This repo contains sample reference implementations and example codes

File: inference_on_video.py

File: http_post_example.py

File: inference_on_video.py

File: extract_frames_from_video.py

This tool allows developers to quickly split a video into frames, with the aim in create training data for model production.

This python script allows developers to either:

1. Go through each frame and manually save the frames you want (default).

$ python3 extract_frames_from_video <path_to_video>

2. Save every ith frame in the video.

$ python3 extract_frames_from_video --every <integer> <path_to_video>

File: convert_label_to_xailient_format.py

A script to convert different annotations formats to the format required by the Xailient Console.

Currently supported formats for this conversion script are pascalvoc/labelimg, labelme, coco, and yolo formats.

Choices for input_format argument are 'voc', 'coco', 'labelme', 'yolo'

• For annotations present in a single file (e.g. COCO), input_path represents the path to the JSON file.

• While for separate annotations for each image (e.g. Pascal VOC, yolo, labelme), input_path represents the path to the folder where the annotations reside.

The output_path is the path and name of the converted xailient annotations.

Examples of usage:

$ python3 convert_label_to_xailient_format.py --input_path example/coco_annotations.json --input_format coco --output_path example/xailient_labels.csv

File: XailientDockerAPI

File: post_processing_script_to_improved_accuracy.py

File: png_to_jpg_converter.py