classifier CLI tool

This is the command line tool from das-element for classification of image, sequence and movie files.

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

How To

Simple usage

`classifier {file}`

The file path can be to a single file (single image or movie file) or a sequence of files.

classifier /path/to/file.#.exr

# result:
{"/path/to/file.#.exr": [{"label": "fire", "description": "rapid oxidation of a material", "value": "Q3196"}]}

Multiple files

You can pass multiple file paths to the software.
classifier {file1} {file2} {file3}

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"}]}

Multiple results

Get the top X predicted categories by using the flag: --top {number}

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"}
]}

Python Example

Here is an example code snippet that you could use in your python code.

# 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

Result format

For each file path you get an list of predictions for labels.
The result is in JSON format. The default string format is Unicode.

{'/path/to/file.mov': [{
                          'value': 'Q327954',
                          'label': 'torch',
                          'description': 'stick with a flaming end used as a source of light'
                        }]}

key	description
`value`	identifier value - see here for more details
`label`	human readable text of this category
`description`	description text for this category

Flags

These are the flags that can be set

flag	description
`--info`	Shows information the software. List all categories that this version of the model can classify. id - the identifier for the class from Wikidata human-readable - readable label synonym - a list of different words for this class
`--top {number}`	Get the top X predictions of labels
`--debug`	debugging mode