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}
classifier /path/to/file.#.exr
# result:
{"/path/to/file.#.exr": [{"label": "fire", "description": "rapid oxidation of a material", "value": "Q3196"}]}
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
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
Software info
Get information about the current software version and the categories that can be classified.
classifier --info
key | description |
|---|---|
id | the identifier for the class from Wikidata |
human-readable | meaningful readable label |
synonym | a list of different words for this class |
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.
File path gets returned as PosixPath with forwards slash, even for Windows.
{'/path/to/file.mov': [{
'value': 'Q327954',
'label': 'torch',
'description': 'stick with a flaming end used as a source of light'
}]}
key | description |
|---|---|
| identifier value - see here for more details |
| human readable text of this category |
| description text for this category |
Flags
These are the flags that can be set
flag | description |
|---|---|
| Shows information the software. |
| Get the top X predictions of labels. The first two predictions are probably the most significant ones. |
| File path to another model file (.wit) |
| Set the number of frames of a filmstrip for a sequence of images or movie file. Example: for a sequence of 1000 frames only a number of frames get validated. This helps to speed up the process and still gets you a good result. |
| debugging mode |
Troubleshooting
issue | solution |
|---|---|
MacOS shows unidentified developer for 'ffprobe' | For MacOS you should add the ffprobe to your trusted applications if you want to use the software. |