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You can deploy it on your render farm to use it in a post-render process for labeling of elements.

 classifier {file}

Valid file paths are …

• single file (single image or movie file):

  • /path/to/file.exr

  • /path/to/file.mov

• sequence of files

  • /path/to/sequence.#.exr

  • /path/to/sequence.%04d.exr

• directory

  • the software will crawl the folder structure to find any media files or sequences

classifier /path/to/files.#.exr /path/to/another/file.mov

# result:
{"/path/to/files.#.exr": [{"label": "fire", "description": "rapid oxidation of a material", "value": "Q3196"}],
"/path/to/another/file.mov": [{"label": "torch", "description": "stick with a flaming end used as a source of light", "value": "Q327954"}]}

classifier -top 3 /path/to/file.mov

# result:
{"/path/to/file.mov": [
      {"label": "torch", "description": "stick with a flaming end used as a source of light", "value": "Q327954"},
      {"label": "fire", "description": "rapid oxidation of a material", "value": "Q3196"},
      {"label": "flame", "description": "visible, gaseous part of a fire", "value": "Q235544"}
]}

# print the top 3 label predictions for a given file path

import json
import subprocess

path = '/path/to/file.mov'
command = ['./classifier', '--top', '3', path]
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
output, error = process.communicate()

if process.returncode != 0:
    print('Something went wrong: {} - Error: {}'.format(path, error))
else:
    result = json.loads(output)
    for path, predictions in result.items():
        wikidata_ids = [item['value'] for item in  predictions]  # list of IDs from wikidata
        readable_labels = [item['label'] for item in predictions]  # list of human readable labels
        print('For path: "{}" predicted the labels: {}'.format(path, ', '.join(readable_labels)))

# result:
# For path: "/path/to/file.mov" predicted the labels: torch, flame, fire