diff --git a/examples/annotation_import/pdf.ipynb b/examples/annotation_import/pdf.ipynb index 47264c02b..b71f17128 100644 --- a/examples/annotation_import/pdf.ipynb +++ b/examples/annotation_import/pdf.ipynb @@ -1,656 +1,664 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": {}, - "cells": [ - { - "metadata": {}, - "source": [ - "\n", - " \n", - "" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# PDF Annotation Import" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "#### PDF Annotation Import\n", - "Supported annotations for PDF assets with text layers\n", - "\n", - "#### Entity annotations\n", - "Supported annotations for PDF assets without text layers\n", - "\n", - "*Annotation types*\n", - "- Checklist classification (including nested classifications)\n", - "- Radio classifications (including nested classifications)\n", - "- Free text classifications\n", - "\n", - "\n", - "*NDJson*\n", - "- Checklist classification (including nested classifications)\n", - "- Radio classifications (including nested classifications)\n", - "- Free text classifications\n", - "- Bounding box \n", - "- Entities " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "### Setup" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "!pip install -q 'labelbox[data]'" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "import labelbox as lb\n", - "import labelbox.types as lb_types\n", - "import uuid\n", - "from uuid import uuid4\n", - "import json\n", - "import uuid\n", - "import numpy as np\n", - "from labelbox.schema.queue_mode import QueueMode" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Replace with your API key\n", - "Guides on https://docs.labelbox.com/docs/create-an-api-key" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# Add your api key\n", - "API_KEY = None\n", - "client = lb.Client(api_key=API_KEY)" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Supported Annotations" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "########## Entity ##########\n", - "# NDJSON\n", - "entities_annotations_ndjson = { \n", - " \"name\": \"named_entity\",\n", - " \"textSelections\": [\n", - " {\n", - " \"tokenIds\": [\n", - " \"\",\n", - " ],\n", - " \"groupId\": \"\",\n", - " \"page\": 1,\n", - " }\n", - " ]\n", - "}" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "########### Radio Classification #########\n", - "\n", - "# Annotation types \n", - "radio_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"radio_question\",\n", - " value=lb_types.Radio(answer = \n", - " lb_types.ClassificationAnswer(name = \"first_radio_answer\")\n", - " )\n", - ")\n", - "# NDJSON\n", - "radio_annotation_ndjson = {\n", - " 'name': 'radio_question',\n", - " 'answer': {'name': 'first_radio_answer'}\n", - "}" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "############ Checklist Classification ###########\n", - "\n", - "# Annotation types \n", - "checklist_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"checklist_question\",\n", - " value=lb_types.Checklist(answer = [\n", - " lb_types.ClassificationAnswer(name = \"first_checklist_answer\"),\n", - " lb_types.ClassificationAnswer(name = \"second_checklist_answer\")\n", - " ])\n", - " )\n", - "\n", - "\n", - "# NDJSON\n", - "checklist_annotation_ndjson = {\n", - " 'name': 'checklist_question',\n", - " 'answer': [\n", - " {'name': 'first_checklist_answer'},\n", - " {'name': 'second_checklist_answer'}\n", - " ]\n", - "}" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "############ Bounding Box ###########\n", - "# Python Annotation \n", - "bbox_annotation = lb_types.ObjectAnnotation(\n", - " name = \"bounding_box\", # must match your ontology feature's name\n", - " value = lb_types.Rectangle(\n", - " start=lb_types.Point(x=42.799, y=86.498), # Top left\n", - " end=lb_types.Point(x=141.911, y=303.195), # Bottom right\n", - " ),\n", - " page = 1,\n", - " unit=\"POINTS\"\n", - ")\n", - "\n", - "bbox_annotation_ndjson = {\n", - " 'name': 'bounding_box',\n", - " 'bbox': {\n", - " \"top\": 42.799,\n", - " \"left\": 86.498,\n", - " \"height\": 141.911,\n", - " \"width\": 303.195\n", - " },\n", - " 'page': 0,\n", - " 'unit': \"POINTS\"\n", - "}" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "# ############ nested classifications ###########\n", - "\n", - "nested_checklist_annotation_ndjson = {\n", - " \"name\": \"nested_checklist_question\",\n", - " \"answer\": [{\n", - " \"name\": \"first_checklist_answer\", \n", - " \"classifications\" : [\n", - " {\n", - " \"name\": \"sub_checklist_question\", \n", - " \"answer\": {\"name\": \"first_sub_checklist_answer\"}\n", - " } \n", - " ] \n", - " }]\n", - "}\n", - "\n", - "nested_radio_annotation_ndjson = {\n", - " 'name': 'nested_radio_question',\n", - " 'answer': {\n", - " 'name': 'first_radio_answer',\n", - " 'classifications': [{\n", - " 'name':'sub_radio_question',\n", - " 'answer': { 'name' : 'first_sub_radio_answer'}\n", - " }]\n", - " }\n", - "}\n", - "\n" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "############## Classification Free-form text ############## \n", - "\n", - "text_annotation = lb_types.ClassificationAnnotation(\n", - " name=\"free_text\", # must match your ontology feature's name\n", - " value=lb_types.Text(answer=\"sample text\")\n", - ")\n", - "\n", - "\n", - "text_annotation_ndjson = {\n", - " 'name': 'free_text',\n", - " 'answer': 'sample text'\n", - "}" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "## Upload Annotations - putting it all together " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "### Step 1: Import data rows into Catalog " - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Text layer url is required for uploading entity annotations\n", - "img_url = {\n", - " \"row_data\": {\n", - " \"pdf_url\": \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\",\n", - " \"text_layer_url\": \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483-lb-textlayer.json\"\n", - " },\n", - " \"global_key\": str(uuid.uuid4())\n", - "}\n", - "\n", - "\n", - "dataset = client.create_dataset(name=\"pdf_demo_dataset\")\n", - "\n", - "data_row = dataset.create_data_row(img_url)\n", - "\n", - "print(data_row)" - ], - "cell_type": "code", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 2: Create/select an Ontology for your project\n", - "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## Setup the ontology and link the tools created above.\n", - "\n", - "ontology_builder = lb.OntologyBuilder(\n", - " classifications=[ # List of Classification objects\n", - " lb.Classification( \n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"radio_question\", \n", - " scope = lb.Classification.Scope.GLOBAL,\n", - " options=[\n", - " lb.Option(value=\"first_radio_answer\"),\n", - " lb.Option(value=\"second_radio_answer\")\n", - " ]\n", - " ),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"checklist_question\", \n", - " scope = lb.Classification.Scope.GLOBAL,\n", - " options=[\n", - " lb.Option(value=\"first_checklist_answer\"),\n", - " lb.Option(value=\"second_checklist_answer\")\n", - " ]\n", - " ), \n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.TEXT,\n", - " name=\"free_text\",\n", - " scope = lb.Classification.Scope.GLOBAL\n", - " ),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"nested_radio_question\",\n", - " scope = lb.Classification.Scope.GLOBAL,\n", - " options=[\n", - " lb.Option(\"first_radio_answer\",\n", - " options=[\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.RADIO,\n", - " name=\"sub_radio_question\",\n", - " options=[lb.Option(\"first_sub_radio_answer\")]\n", - " )\n", - " ]\n", - " )\n", - " ] \n", - " ),\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"nested_checklist_question\",\n", - " scope = lb.Classification.Scope.GLOBAL,\n", - " options=[\n", - " lb.Option(\"first_checklist_answer\",\n", - " options=[\n", - " lb.Classification(\n", - " class_type=lb.Classification.Type.CHECKLIST,\n", - " name=\"sub_checklist_question\", \n", - " options=[lb.Option(\"first_sub_checklist_answer\")]\n", - " )\n", - " ]\n", - " )\n", - " ]\n", - " ), \n", - " ],\n", - " tools=[ # List of Tool objects\n", - " lb.Tool( \n", - " tool=lb.Tool.Type.BBOX,\n", - " name=\"bounding_box\"), \n", - " lb.Tool(\n", - " tool=lb.Tool.Type.NER, \n", - " name=\"named_entity\")]\n", - ")\n", - "\n", - "ontology = client.create_ontology(\"Document Annotation Import Demo\",\n", - " ontology_builder.asdict(),\n", - " media_type=lb.MediaType.Document)" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 3: Creating a labeling project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# Create a Labelbox project\n", - "project = client.create_project(name=\"PDF_annotation_demo\", \n", - " queue_mode=QueueMode.Batch,\n", - " media_type=lb.MediaType.Document)\n", - "project.setup_editor(ontology)" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 4: Send a batch of data rows to the project" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "project.create_batch(\n", - " \"PDF_annotation_batch\", # Each batch in a project must have a unique name\n", - " dataset.export_data_rows(), # A list of data rows or data row ids\n", - " 5 # priority between 1(Highest) - 5(lowest)\n", - ")" - ], - "cell_type": "code", - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 115, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 5. Create the annotation payload\n", - "Create the annotations payload using the snippets of code in Supported predictions section.\n", - "\n", - "Labelbox support NDJSON only for this data type.\n", - "\n", - "The resulting label_ndjson should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "## We need to construct our entity annotation using our text layer. \n", - "\n", - "import requests\n", - "import json\n", - "\n", - "## To learn how to generate a text layer for your documents please refer to the following repositories/files: \n", - "# https://github.com/Labelbox/PDF-OCR-Transform-CLI/blob/main/src/scripts/gcloud/gcp-vision-to-lb-text-layer.py\n", - "# https://github.com/Labelbox/PDF-OCR-Transform-CLI/blob/main/src/scripts/adobe/adobe-ocr-to-lb-text-layer.py\n", - "\n", - "text_layer = \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483-lb-textlayer.json\"\n", - "\n", - "## Fetch the content of the text layer\n", - "res = requests.get(text_layer) \n", - "\n", - "## Parse the text layer\n", - "for obj in json.loads(res.text):\n", - " for group in obj['groups']: \n", - " ## Find the text group that we are interested in annotating\n", - " if group['content'] == \"Metal-insulator (MI) transitions have been one of the\":\n", - " ## We now need all the tokens associated with each word in this text group\n", - " list_tokens = [x['id'] for x in group['tokens']]\n", - " entities_annotations_ndjson.update(\n", - " {\n", - " \"textSelections\": [\n", - " {\n", - " \"groupId\": group['id'], #id associated with the group of words\n", - " \"tokenIds\": list_tokens, #id associated with each word in a sentence group\n", - " \"page\": 1,\n", - " }\n", - " ]\n", - " }\n", - " )\n", - " " - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### Python annotation\n", - "Here we create the complete label ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. Note that only a handful of python annotation types are supported for PDF documents." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "# create a Label\n", - "\n", - "label = []\n", - "for data_row in dataset.export_data_rows():\n", - " label.append(lb_types.Label(\n", - " data=lb_types.TextData(\n", - " uid=data_row.uid),\n", - " annotations = [\n", - " checklist_annotation, \n", - " text_annotation,\n", - " radio_annotation\n", - " ]\n", - " )\n", - ")" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "#### NDJson annotations\n", - "Here we create the complete label ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above." - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "ndjson_annotation = []\n", - "for annot in [\n", - " entities_annotations_ndjson,\n", - " bbox_annotation_ndjson,\n", - " text_annotation_ndjson,\n", - " checklist_annotation_ndjson,\n", - " nested_checklist_annotation_ndjson,\n", - " nested_radio_annotation_ndjson,\n", - " radio_annotation_ndjson\n", - " ]:\n", - " annot.update({\n", - " 'dataRow': {'id': data_row.uid},\n", - " })\n", - " ndjson_annotation.append(annot)\n", - "\n" - ], - "cell_type": "code", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "### Step 6: Import the annotation payload" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "Option A: Upload to a labeling project as pre-labels (MAL)" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "upload_job = lb.MALPredictionImport.create_from_objects(\n", - " client = client,\n", - " project_id = project.uid,\n", - " name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n", - " predictions=ndjson_annotation)\n", - "\n", - "upload_job.wait_until_done()\n", - "# Errors will appear for annotation uploads that failed.\n", - "print(\"Errors:\", upload_job.errors)" - ], - "cell_type": "code", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Errors: []\n" - ] - } - ], - "execution_count": null - }, - { - "metadata": {}, - "source": [ - "Option B: Upload to a labeling project using ground truth" - ], - "cell_type": "markdown" - }, - { - "metadata": {}, - "source": [ - "\n", - "upload_job = lb.LabelImport.create_from_objects(\n", - " client = client, \n", - " project_id = project.uid, \n", - " name=\"label_import_job\"+str(uuid.uuid4()), \n", - " labels=ndjson_annotation)\n", - "\n", - "print(\"Errors:\", upload_job.errors)" - ], - "cell_type": "code", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Errors: []\n" - ] - } - ], - "execution_count": null - } - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PDF Annotation Import" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PDF Annotation Import\n", + "Supported annotations for PDF assets with text layers\n", + "\n", + "#### Entity annotations\n", + "Supported annotations for PDF assets without text layers\n", + "\n", + "*Annotation types*\n", + "- Checklist classification (including nested classifications)\n", + "- Radio classifications (including nested classifications)\n", + "- Free text classifications\n", + "\n", + "\n", + "*NDJson*\n", + "- Checklist classification (including nested classifications)\n", + "- Radio classifications (including nested classifications)\n", + "- Free text classifications\n", + "- Bounding box \n", + "- Entities " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q 'labelbox[data]'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -e ../.." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import cast\n", + "import uuid\n", + "from uuid import uuid4\n", + "\n", + "import labelbox as lb\n", + "import labelbox.types as lb_types\n", + "from labelbox.schema.queue_mode import QueueMode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replace with your API key\n", + "Guides on https://docs.labelbox.com/docs/create-an-api-key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add your api key\n", + "API_KEY = None\n", + "client = lb.Client(api_key=API_KEY)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Supported Annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "########## Entity ##########\n", + "# NDJSON\n", + "entities_annotations_ndjson = { \n", + " \"name\": \"named_entity\",\n", + " \"textSelections\": [\n", + " {\n", + " \"tokenIds\": [\n", + " \"\",\n", + " ],\n", + " \"groupId\": \"\",\n", + " \"page\": 1,\n", + " }\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "########### Radio Classification #########\n", + "\n", + "# Annotation types \n", + "radio_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"radio_question\",\n", + " value=lb_types.Radio(answer = \n", + " lb_types.ClassificationAnswer(name = \"first_radio_answer\")\n", + " )\n", + ")\n", + "# NDJSON\n", + "radio_annotation_ndjson = {\n", + " 'name': 'radio_question',\n", + " 'answer': {'name': 'first_radio_answer'}\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "############ Checklist Classification ###########\n", + "\n", + "# Annotation types \n", + "checklist_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"checklist_question\",\n", + " value=lb_types.Checklist(answer = [\n", + " lb_types.ClassificationAnswer(name = \"first_checklist_answer\"),\n", + " lb_types.ClassificationAnswer(name = \"second_checklist_answer\")\n", + " ])\n", + " )\n", + "\n", + "\n", + "# NDJSON\n", + "checklist_annotation_ndjson = {\n", + " 'name': 'checklist_question',\n", + " 'answer': [\n", + " {'name': 'first_checklist_answer'},\n", + " {'name': 'second_checklist_answer'}\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "############ Bounding Box ###########\n", + "# Python Annotation \n", + "bbox_annotation = lb_types.ObjectAnnotation(\n", + " name = \"bounding_box\", # must match your ontology feature's name\n", + " value = lb_types.Rectangle(\n", + " start=lb_types.Point(x=42.799, y=86.498), # Top left\n", + " end=lb_types.Point(x=141.911, y=303.195), # Bottom right\n", + " ),\n", + " page = 1,\n", + " unit=\"POINTS\"\n", + ")\n", + "\n", + "bbox_annotation_ndjson = {\n", + " 'name': 'bounding_box',\n", + " 'bbox': {\n", + " \"top\": 42.799,\n", + " \"left\": 86.498,\n", + " \"height\": 141.911,\n", + " \"width\": 303.195\n", + " },\n", + " 'page': 0,\n", + " 'unit': \"POINTS\"\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ############ nested classifications ###########\n", + "\n", + "nested_checklist_annotation_ndjson = {\n", + " \"name\": \"nested_checklist_question\",\n", + " \"answer\": [{\n", + " \"name\": \"first_checklist_answer\", \n", + " \"classifications\" : [\n", + " {\n", + " \"name\": \"sub_checklist_question\", \n", + " \"answer\": {\"name\": \"first_sub_checklist_answer\"}\n", + " } \n", + " ] \n", + " }]\n", + "}\n", + "\n", + "nested_radio_annotation_ndjson = {\n", + " 'name': 'nested_radio_question',\n", + " 'answer': {\n", + " 'name': 'first_radio_answer',\n", + " 'classifications': [{\n", + " 'name':'sub_radio_question',\n", + " 'answer': { 'name' : 'first_sub_radio_answer'}\n", + " }]\n", + " }\n", + "}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "############## Classification Free-form text ############## \n", + "\n", + "text_annotation = lb_types.ClassificationAnnotation(\n", + " name=\"free_text\", # must match your ontology feature's name\n", + " value=lb_types.Text(answer=\"sample text\")\n", + ")\n", + "\n", + "\n", + "text_annotation_ndjson = {\n", + " 'name': 'free_text',\n", + " 'answer': 'sample text'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upload Annotations - putting it all together " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1: Import data rows into Catalog " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Text layer url is required for uploading entity annotations\n", + "img_url = {\n", + " \"row_data\": {\n", + " \"pdf_url\": \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483.pdf\",\n", + " \"text_layer_url\": \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483-lb-textlayer.json\"\n", + " },\n", + " \"global_key\": str(uuid.uuid4())\n", + "}\n", + "\n", + "\n", + "dataset = client.create_dataset(name=\"pdf_demo_dataset\")\n", + "\n", + "data_row = dataset.create_data_row(img_url)\n", + "\n", + "print(data_row)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Create/select an Ontology for your project\n", + "Your project should have the correct ontology setup with all the tools and classifications supported for your annotations, and the tool names and classification instructions should match the name/instructions fields in your annotations to ensure the correct feature schemas are matched." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Setup the ontology and link the tools created above.\n", + "\n", + "ontology_builder = lb.OntologyBuilder(\n", + " classifications=[ # List of Classification objects\n", + " lb.Classification( \n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"radio_question\", \n", + " scope = lb.Classification.Scope.GLOBAL,\n", + " options=[\n", + " lb.Option(value=\"first_radio_answer\"),\n", + " lb.Option(value=\"second_radio_answer\")\n", + " ]\n", + " ),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"checklist_question\", \n", + " scope = lb.Classification.Scope.GLOBAL,\n", + " options=[\n", + " lb.Option(value=\"first_checklist_answer\"),\n", + " lb.Option(value=\"second_checklist_answer\")\n", + " ]\n", + " ), \n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.TEXT,\n", + " name=\"free_text\",\n", + " scope = lb.Classification.Scope.GLOBAL\n", + " ),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"nested_radio_question\",\n", + " scope = lb.Classification.Scope.GLOBAL,\n", + " options=[\n", + " lb.Option(\"first_radio_answer\",\n", + " options=[\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.RADIO,\n", + " name=\"sub_radio_question\",\n", + " options=[lb.Option(\"first_sub_radio_answer\")]\n", + " )\n", + " ]\n", + " )\n", + " ] \n", + " ),\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"nested_checklist_question\",\n", + " scope = lb.Classification.Scope.GLOBAL,\n", + " options=[\n", + " lb.Option(\"first_checklist_answer\",\n", + " options=[\n", + " lb.Classification(\n", + " class_type=lb.Classification.Type.CHECKLIST,\n", + " name=\"sub_checklist_question\", \n", + " options=[lb.Option(\"first_sub_checklist_answer\")]\n", + " )\n", + " ]\n", + " )\n", + " ]\n", + " ), \n", + " ],\n", + " tools=[ # List of Tool objects\n", + " lb.Tool( \n", + " tool=lb.Tool.Type.BBOX,\n", + " name=\"bounding_box\"), \n", + " lb.Tool(\n", + " tool=lb.Tool.Type.NER, \n", + " name=\"named_entity\")]\n", + ")\n", + "\n", + "ontology = client.create_ontology(\"Document Annotation Import Demo\",\n", + " ontology_builder.asdict(),\n", + " media_type=lb.MediaType.Document)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3: Creating a labeling project" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Labelbox project\n", + "project = client.create_project(name=\"PDF_annotation_demo\", \n", + " queue_mode=QueueMode.Batch,\n", + " media_type=lb.MediaType.Document)\n", + "project.setup_editor(ontology)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4: Send a batch of data rows to the project" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project.create_batch(\n", + " \"PDF_annotation_batch\", # Each batch in a project must have a unique name\n", + " list(dataset.export_data_rows()), # A list of data rows or data row ids\n", + " 5 # priority between 1(Highest) - 5(lowest)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create the annotation payload\n", + "Create the annotations payload using the snippets of code in Supported predictions section.\n", + "\n", + "Labelbox support NDJSON only for this data type.\n", + "\n", + "The resulting label_ndjson should have exactly the same content for annotations that are supported by both (with exception of the uuid strings that are generated)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## We need to construct our entity annotation using our text layer. \n", + "\n", + "import requests\n", + "import json\n", + "\n", + "\n", + "## To learn how to generate a text layer for your documents please refer to the following repositories/files: \n", + "# https://github.com/Labelbox/PDF-OCR-Transform-CLI/blob/main/src/scripts/gcloud/gcp-vision-to-lb-text-layer.py\n", + "# https://github.com/Labelbox/PDF-OCR-Transform-CLI/blob/main/src/scripts/adobe/adobe-ocr-to-lb-text-layer.py\n", + "\n", + "text_layer = \"https://storage.googleapis.com/labelbox-datasets/arxiv-pdf/data/99-word-token-pdfs/0801.3483-lb-textlayer.json\"\n", + "\n", + "## Fetch the content of the text layer\n", + "res = requests.get(text_layer) \n", + "\n", + "\n", + "\n", + "## Parse the text layer\n", + "text_selections = []\n", + "for obj in json.loads(res.text):\n", + " for group in obj['groups']: \n", + " ## Find the text group that we are interested in annotating\n", + " if group['content'] == \"Metal-insulator (MI) transitions have been one of the\":\n", + " ## We now need all the tokens associated with each word in this text group\n", + " list_tokens = [x['id'] for x in group['tokens']]\n", + " document_text_selection = lb_types.DocumentTextSelection(groupId=group['id'], tokenIds=list_tokens, page=1)\n", + " text_selections.append(document_text_selection)\n", + " entities_annotations_ndjson.update(\n", + " {\n", + " \"textSelections\": [\n", + " {\n", + " \"groupId\": group['id'], #id associated with the group of words\n", + " \"tokenIds\": list_tokens, #id associated with each word in a sentence group\n", + " \"page\": 1,\n", + " }\n", + " ]\n", + " }\n", + " )\n", + "entities_annotation_document_entity = lb_types.DocumentEntity(name=\"named_entity\", \n", + " textSelections = text_selections)\n", + "entities_annotation = lb_types.ObjectAnnotation(name=\"named_entity\",\n", + " value=entities_annotation_document_entity)\n", + " \n", + "print(f\"entities_annotations_ndjson={entities_annotations_ndjson}\")\n", + "print(f\"entities_annotation={entities_annotation}\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python annotation\n", + "Here we create the complete labels ndjson payload of annotations only using python annotation format. There is one annotation for each reference to an annotation that we created. Note that only a handful of python annotation types are supported for PDF documents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create a Label\n", + "\n", + "labels = []\n", + "for data_row in dataset.export_data_rows():\n", + " labels.append(lb_types.Label(\n", + " data=lb_types.TextData(\n", + " uid=data_row.uid),\n", + " annotations = [\n", + " entities_annotation,\n", + " checklist_annotation, \n", + " text_annotation,\n", + " radio_annotation\n", + " ]\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### NDJson annotations\n", + "Here we create the complete labels ndjson payload of annotations only using NDJSON format. There is one annotation for each reference to an annotation that we created above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "ndjson_annotation = []\n", + "for annot in [\n", + " entities_annotations_ndjson,\n", + " bbox_annotation_ndjson,\n", + " text_annotation_ndjson,\n", + " checklist_annotation_ndjson,\n", + " nested_checklist_annotation_ndjson,\n", + " nested_radio_annotation_ndjson,\n", + " radio_annotation_ndjson\n", + " ]:\n", + " annot.update({\n", + " 'dataRow': {'id': data_row.uid},\n", + " })\n", + " ndjson_annotation.append(annot)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6: Import the annotation payload" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Option A: Upload to a labeling project as pre-labels (MAL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# upload_job = lb.MALPredictionImport.create_from_objects(\n", + "# client = client,\n", + "# project_id = project.uid,\n", + "# name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n", + "# predictions=ndjson_annotation)\n", + "\n", + "upload_job = lb.MALPredictionImport.create_from_objects(\n", + " client = client,\n", + " project_id = project.uid,\n", + " name=\"pdf_annotation_upload\" + str(uuid.uuid4()),\n", + " predictions=labels)\n", + "\n", + "upload_job.wait_until_done()\n", + "# Errors will appear for annotation uploads that failed.\n", + "print(\"Errors:\", upload_job.errors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Option B: Upload to a labeling project using ground truth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "upload_job = lb.LabelImport.create_from_objects(\n", + " client = client, \n", + " project_id = project.uid, \n", + " name=\"label_import_job\"+str(uuid.uuid4()), \n", + " labels=ndjson_annotation)\n", + "\n", + "print(\"Errors:\", upload_job.errors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 } diff --git a/labelbox/data/annotation_types/__init__.py b/labelbox/data/annotation_types/__init__.py index d2b3bd668..7c8926c78 100644 --- a/labelbox/data/annotation_types/__init__.py +++ b/labelbox/data/annotation_types/__init__.py @@ -11,6 +11,8 @@ from .annotation import VideoObjectAnnotation from .ner import TextEntity +from .ner import DocumentEntity +from .ner import DocumentTextSelection from .classification import Checklist from .classification import ClassificationAnswer diff --git a/labelbox/data/annotation_types/annotation.py b/labelbox/data/annotation_types/annotation.py index e4f94b6dd..5df63f3ae 100644 --- a/labelbox/data/annotation_types/annotation.py +++ b/labelbox/data/annotation_types/annotation.py @@ -6,7 +6,7 @@ from .classification import Checklist, Dropdown, Radio, Text from .feature import FeatureSchema from .geometry import Geometry, Rectangle, Point -from .ner import TextEntity +from .ner import DocumentEntity, TextEntity class BaseAnnotation(FeatureSchema, abc.ABC): @@ -51,7 +51,7 @@ class ObjectAnnotation(BaseAnnotation, ConfidenceMixin): classifications (Optional[List[ClassificationAnnotation]]): Optional sub classification of the annotation extra (Dict[str, Any]) """ - value: Union[TextEntity, Geometry] + value: Union[TextEntity, DocumentEntity, Geometry] classifications: List[ClassificationAnnotation] = [] diff --git a/labelbox/data/annotation_types/label.py b/labelbox/data/annotation_types/label.py index ad6b10a03..277619137 100644 --- a/labelbox/data/annotation_types/label.py +++ b/labelbox/data/annotation_types/label.py @@ -6,6 +6,7 @@ import labelbox from labelbox.data.annotation_types.data.tiled_image import TiledImageData +from labelbox.data.annotation_types.ner import DocumentEntity from labelbox.schema import ontology from .annotation import (ClassificationAnnotation, ObjectAnnotation, VideoClassificationAnnotation, VideoObjectAnnotation) diff --git a/labelbox/data/annotation_types/ner/__init__.py b/labelbox/data/annotation_types/ner/__init__.py new file mode 100644 index 000000000..2403c1107 --- /dev/null +++ b/labelbox/data/annotation_types/ner/__init__.py @@ -0,0 +1,2 @@ +from .text_entity import TextEntity +from .document_entity import DocumentEntity, DocumentTextSelection diff --git a/labelbox/data/annotation_types/ner/document_entity.py b/labelbox/data/annotation_types/ner/document_entity.py new file mode 100644 index 000000000..d4c74b766 --- /dev/null +++ b/labelbox/data/annotation_types/ner/document_entity.py @@ -0,0 +1,23 @@ +from typing import List + +from pydantic import BaseModel, validator + +from labelbox.utils import _CamelCaseMixin + + +class DocumentTextSelection(_CamelCaseMixin, BaseModel): + token_ids: List[str] + group_id: str + page: int + + @validator("page") + def validate_page(cls, v): + if v < 1: + raise ValueError("Page must be greater than 1") + return v + + +class DocumentEntity(_CamelCaseMixin, BaseModel): + """ Represents a text entity """ + name: str + text_selections: List[DocumentTextSelection] diff --git a/labelbox/data/annotation_types/ner.py b/labelbox/data/annotation_types/ner/text_entity.py similarity index 100% rename from labelbox/data/annotation_types/ner.py rename to labelbox/data/annotation_types/ner/text_entity.py diff --git a/labelbox/data/serialization/ndjson/converter.py b/labelbox/data/serialization/ndjson/converter.py index 29db466ba..4d95faca2 100644 --- a/labelbox/data/serialization/ndjson/converter.py +++ b/labelbox/data/serialization/ndjson/converter.py @@ -40,6 +40,7 @@ def serialize( Returns: A generator for accessing the ndjson representation of the data """ + for example in NDLabel.from_common(labels): res = example.dict(by_alias=True) for k, v in list(res.items()): diff --git a/labelbox/data/serialization/ndjson/objects.py b/labelbox/data/serialization/ndjson/objects.py index 70e672816..ca2849d74 100644 --- a/labelbox/data/serialization/ndjson/objects.py +++ b/labelbox/data/serialization/ndjson/objects.py @@ -12,7 +12,7 @@ from labelbox.data.annotation_types.data.video import VideoData from ...annotation_types.data import ImageData, TextData, MaskData -from ...annotation_types.ner import TextEntity +from ...annotation_types.ner import DocumentEntity, DocumentTextSelection, TextEntity from ...annotation_types.types import Cuid from ...annotation_types.geometry import Rectangle, Polygon, Line, Point, Mask from ...annotation_types.annotation import ClassificationAnnotation, ObjectAnnotation, VideoObjectAnnotation @@ -372,6 +372,33 @@ def from_common(cls, confidence=confidence) +class NDDocumentEntity(NDBaseObject, ConfidenceMixin): + name: str + text_selections: List[DocumentTextSelection] + + def to_common(self) -> DocumentEntity: + return DocumentEntity(name=self.name, + text_selections=self.text_selections) + + @classmethod + def from_common(cls, + document_entity: DocumentEntity, + classifications: List[ClassificationAnnotation], + name: str, + feature_schema_id: Cuid, + extra: Dict[str, Any], + data: Union[ImageData, TextData], + confidence: Optional[float] = None) -> "NDDocumentEntity": + + return cls(text_selections=document_entity.text_selections, + dataRow=DataRow(id=data.uid), + name=name, + schema_id=feature_schema_id, + uuid=extra.get('uuid'), + classifications=classifications, + confidence=confidence) + + class NDObject: @staticmethod @@ -434,7 +461,8 @@ def lookup_object( Polygon: NDPolygon, Rectangle: NDRectangle, Mask: NDMask, - TextEntity: NDTextEntity + TextEntity: NDTextEntity, + DocumentEntity: NDDocumentEntity, }.get(type(annotation.value)) if result is None: raise TypeError( @@ -444,6 +472,6 @@ def lookup_object( NDObjectType = Union[NDLine, NDPolygon, NDPoint, NDRectangle, NDMask, - NDTextEntity] + NDTextEntity, NDDocumentEntity] NDFrameObjectType = NDFrameRectangle, NDFramePoint, NDFrameLine diff --git a/tests/assets/arxiv-pdf_data_99-word-token-pdfs_0801.3483-lb-textlayer.json b/tests/assets/arxiv-pdf_data_99-word-token-pdfs_0801.3483-lb-textlayer.json new file mode 100644 index 000000000..03921dd83 --- /dev/null +++ b/tests/assets/arxiv-pdf_data_99-word-token-pdfs_0801.3483-lb-textlayer.json @@ -0,0 +1 @@ +[{"number":1,"units":"PERCENT","groups":[{"id":"ed53dd86-ef39-4634-9505-ee0eebedef44","geometry":{"top":0.06709828227758408,"left":0.15232421457767487,"width":0.7007846683263779,"height":0.015183515846729279},"content":"Mott transition and superconductivity in the strongly correlated organic","tokens":[{"id":"543ea587-6817-4d2b-a9c1-6d8dd797e8d7","content":"Mott","geometry":{"top":0.06709828227758408,"left":0.15232421457767487,"width":0.049093395471572876,"height":0.011701948940753937}},{"id":"f4c15744-c9fe-48f2-89dc-8f52fafda028","content":"transition","geometry":{"top":0.0673278197646141,"left":0.2083045393228531,"width":0.09329061210155487,"height":0.011506609618663788}},{"id":"43867d16-0236-47cd-b348-5190143a3ccb","content":"and","geometry":{"top":0.0671420469880104,"left":0.3090585172176361,"width":0.03487974405288696,"height":0.011732921004295349}},{"id":"521f705e-b276-4ac7-8e5b-2e38e037f80f","content":"superconductivity","geometry":{"top":0.06714753806591034,"left":0.35155054926872253,"width":0.1713433563709259,"height":0.014519907534122467}},{"id":"4c1475f9-3576-4171-91a9-7cfb369c73d8","content":"in","geometry":{"top":0.0671587809920311,"left":0.5301722884178162,"width":0.018753528594970703,"height":0.011397771537303925}},{"id":"a75658b1-dd91-4f9f-b7d8-572eee7fd707","content":"the","geometry":{"top":0.06706593930721283,"left":0.5560554265975952,"width":0.031216084957122803,"height":0.011679284274578094}},{"id":"480a3484-28d9-4690-a466-0b8e24a24c3a","content":"strongly","geometry":{"top":0.06771419942378998,"left":0.5940885543823242,"width":0.07808887958526611,"height":0.014701396226882935}},{"id":"d8fcd3cb-4bf7-461c-a91f-f9142b6bc3c3","content":"correlated","geometry":{"top":0.06704511493444443,"left":0.6800980567932129,"width":0.09604448080062866,"height":0.011917665600776672}},{"id":"4ec0bac5-e5d7-4eda-802b-3962ad2db82c","content":"organic","geometry":{"top":0.06715976446866989,"left":0.7830923199653625,"width":0.07001626491546631,"height":0.014884158968925476}}]},{"id":"1a0c104a-e044-4192-a804-23bdee291222","geometry":{"top":0.08412960171699524,"left":0.27643412351608276,"width":0.4505969285964966,"height":0.015744037926197052},"content":"superconductor k-(BEDT-TTF)2CuN(CN)2Br","tokens":[{"id":"980f4d86-406a-4a98-9a16-260a3c365ca7","content":"superconductor","geometry":{"top":0.08495695143938065,"left":0.27643492817878723,"width":0.14872518181800842,"height":0.014116048812866211}},{"id":"875e8050-c4c4-4764-b03e-3e1ba6cc0566","content":"k-(BEDT-TTF)2CuN(CN)2Br","geometry":{"top":0.08401388674974442,"left":0.4329206049442291,"width":0.2941104471683502,"height":0.015859752893447876}}]},{"id":"80e1e326-0194-4f38-845c-d9d22b0054fb","geometry":{"top":0.11619369685649872,"left":0.33495020866394043,"width":0.3356838822364807,"height":0.012528747320175171},"content":"T. 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if isinstance(data[key], float): + if isinstance(data[key], (int, float)): data[key] = int(data[key]) elif isinstance(data[key], dict): data[key] = round_dict(data[key]) @@ -28,6 +28,20 @@ def test_pdf(): f.close() +def test_pdf_document_entity(): + """ + Tests a pdf file with bbox annotations only + """ + with open('tests/data/assets/ndjson/pdf_document_entity_import.json', + 'r') as f: + data = json.load(f) + res = list(NDJsonConverter.deserialize(data)) + res = list(NDJsonConverter.serialize(res)) + assert [round_dict(x) for x in res] == [round_dict(x) for x in data] + + f.close() + + def test_pdf_with_name_only(): """ Tests a pdf file with bbox annotations only diff --git a/tests/integration/annotation_import/conftest.py b/tests/integration/annotation_import/conftest.py index e8cc860e1..1cb7e0089 100644 --- a/tests/integration/annotation_import/conftest.py +++ b/tests/integration/annotation_import/conftest.py @@ -114,10 +114,17 @@ def ontology(): 'options': [] }] } + named_entity = { + 'tool': 'named-entity', + 'name': 'named-entity', + 'required': False, + 'color': '#A30059', + 'classifications': [], + } tools = [ bbox_tool, polygon_tool, polyline_tool, point_tool, entity_tool, - segmentation_tool + segmentation_tool, named_entity ] classifications = [checklist, free_form_text, radio] return {"tools": tools, "classifications": classifications} @@ -152,7 +159,7 @@ def configured_project_pdf(client, ontology, rand_gen, pdf_url): where=LabelingFrontend.name == "editor"))[0] project.setup(editor, ontology) data_row_ids = [] - data_row_ids.append(dataset.create_data_row(row_data=pdf_url).uid) + data_row_ids.append(dataset.create_data_row(pdf_url).uid) project.datasets.connect(dataset) project.data_row_ids = data_row_ids yield project @@ -160,6 +167,16 @@ def configured_project_pdf(client, ontology, rand_gen, pdf_url): dataset.delete() +@pytest.fixture +def dataset_pdf_entity(client, rand_gen, pdf_entity_data_row): + dataset = client.create_dataset(name=rand_gen(str)) + data_row_ids = [] + data_row = dataset.create_data_row(pdf_entity_data_row) + data_row_ids.append(data_row.uid) + yield dataset, data_row_ids + dataset.delete() + + @pytest.fixture def configured_project_without_data_rows(client, configured_project, rand_gen): project = client.create_project(name=rand_gen(str), diff --git a/tests/integration/annotation_import/test_bulk_import_request.py b/tests/integration/annotation_import/test_bulk_import_request.py index 1aa8a299d..54f24d14a 100644 --- a/tests/integration/annotation_import/test_bulk_import_request.py +++ b/tests/integration/annotation_import/test_bulk_import_request.py @@ -2,6 +2,10 @@ import ndjson import pytest import random +from labelbox.data.annotation_types.annotation import ObjectAnnotation +from labelbox.data.annotation_types.label import Label +from labelbox.data.annotation_types.data.text import TextData +from labelbox.data.annotation_types.ner import DocumentEntity, DocumentTextSelection from labelbox.data.serialization import NDJsonConverter from labelbox.exceptions import MALValidationError, UuidError @@ -287,3 +291,50 @@ def test_pdf_mal_bbox(client, configured_project_pdf): import_annotations.wait_until_done() assert import_annotations.errors == [] + + +def test_pdf_document_entity(client, configured_project_without_data_rows, + dataset_pdf_entity, rand_gen): + # for content "Metal-insulator (MI) transitions have been one of the" in OCR JSON extract tests/assets/arxiv-pdf_data_99-word-token-pdfs_0801.3483-lb-textlayer.json + document_text_selection = DocumentTextSelection( + group_id="2f4336f4-a07e-4e0a-a9e1-5629b03b719b", + token_ids=[ + "3f984bf3-1d61-44f5-b59a-9658a2e3440f", + "3bf00b56-ff12-4e52-8cc1-08dbddb3c3b8", + "6e1c3420-d4b7-4c5a-8fd6-ead43bf73d80", + "87a43d32-af76-4a1d-b262-5c5f4d5ace3a", + "e8606e8a-dfd9-4c49-a635-ad5c879c75d0", + "67c7c19e-4654-425d-bf17-2adb8cf02c30", + "149c5e80-3e07-49a7-ab2d-29ddfe6a38fa", + "b0e94071-2187-461e-8e76-96c58738a52c" + ], + page=1) + + entities_annotation_document_entity = DocumentEntity( + name="named_entity", text_selections=[document_text_selection]) + entities_annotation = ObjectAnnotation( + name="named-entity", value=entities_annotation_document_entity) + + labels = [] + _, data_row_uids = dataset_pdf_entity + configured_project_without_data_rows.create_batch( + rand_gen(str), + data_row_uids, # sample of data row objects + 5 # priority between 1(Highest) - 5(lowest) + ) + + for data_row_uid in data_row_uids: + labels.append( + Label(data=TextData(uid=data_row_uid), + annotations=[ + entities_annotation, + ])) + + import_annotations = MALPredictionImport.create_from_objects( + client=client, + project_id=configured_project_without_data_rows.uid, + name=f"import {str(uuid.uuid4())}", + predictions=labels) + import_annotations.wait_until_done() + + assert import_annotations.errors == [] diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 019603bdf..82728ccdb 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -173,7 +173,25 @@ def image_url(client): @pytest.fixture(scope="session") def pdf_url(client): - return client.upload_file('tests/assets/loremipsum.pdf') + pdf_url = client.upload_file('tests/assets/loremipsum.pdf') + return {"row_data": {"pdf_url": pdf_url,}, "global_key": str(uuid.uuid4())} + + +@pytest.fixture(scope="session") +def pdf_entity_data_row(client): + pdf_url = client.upload_file( + 'tests/assets/arxiv-pdf_data_99-word-token-pdfs_0801.3483.pdf') + text_layer_url = client.upload_file( + 'tests/assets/arxiv-pdf_data_99-word-token-pdfs_0801.3483-lb-textlayer.json' + ) + + return { + "row_data": { + "pdf_url": pdf_url, + "text_layer_url": text_layer_url + }, + "global_key": str(uuid.uuid4()) + } @pytest.fixture