## Fish Body Segmentation (YOLOv8-seg)

This folder provides a quick pipeline to train a **body-only** fish segmentation model using **Labelme polygon annotations**.

The goal is to produce a mask that **excludes fins and tail** (as much as possible), so the depth->pointcloud becomes cleaner for weight estimation.

### 1) Labeling in Labelme

- Use `Labelme` polygon tool.
- Recommended class name: `body` (you can use other names; see `--classes` below).
- Each image produces a `.json` annotation file.

### 2) Convert Labelme JSON -> YOLOv8-seg dataset

This will create a YOLO dataset folder:

```
<out_dir>/
  images/train, images/val, images/test
  labels/train, labels/val, labels/test
  dataset.yaml
```

Example:

```bash
python3 segmentation/prepare_yolo_seg_dataset.py \
  --source_dir /path/to/labelme_export \
  --out_dir ./datasets/fish_body_seg \
  --classes body \
  --train_ratio 0.8 --val_ratio 0.1 --test_ratio 0.1 \
  --seed 42 \
  --copy
```

Notes:
- YOLOv8-seg label format is: `<class_id> x1 y1 x2 y2 ... xn yn` (all normalized to [0,1]).
- If an image has no valid polygons, an empty label file will be written (you can change this later if desired).

### 2b) Filter existing prepared dataset (if only some images are labeled)

If you already have a prepared YOLO-seg dataset but only some images have labels, use this mode to filter and keep only labeled images:

```bash
python3 segmentation/prepare_yolo_seg_dataset.py \
  --prepared_dataset /home/ubuntu/data/fish/fish_measure_intermediates/yolo_seg \
  --out_dir ./datasets/fish_body_seg_filtered \
  --classes body \
  --copy
```

This will:
- Scan `images/train/`, `images/val/`, `images/test/` for images
- Check for corresponding `.txt` label files in `labels/train/`, `labels/val/`, `labels/test/`
- Only copy/symlink images that have labels
- Generate a clean `dataset.yaml` for training

### 3) Visualize labels (optional - verify conversion correctness)

Before training, you can visualize the converted labels to verify they're correct:

```bash
python3 segmentation/visualize_yolo_seg_labels.py \
  --dataset ./datasets/fish_body_seg_filtered \
  --output ./visualizations/yolo_seg_labels \
  --split train \
  --max_images 50 \
  --classes fishbody \
  --alpha 0.5
```

This will:
- Load images and their corresponding `.txt` label files
- Draw polygon masks (semi-transparent overlay) on images
- Save visualized images to the output directory
- Useful for checking that Labelme → YOLO conversion preserved polygon shapes correctly

### 4) Train YOLOv8 segmentation

```bash
python3 segmentation/train_yolo_seg.py \
  --data ./datasets/fish_body_seg/dataset.yaml \
  --model yolov8s-seg.pt \
  --epochs 200 \
  --batch 16 \
  --imgsz 640 \
  --project runs/seg \
  --name fish_body_seg_$(date +%Y%m%d_%H%M%S)
```

Outputs:
- `runs/seg/<name>/weights/best.pt`

### 5) Next step (pipeline integration)

After training, you can run segmentation on:
- full image, or
- **cropped image from detector bbox** (often better when fish is small in the frame).