FAQs

Threshold based object segmentation

Added track bars for users to select HSV values to segment ROIs in the provided video.

python annolid/main.py -v /path/to/my_video.mp4 --segmentation=threshold

Threshold based segmentation

Convert WMV format to mp4 format using ffmpeg

ffmpeg -i /path/to/my_video.wmv -c:a aac /path/to/my_video.mp4

Save the extracted frames to a user selected output directory

If not selected, it will save the extracted frames to a folder named with the video name without extension. For example, if the input video path is /path/to/my_video.mp4, the extracted frames will be saved in the folder /path/to/my_video. The output directory is provided, the extracted frames will be saved /path/to/dest/my_video.

cd annolid
python main.py -v /path/to/my_video.mp4 --extract_frames=20 --to /path/to/dest --algo=uniform

How to track multiple objects in the video?

YOLO-based tracking has been removed from Annolid.

How to convert labelme labeled dataset to COCO format?

Use the GUI export dialog:

Open Annolid.
Go to Convert -> LabelMe -> COCO.
Select your LabelMe annotation directory.
Optionally set output directory and labels file.
Choose train split and output mode (Segmentation or Keypoints), then click OK.

You can also run the CLI:

python annolid/main.py \
  --labelme2coco=/path/to/my_labeled_images \
  --to /path/to/my_dataset_coco \
  --labels=/path/to/my_labels.txt

The dataset is structured as:

../../datasets/mydataset_coco/
├── data.yaml
├── annotations_train.json
├── annotations_valid.json
├── train
│   ├── annotations.json
│   └── JPEGImages
│       ├── 00000444.jpg
└── valid
    ├── annotations.json
    └── JPEGImages
        ├── 00000443.jpg

Convert the tracking results csv file to Glitter2 csv format

The result csv file named as tracking_results_nix.csv in the folder as provided in –to option.

python annolid/main.py -v /path/to/my_video.mkv --tracks2glitter /path/to/tracking_results.csv --to /path/to/results_dir/

Convert the keypoint annotations to labelme format

e.g. DeepLabCut Mouse dataset

python annolid/main.py --keypoints2labelme /path/to/mouse_m7s3/  --keypoints /path/to/mouse_m7s3/CollectedData_xxxx.h5

Example