README.md

# 0. Directory structure:

```
Hens Tracking

├── data
│   ├── frames
│   │   └── frame_0.jpg, frame_1.jpg, ..., frame_204.jpg
│   └── video
│       └──  video.mkv
│
├──  train
│   ├── images
│   │   └── first 185 images
│   └── labels
│
├──  val
│   ├── images
│   │   └── remaining 20 images
│   └── labels
│
├──  annotate.py
│
├──  get_stuff.py
│
├──  track.py
│
└── data.yaml
```

# 1. Annotated the frames using `ultralytics auto annotator`

> Run `python annotate.py` 

(See output `annotate_output.txt`)

This creates a folder under `data` named 'frames_auto_annotate_labels' and gives a .txt file for each frame containing the segmentations of the hens it detected.


# 2. Put the labels into a proper structure for training i.e. specify custom class (hen)

> Run `python get_stuff.py`

This will put all the necesaary files under `train/labels`. Move the last 20 files to `val/labels` for validation in the next stripped.


# 3. The model is now trained using a pretrained `yolov8x-seg` 
Ensure that the directory path is good

> Run `yolo task=segment mode=train model=yolov8x-seg data=data.yaml epochs=20 imgsz=640` in a terminal opened in the directory

(See output `train_output.txt`)

This will create a `runs` folder and all the metrics and model weights will be stored there.


# 4. Use the best weights as input model for tracking

> Run `python track.py` 

(See `output track_output.txt`, uncomment line no. 34 if you want to visualize the output alongside running)

The final tracking video would be saved under the directory.