Project, and Project_setup notebooks for more information on this section.\n",
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"project = client.create_project(name=\"video_mal_project\")\n",
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"dataset = client.create_dataset(name=\"video_mal_dataset\")\n",
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"dataset.create_data_row(\n",
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" row_data=\"https://storage.labelbox.com/cjhfn5y6s0pk507024nz1ocys%2Fb8837f3b-b071-98d9-645e-2e2c0302393b-jellyfish2-100-110.mp4\")\n",
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"editor = next(\n",
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" client.get_labeling_frontends(where=LabelingFrontend.name == \"Editor\")\n",
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")\n",
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"project.setup(editor, ontology_builder.asdict())\n",
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"project.datasets.connect(dataset)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "portable-grenada",
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"metadata": {},
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"source": [
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"#### Grab featureSchemaIds"
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]
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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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"id": "abstract-fifteen",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'jellyfish': 'cky3dt2lja37d0z9t26wf3qo5'}\n"
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]
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}
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],
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"source": [
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"# When we created a project with the ontology defined above, all of the ids were assigned.\n",
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"# So lets reconstruct the ontology builder with all of the ids.\n",
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"ontology = ontology_builder.from_project(project)\n",
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"# We want all of the feature schemas to be easily accessible by name.\n",
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"schema_lookup = {tool.name: tool.feature_schema_id for tool in ontology.tools}\n",
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"print(schema_lookup)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "portuguese-arthur",
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"metadata": {},
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"source": [
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"## Import Format\n",
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"\n",
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"* [Documentation](https://docs.labelbox.com/docs/bounding-box-json)\n",
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"\n",
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"\n",
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"```\n",
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"Each row of the import is a unique instance\n",
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"\n",
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"schemaId: <featureSchemaId>\n",
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"dataRow:\n",
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" id: <dataRowId>\n",
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"Instance:\n",
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" [Segments]:\n",
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" [KeyFrames]:\n",
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" frame:\n",
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" bbox:\n",
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" top:\n",
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" bottom:\n",
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" height:\n",
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" width:\n",
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"```\n",
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"\n",
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"**segments**: A segment represents a continuous section where an object is visible. If an instance disappears then the segment ends. If it re-appears, a new segment is created.\n",
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"\n",
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"**keyframes**: Key frames identify the location of an instance. Between keyframes, the location of the instance is interpolated.\n",
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"\n",
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"**bbox**: The coordinates of the bounding box"
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]
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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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"id": "5fc417c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"segments = [\n",
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" {\n",
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" \"keyframes\": [\n",
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" {\n",
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" \"frame\": 1,\n",
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" \"bbox\": {\n",
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" \"top\": 80,\n",
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" \"left\": 80,\n",
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" \"height\": 80,\n",
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" \"width\": 80\n",
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" }\n",
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" },\n",
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" {\n",
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" \"frame\": 20,\n",
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" \"bbox\": {\n",
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" \"top\": 125,\n",
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" \"left\": 125,\n",
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" \"height\": 200,\n",
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" \"width\": 300\n",
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" }\n",
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" }\n",
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" ]\n",
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" },\n",
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" {\n",
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" \"keyframes\": [\n",
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" {\n",
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" \"frame\": 27,\n",
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" \"bbox\": {\n",
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" \"top\": 80,\n",
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" \"left\": 50,\n",
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" \"height\": 80,\n",
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" \"width\": 50\n",
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" }\n",
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" }\n",
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" ]\n",
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" }\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "convertible-entry",
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"metadata": {},
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"source": [
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"##### Create helper functions to make this much easier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "developing-beauty",
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"metadata": {},
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"outputs": [],
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"source": [
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"def create_video_bbox_ndjson(datarow_id: str, schema_id: str, segments: Dict[str, Any]) -> Dict[str, Any]:\n",
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" return {\n",
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" \"uuid\": str(uuid.uuid4()),\n",
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" \"schemaId\": schema_id,\n",
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" \"dataRow\": {\"id\": datarow_id},\n",
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" \"segments\": segments\n",
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" }"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "asian-savings",
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"metadata": {},
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"outputs": [],
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"source": [
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"uploads = []\n",
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"\n",
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"for data_row in dataset.data_rows():\n",
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" uploads.append(create_video_bbox_ndjson(data_row.uid, schema_lookup['jellyfish'], segments))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "perfect-seafood",
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"metadata": {},
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"source": [
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"### Upload the annotations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "entire-community",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's upload!\n",
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"# Validate must be set to false for video bounding boxes\n",
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"upload_task = project.upload_annotations(name=f\"upload-job-{uuid.uuid4()}\",\n",
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" annotations=uploads,\n",
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" validate=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "hollywood-faculty",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[]\n"
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]
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}
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],
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"source": [
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"# Wait for upload to finish (Will take up to five minutes)\n",
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"upload_task.wait_until_done()\n",
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"# Review the upload status\n",
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"print(upload_task.errors)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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