100 lines
3.0 KiB
Markdown
100 lines
3.0 KiB
Markdown
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## Fish Body Segmentation (YOLOv8-seg)
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This folder provides a quick pipeline to train a **body-only** fish segmentation model using **Labelme polygon annotations**.
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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.
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### 1) Labeling in Labelme
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- Use `Labelme` polygon tool.
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- Recommended class name: `body` (you can use other names; see `--classes` below).
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- Each image produces a `.json` annotation file.
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### 2) Convert Labelme JSON -> YOLOv8-seg dataset
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This will create a YOLO dataset folder:
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```
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<out_dir>/
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images/train, images/val, images/test
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labels/train, labels/val, labels/test
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dataset.yaml
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```
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Example:
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```bash
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python3 segmentation/prepare_yolo_seg_dataset.py \
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--source_dir /path/to/labelme_export \
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--out_dir ./datasets/fish_body_seg \
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--classes body \
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--train_ratio 0.8 --val_ratio 0.1 --test_ratio 0.1 \
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--seed 42 \
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--copy
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```
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Notes:
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- YOLOv8-seg label format is: `<class_id> x1 y1 x2 y2 ... xn yn` (all normalized to [0,1]).
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- If an image has no valid polygons, an empty label file will be written (you can change this later if desired).
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### 2b) Filter existing prepared dataset (if only some images are labeled)
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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:
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```bash
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python3 segmentation/prepare_yolo_seg_dataset.py \
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--prepared_dataset /home/ubuntu/data/fish/fish_measure_intermediates/yolo_seg \
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--out_dir ./datasets/fish_body_seg_filtered \
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--classes body \
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--copy
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```
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This will:
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- Scan `images/train/`, `images/val/`, `images/test/` for images
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- Check for corresponding `.txt` label files in `labels/train/`, `labels/val/`, `labels/test/`
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- Only copy/symlink images that have labels
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- Generate a clean `dataset.yaml` for training
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### 3) Visualize labels (optional - verify conversion correctness)
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Before training, you can visualize the converted labels to verify they're correct:
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```bash
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python3 segmentation/visualize_yolo_seg_labels.py \
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--dataset ./datasets/fish_body_seg_filtered \
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--output ./visualizations/yolo_seg_labels \
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--split train \
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--max_images 50 \
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--classes fishbody \
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--alpha 0.5
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```
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This will:
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- Load images and their corresponding `.txt` label files
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- Draw polygon masks (semi-transparent overlay) on images
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- Save visualized images to the output directory
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- Useful for checking that Labelme → YOLO conversion preserved polygon shapes correctly
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### 4) Train YOLOv8 segmentation
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```bash
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python3 segmentation/train_yolo_seg.py \
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--data ./datasets/fish_body_seg/dataset.yaml \
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--model yolov8s-seg.pt \
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--epochs 200 \
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--batch 16 \
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--imgsz 640 \
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--project runs/seg \
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--name fish_body_seg_$(date +%Y%m%d_%H%M%S)
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```
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Outputs:
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- `runs/seg/<name>/weights/best.pt`
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### 5) Next step (pipeline integration)
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After training, you can run segmentation on:
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- full image, or
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- **cropped image from detector bbox** (often better when fish is small in the frame).
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