> ## Documentation Index > Fetch the complete documentation index at: https://docs.labelbox.com/llms.txt > Use this file to discover all available pages before exploring further. # Export video annotations > How to export video annotations and sample export formats. ## Export as JSON ```python Export theme={null} # Set the export params to include/exclude certain fields. export_params= { "attachments": True, "metadata_fields": True, "data_row_details": True, "project_details": True, "label_details": True, "performance_details": True, "interpolated_frames": True } # Note: Filters follow AND logic, so typically using one filter is sufficient. filters= { "last_activity_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"], "workflow_status": "" } export_task = project.export(params=export_params, filters=filters) export_task.wait_till_done() # Stream the export using a callback function def json_stream_handler(output: labelbox.BufferedJsonConverterOutput): print(output.json) export_task.get_buffered_stream(stream_type=labelbox.StreamType.RESULT).start(stream_handler=json_stream_handler) # Collect all exported data into a list export_json = [data_row.json for data_row in export_task.get_buffered_stream()] print("file size: ", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT)) print("line count: ", export_task.get_total_lines(stream_type=lb.StreamType.RESULT)) ``` ## Annotation export formats ### Bounding box ```json JSON theme={null} { "feature_id": "cltd97cag01th14ys6fyyxyk9", "feature_schema_id": "cltd977no037j07x5d82dacbc", "name": "bbox_video", "value": "bbox_video", "annotation_kind": "VideoBoundingBox", "classifications": [], "bounding_box": { "top": 617.0, "left": 1371.0, "height": 419.0, "width": 505.0 } } ``` ### Point ```json JSON theme={null} { "feature_id": "cltd97cag01to14yslakirofl", "feature_schema_id": "cltd977no037l07x56wfu4536", "name": "point_video", "value": "point_video", "annotation_kind": "VideoPoint", "classifications": [], "point": { "x": 660.134, "y": 407.926 } } ``` ### Polyline ```json JSON theme={null} { "feature_id": "cltd97cag01tp14ysz3my848t", "feature_schema_id": "cltd977np037n07x5d1buckz9", "name": "line_video_frame", "value": "line_video_frame", "annotation_kind": "VideoPolyline", "classifications": [], "line": [ { "x": 680.0, "y": 100.0 }, { "x": 100.0, "y": 190.0 } ] } ``` ### Mask Regardless of which [tool](/docs/image-editor#segmentation-mask) is used to create a segmentation mask, the annotation will appear in the following form in the export. Whether you use AutoSegment, the brush tool, the pen tool, or the fill tool, all masks take the same format in exports. #### Composite masks [Open this Colab](https://colab.research.google.com/github/Labelbox/labelbox-notebooks/blob/main/exports/composite_mask_export.ipynb) for an interactive tutorial on exporting composite masks. A composite mask is a cloud-hosted PNG file that contains all raster segmentation mask instances on a labeled data row (one composite mask per label). This screenshot shows an example composite mask in PNG format. Here, the image has eight segmentation mask instances, each using the schema named "Human". When you export data rows containing segmentation masks, you can use `composite_mask.url` to access all segmentation masks on that data row. To access a single instance of a segmentation mask, use the `color_rgb` values. The `color_rgb` value represents the color of a single annotation or instance mask in a label. ##### Sample composite masks export ```json JSON theme={null} { "feature_id": "clsivokxg00043b70xuf7x5tz", "feature_schema_id": "clsgbcaqw000j071t2ga23jnc", "name": "car", "value": "car", "annotation_kind": "ImageSegmentationMask", "classifications": [], "mask": { "url": "https://api.labelbox.com/api/v1/projects/clsivnieg050k074i2jqaa0c3/annotations/clsivokxg00043b70xuf7x5tz/index/1/mask" }, "composite_mask": { // This URL contains all mask instances on the data row "url": "https://api.labelbox.com/api/v1/tasks/clsiw88n205mw072ke4n78y5o/masks/clsivok5f00023b70r1a4fgzy", // This RGB reflects a single mask instance "color_rgb": [ 31, 185, 101 ] } } ``` #### Access mask data To access the mask data, you must pass your Labelbox API key stored in `client.headers` in an API request. When you grab a URL from the mask annotation in the export, the `project_id` and `feature_id` will already be in place, as shown above. Here, we provide the framework for structuring a URL with any project ID and feature ID. ```python Python theme={null} import urllib.request from PIL import Image # Provide a project ID and feature ID if using instance masks. Alternatively, replace the entire mask_url with a URL grabbed from your export. project_id = "" feature_id = "" mask_url = f"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask" # Instead of providing the individual variables, we recommend utilizing the URL from export for composite mask composite_mask_url = f"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1" # Make the API request req = urllib.request.Request(mask_url, headers=client.headers) # Optionally, print the image of the mask image = Image.open(urllib.request.urlopen(req)) image.show() ``` ### Classification - Radio ```json JSON theme={null} { "feature_id": "cltd97cag01tf14ysho6tfl7j", "feature_schema_id": "cltd977np038907x5ah8l9ha7", "name": "radio_class", "value": "radio_class", "radio_answer": { "feature_id": "cltd97cag01tg14ys171a3qqr", "feature_schema_id": "cltd977np038a07x515axagkl", "name": "first_radio_answer", "value": "first_radio_answer", "classifications": [] } } ``` ### Classification - Checklist ```json JSON theme={null} { "feature_id": "cltd97cag01tq14ysb4m3nyjo", "feature_schema_id": "cltd977nq039107x5dsml2knm", "name": "checklist_class_global", "value": "checklist_class_global", "checklist_answers": [ { "feature_id": "cltd97cag01tr14ysozjbx0c6", "feature_schema_id": "cltd977nq039207x5ei1605s1", "name": "first_checklist_answer", "value": "first_checklist_answer", "classifications": [] }, { "feature_id": "cltd97cag01ts14ys4vvxihnw", "feature_schema_id": "cltd977nq039407x50qs3g61l", "name": "second_checklist_answer", "value": "second_checklist_answer", "classifications": [] } ] } ``` ### Classification - Free-form text ```json JSON theme={null} { "feature_id": "cltd97cag01u314ysx1h72f0h", "feature_schema_id": "cltd977nq039707x535ufeo39", "name": "free_text", "value": "free_text", "text_answer": { "content": "sample text" } } ``` ## Sample project export ```json JSON expandable theme={null} { "data_row": { "id": "clp1jolki07uy0725flku4bmv", "global_key": "sample-video-jellyfish.mp4", "row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4", "details": { "dataset_id": "clp1jokqp000p07307gtfy1w3", "dataset_name": "video_demo_dataset", "created_at": "2023-11-16T18:48:51.208+00:00", "updated_at": "2023-11-16T18:48:51.801+00:00", "last_activity_at": "2024-04-08T17:09:54.000+00:00", "created_by": "[email protected]" } }, "media_attributes": { "height": 1080, "width": 1920, "mime_type": "video/mp4", "frame_rate": 10, "frame_count": 100 }, "attachments": [], "metadata_fields": [], "embeddings": [ { "id": "c400000000000000000000000", "name": "Video Embedding V2 (Google MultiModal)", "dimensions": 1408, "is_custom": false, "values": [ { "value": [ -0.00489852577, 0.0287129097 ... ] } ] } ], "projects": { "cltd977uu016y08xyggep8aiv": { "name": "Video Annotation Import Demo", "labels": [ { "label_kind": "Video", "version": "1.0.0", "id": 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"first_radio_answer", "classifications": [ { "feature_id": "cltd97cag01u114ysktcruoro", "feature_schema_id": "cltd977nq038h07x59zx07wfe", "name": "sub_radio_question", "value": "sub_radio_question", "radio_answer": { "feature_id": "cltd97cag01u214ysxi71u4qi", "feature_schema_id": "cltd977nq038i07x5et0717w6", "name": "first_sub_radio_answer", "value": "first_sub_radio_answer", "classifications": [] } } ] } }, { "feature_id": "cltd97cag01u314ysx1h72f0h", "feature_schema_id": "cltd977nq039707x535ufeo39", "name": "free_text", "value": "free_text", "text_answer": { "content": "sample text" } } ] } } ], "project_details": { "ontology_id": "cltd977mx037i07x52amua8iw", "task_name": "Done", "batch_id": "1026dd60-da52-11ee-9774-cfce344359c8", "batch_name": "first-batch-video-demo2", "workflow_status": "DONE", "priority": 5, "consensus_expected_label_count": 1, "workflow_history": [] }, "project_tags": [] } } } ``` ## Sample model run export ```json JSON expandable theme={null} { "data_row": { "id": "cldtd2nai107407y5979xbu97", "global_key": "3548cfa1-1a42-40f9-a246-2aa7c696dcbe", "row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4", "details": { "dataset_id": "cldtd2mox11dz07wh3gkk9awx", "dataset_name": "video_prediction_demo", "created_at": "2023-02-06T22:04:54.000+00:00", "updated_at": "2023-02-06T22:04:54.000+00:00", "created_by": "[email protected]" } }, "media_attributes": { "height": 1080, "width": 1920, "mime_type": "video/mp4", "frame_rate": 10, "frame_count": 100 }, "attachments": [], "metadata_fields": [], "experiments": { "a088408e-81af-0152-f2ca-0b870c7a2bfa": { "name": "video_model_run_8d7dbe3d-30e0-4c9c-becd-26afb2386bc9", "runs": { "a088408e-ce36-09c1-cbd9-d43c677f7773": { "name": "iteration 1", "run_data_row_id": "999a247e-0b57-415e-af7e-d3a87cc6559b", "labels": [ { "label_kind": "Video", "version": "1.0.0", "id": "cldteelzh08us1331q4pzj2ke", "annotations": { "frames": { "17": { "objects": { 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