commit aed092f09cebb9a8ad86a8d8a6ef3beedf35f057 Author: dillonj Date: Mon Dec 22 14:11:39 2025 -0700 Initial commit: Wood knot detection model and GUI diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..7ee09afe --- /dev/null +++ b/.gitignore @@ -0,0 +1,56 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +*.egg-info/ +dist/ +build/ +*.egg + +# Virtual Environment +.venv/ +venv/ +ENV/ +env/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# Model checkpoints and weights +*.pt +*.pth +*.onnx +*.blob +*.xml +*.bin +checkpoints/ +weights/ +runs/ + +# Dataset (large files) +IMAGE/ +images/ +*.jpg +*.jpeg +*.png +*.gif +bbox_coco_dataset.json + +# Training outputs +train_output/ +results/ +*.log + +# Jupyter +.ipynb_checkpoints/ +*.ipynb + +# OS +.DS_Store +Thumbs.db diff --git a/GUI_README.md b/GUI_README.md new file mode 100644 index 00000000..0f3e936c --- /dev/null +++ b/GUI_README.md @@ -0,0 +1,148 @@ +# Custom Annotation GUI + +A simple, **fully customizable** annotation tool built with Gradio (pure Python). + +## Features + +✅ **Auto-labeling** with your trained RF-DETR model +✅ **Manual annotation** by entering box coordinates +✅ **Edit/delete** annotations easily +✅ **Navigation** between images +✅ **Export** to COCO JSON format +✅ **100% Python** - easy to modify and extend + +## Quick Start + +### 1. Install dependencies +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python -m pip install gradio>=4.0.0 +``` + +### 2. Run the GUI + +**With auto-labeling (requires trained model):** +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python annotation_gui.py \ + --images-dir /path/to/images \ + --model-weights runs/knot_rfdetr_medium/checkpoint_best_total.pth +``` + +**Manual annotation only:** +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python annotation_gui.py \ + --images-dir /path/to/images +``` + +### 3. Open in browser +Opens automatically at http://localhost:7860 + +## Usage + +1. **Auto-Label**: Click "🤖 Auto-Label" to detect knots with your model +2. **Adjust threshold**: Lower = more detections, Higher = only confident ones +3. **Manual boxes**: Enter coordinates (x1, y1, x2, y2) and click "➕ Add Box" +4. **Delete mistakes**: Click "🗑️ Delete Last" to remove last box +5. **Navigate**: Use "Previous" / "Next" buttons +6. **Export**: Click "💾 Export COCO" when done + +## Customization Examples + +### Add keyboard shortcuts +```python +# In create_ui(), add: +image_display.keyboard_shortcuts = { + "d": delete_btn.click, # Press 'd' to delete + "n": next_btn.click, # Press 'n' for next +} +``` + +### Add interactive drawing +```python +# Replace manual coordinates with image annotator: +from gradio_image_annotation import image_annotator + +annotator = image_annotator( + label="Draw boxes", + type="numpy" +) +``` + +### Change box colors by confidence +```python +# In draw_boxes_on_image(): +color = "green" if conf > 0.8 else "yellow" if conf > 0.5 else "red" +draw.rectangle([x1, y1, x2, y2], outline=color, width=3) +``` + +### Add multiple label classes +```python +# Add a dropdown: +label_choice = gr.Dropdown( + choices=["knot", "crack", "hole"], + value="knot", + label="Label Type" +) + +# Update box dict: +box = { + "bbox": [x1, y1, x2, y2], + "label": label_choice_value, # from the dropdown + "confidence": 1.0 +} +``` + +### Save checkpoints automatically +```python +# In _save_annotations(), add: +import shutil +backup_path = self.ann_file.with_suffix('.backup.json') +shutil.copy(self.ann_file, backup_path) +``` + +### Add image filters/preprocessing +```python +# Add before annotation: +def preprocess_image(img: Image.Image) -> Image.Image: + from PIL import ImageEnhance + enhancer = ImageEnhance.Contrast(img) + return enhancer.enhance(1.5) # Increase contrast +``` + +## File Structure + +``` +annotation_gui.py +├── AnnotationApp # Main logic (easy to extend) +│ ├── auto_label_current() # Modify for different models +│ ├── add_box_manual() # Customize annotation format +│ ├── export_to_coco() # Change export format +│ └── draw_boxes_on_image() # Customize visualization +└── create_ui() # Gradio interface (add components) +``` + +## Advantages vs Label Studio + +| Feature | Custom GUI | Label Studio | +|---------|-----------|--------------| +| **Modify code** | ✅ Easy (pure Python) | ❌ Complex (React + Python) | +| **Add features** | ✅ ~10-50 lines | ❌ Hundreds of lines | +| **Custom models** | ✅ Direct integration | ⚠️ Need ONNX export | +| **Learning curve** | ✅ Simple Gradio | ⚠️ Larger codebase | +| **Setup** | ✅ pip install | ⚠️ Docker/complex | + +## Troubleshooting + +**Port already in use:** +```bash +python annotation_gui.py --images-dir /path --port 7861 +``` + +**Model not loading:** +- Check the weights path exists +- Verify it's a valid checkpoint file +- Try without `--model-weights` for manual-only mode + +**Need more features?** +- Check Gradio docs: https://www.gradio.app/docs/ +- Add custom components easily +- Fork and modify the code freely! diff --git a/LABELING_GUIDE.md b/LABELING_GUIDE.md new file mode 100644 index 00000000..a050f731 --- /dev/null +++ b/LABELING_GUIDE.md @@ -0,0 +1,244 @@ +# Image Labeling Guide for Knot Detection + +## Quick Start: Label Studio (Recommended) + +### 1. Install and Launch +```bash +# Install (outside your project venv is fine) +pip install label-studio + +# Start the server +label-studio start +``` + +Open http://localhost:8080 in your browser. + +### 2. Create Your Project +1. Click "Create Project" +2. Project Name: "Wood Knot Detection" +3. Data Import: Click "Upload Files" → select your wood images +4. Labeling Setup: + - Template: "Object Detection with Bounding Boxes" + - Add label: `knot` (or multiple types: `sound_knot`, `dead_knot`, etc.) + +### 3. Label Images +**Keyboard Shortcuts (speeds up 3-5x):** +- `Alt + Click` = Create bounding box +- `Alt + R` = Select rectangle tool +- `Ctrl + Enter` = Submit and move to next +- `Ctrl + Z` = Undo + +**Best Practices:** +- Draw boxes tight around each knot +- Include partial knots at image edges +- Label consistently (all knots, or only specific types) +- Take breaks every 30-50 images to maintain quality + +### 4. Export to COCO Format +1. Click project name → **Export** +2. Format: **"COCO"** +3. Download the zip file +4. Extract and organize for RF-DETR: + ```bash + # After extracting export.zip: + unzip export.zip -d exported/ + + # Organize into RF-DETR format + mkdir -p dataset/train dataset/valid dataset/test + + # Move images and rename JSON + mv exported/images/* dataset/train/ + mv exported/result.json dataset/train/_annotations.coco.json + + # Split 80/10/10 manually or use a script + # Move ~10% of images + their annotations to valid/ + # Move ~10% to test/ + ``` + +**Tip**: Label Studio keeps all annotations in one JSON. You'll need to split it into train/valid/test. The `validate_coco_dataset.py` script can help verify the structure. + +--- + +## Alternative: CVAT (More Powerful) + +### Setup with Docker +```bash +git clone https://github.com/opencv/cvat +cd cvat +docker compose up -d +``` + +Open http://localhost:8080 (default login: admin/admin) + +### Features +- **Keyboard shortcuts**: `N` (new box), `Shift+arrows` (adjust box) +- **Interpolation**: Auto-label between frames (for video) +- **Team mode**: Multiple annotators on same project +- **Quality control**: Review mode for double-checking labels + +### Export +1. Actions → Export task dataset +2. Format: "COCO 1.0" +3. Restructure files to match RF-DETR's expected format + +--- + +## Alternative: labelImg (Desktop App) + +### Quick Setup +```bash +pip install labelImg +labelImg /path/to/images +``` + +**Pros:** +- No web server needed +- Works offline +- Very simple interface + +**Cons:** +- Exports Pascal VOC by default (not COCO) +- Need to convert format: + ```bash + # Use roboflow or pylabel library to convert VOC → COCO + ``` + +--- + +## Model-Assisted Labeling Workflow + +After you have a trained model, speed up labeling 10-20x: + +### Step 1: Auto-label new images +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python auto_label_images.py \ + --weights runs/knot_rfdetr_medium/checkpoint_best_total.pth \ + --images-dir unlabeled_images/ \ + --output-json predictions.json \ + --threshold 0.3 +``` + +**Use low threshold (0.3)** to capture more candidates - easier to delete false positives than add missed knots. + +### Step 2: Convert to Label Studio format +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python convert_to_label_studio.py \ + --coco-json predictions.json \ + --images-dir unlabeled_images/ \ + --output-json label_studio_tasks.json +``` + +### Step 3: Import predictions into Label Studio +1. In Label Studio, open your project +2. **Settings → Storage → Add Source Storage → Local files**: + - Storage Type: Local files + - Absolute local path: `/full/path/to/unlabeled_images` + - Click "Add Storage" then "Sync Storage" +3. **Import → Upload Files** → select `label_studio_tasks.json` +4. Each image now loads with **pre-drawn boxes from your model** +5. Click through images, fixing mistakes (much faster than labeling from scratch!) + +### Step 4: Active Learning Loop with Label Studio +1. **Initial labeling**: Label 50-100 images manually in Label Studio +2. **Export & prepare**: + ```bash + # Export from Label Studio as COCO format + # Split into train/valid/test folders + ``` +3. **Train RF-DETR**: Run for just 10 epochs (faster iteration) + ```bash + /home/dillon/_code/saw_mill_knot_detection/.venv/bin/python train_rfdetr.py \ + --dataset-dir dataset/ \ + --output-dir runs/iteration_1 \ + --model medium \ + --epochs 10 + ``` +4. **Auto-label new batch**: Get predictions on 500 unlabeled images + ```bash + /home/dillon/_code/saw_mill_knot_detection/.venv/bin/python auto_label_images.py \ + --weights runs/iteration_1/checkpoint_best_total.pth \ + --images-dir batch_2_images/ \ + --output-json batch_2_predictions.json \ + --threshold 0.3 + + /home/dillon/_code/saw_mill_knot_detection/.venv/bin/python convert_to_label_studio.py \ + --coco-json batch_2_predictions.json \ + --images-dir batch_2_images/ \ + --output-json batch_2_ls_tasks.json + ``` +5. **Review in Label Studio**: Import `batch_2_ls_tasks.json` → review/correct (10x faster than from scratch) +6. **Export & retrain**: Add corrected labels to dataset, retrain for 20-50 epochs +7. **Repeat**: Continue with batch 3, 4, etc. + +This iterative approach typically achieves **95%+ accuracy with 5-10x less manual effort**. + +--- + +## Tips for High-Quality Labels + +### Consistency is Key +- **Same criteria every time**: Decide upfront if you label tiny knots, damaged areas, etc. +- **Box boundaries**: Tight around knot, or include some margin? Pick one and stick to it. +- **Occlusions**: Label partially visible knots? Document your decision. + +### Speed vs. Quality +- **First 100 images**: Take your time, establish consistency +- **After 100**: Speed up - model will help catch inconsistencies later +- **Every 500 images**: Audit 20-30 random labels to check quality + +### Common Mistakes +1. ❌ Inconsistent box sizes (sometimes tight, sometimes loose) +2. ❌ Missing small knots in some images but labeling them in others +3. ❌ Labeling knot-like wood grain patterns +4. ❌ Fatigue errors after 2+ hours - take breaks! + +### Dataset Size Guidelines +- **Minimum**: 200 labeled images (split: 150 train, 30 valid, 20 test) +- **Good**: 500-1000 images +- **Excellent**: 2000+ images +- **With active learning**: Start with 100, grow to 500+ iteratively + +--- + +## Converting Other Formats to COCO + +If you have labels in another format: + +### From YOLO format: +```python +from pylabel import importer + +dataset = importer.ImportYoloV5( + path="yolo_labels/", + img_path="images/", + cat_names=['knot'] +) +dataset.export.ExportToCoco(output_path="coco_format/") +``` + +### From Pascal VOC: +```python +from pylabel import importer + +dataset = importer.ImportVOC(path="voc_annotations/") +dataset.export.ExportToCoco() +``` + +--- + +## Troubleshooting + +**Label Studio won't start:** +- Try: `label-studio reset` then `label-studio start` + +**CVAT Docker issues:** +- Check: `docker compose logs` +- Ensure ports 8080, 8070 are free + +**Export format doesn't match RF-DETR:** +- See [validate_coco_dataset.py](validate_coco_dataset.py) to check your format +- Your JSON needs: `images`, `annotations`, `categories` keys + +**Need help?** +- Label Studio docs: https://labelstud.io/guide/ +- CVAT docs: https://opencv.github.io/cvat/docs/ diff --git a/MODEL_COMPARISON.md b/MODEL_COMPARISON.md new file mode 100644 index 00000000..c19d47a3 --- /dev/null +++ b/MODEL_COMPARISON.md @@ -0,0 +1,92 @@ +# Model Framework Comparison + +## License Comparison + +| Framework | License | Commercial Use | OAK-D Support | +|-----------|---------|----------------|---------------| +| **RT-DETR** | Apache 2.0 | ✅ Free | ⭐ Excellent | +| **YOLOv6** | MIT | ✅ Free | ⭐ Excellent | +| **YOLOX** | MIT | ✅ Free | ⭐ Excellent | +| RF-DETR | Check repo | ⚠️ Unknown | ⚠️ May need conversion | +| YOLOv8/v11 | AGPL-3.0 | ❌ Paid ($1k-5k/yr) | Excellent | + +## Performance on OAK-D 4 Pro (48 TOPS INT8) + +| Model | Size | Speed (FPS) | Accuracy | Training Time | +|-------|------|-------------|----------|---------------| +| RT-DETR r18 | ~15MB | 30-40 | Good | Fast | +| RT-DETR r34 | ~30MB | 20-30 | Better | Medium | +| YOLOv6n | ~10MB | 40-50 | Good | Fast | +| YOLOv6s | ~20MB | 30-40 | Better | Medium | +| YOLOX nano | ~6MB | 50-60 | Good | Fast | +| YOLOX-s | ~18MB | 35-45 | Better | Medium | + +## Which to Choose? + +### For Maximum Speed (50-60 FPS): +**YOLOX nano** - Smallest, fastest, proven + +### For Best Balance (30-40 FPS): +**RT-DETR r18** or **YOLOv6n** - Modern, accurate + +### For Best Accuracy (20-30 FPS): +**RT-DETR r34** or **YOLOv6s** - Larger models + +### Recommended Starting Point: +**YOLOv6n** - Great balance, proven OAK compatibility, MIT license + +## Training Commands + +All models use the same workflow in the GUI, or from command line: + +### RT-DETR +```bash +.venv/bin/python train_rtdetr.py \ + --dataset-dir dataset_prepared \ + --model rtdetr-r18 \ + --epochs 100 +``` + +### YOLOv6 +```bash +.venv/bin/python train_yolov6.py \ + --dataset-dir dataset_prepared \ + --model yolov6n \ + --epochs 100 +``` + +### YOLOX (YOLOv8 equivalent) +```bash +.venv/bin/python train_yolox.py \ + --dataset-dir dataset_prepared \ + --model yolox-nano \ + --epochs 100 +``` + +## Export for OAK-D + +All models export to OpenVINO format for OAK deployment: + +```bash +# RT-DETR +.venv/bin/python export_rtdetr_oak.py --weights runs/rtdetr_training/training/weights/best.pt + +# YOLOv6/YOLOX use Ultralytics export +.venv/bin/python -c " +from ultralytics import YOLO +model = YOLO('runs/yolov6_training/training/weights/best.pt') +model.export(format='openvino', imgsz=640, half=False) +" +``` + +Then convert to blob: +- Online: https://blobconverter.luxonis.com/ +- CLI: `blobconverter --openvino-xml model.xml` + +## Tips + +1. **Start with nano/r18 models** for fast iteration +2. **Train for 100-200 epochs** - use early stopping +3. **Collect 200+ images** for good accuracy +4. **Test on OAK-D** before collecting more data +5. **Use INT8 quantization** for full 48 TOPS speed diff --git a/README.md b/README.md new file mode 100644 index 00000000..4133a2cb --- /dev/null +++ b/README.md @@ -0,0 +1,153 @@ +# Saw Mill Knot Detection (RF-DETR) + +This repo contains a minimal training pipeline to fine-tune **RF-DETR** to detect knots in wood. + +**Dataset Source**: The wood defect images and annotations used in this project come from [Kaggle Wood Surface Defects Dataset](https://www.kaggle.com/datasets/kirs0816/wood-surface-defects?resource=download). + +## 1) Dataset format (required) +RF-DETR expects **COCO format**, split into `train/`, `valid/`, `test/`, each with its own `_annotations.coco.json`. + +Example: + +``` +dataset/ +├── train/ +│ ├── _annotations.coco.json +│ ├── 0001.jpg +│ └── ... +├── valid/ +│ ├── _annotations.coco.json +│ ├── 0101.jpg +│ └── ... +└── test/ + ├── _annotations.coco.json + ├── 0201.jpg + └── ... +``` + +Your COCO JSON should include a `categories` entry for your class(es), e.g. `knot`. + +## 2) Setup + +Create venv (already created if you used the VS Code prompt) and install deps: + +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python -m pip install -U pip +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python -m pip install -r requirements.txt +``` + +## 3) Validate dataset + +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python validate_coco_dataset.py --dataset-dir /path/to/dataset +``` + +## 4) Train + +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python train_rfdetr.py \ + --dataset-dir /path/to/dataset \ + --output-dir runs/knot_rfdetr_medium \ + --model medium \ + --epochs 50 \ + --batch-size 4 \ + --grad-accum-steps 4 \ + --lr 1e-4 +``` + +Notes: +- Keep **effective batch size** near 16: `batch_size * grad_accum_steps * num_gpus ≈ 16`. +- Checkpoints are written into `--output-dir` (including `checkpoint_best_total.pth`). + +## 5) Auto-label new images (automatic) + +Use your trained model to generate annotations on unlabeled images: + +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python auto_label_images.py \ + --weights runs/knot_rfdetr_medium/checkpoint_best_total.pth \ + --images-dir /path/to/new_images \ + --output-json auto_labeled.json \ + --threshold 0.4 +``` + +This outputs a COCO JSON with predicted bounding boxes. You can then review/correct them manually. + +## 6) Manual labeling (recommended tools) + +**Don't build your own GUI** - use these proven open-source tools instead: + +### Option A: Label Studio (Recommended - Easiest) +**Best for**: Quick setup, modern UI, ML-assisted labeling + +```bash +# Install Label Studio +pip install label-studio + +# Start the server +label-studio start +``` + +Then open http://localhost:8080 in your browser: +1. Create a new project for "Object Detection with Bounding Boxes" +2. Import your images +3. Start labeling manually OR: + - Use the auto-label script to generate initial annotations: + ```bash + /home/dillon/_code/saw_mill_knot_detection/.venv/bin/python auto_label_images.py \ + --weights runs/knot_rfdetr_medium/checkpoint_best_total.pth \ + --images-dir /path/to/images \ + --output-json predictions.json \ + --threshold 0.3 + ``` + - Import the predictions into Label Studio + - Review and correct them +4. Export in COCO format when done + +### Option B: CVAT (Most Powerful) +**Best for**: Large-scale projects, team collaboration + +```bash +# Using Docker (easiest) +git clone https://github.com/opencv/cvat +cd cvat +docker compose up -d +``` + +Open http://localhost:8080: +- Create project → upload images → annotate +- Supports keyboard shortcuts, interpolation, and advanced features +- Export directly to COCO JSON + +[CVAT Documentation](https://opencv.github.io/cvat/docs/) + +### Option C: labelImg (Simplest Desktop App) +**Best for**: Offline labeling, no server needed + +```bash +pip install labelImg +labelImg +``` + +- Simple desktop app with no web server +- Exports to Pascal VOC (needs conversion to COCO) +- Good for small datasets + +### Workflow with Model Assistance: +1. **Initial batch**: Manually label 50-100 images +2. **Train RF-DETR**: Use your training script +3. **Auto-label**: Run `auto_label_images.py` on remaining images +4. **Review**: Import predictions into Label Studio/CVAT +5. **Correct**: Fix any mistakes (much faster than labeling from scratch) +6. **Iterate**: Retrain with corrected labels, repeat + +This semi-supervised approach is **10-20x faster** than manual labeling alone. + +## 7) Quick inference sanity check + +```bash +/home/dillon/_code/saw_mill_knot_detection/.venv/bin/python predict_rfdetr.py \ + --weights runs/knot_rfdetr_medium/checkpoint_best_total.pth \ + --image /path/to/example.jpg \ + --threshold 0.4 +``` diff --git a/RTDETR_README.md b/RTDETR_README.md new file mode 100644 index 00000000..3bd9396d --- /dev/null +++ b/RTDETR_README.md @@ -0,0 +1,188 @@ +# RT-DETR Training for OAK-D Camera Deployment + +RT-DETR (Real-Time Detection Transformer) is Apache 2.0 licensed - **free for commercial use**. It's designed for real-time detection and works great on edge devices like the OAK-D 4 Pro. + +## Why RT-DETR? + +- ✅ **Apache 2.0 license** - truly free for commercial use +- ✅ **Excellent OAK camera compatibility** - exports cleanly to OpenVINO +- ✅ **Real-time performance** - 30-60 FPS on OAK-D 4 Pro +- ✅ **Modern transformer architecture** - competitive accuracy with YOLO +- ✅ **Easy deployment** - direct export to OpenVINO format + +## Quick Start + +### 1. Annotate Images + +Use the annotation GUI: +```bash +.venv/bin/python annotation_gui.py +``` + +- Load your images from Settings +- Annotate knots manually or use auto-labeling +- Aim for 100+ annotated images for good results + +### 2. Train Model + +From the GUI: +1. Go to **Training** tab +2. Click "Prepare Dataset" (creates train/valid/test splits) +3. Select **RT-DETR** framework +4. Choose model size: + - `nano` (r18): Fastest, 30-40 FPS on OAK + - `small` (r34): Balanced + - `medium` (r50): More accurate + - `base` (l): Best accuracy, slower +5. Click "Start Training" + +Or from command line: +```bash +.venv/bin/python train_rtdetr.py \ + --dataset-dir dataset_prepared \ + --model rtdetr-r18 \ + --epochs 100 \ + --batch-size 8 +``` + +### 3. Test Model + +```bash +.venv/bin/python predict_rtdetr.py \ + --weights runs/rtdetr_training/training/weights/best.pt \ + --image test_image.jpg +``` + +### 4. Export for OAK-D + +Export to OpenVINO format: +```bash +.venv/bin/python export_rtdetr_oak.py \ + --weights runs/rtdetr_training/training/weights/best.pt \ + --img-size 640 +``` + +This creates: +- `best_openvino_model/` - OpenVINO IR format (.xml + .bin files) +- `best.onnx` - ONNX format (intermediate) + +### 5. Convert to Blob for OAK + +**Option A: Online converter** (easiest) +1. Go to https://blobconverter.luxonis.com/ +2. Upload `best_openvino_model/model.xml` +3. Select "OAK-D 4 Pro" +4. Download `.blob` file + +**Option B: Command line** +```bash +pip install blobconverter +blobconverter --openvino-xml best_openvino_model/model.xml \ + --shaves 6 +``` + +### 6. Deploy to OAK-D Camera + +Example DepthAI script: +```python +import depthai as dai +import cv2 + +# Create pipeline +pipeline = dai.Pipeline() + +# Camera +cam = pipeline.createColorCamera() +cam.setPreviewSize(640, 640) +cam.setInterleaved(False) + +# Neural network +nn = pipeline.createNeuralNetwork() +nn.setBlobPath("best.blob") +cam.preview.link(nn.input) + +# Output +xout = pipeline.createXLinkOut() +xout.setStreamName("detections") +nn.out.link(xout.input) + +# Run +with dai.Device(pipeline) as device: + queue = device.getOutputQueue("detections") + + while True: + detections = queue.get() + # Process detections... +``` + +## Model Comparison + +| Model | Size | Speed (OAK-D) | Accuracy | License | +|-------|------|---------------|----------|---------| +| RT-DETR r18 | ~15MB | 30-40 FPS | Good | Apache 2.0 ✅ | +| RT-DETR r34 | ~30MB | 20-30 FPS | Better | Apache 2.0 ✅ | +| YOLOv11n | ~6MB | 50-60 FPS | Good | AGPL ❌ | +| YOLOv6n | ~10MB | 40-50 FPS | Good | MIT ✅ | +| RF-DETR nano | ~15MB | 10-20 FPS* | Good | Check repo | + +*May have compatibility issues with OpenVINO + +## Training Tips + +1. **Dataset size**: + - Minimum: 50 images + - Good: 200+ images + - Excellent: 1000+ images + +2. **Data diversity**: + - Different wood types + - Various lighting conditions + - Multiple knot sizes/types + - Different angles + +3. **Training settings**: + - Start with `rtdetr-r18` for fastest iteration + - Use `batch-size=8` if you have 8GB+ GPU + - Train for 100-200 epochs + - Use early stopping (patience=20) + +4. **Data augmentation** (automatic): + - Flips, rotations + - Color adjustments + - Crops and scales + +## Troubleshooting + +**Training is slow:** +- Reduce batch size +- Use smaller model (r18) +- Check GPU usage with `nvidia-smi` + +**Low accuracy:** +- Add more training data +- Train longer (more epochs) +- Use larger model (r34 or r50) +- Check your annotations for errors + +**OAK deployment fails:** +- Ensure OpenVINO export succeeded +- Check blob size (<200MB for OAK-D) +- Verify input size matches training (640x640) +- Try FP16 instead of FP32 to reduce size + +## Resources + +- [RT-DETR Paper](https://arxiv.org/abs/2304.08069) +- [Ultralytics RT-DETR Docs](https://docs.ultralytics.com/models/rtdetr/) +- [OAK-D Docs](https://docs.luxonis.com/) +- [DepthAI Examples](https://github.com/luxonis/depthai-experiments) + +## License + +RT-DETR is Apache 2.0 licensed - you can use it for: +- ✅ Personal projects +- ✅ Commercial products +- ✅ Internal business tools +- ✅ Proprietary software + +No restrictions, no paid licenses required! diff --git a/annotation_gui.py b/annotation_gui.py new file mode 100644 index 00000000..33fedf2b --- /dev/null +++ b/annotation_gui.py @@ -0,0 +1,824 @@ +""" +Simple customizable annotation GUI with auto-labeling support. + +Built with Gradio - easy to modify and extend. +Run: python annotation_gui.py + +To set default paths, edit config.py +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import threading +from pathlib import Path +from typing import Any + +import gradio as gr +from PIL import Image, ImageDraw + +# Try to load config, use fallbacks if not available +try: + from config import ( + DEFAULT_IMAGES_DIR, DEFAULT_MODEL_WEIGHTS, DEFAULT_PORT, + DEFAULT_DETECTION_THRESHOLD, DEFAULT_TRAIN_EPOCHS, + DEFAULT_BATCH_SIZE, DEFAULT_LEARNING_RATE, DEFAULT_MODEL_SIZE + ) +except ImportError: + DEFAULT_IMAGES_DIR = None + DEFAULT_MODEL_WEIGHTS = None + DEFAULT_PORT = 7860 + DEFAULT_DETECTION_THRESHOLD = 0.5 + DEFAULT_TRAIN_EPOCHS = 20 + DEFAULT_BATCH_SIZE = 4 + DEFAULT_LEARNING_RATE = 1e-4 + DEFAULT_MODEL_SIZE = "small" + + +class AnnotationApp: + def __init__(self, images_dir: Path | None = None, model_weights: Path | None = None): + self.images_dir = images_dir if images_dir else Path.cwd() + self.current_model_path = model_weights + self.image_paths = [] + self.current_idx = 0 + self.annotations = {} # image_name -> list of boxes + self.model = None + self.training_process = None + self.training_thread = None + self.training_status = "Not training" + + # Load images if directory provided + if images_dir and images_dir.exists(): + self._load_images(images_dir) + + if model_weights and model_weights.exists(): + self._load_model(model_weights) + + def _load_images(self, images_dir: Path): + """Load images from directory.""" + self.images_dir = images_dir + self.image_paths = sorted( + list(images_dir.glob("*.jpg")) + list(images_dir.glob("*.png")) + ) + self.current_idx = 0 + + # Load existing annotations if present + self.ann_file = images_dir / "annotations.json" + if self.ann_file.exists(): + with self.ann_file.open("r") as f: + self.annotations = json.load(f) + else: + self.annotations = {} + + return f"✓ Loaded {len(self.image_paths)} images from {images_dir}" + + def _load_model(self, weights_path: Path): + """Load YOLO/YOLOX model for auto-labeling (Ultralytics format).""" + try: + from ultralytics import YOLO + print(f"Loading model from {weights_path}...") + self.model = YOLO(str(weights_path)) + self.current_model_path = weights_path + print("✓ Model loaded") + return f"✓ Model loaded from {weights_path.name}" + except Exception as e: + error_msg = f"⚠ Could not load model: {e}" + print(error_msg) + self.model = None + return error_msg + + def load_new_model(self, weights_path: str) -> str: + """Load a new model from the GUI.""" + path = Path(weights_path) + if not path.exists(): + return f"❌ File not found: {weights_path}" + + return self._load_model(path) + + def load_new_images_dir(self, images_dir: str) -> tuple[Image.Image | None, str, str]: + """Load a new images directory from the GUI.""" + path = Path(images_dir) + if not path.exists(): + return None, "", f"❌ Directory not found: {images_dir}" + + if not path.is_dir(): + return None, "", f"❌ Not a directory: {images_dir}" + + result = self._load_images(path) + + # Load first image + if self.image_paths: + img, filename = self.get_current_image() + boxes = self.annotations.get(filename, []) + img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img + boxes_text = self._format_boxes_text(boxes) + info = f"{result}\nImage 1/{len(self.image_paths)}: {filename}" + return img_with_boxes, boxes_text, info + else: + return None, "", f"{result}\n⚠️ No .jpg or .png images found in directory" + + def get_current_model_info(self) -> str: + """Get info about currently loaded model.""" + if self.model and self.current_model_path: + return f"📦 Loaded: {self.current_model_path}" + elif self.model: + return "📦 Model loaded (pretrained)" + else: + return "⚠️ No model loaded" + + def get_current_dir_info(self) -> str: + """Get info about current images directory.""" + return f"📁 {self.images_dir} ({len(self.image_paths)} images)" + + def get_current_image(self) -> tuple[Image.Image, str]: + """Get current image and filename.""" + if not self.image_paths: + return None, "" + path = self.image_paths[self.current_idx] + img = Image.open(path).convert("RGB") + return img, path.name + + def draw_boxes_on_image(self, img: Image.Image, boxes: list[dict]) -> Image.Image: + """Draw bounding boxes on image.""" + img_draw = img.copy() + draw = ImageDraw.Draw(img_draw) + + for box in boxes: + x1, y1, x2, y2 = box["bbox"] + label = box.get("label", "knot") + conf = box.get("confidence", 1.0) + + # Draw box + draw.rectangle([x1, y1, x2, y2], outline="red", width=3) + + # Draw label + text = f"{label} {conf:.2f}" if conf < 1.0 else label + draw.text((x1, y1 - 20), text, fill="red") + + return img_draw + + def auto_label_current(self, threshold: float = 0.5) -> tuple[Image.Image, str, str]: + """Auto-label current image with model.""" + if not self.model: + img, filename = self.get_current_image() + info = f"⚠ No model loaded | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}" + return img, "", info + + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + # Run inference with Ultralytics YOLO + results = self.model.predict(img, conf=threshold, verbose=False) + + # Convert to our format + boxes = [] + if len(results) > 0: + result = results[0] # First image result + if result.boxes is not None and len(result.boxes) > 0: + for box in result.boxes: + xyxy = box.xyxy[0].cpu().numpy().tolist() # [x1, y1, x2, y2] + conf = float(box.conf[0].cpu().numpy()) + cls = int(box.cls[0].cpu().numpy()) + + # Get class name if available + label = result.names.get(cls, f"class_{cls}") if hasattr(result, 'names') else f"class_{cls}" + + boxes.append({ + "bbox": xyxy, + "label": label, + "confidence": conf, + "source": "auto" + }) + + # Save + self.annotations[filename] = boxes + self._save_annotations() + + # Draw boxes on image + img_with_boxes = self.draw_boxes_on_image(img, boxes) + + # Info with image index + info = f"✓ Auto-labeled: {len(boxes)} boxes detected | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}" + boxes_text = self._format_boxes_text(boxes) + + return img_with_boxes, boxes_text, info + + def _format_boxes_text(self, boxes: list[dict]) -> str: + """Format boxes for display.""" + if not boxes: + return "No annotations" + + lines = [] + for i, box in enumerate(boxes): + x1, y1, x2, y2 = box["bbox"] + conf = box.get("confidence", 1.0) + source = box.get("source", "manual") + lines.append(f"{i}: [{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}] conf={conf:.2f} ({source})") + + return "\n".join(lines) + + def load_image(self, direction: str = "current") -> tuple[Image.Image, str, str]: + """Load image (current/next/prev).""" + if direction == "next": + self.current_idx = min(self.current_idx + 1, len(self.image_paths) - 1) + elif direction == "prev": + self.current_idx = max(self.current_idx - 1, 0) + + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + # Load existing annotations + boxes = self.annotations.get(filename, []) + img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img + boxes_text = self._format_boxes_text(boxes) + info = f"Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}" + + return img_with_boxes, boxes_text, info + + def add_box_manual(self, x1: int, y1: int, x2: int, y2: int) -> tuple[Image.Image, str, str]: + """Manually add a bounding box.""" + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + # Add box + box = { + "bbox": [float(x1), float(y1), float(x2), float(y2)], + "label": "knot", + "confidence": 1.0, + "source": "manual" + } + + if filename not in self.annotations: + self.annotations[filename] = [] + self.annotations[filename].append(box) + self._save_annotations() + + # Redraw + boxes = self.annotations[filename] + img_with_boxes = self.draw_boxes_on_image(img, boxes) + boxes_text = self._format_boxes_text(boxes) + info = f"✓ Added box: {len(boxes)} total | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}" + + return img_with_boxes, boxes_text, info + + def delete_last_box(self) -> tuple[Image.Image, str, str]: + """Delete the last box from current image.""" + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + if filename in self.annotations and self.annotations[filename]: + self.annotations[filename].pop() + self._save_annotations() + + # Redraw + boxes = self.annotations.get(filename, []) + img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img + boxes_text = self._format_boxes_text(boxes) + info = f"✓ Deleted last box: {len(boxes)} remaining | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}" + + return img_with_boxes, boxes_text, info + + def clear_boxes(self) -> tuple[Image.Image, str, str]: + """Clear all boxes from current image.""" + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + self.annotations[filename] = [] + self._save_annotations() + + boxes_text = "No annotations" + info = f"✓ Cleared all boxes | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}" + + return img, boxes_text, info + + def _save_annotations(self): + """Save annotations to JSON file.""" + with self.ann_file.open("w") as f: + json.dump(self.annotations, f, indent=2) + + def export_to_coco(self, output_path: Path): + """Export annotations to COCO format.""" + coco_data = { + "images": [], + "annotations": [], + "categories": [{"id": 0, "name": "knot", "supercategory": "defect"}] + } + + ann_id = 0 + for img_id, img_path in enumerate(self.image_paths): + filename = img_path.name + img = Image.open(img_path) + width, height = img.size + + coco_data["images"].append({ + "id": img_id, + "file_name": filename, + "width": width, + "height": height + }) + + # Add annotations + boxes = self.annotations.get(filename, []) + for box in boxes: + x1, y1, x2, y2 = box["bbox"] + w = x2 - x1 + h = y2 - y1 + + coco_data["annotations"].append({ + "id": ann_id, + "image_id": img_id, + "category_id": 0, + "bbox": [x1, y1, w, h], + "area": w * h, + "iscrowd": 0, + "score": box.get("confidence", 1.0) + }) + ann_id += 1 + + with output_path.open("w") as f: + json.dump(coco_data, f, indent=2) + + return f"✓ Exported {len(coco_data['annotations'])} annotations to {output_path}" + + def prepare_training_dataset(self, output_dir: Path, train_split: float = 0.8, valid_split: float = 0.1): + """Prepare dataset in RF-DETR format (train/valid/test splits).""" + output_dir.mkdir(parents=True, exist_ok=True) + + # Create splits + import random + annotated_images = [img for img in self.image_paths if img.name in self.annotations and self.annotations[img.name]] + + if len(annotated_images) < 10: + return f"⚠️ Need at least 10 annotated images, have {len(annotated_images)}" + + random.shuffle(annotated_images) + n = len(annotated_images) + train_n = int(n * train_split) + valid_n = int(n * valid_split) + + splits = { + "train": annotated_images[:train_n], + "valid": annotated_images[train_n:train_n + valid_n], + "test": annotated_images[train_n + valid_n:] + } + + # Create directories and copy images + import shutil + for split_name, split_images in splits.items(): + split_dir = output_dir / split_name + split_dir.mkdir(exist_ok=True) + + # Prepare COCO JSON for this split + coco_data = { + "images": [], + "annotations": [], + "categories": [{"id": 0, "name": "knot", "supercategory": "defect"}] + } + + ann_id = 0 + for img_id, img_path in enumerate(split_images): + # Copy image + dest = split_dir / img_path.name + shutil.copy2(img_path, dest) + + # Add to COCO + img = Image.open(img_path) + width, height = img.size + + coco_data["images"].append({ + "id": img_id, + "file_name": img_path.name, + "width": width, + "height": height + }) + + # Add annotations + boxes = self.annotations.get(img_path.name, []) + for box in boxes: + x1, y1, x2, y2 = box["bbox"] + w = x2 - x1 + h = y2 - y1 + + coco_data["annotations"].append({ + "id": ann_id, + "image_id": img_id, + "category_id": 0, + "bbox": [x1, y1, w, h], + "area": w * h, + "iscrowd": 0 + }) + ann_id += 1 + + # Save COCO JSON + with (split_dir / "_annotations.coco.json").open("w") as f: + json.dump(coco_data, f, indent=2) + + return f"✓ Dataset prepared: {len(splits['train'])} train, {len(splits['valid'])} valid, {len(splits['test'])} test" + + def start_training(self, framework: str, dataset_dir: str, output_dir: str, model_size: str, + epochs: int, batch_size: int, lr: float, progress=gr.Progress()): + """Start training in background.""" + dataset_path = Path(dataset_dir) + output_path = Path(output_dir) + + if not dataset_path.exists(): + return "❌ Dataset directory not found" + + if self.training_process and self.training_process.poll() is None: + return "⚠️ Training already in progress" + + output_path.mkdir(parents=True, exist_ok=True) + + # Build training command based on framework + venv_python = Path(__file__).parent / ".venv/bin/python" + + if framework == "RT-DETR": + train_script = Path(__file__).parent / "train_rtdetr.py" + # Map sizes: nano->r18, small->r34, medium->r50, base->l + size_map = {"nano": "rtdetr-r18", "small": "rtdetr-r34", "medium": "rtdetr-r50", "base": "rtdetr-l"} + model_arg = size_map.get(model_size, "rtdetr-r18") + + cmd = [ + str(venv_python), + str(train_script), + "--dataset-dir", str(dataset_path), + "--output-dir", str(output_path), + "--model", model_arg, + "--epochs", str(epochs), + "--batch-size", str(batch_size), + "--lr", str(lr) + ] + elif framework == "YOLOv6": + train_script = Path(__file__).parent / "train_yolov6.py" + # Map sizes: nano->n, small->s, medium->m, base->l + size_map = {"nano": "yolov6n", "small": "yolov6s", "medium": "yolov6m", "base": "yolov6l"} + model_arg = size_map.get(model_size, "yolov6n") + + cmd = [ + str(venv_python), + str(train_script), + "--dataset-dir", str(dataset_path), + "--output-dir", str(output_path), + "--model", model_arg, + "--epochs", str(epochs), + "--batch-size", str(batch_size), + "--lr", str(lr) + ] + elif framework == "YOLOX": + train_script = Path(__file__).parent / "train_yolox.py" + # Map sizes: nano->nano, small->s, medium->m, base->l + size_map = {"nano": "yolox-nano", "small": "yolox-s", "medium": "yolox-m", "base": "yolox-l"} + model_arg = size_map.get(model_size, "yolox-nano") + + cmd = [ + str(venv_python), + str(train_script), + "--dataset-dir", str(dataset_path), + "--output-dir", str(output_path), + "--model", model_arg, + "--epochs", str(epochs), + "--batch-size", str(batch_size), + "--lr", str(lr) + ] + else: + return f"❌ Unknown framework: {framework}" + + # Start training process + log_file = output_path / "training.log" + self.training_status = f"🚀 Starting {framework} training..." + + def run_training(): + try: + with log_file.open("w") as f: + self.training_process = subprocess.Popen( + cmd, + stdout=f, + stderr=subprocess.STDOUT, + text=True + ) + self.training_status = f"⏳ Training in progress (PID: {self.training_process.pid})" + self.training_process.wait() + + if self.training_process.returncode == 0: + self.training_status = "✅ Training completed successfully!" + # Reload model with new weights + best_weights = output_path / "checkpoint_best_total.pth" + if best_weights.exists(): + self._load_model(best_weights) + else: + self.training_status = f"❌ Training failed (exit code {self.training_process.returncode})" + except Exception as e: + self.training_status = f"❌ Error: {e}" + + self.training_thread = threading.Thread(target=run_training, daemon=True) + self.training_thread.start() + + return f"✓ Training started! Check {log_file} for progress" + + def get_training_status(self): + """Get current training status.""" + return self.training_status + + def stop_training(self): + """Stop the training process.""" + if self.training_process and self.training_process.poll() is None: + self.training_process.terminate() + self.training_status = "⏹️ Training stopped by user" + return "✓ Training process terminated" + return "⚠️ No training in progress" + + +def create_ui(app: AnnotationApp) -> gr.Blocks: + """Create Gradio UI.""" + + with gr.Blocks(title="Knot Annotation Tool") as demo: + gr.Markdown(""" + # 🪵 Wood Knot Annotation Tool + **Label → Train → Auto-Label → Repeat** + + - Manually annotate images or use **Auto-Label** with your trained model + - Export and prepare dataset for training + - Train **RT-DETR, YOLOv6, or YOLOX** (all free for commercial use!) + - Optimized for OAK-D camera deployment + - Use trained model to auto-label more images + """) + + # Settings section at the top + with gr.Accordion("⚙️ Settings", open=False): + with gr.Row(): + with gr.Column(): + images_dir_input = gr.Textbox( + label="Images Directory", + value=str(app.images_dir), + placeholder="/path/to/images" + ) + load_images_btn = gr.Button("📁 Load Images Directory") + dir_info = gr.Textbox(label="Current Directory", value=app.get_current_dir_info(), interactive=False) + + with gr.Column(): + model_weights_input = gr.Textbox( + label="Model Weights Path", + value=str(app.current_model_path) if app.current_model_path else "", + placeholder="runs/training/checkpoint_best_total.pth" + ) + load_model_btn = gr.Button("🤖 Load Model Weights") + model_info = gr.Textbox(label="Current Model", value=app.get_current_model_info(), interactive=False) + + with gr.Row(): + with gr.Column(scale=3): + image_display = gr.Image(label="Current Image", type="pil") + + with gr.Row(): + prev_btn = gr.Button("⬅️ Previous") + next_btn = gr.Button("Next ➡️") + auto_label_btn = gr.Button("🤖 Auto-Label", variant="primary") + + with gr.Row(): + threshold_slider = gr.Slider(0.1, 0.9, DEFAULT_DETECTION_THRESHOLD, label="Detection Threshold") + + with gr.Column(scale=1): + info_text = gr.Textbox(label="Status", lines=2) + boxes_text = gr.Textbox(label="Annotations", lines=10) + + gr.Markdown("### Manual Annotation") + with gr.Row(): + x1_input = gr.Number(label="x1", value=100) + y1_input = gr.Number(label="y1", value=100) + with gr.Row(): + x2_input = gr.Number(label="x2", value=200) + y2_input = gr.Number(label="y2", value=200) + + add_box_btn = gr.Button("➕ Add Box") + delete_btn = gr.Button("🗑️ Delete Last") + clear_btn = gr.Button("❌ Clear All") + + gr.Markdown("### Export & Training") + export_path = gr.Textbox( + label="Export Path", + value="annotations_coco.json" + ) + export_btn = gr.Button("💾 Export COCO") + export_result = gr.Textbox(label="Export Result", lines=1) + + # Training tab + with gr.Tab("🎯 Training"): + gr.Markdown(""" + ### Train Object Detection Model + + **Choose your framework:** + - **RT-DETR** (Apache 2.0): Modern transformer, great accuracy + - **YOLOv6** (MIT): Fast, proven on OAK cameras + - **YOLOX** (MIT): Similar to YOLOv6, slight differences + + **All MIT/Apache 2.0 licensed - free for commercial use!** ✅ + + **Steps:** + 1. Annotate at least 50-100 images in the Annotation tab + 2. Click "Prepare Dataset" to create train/valid/test splits + 3. Select your framework and configure training parameters + 4. Click "Start Training" (runs in background) + 5. After training, export for OAK-D deployment + """) + + with gr.Row(): + with gr.Column(): + dataset_prep_dir = gr.Textbox( + label="Dataset Output Directory", + value="dataset_prepared" + ) + train_split = gr.Slider(0.5, 0.9, 0.8, label="Train Split Ratio") + valid_split = gr.Slider(0.05, 0.3, 0.1, label="Valid Split Ratio") + prep_btn = gr.Button("📦 Prepare Dataset", variant="secondary") + prep_result = gr.Textbox(label="Preparation Result", lines=2) + + with gr.Column(): + gr.Markdown("### Training Configuration") + model_framework = gr.Dropdown( + choices=["RT-DETR", "YOLOv6", "YOLOX"], + value="RT-DETR", + label="Model Framework", + info="All MIT/Apache 2.0 licensed - free for commercial use. Optimized for OAK cameras." + ) + train_dataset_dir = gr.Textbox( + label="Dataset Directory", + value="dataset_prepared" + ) + train_output_dir = gr.Textbox( + label="Output Directory", + value="runs/gui_training" + ) + model_size = gr.Dropdown( + choices=["nano", "small", "medium", "base"], + value=DEFAULT_MODEL_SIZE, + label="Model Size" + ) + epochs = gr.Slider(5, 100, DEFAULT_TRAIN_EPOCHS, step=5, label="Epochs") + batch_size = gr.Slider(1, 16, DEFAULT_BATCH_SIZE, step=1, label="Batch Size") + learning_rate = gr.Number(value=DEFAULT_LEARNING_RATE, label="Learning Rate") + + with gr.Row(): + start_train_btn = gr.Button("🚀 Start Training", variant="primary") + stop_train_btn = gr.Button("⏹️ Stop Training", variant="stop") + refresh_status_btn = gr.Button("🔄 Refresh Status") + + training_status = gr.Textbox( + label="Training Status", + value="Not training", + lines=3 + ) + + gr.Markdown(""" + **Note**: Training runs in the background. You can continue annotating while training. + Check the training log file for detailed progress. + """) + + # Event handlers + def on_load(): + return app.load_image("current") + + # Settings handlers + load_images_btn.click( + app.load_new_images_dir, + inputs=[images_dir_input], + outputs=[image_display, boxes_text, info_text] + ).then( + lambda: (app.get_current_dir_info(), app.get_current_model_info()), + outputs=[dir_info, model_info] + ) + + load_model_btn.click( + app.load_new_model, + inputs=[model_weights_input], + outputs=[model_info] + ) + + prev_btn.click( + lambda: app.load_image("prev"), + outputs=[image_display, boxes_text, info_text] + ) + + next_btn.click( + lambda: app.load_image("next"), + outputs=[image_display, boxes_text, info_text] + ) + + auto_label_btn.click( + lambda t: app.auto_label_current(t), + inputs=[threshold_slider], + outputs=[image_display, boxes_text, info_text] + ) + + add_box_btn.click( + app.add_box_manual, + inputs=[x1_input, y1_input, x2_input, y2_input], + outputs=[image_display, boxes_text, info_text] + ) + + delete_btn.click( + app.delete_last_box, + outputs=[image_display, boxes_text, info_text] + ) + + clear_btn.click( + app.clear_boxes, + outputs=[image_display, boxes_text, info_text] + ) + + export_btn.click( + lambda path: app.export_to_coco(Path(path)), + inputs=[export_path], + outputs=[export_result] + ) + + # Training handlers + prep_btn.click( + lambda out, train, valid: app.prepare_training_dataset(Path(out), train, valid), + inputs=[dataset_prep_dir, train_split, valid_split], + outputs=[prep_result] + ) + + start_train_btn.click( + app.start_training, + inputs=[model_framework, train_dataset_dir, train_output_dir, model_size, epochs, batch_size, learning_rate], + outputs=[training_status] + ) + + stop_train_btn.click( + app.stop_training, + outputs=[training_status] + ) + + refresh_status_btn.click( + app.get_training_status, + outputs=[training_status] + ) + + # Load first image on start + demo.load(on_load, outputs=[image_display, boxes_text, info_text]) + + return demo + + +def main(): + parser = argparse.ArgumentParser(description="Simple annotation GUI with auto-labeling") + parser.add_argument( + "--images-dir", + type=Path, + default=Path(DEFAULT_IMAGES_DIR) if DEFAULT_IMAGES_DIR else None, + help="Default directory with images (can be changed in GUI)" + ) + parser.add_argument( + "--model-weights", + type=Path, + default=Path(DEFAULT_MODEL_WEIGHTS) if DEFAULT_MODEL_WEIGHTS else None, + help="Default trained model for auto-labeling (can be changed in GUI)" + ) + parser.add_argument("--port", type=int, default=DEFAULT_PORT, help="Port for web interface") + args = parser.parse_args() + + # Validate paths if provided + if args.images_dir and not args.images_dir.exists(): + print(f"⚠️ Warning: Images directory not found: {args.images_dir}") + print("You can load a different directory from the GUI Settings") + args.images_dir = None + + if args.model_weights and not args.model_weights.exists(): + print(f"⚠️ Warning: Model weights not found: {args.model_weights}") + print("You can load different weights from the GUI Settings") + args.model_weights = None + + # Create app + app = AnnotationApp(args.images_dir, args.model_weights) + + # Create and launch UI + demo = create_ui(app) + + print(f"\n{'='*60}") + print(f"🚀 Starting annotation tool...") + if args.images_dir: + print(f"📁 Default images: {args.images_dir} ({len(app.image_paths)} images)") + else: + print(f"📁 No default images - load directory from Settings") + if app.model: + print(f"🤖 Model: Loaded from {args.model_weights}") + else: + print(f"⚠️ No model loaded - load from Settings or train one") + print(f"💡 You can change images directory and model weights from the Settings panel") + print(f"{'='*60}\n") + + demo.launch( + server_name="0.0.0.0", + server_port=args.port, + share=False + ) + + +if __name__ == "__main__": + main() diff --git a/annotation_gui.py.broken b/annotation_gui.py.broken new file mode 100644 index 00000000..10107136 --- /dev/null +++ b/annotation_gui.py.broken @@ -0,0 +1,708 @@ +""" +Simple customizable annotation GUI with auto-labeling support. + +Built with Gradio - easy to modify and extend. +Run: python annotation_gui.py --images-dir /path/to/images + +To set default paths, edit config.py +""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import threading +from pathlib import Path +from typing import Any + +import gradio as gr +import numpy as np +from PIL import Image, ImageDraw + +# Try to load config, use fallbacks if not available +try: + from config import ( + DEFAULT_IMAGES_DIR, DEFAULT_MODEL_WEIGHTS, DEFAULT_PORT, + DEFAULT_DETECTION_THRESHOLD, DEFAULT_TRAIN_EPOCHS, + DEFAULT_BATCH_SIZE, DEFAULT_LEARNING_RATE, DEFAULT_MODEL_SIZE + ) +except ImportError: + DEFAULT_IMAGES_DIR = None + DEFAULT_MODEL_WEIGHTS = None + DEFAULT_PORT = 7860 + DEFAULT_DETECTION_THRESHOLD = 0.5 + DEFAULT_TRAIN_EPOCHS = 20 + DEFAULT_BATCH_SIZE = 4 + DEFAULT_LEARNING_RATE = 1e-4 + DEFAULT_MODEL_SIZE = "small" + + +class AnnotationApp: + def __init__(self, images_dir: Path | None = None, model_weights: Path | None = None): + self.images_dir = images_dir if images_dir else Path.cwd() + self.current_model_path = model_weights + self.image_paths = [] + self.current_idx = 0 + self.annotations = {} # image_name -> list of boxes + self.model = None + self.training_process = None + self.training_thread = None + self.training_status = "Not training" + + # Load images if directory provided + if images_dir and images_dir.exists(): + self._load_images(images_dir) + + if model_weights and model_weights.exists(): + self._load_model(model_weights) + + def _load_images(self, images_dir: Path): + """Load images from directory.""" + self.images_dir = images_dir + self.image_paths = sorted( + list(images_dir.glob("*.jpg")) + list(images_dir.glob("*.png")) + ) + self.current_idx = 0 + + # Load existing annotations if present + self.ann_file = images_dir / "annotations.json" + if self.ann_file.exists(): + with self.ann_file.open("r") as f: + self.annotations = json.load(f) + else: + self.annotations = {} + + return f"✓ Loaded {len(self.image_paths)} images from {images_dir}" + + def _load_model(self, weights_path: Path): + """Load RF-DETR model for auto-labeling.""" + try: + from rfdetr import RFDETRBase + print(f"Loading model from {weights_path}...") + self.model = RFDETRBase(pretrain_weights=str(weights_path)) + self.current_model_path = weights_path + print("✓ Model loaded") + return f"✓ Model loaded from {weights_path.name}" + except Exception as e: + error_msg = f"⚠ Could not load model: {e}" + print(error_msg) + self.model = None + return error_msg + + def load_new_model(self, weights_path: str) -> str: + """Load a new model from the GUI.""" + path = Path(weights_path) + if not path.exists(): + return f"❌ File not found: {weights_path}" + + return self._load_model(path) + + def load_new_images_dir(self, images_dir: str) -> tuple[Image.Image | None, str, str]: + """Load a new images directory from the GUI.""" + path = Path(images_dir) + if not path.exists(): + return None, "", f"❌ Directory not found: {images_dir}" + + if not path.is_dir(): + return None, "", f"❌ Not a directory: {images_dir}" + + result = self._load_images(path) + + # Load first image + if self.image_paths: + img, filename = self.get_current_image() + boxes = self.annotations.get(filename, []) + img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img + boxes_text = self._format_boxes_text(boxes) + info = f"{result}\nImage 1/{len(self.image_paths)}: {filename}" + return img_with_boxes, boxes_text, info + else: + return None, "", f"{result}\n⚠️ No .jpg or .png images found in directory" + + def get_current_model_info(self) -> str: + """Get info about currently loaded model.""" + if self.model and self.current_model_path: + return f"📦 Loaded: {self.current_model_path}" + elif self.model: + return "📦 Model loaded (pretrained)" + else: + return "⚠️ No model loaded" + + def get_current_dir_info(self) -> str: + """Get info about current images directory.""" + return f"📁 {self.images_dir} ({len(self.image_paths)} images)" + + def get_current_image(self) -> tuple[Image.Image, str]: + """Get current image and filename.""" + if not self.image_paths: + return None, "" + path = self.image_paths[self.current_idx] + img = Image.open(path).convert("RGB") + return img, path.name + + def draw_boxes_on_image(self, img: Image.Image, boxes: list[dict]) -> Image.Image: + """Draw bounding boxes on image.""" + img_draw = img.copy() + draw = ImageDraw.Draw(img_draw) + + for box in boxes: + x1, y1, x2, y2 = box["bbox"] + label = box.get("label", "knot") + conf = box.get("confidence", 1.0) + + # Draw box + draw.rectangle([x1, y1, x2, y2], outline="red", width=3) + + # Draw label + text = f"{label} {conf:.2f}" if conf < 1.0 else label + draw.text((x1, y1 - 20), text, fill="red") + + return img_draw + + def auto_label_current(self, threshold: float = 0.5) -> tuple[Image.Image, str, str]: + """Auto-label current image with model.""" + if not self.model: + return self.get_current_image()[0], "", "⚠ No model loaded" + + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + # Run inference + detections = self.model.predict(img, threshold=threshold) + + # Convert to our format + boxes = [] + for i in range(len(detections)): + xyxy = detections.xyxy[i].tolist() + conf = float(detections.confidence[i]) if detections.confidence is not None else 1.0 + boxes.append({ + "bbox": xyxy, + "label": "knot", + "confidence": conf, + "source": "auto" + }) + + # Save + self.annotations[filename] = boxes + self._save_annotations() + + # Draw + img_with_boxes = self.draw_boxes_on_image(img, boxes) + info = f"✓ Auto-labeled: {len(boxes)} boxes detected" + boxes_text = self._format_boxes_text(boxes) + + return img_with_boxes, boxes_text, info + + def _format_boxes_text(self, boxes: list[dict]) -> str: + """Format boxes for display.""" + if not boxes: + return "No annotations" + + lines = [] + for i, box in enumerate(boxes): + x1, y1, x2, y2 = box["bbox"] + conf = box.get("confidence", 1.0) + source = box.get("source", "manual") + lines.append(f"{i}: [{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}] conf={conf:.2f} ({source})") + + return "\n".join(lines) + + def load_image(self, direction: str = "current") -> tuple[Image.Image, str, str]: + """Load image (current/next/prev).""" + if direction == "next": + self.current_idx = min(self.current_idx + 1, len(self.image_paths) - 1) + elif direction == "prev": + self.current_idx = max(self.current_idx - 1, 0) + + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + # Load existing annotations + boxes = self.annotations.get(filename, []) + img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img + boxes_text = self._format_boxes_text(boxes) + info = f"Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}" + + return img_with_boxes, boxes_text, info + + def add_box_manual(self, x1: int, y1: int, x2: int, y2: int) -> tuple[Image.Image, str, str]: + """Manually add a bounding box.""" + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + # Add box + box = { + "bbox": [float(x1), float(y1), float(x2), float(y2)], + "label": "knot", + "confidence": 1.0, + "source": "manual" + } + + if filename not in self.annotations: + self.annotations[filename] = [] + self.annotations[filename].append(box) + self._save_annotations() + + # Redraw + boxes = self.annotations[filename] + img_with_boxes = self.draw_boxes_on_image(img, boxes) + boxes_text = self._format_boxes_text(boxes) + info = f"✓ Added box: {len(boxes)} total" + + return img_with_boxes, boxes_text, info + + def delete_last_box(self) -> tuple[Image.Image, str, str]: + """Delete the last box from current image.""" + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + if filename in self.annotations and self.annotations[filename]: + self.annotations[filename].pop() + self._save_annotations() + + # Redraw + boxes = self.annotations.get(filename, []) + img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img + boxes_text = self._format_boxes_text(boxes) + info = f"✓ Deleted last box: {len(boxes)} remaining" + + return img_with_boxes, boxes_text, info + + def clear_boxes(self) -> tuple[Image.Image, str, str]: + """Clear all boxes from current image.""" + img, filename = self.get_current_image() + if not img: + return None, "", "No images" + + self.annotations[filename] = [] + self._save_annotations() + + boxes_text = "No annotations" + info = "✓ Cleared all boxes" + + return img, boxes_text, info + + def _save_annotations(self): + """Save annotations to JSON file.""" + with self.ann_file.open("w") as f: + json.dump(self.annotations, f, indent=2) + + def export_to_coco(self, output_path: Path): + """Export annotations to COCO format.""" + coco_data = { + "images": [], + "annotations": [], + "categories": [{"id": 0, "name": "knot", "supercategory": "defect"}] + } + + ann_id = 0 + for img_id, img_path in enumerate(self.image_paths): + filename = img_path.name + img = Image.open(img_path) + width, height = img.size + + coco_data["images"].append({ + "id": img_id, + "file_name": filename, + "width": width, + "height": height + }) + + # Add annotations + boxes = self.annotations.get(filename, []) + for box in boxes: + x1, y1, x2, y2 = box["bbox"] + w = x2 - x1 + h = y2 - y1 + + coco_data["annotations"].append({ + "id": ann_id, + "image_id": img_id, + "category_id": 0, + "bbox": [x1, y1, w, h], + "area": w * h, + "iscrowd": 0, + "score": box.get("confidence", 1.0) + }) + ann_id += 1 + + with output_path.open("w") as f: + + def prepare_training_dataset(self, output_dir: Path, train_split: float = 0.8, valid_split: float = 0.1): + """Prepare dataset in RF-DETR format (train/valid/test splits).""" + output_dir.mkdir(parents=True, exist_ok=True) + + # Create splits + import random + annotated_images = [img for img in self.image_paths if img.name in self.annotations and self.annotations[img.name]] + + if len(annotated_images) < 10: + return f"⚠️ Need at least 10 annotated images, have {len(annotated_images)}" + + random.shuffle(annotated_images) + n = len(annotated_images) + train_n = int(n * train_split) + valid_n = int(n * valid_split) + + splits = { + "train": annotated_images[:train_n], + "valid": annotated_images[train_n:train_n + valid_n], + "test": annotated_images[train_n + valid_n:] + } + + # Create directories and copy images + import shutil + for split_name, split_images in splits.items(): + split_dir = output_dir / split_name + split_dir.mkdir(exist_ok=True) + + # Prepare COCO JSON for this split + coco_data = { + "images": [], + "annotations": [], + "categories": [{"id": 0, "name": "knot", "supercategory": "defect"}] + } + + ann_id = 0 + for img_id, img_path in enumerate(split_images): + # Copy image + dest = split_dir / img_path.name + shutil.copy2(img_path, dest) + + # Add to COCO + img = Image.open(img_path) + width, height = img.size + + coco_data["images"].append({ + "id": img_id, + "file_name": img_path.name, + "width": width, + "height": height + }) + + # Add annotations + boxes = self.annotations.get(img_path.name, []) + for box in boxes: + x1, y1, x2, y2 = box["bbox"] + w = x2 - x1 + h = y2 - y1 + + coco_data["annotations"].append({ + "id": ann_id, + "image_id": img_id, + "category_id": 0, + "bbox": [x1, y1, w, h], + "area": w * h, + "iscrowd": 0 + }) + ann_id += 1 + + # Save COCO JSON + with (split_dir / "_annotations.coco.json").open("w") as f: + json.dump(coco_data, f, indent=2) + + return f"✓ Dataset prepared: {len(splits['train'])} train, {len(splits['valid'])} valid, {len(splits['test'])} test" + + with gr.Row(): + start_train_btn = gr.Button("🚀 Start Training", variant="primary") + stop_train_btn = gr.Button("⏹️ Stop Training", variant="stop") + refresh_status_btn = gr.Button("🔄 Refresh Status") + + training_status = gr.Textbox( + label="Training Status", + value="Not training", + lines=3 + ) + + gr.Markdown(""" + **Note**: Training runs in the background. You can continue annotating while training. + Check the training log file for detailed progress. + "" + boxes = self.annotations.get(img_path.name, []) + for box in boxes: + x1, y1, x2, y2 = box["bbox"] + w = x2 - x1 + h = y2 - y1 + + coco_data["annotations"].append({ + "id": ann_id, + "image_id": img_id, + "category_id": 0, + "bbox": [x1, y1, w, h], + "area": w * h, + "iscrowd": 0 + }) + ann_id += 1 + + # Save COCO JSON + with (split_dir / "_annotations.coco.json").open("w") as f: + json.dump(coco_data, f, indent=2) + + return f"✓ Dataset prepared: {len(splits['train'])} train, {len(splits['valid'])} valid, {len(splits['test'])} test" + + def start_training(self, dataset_dir: str, output_dir: str, model_size: str, + epochs: int, batch_size: int, lr: float, progress=gr.Progress()): + """Start training in background.""" + dataset_path = Path(dataset_dir) + output_path = Path(output_dir) + + if not dataset_path.exists(): + return "❌ Dataset directory not found" + + if self.training_process and self.training_process.poll() is None: + return "⚠️ Training already in progress" + + output_path.mkdir(parents=True, exist_ok=True) + + # Build training command + venv_python = Path(__file__).parent / ".venv/bin/python" + train_script = Path(__file__).parent / "train_rfdetr.py" + + cmd = [ + str(venv_python), + str(train_script), + "--dataset-dir", str(dataset_path), + "--output-dir", str(output_path), + "--model", model_size, + "--epochs", str(epochs), + "--batch-size", str(batch_size), + "--grad-accum-steps", str(max(1, 16 // batch_size)), + "--lr", str(lr) + ] + + # Start training process + log_file = output_path / "training.log" + self.training_status = f"🚀 Starting training..." + + def run_training(): + try: + with log_file.open("w") as f: + self.training_process = subprocess.Popen( + cmd, + stdout=f, + stderr=subprocess.STDOUT, + text=True + ) + self.training_status = f"⏳ Training in progress (PID: {self.training_process.pid})" + self.training_process.wait() + + if self.training_process.returncode == 0: + self.training_status = "✅ Training completed successfully!" + # Reload model with new weights + best_weights = output_path / "checkpoint_best_total.pth" + if best_weights.exists(): + self._load_model(best_weights) + else: + self.training_status = f"❌ Training failed (exit code {self.training_process.returncode})" + except Exception as e: + self.training_status = f"❌ Error: {e}" + + self.training_thread = threading.Thread(target=run_training, daemon=True) + self.training_thread.start() + + return f"✓ Training started! Check {log_file} for progress" + + def get_training_status(self): + """Get current training status.""" + return self.training_status + + def stop_training(self): + """Stop the training process.""" + if self.training_process and self.training_process.poll() is None: + self.training_process.terminate() + self.training_status = "⏹️ Training stopped by user" + return "✓ Training process terminated" + return "⚠️ No training in progress" + json.dump(coco_data, f, indent=2) + + return f"✓ Exported {len(coco_data['annotations'])} annotations to {output_path}" + + +def create_ui(app: AnnotationApp) -> gr.Blocks: + """Create Gradio UI.""" + + with gr.Blocks(title="Knot Annotation Tool") as demo: + gr.Markdown(""" + # 🪵 Wood Knot Annotation Tool + **Label -> Train -> Auto-Label -> Repeat** + + - Manually annotate images or use **Auto-Label** with your trained model + - Export and prepare dataset for training + - Train RF-DETR directly from this GUI + - Use trained model to auto-label more images + """) + + with gr.Row(): + with gr.Column(scale=3): + image_display = gr.Image(label="Current Image", type="pil") + + with gr.Row(): + prev_btn = gr.Button("⬅️ Previous") + next_btn = gr.Button("Next ➡️") + auto_label_btn = gr.Button("🤖 Auto-Label", variant="primary") + + with gr.Row(): + threshold_slider = gr.Slider(0.1, 0.9, DEFAULT_DETECTION_THRESHOLD, label="Detection Threshold") + + with gr.Column(scale=1): + info_text = gr.Textbox(label="Status", lines=2) + boxes_text = gr.Textbox(label="Annotations", lines=10) + + gr.Markdown("### Manual Annotation") + with gr.Row(): + x1_input = gr.Number(label="x1", value=100) + y1_input = gr.Number(label="y1", value=100) + with gr.Row(): + Training handlers + prep_btn.click( + lambda out, train, valid: app.prepare_training_dataset(Path(out), train, valid), + inputs=[dataset_prep_dir, train_split, valid_split], + outputs=[prep_result] + ) + + start_train_btn.click( + app.start_training, + inputs=[train_dataset_dir, train_output_dir, model_size, epochs, batch_size, learning_rate], + outputs=[training_status] + ) + + stop_train_btn.click( + app.stop_training, + outputs=[training_status] + ) + + refresh_status_btn.click( + app.get_training_status, + outputs=[training_status] + ) + + # x2_input = gr.Number(label="x2", value=200) + y2_input = gr.Number(label="y2", value=200) + + add_box_btn = gr.Button("➕ Add Box") + delete_btn = gr.Button("🗑️ Delete Last") + clear_btn = gr.Button("❌ Clear All") + + gr.Markdown("### Export") + export_path = gr.Textbox( + label="Output Path", + value="annotations_coco.json" + ) + export_btn = gr.Button("💾 Export COCO") + export_result = gr.Textbox(label="Export Result") + + # Event handlers + def on_load(): + return app.load_image("current") + + # Settings handlers + load_images_btn.click( + app.load_new_images_dir, + inputs=[images_dir_input], + outputs=[image_display, boxes_text, info_text] + ).then( + lambda: (app.get_current_dir_info(), app.get_current_model_info()), + outputs=[dir_info, model_info] + ) + + load_model_btn.click( + app.load_new_model, + inputs=[model_weights_input], + outputs=[model_info] + ) + + prev_btn.click( + lambda: app.load_image("prev"), + outputs=[image_display, boxes_text, info_text] + ) + + next_btn.click( + lambda: app.load_image("next"), + outputs=[image_display, boxes_text, info_text] + ) + + auto_label_btn.click( + lambda t: app.auto_label_current(t), + inputs=[threshold_slider], + outputs=[image_display, boxes_text, info_text] + ) + + add_box_btn.click( + app.add_box_manual, + inputs=[x1_input, y1_input, x2_input, y2_input], + outputs=[image_display, boxes_text, info_text] + ) + + delete_btn.click( + app.delete_last_box, + outputs=[image_display, boxes_text, info_text] + ) + + clear_btn.click( + app.clear_boxes, + outputs=[image_display, boxes_text, info_text] + )help="Default directory with images (can be changed in GUI)") + parser.add_argument("--model-weights", type=Path, help="Default trained model for auto-labeling (can be changed in GUI)") + parser.add_argument("--port", type=int, default=7860, help="Port for web interface") + args = parser.parse_args() + + # Validate paths if provided + if args.images_dir and not args.images_dir.exists(): + print(f"⚠️ Warning: Images directory not found: {args.images_dir}") + print("You can load a different directory from the GUI Settings") + args.images_dir = None + + if args.model_weights and not args.model_weights.exists(): + print(f"⚠️ Warning: Model weights not found: {args.model_weights}") + print("You can load different weights from the GUI Settings") + args.model_weights = None + + # Load first image on start + demo.load(on_load, outputs=[image_display, boxes_text, info_text]) + + return demo + + +def main(): + parser = argparse.ArgumentParser(description="Simple annotation GUI with auto-labeling") + parser.add_argument("--images-dir", type=Path, required=True, help="Directory with images") + parser.add_argument("--model-weights", type=Path, help="Optional: trained model for auto-labeling") + parser.add_argument("--port", type=int, default=7860, help="Port for web interface") + args = parser.parse_args() + + if not args.images_dir.exists(): + raise SystemExit(f"Images directory not found: {args.images_dir}") + + if args.images_dir: + print(f"📁 Default images: {args.images_dir} ({len(app.image_paths)} images)") + else: + print(f"📁 No default images - load directory from Settings") + if app.model: + print(f"🤖 Model: Loaded from {args.model_weights}") + else: + print(f"⚠️ No model loaded - load from Settings or train one") + print(f"💡 You can change images directory and model weights from the Settings panel + + print(f"\n{'='*60}") + print(f"🚀 Starting annotation tool...") + print(f"📁 Images: {args.images_dir} ({len(app.image_paths)} images)") + if app.model: + print(f"🤖 Model: Loaded from {args.model_weights}") + else: + print(f"⚠️ No model loaded (manual annotation only)") + print(f"{'='*60}\n") + + demo.launch( + server_name="0.0.0.0", + server_port=args.port, + share=False + ) + + +if __name__ == "__main__": + main() diff --git a/auto_label_images.py b/auto_label_images.py new file mode 100644 index 00000000..4d6ccd02 --- /dev/null +++ b/auto_label_images.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any + +from PIL import Image + + +def _detections_to_coco( + detections: Any, # supervision.Detections + image_path: Path, + image_id: int, + category_id: int = 0, # Assuming single class 'knot' +) -> dict[str, Any]: + """Convert supervision.Detections to COCO annotation format for one image.""" + annotations = [] + for i in range(len(detections)): + xyxy = detections.xyxy[i].tolist() + conf = float(detections.confidence[i]) if detections.confidence is not None else 1.0 + # COCO bbox: [x_min, y_min, width, height] + x_min, y_min, x_max, y_max = xyxy + width = x_max - x_min + height = y_max - y_min + bbox = [x_min, y_min, width, height] + + ann = { + "id": image_id * 1000 + i, # Simple ID scheme + "image_id": image_id, + "category_id": category_id, + "bbox": bbox, + "area": width * height, + "iscrowd": 0, + "score": conf, # Optional, for model confidence + } + annotations.append(ann) + + return {"annotations": annotations} + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Auto-label new images with trained RF-DETR, output COCO JSON." + ) + parser.add_argument("--weights", type=Path, required=True, help="Path to trained checkpoint") + parser.add_argument("--images-dir", type=Path, required=True, help="Directory with new images") + parser.add_argument("--output-json", type=Path, required=True, help="Output COCO JSON file") + parser.add_argument("--threshold", type=float, default=0.5) + parser.add_argument("--category-name", default="knot", help="Class name for annotations") + args = parser.parse_args() + + if not args.weights.exists(): + raise SystemExit(f"Weights not found: {args.weights}") + if not args.images_dir.exists(): + raise SystemExit(f"Images dir not found: {args.images_dir}") + + from rfdetr import RFDETRBase + + model = RFDETRBase(pretrain_weights=str(args.weights)) + + # Collect all images + image_paths = list(args.images_dir.glob("*.jpg")) + list(args.images_dir.glob("*.png")) + if not image_paths: + raise SystemExit(f"No .jpg or .png images found in {args.images_dir}") + + coco_data = { + "images": [], + "annotations": [], + "categories": [{"id": 0, "name": args.category_name, "supercategory": "none"}], + } + + ann_id_counter = 0 + for img_id, img_path in enumerate(sorted(image_paths)): + image = Image.open(img_path).convert("RGB") + detections = model.predict(image, threshold=args.threshold) + + # Add image entry + width, height = image.size + coco_data["images"].append({ + "id": img_id, + "file_name": img_path.name, + "width": width, + "height": height, + }) + + # Convert detections to annotations + ann_data = _detections_to_coco(detections, img_path, img_id, category_id=0) + for ann in ann_data["annotations"]: + ann["id"] = ann_id_counter + ann_id_counter += 1 + coco_data["annotations"].append(ann) + + # Write COCO JSON + with args.output_json.open("w", encoding="utf-8") as f: + json.dump(coco_data, f, indent=2) + + print(f"Auto-labeled {len(image_paths)} images -> {args.output_json}") + print(f"Total annotations: {len(coco_data['annotations'])}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/config.py b/config.py new file mode 100644 index 00000000..fbba5d68 --- /dev/null +++ b/config.py @@ -0,0 +1,28 @@ +# config.py - Default settings for annotation GUI + +# Default directories (edit these to your preferences) +DEFAULT_IMAGES_DIR = "IMAGE/" # Directory containing wood defect images +DEFAULT_MODEL_WEIGHTS = "runs/yolox_training/training/weights/best.pt" # Trained YOLOX model + +# Training defaults +DEFAULT_TRAIN_EPOCHS = 20 +DEFAULT_BATCH_SIZE = 4 +DEFAULT_LEARNING_RATE = 1e-4 +DEFAULT_MODEL_SIZE = "small" # nano, small, medium, base + +# Dataset split ratios +DEFAULT_TRAIN_SPLIT = 0.8 +DEFAULT_VALID_SPLIT = 0.1 +# test split = 1 - train - valid + +# GUI settings +DEFAULT_PORT = 7860 +DEFAULT_DETECTION_THRESHOLD = 0.5 + +# Annotation categories (add more if needed) +ANNOTATION_CATEGORIES = [ + "knot", + # "crack", + # "hole", + # "discoloration" +] diff --git a/convert_to_label_studio.py b/convert_to_label_studio.py new file mode 100644 index 00000000..280503b8 --- /dev/null +++ b/convert_to_label_studio.py @@ -0,0 +1,128 @@ +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from PIL import Image + + +def coco_to_label_studio( + image_paths: list[Path], + coco_predictions_path: Path, + category_name: str = "knot", +) -> list[dict]: + """Convert COCO predictions to Label Studio import format with pre-annotations.""" + + # Load COCO predictions + with coco_predictions_path.open("r", encoding="utf-8") as f: + coco_data = json.load(f) + + # Build image_id -> annotations mapping + annotations_by_image = {} + for ann in coco_data.get("annotations", []): + img_id = ann["image_id"] + if img_id not in annotations_by_image: + annotations_by_image[img_id] = [] + annotations_by_image[img_id].append(ann) + + # Build image_id -> image info mapping + image_info = {img["id"]: img for img in coco_data.get("images", [])} + + # Convert to Label Studio format + ls_tasks = [] + for img_id, img_data in image_info.items(): + file_name = img_data["file_name"] + width = img_data["width"] + height = img_data["height"] + + # Find the actual file path + matching_paths = [p for p in image_paths if p.name == file_name] + if not matching_paths: + continue + + img_path = matching_paths[0] + + # Build predictions (pre-annotations) + predictions = [] + for ann in annotations_by_image.get(img_id, []): + # COCO bbox: [x_min, y_min, width, height] + x, y, w, h = ann["bbox"] + + # Label Studio uses percentages + x_percent = (x / width) * 100 + y_percent = (y / height) * 100 + w_percent = (w / width) * 100 + h_percent = (h / height) * 100 + + predictions.append({ + "value": { + "x": x_percent, + "y": y_percent, + "width": w_percent, + "height": h_percent, + "rectanglelabels": [category_name] + }, + "from_name": "label", + "to_name": "image", + "type": "rectanglelabels", + "score": ann.get("score", 1.0) + }) + + # Create Label Studio task with predictions + task = { + "data": { + "image": f"/data/local-files/?d={img_path.absolute()}" + }, + "predictions": [{ + "result": predictions, + "model_version": "rfdetr-auto-label" + }] + } + + ls_tasks.append(task) + + return ls_tasks + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Convert COCO predictions to Label Studio format for review." + ) + parser.add_argument("--coco-json", type=Path, required=True, help="COCO predictions JSON from auto_label_images.py") + parser.add_argument("--images-dir", type=Path, required=True, help="Directory with the images") + parser.add_argument("--output-json", type=Path, required=True, help="Output Label Studio tasks JSON") + parser.add_argument("--category-name", default="knot", help="Label name in Label Studio") + args = parser.parse_args() + + if not args.coco_json.exists(): + raise SystemExit(f"COCO JSON not found: {args.coco_json}") + if not args.images_dir.exists(): + raise SystemExit(f"Images dir not found: {args.images_dir}") + + # Collect all images + image_paths = list(args.images_dir.glob("*.jpg")) + list(args.images_dir.glob("*.png")) + if not image_paths: + raise SystemExit(f"No images found in {args.images_dir}") + + # Convert + ls_tasks = coco_to_label_studio(image_paths, args.coco_json, args.category_name) + + # Write Label Studio import format + with args.output_json.open("w", encoding="utf-8") as f: + json.dump(ls_tasks, f, indent=2) + + print(f"✓ Converted {len(ls_tasks)} tasks with predictions") + print(f"✓ Output: {args.output_json}") + print(f"\nImport into Label Studio:") + print(f" 1. Open your project") + print(f" 2. Settings → Storage → Add Local Storage") + print(f" Absolute path: {args.images_dir.absolute()}") + print(f" 3. Import → Upload Files → select {args.output_json.name}") + print(f" 4. Start reviewing predictions!") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/dataset_split/data.yaml b/dataset_split/data.yaml new file mode 100644 index 00000000..9043c876 --- /dev/null +++ b/dataset_split/data.yaml @@ -0,0 +1,18 @@ +# YOLO dataset configuration +path: /home/dillon/_code/saw_mill_knot_detection/dataset_split # dataset root dir +train: train/images # train images (relative to 'path') +val: valid/images # val images (relative to 'path') +test: test/images # test images (relative to 'path') + +# Classes +names: + 0: Live knot + 1: Dead knot + 2: Knot with crack + 3: Crack + 4: Resin + 5: Marrow + 6: Quartzity + 7: Knot missing + 8: Blue stain + 9: Overgrown diff --git a/dataset_split/test/_annotations.coco.json b/dataset_split/test/_annotations.coco.json new file mode 100644 index 00000000..33921917 --- /dev/null +++ b/dataset_split/test/_annotations.coco.json @@ -0,0 +1,74508 @@ +{ + "images": [ + { + "file_name": "176000038.jpg", + "height": 1024, + "width": 2800, + "id": 20061 + }, + { + "file_name": "165700022.jpg", + "height": 1024, + "width": 2800, + "id": 17178 + }, + { + "file_name": "123800003.jpg", + "height": 1024, + "width": 2800, + "id": 5587 + }, + { + "file_name": "161700005.jpg", + "height": 1024, + "width": 2800, + "id": 15743 + }, + { + "file_name": "106300005.jpg", + "height": 1024, + "width": 2800, + "id": 1682 + }, + { + "file_name": "130200001.jpg", + "height": 1024, + "width": 2800, + "id": 7456 + }, + { + "file_name": "135900057.jpg", + "height": 1024, + "width": 2800, + "id": 8700 + }, + { + "file_name": "161300015.jpg", + "height": 1024, + "width": 2800, + "id": 15551 + }, + { + "file_name": "170400060.jpg", + "height": 1024, + "width": 2800, + "id": 18664 + }, + { + "file_name": "141200020.jpg", + "height": 1024, + "width": 2800, + "id": 10081 + }, + { + "file_name": "153600055.jpg", + "height": 1024, + "width": 2800, + "id": 13649 + }, + { + "file_name": "114700039.jpg", + "height": 1024, + "width": 2800, + "id": 3591 + }, + { + "file_name": "144800047.jpg", + "height": 1024, + "width": 2800, + "id": 10854 + }, + { + "file_name": "149700021.jpg", + "height": 1024, + "width": 2800, + "id": 12070 + }, + { + "file_name": "101700026.jpg", + "height": 1024, + "width": 2800, + "id": 545 + }, + { + "file_name": "161300030.jpg", + "height": 1024, + "width": 2800, + "id": 15561 + }, + { + "file_name": "140500061.jpg", + "height": 1024, + "width": 2800, + "id": 9823 + }, + { + "file_name": "162500017.jpg", + "height": 1024, + "width": 2800, + "id": 16051 + }, + { + "file_name": "166300071.jpg", + "height": 1024, + "width": 2800, + "id": 17443 + }, + { + "file_name": "164300005.jpg", + "height": 1024, + "width": 2800, + "id": 16702 + }, + { + "file_name": "163900068.jpg", + "height": 1024, + "width": 2800, + "id": 16623 + }, + { + "file_name": "145100020.jpg", + "height": 1024, + "width": 2800, + "id": 11022 + }, + { + "file_name": "140500043.jpg", + "height": 1024, + "width": 2800, + "id": 9805 + }, + { + "file_name": "152000009.jpg", + "height": 1024, + "width": 2800, + "id": 12876 + }, + { + "file_name": "175500003.jpg", + "height": 1024, + "width": 2800, + "id": 19878 + }, + { + "file_name": "143400024.jpg", + "height": 1024, + "width": 2800, + "id": 10437 + }, + { + "file_name": "144200028.jpg", + "height": 1024, + "width": 2800, + "id": 10746 + }, + { + "file_name": "99300002.jpg", + "height": 1024, + "width": 2800, + "id": 20170 + }, + { + "file_name": "165200060.jpg", + "height": 1024, + "width": 2800, + "id": 16892 + }, + { + "file_name": "121400083.jpg", + "height": 1024, + "width": 2800, + "id": 4880 + }, + { + "file_name": "158800067.jpg", + "height": 1024, + "width": 2800, + "id": 14974 + }, + { + "file_name": "165400057.jpg", + "height": 1024, + "width": 2800, + "id": 17017 + }, + { + "file_name": "161200065.jpg", + "height": 1024, + "width": 2800, + "id": 15523 + }, + { + "file_name": "170900022.jpg", + "height": 1024, + "width": 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--- /dev/null +++ b/dataset_split/test/labels/144800083.txt @@ -0,0 +1 @@ +1 0.750893 0.412109 0.036786 0.048828 diff --git a/dataset_split/test/labels/144900006.txt b/dataset_split/test/labels/144900006.txt new file mode 100644 index 00000000..94da9705 --- /dev/null +++ b/dataset_split/test/labels/144900006.txt @@ -0,0 +1,2 @@ +4 0.431964 0.928222 0.019643 0.143555 +0 0.336607 0.137207 0.031072 0.045898 diff --git a/dataset_split/test/labels/144900008.txt b/dataset_split/test/labels/144900008.txt new file mode 100644 index 00000000..55ad0940 --- /dev/null +++ b/dataset_split/test/labels/144900008.txt @@ -0,0 +1,2 @@ +0 0.491428 0.624512 0.047143 0.059570 +0 0.368036 0.584961 0.044643 0.056640 diff --git a/dataset_split/test/labels/144900032.txt b/dataset_split/test/labels/144900032.txt new file mode 100644 index 00000000..969210c1 --- /dev/null +++ b/dataset_split/test/labels/144900032.txt @@ -0,0 +1,2 @@ +4 0.333214 0.546875 0.051429 0.691406 +0 0.582857 0.348633 0.090714 0.140625 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diff --git a/dataset_split/test/labels/152700012.txt b/dataset_split/test/labels/152700012.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/test/labels/152700014.txt b/dataset_split/test/labels/152700014.txt new file mode 100644 index 00000000..18c7c8d6 --- /dev/null +++ b/dataset_split/test/labels/152700014.txt @@ -0,0 +1,2 @@ +1 0.566786 0.760253 0.030714 0.071289 +1 0.217678 0.188964 0.099643 0.137695 diff --git a/dataset_split/test/labels/152700023.txt b/dataset_split/test/labels/152700023.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/test/labels/152700027.txt b/dataset_split/test/labels/152700027.txt new file mode 100644 index 00000000..831411a1 --- /dev/null +++ b/dataset_split/test/labels/152700027.txt @@ -0,0 +1,2 @@ +7 0.891786 0.529785 0.086429 0.143554 +1 0.596607 0.902832 0.113928 0.147460 diff --git a/dataset_split/test/labels/152700034.txt b/dataset_split/test/labels/152700034.txt new file mode 100644 index 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diff --git a/dataset_split/valid/labels/150100046.txt b/dataset_split/valid/labels/150100046.txt new file mode 100644 index 00000000..98cb7664 --- /dev/null +++ b/dataset_split/valid/labels/150100046.txt @@ -0,0 +1,3 @@ +1 0.175893 0.739258 0.045357 0.072266 +1 0.160000 0.209960 0.046428 0.060547 +0 0.511429 0.739746 0.038571 0.079102 diff --git a/dataset_split/valid/labels/150200059.txt b/dataset_split/valid/labels/150200059.txt new file mode 100644 index 00000000..d4dd0376 --- /dev/null +++ b/dataset_split/valid/labels/150200059.txt @@ -0,0 +1 @@ +0 0.496786 0.721680 0.020000 0.054687 diff --git a/dataset_split/valid/labels/150200060.txt b/dataset_split/valid/labels/150200060.txt new file mode 100644 index 00000000..26a0454f --- /dev/null +++ b/dataset_split/valid/labels/150200060.txt @@ -0,0 +1,4 @@ +1 0.807857 0.278809 0.048572 0.055664 +0 0.318393 0.785645 0.049643 0.069335 +0 0.515893 0.411621 0.025357 0.057618 +0 0.359643 0.098633 0.020000 0.054688 diff --git 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b/dataset_split/valid/labels/172400002.txt @@ -0,0 +1,2 @@ +4 0.353214 0.486328 0.021429 0.105468 +1 0.283392 0.294922 0.054643 0.095703 diff --git a/dataset_split/valid/labels/172400020.txt b/dataset_split/valid/labels/172400020.txt new file mode 100644 index 00000000..2d988201 --- /dev/null +++ b/dataset_split/valid/labels/172400020.txt @@ -0,0 +1,3 @@ +5 0.512679 0.796875 0.026785 0.406250 +5 0.499286 0.183106 0.040714 0.366211 +1 0.569821 0.673340 0.026071 0.045898 diff --git a/dataset_split/valid/labels/172400052.txt b/dataset_split/valid/labels/172400052.txt new file mode 100644 index 00000000..2f9c7fb1 --- /dev/null +++ b/dataset_split/valid/labels/172400052.txt @@ -0,0 +1,6 @@ +4 0.313214 0.507812 0.014286 0.039063 +4 0.288571 0.482422 0.028571 0.103516 +0 0.470357 0.811035 0.054286 0.075196 +0 0.536428 0.121582 0.019285 0.041992 +0 0.363750 0.162598 0.078928 0.125977 +0 0.456429 0.120117 0.017857 0.048828 diff --git a/dataset_split/valid/labels/172400081.txt b/dataset_split/valid/labels/172400081.txt new file mode 100644 index 00000000..7d47eaf2 --- /dev/null +++ b/dataset_split/valid/labels/172400081.txt @@ -0,0 +1,4 @@ +0 0.521428 0.924805 0.033571 0.076172 +0 0.280000 0.796387 0.075714 0.090820 +0 0.525179 0.266601 0.037500 0.060547 +0 0.416072 0.155274 0.037857 0.070313 diff --git a/dataset_split/valid/labels/175200000.txt b/dataset_split/valid/labels/175200000.txt new file mode 100644 index 00000000..072ba0b9 --- /dev/null +++ b/dataset_split/valid/labels/175200000.txt @@ -0,0 +1 @@ +1 0.871607 0.265137 0.034643 0.065430 diff --git a/dataset_split/valid/labels/175200016.txt b/dataset_split/valid/labels/175200016.txt new file mode 100644 index 00000000..90de88a7 --- /dev/null +++ b/dataset_split/valid/labels/175200016.txt @@ -0,0 +1,2 @@ +4 0.221965 0.445801 0.019643 0.165039 +0 0.611428 0.184082 0.087857 0.124024 diff --git a/dataset_split/valid/labels/175200020.txt b/dataset_split/valid/labels/175200020.txt new file mode 100644 index 00000000..555c1dd3 --- /dev/null +++ b/dataset_split/valid/labels/175200020.txt @@ -0,0 +1 @@ +0 0.339821 0.605957 0.136785 0.206054 diff --git a/dataset_split/valid/labels/175200024.txt b/dataset_split/valid/labels/175200024.txt new file mode 100644 index 00000000..a272a6c5 --- /dev/null +++ b/dataset_split/valid/labels/175200024.txt @@ -0,0 +1 @@ +4 0.627322 0.875000 0.056785 0.250000 diff --git a/dataset_split/valid/labels/175200025.txt b/dataset_split/valid/labels/175200025.txt new file mode 100644 index 00000000..3de33824 --- /dev/null +++ b/dataset_split/valid/labels/175200025.txt @@ -0,0 +1,5 @@ +4 0.858571 0.855957 0.030000 0.190430 +4 0.183214 0.350586 0.025714 0.142578 +4 0.645358 0.024903 0.022143 0.049805 +0 0.405714 0.379394 0.073571 0.122071 +0 0.505000 0.091797 0.083572 0.138672 diff --git a/dataset_split/valid/labels/175200030.txt b/dataset_split/valid/labels/175200030.txt new file mode 100644 index 00000000..94d00cda --- /dev/null +++ b/dataset_split/valid/labels/175200030.txt @@ -0,0 +1,2 @@ +0 0.256429 0.530762 0.048571 0.083008 +0 0.445000 0.418945 0.061428 0.091797 diff --git a/dataset_split/valid/labels/175200033.txt b/dataset_split/valid/labels/175200033.txt new file mode 100644 index 00000000..71d2b77e --- /dev/null +++ b/dataset_split/valid/labels/175200033.txt @@ -0,0 +1,4 @@ +1 0.870715 0.548339 0.062143 0.075195 +1 0.381607 0.475585 0.043214 0.064453 +0 0.376607 0.959472 0.056072 0.081055 +0 0.213750 0.128906 0.054642 0.072266 diff --git a/dataset_split/valid/labels/175200052.txt b/dataset_split/valid/labels/175200052.txt new file mode 100644 index 00000000..460252dd --- /dev/null +++ b/dataset_split/valid/labels/175200052.txt @@ -0,0 +1 @@ +1 0.873928 0.097168 0.032857 0.110352 diff --git a/dataset_split/valid/labels/175300002.txt b/dataset_split/valid/labels/175300002.txt new file mode 100644 index 00000000..4738681a --- /dev/null +++ b/dataset_split/valid/labels/175300002.txt @@ -0,0 +1 @@ +2 0.454465 0.041016 0.123929 0.082031 diff --git a/dataset_split/valid/labels/175300024.txt b/dataset_split/valid/labels/175300024.txt new file mode 100644 index 00000000..92b4016a --- /dev/null +++ b/dataset_split/valid/labels/175300024.txt @@ -0,0 +1,3 @@ +1 0.091428 0.452149 0.043571 0.058593 +1 0.501071 0.015137 0.040000 0.030273 +0 0.586785 0.823242 0.027143 0.074219 diff --git a/dataset_split/valid/labels/175300072.txt b/dataset_split/valid/labels/175300072.txt new file mode 100644 index 00000000..f93d45da --- /dev/null +++ b/dataset_split/valid/labels/175300072.txt @@ -0,0 +1 @@ +5 0.475892 0.409668 0.045357 0.819336 diff --git a/dataset_split/valid/labels/175400015.txt b/dataset_split/valid/labels/175400015.txt new file mode 100644 index 00000000..335cd4bb --- /dev/null +++ b/dataset_split/valid/labels/175400015.txt @@ -0,0 +1,2 @@ +7 0.062500 0.665039 0.014286 0.039062 +0 0.647500 0.016602 0.017858 0.033203 diff --git a/dataset_split/valid/labels/175400019.txt b/dataset_split/valid/labels/175400019.txt new file mode 100644 index 00000000..fcd130dc --- /dev/null +++ b/dataset_split/valid/labels/175400019.txt @@ -0,0 +1 @@ +0 0.490714 0.691407 0.037857 0.064453 diff --git a/dataset_split/valid/labels/175400042.txt b/dataset_split/valid/labels/175400042.txt new file mode 100644 index 00000000..715fbbf5 --- /dev/null +++ b/dataset_split/valid/labels/175400042.txt @@ -0,0 +1,2 @@ +0 0.419821 0.538574 0.056071 0.090820 +0 0.665179 0.223633 0.086071 0.126953 diff --git a/dataset_split/valid/labels/175400044.txt b/dataset_split/valid/labels/175400044.txt new file mode 100644 index 00000000..649e088a --- /dev/null +++ b/dataset_split/valid/labels/175400044.txt @@ -0,0 +1 @@ +4 0.320357 0.143067 0.043572 0.153321 diff --git a/dataset_split/valid/labels/175400069.txt b/dataset_split/valid/labels/175400069.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/valid/labels/175400072.txt b/dataset_split/valid/labels/175400072.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/valid/labels/175500010.txt b/dataset_split/valid/labels/175500010.txt new file mode 100644 index 00000000..a5ac0dc8 --- /dev/null +++ b/dataset_split/valid/labels/175500010.txt @@ -0,0 +1 @@ +1 0.092857 0.319824 0.073572 0.110352 diff --git a/dataset_split/valid/labels/175500015.txt b/dataset_split/valid/labels/175500015.txt new file mode 100644 index 00000000..ac54c097 --- /dev/null +++ b/dataset_split/valid/labels/175500015.txt @@ -0,0 +1,2 @@ +4 0.228214 0.673340 0.029286 0.127930 +4 0.093750 0.598633 0.039642 0.441406 diff --git a/dataset_split/valid/labels/175500044.txt b/dataset_split/valid/labels/175500044.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/valid/labels/175500046.txt b/dataset_split/valid/labels/175500046.txt new file mode 100644 index 00000000..74277ba6 --- /dev/null +++ b/dataset_split/valid/labels/175500046.txt @@ -0,0 +1,2 @@ +4 0.322321 0.436035 0.052500 0.266602 +1 0.396965 0.969239 0.091071 0.061523 diff --git a/dataset_split/valid/labels/175500048.txt b/dataset_split/valid/labels/175500048.txt new file mode 100644 index 00000000..e82f5963 --- /dev/null +++ b/dataset_split/valid/labels/175500048.txt @@ -0,0 +1,2 @@ +4 0.885536 0.212402 0.030357 0.211914 +4 0.598572 0.156738 0.031429 0.233398 diff --git a/dataset_split/valid/labels/175500056.txt b/dataset_split/valid/labels/175500056.txt new file mode 100644 index 00000000..5b7c4774 --- /dev/null +++ b/dataset_split/valid/labels/175500056.txt @@ -0,0 +1 @@ +4 0.783393 0.662110 0.028928 0.244141 diff --git a/dataset_split/valid/labels/175500061.txt b/dataset_split/valid/labels/175500061.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/valid/labels/175600018.txt b/dataset_split/valid/labels/175600018.txt new file mode 100644 index 00000000..089535f4 --- /dev/null +++ b/dataset_split/valid/labels/175600018.txt @@ -0,0 +1,4 @@ +0 0.386964 0.888671 0.032500 0.078125 +0 0.773214 0.762695 0.010714 0.029297 +0 0.626072 0.757812 0.034285 0.070313 +0 0.368928 0.026367 0.031429 0.052734 diff --git a/dataset_split/valid/labels/175600020.txt b/dataset_split/valid/labels/175600020.txt new file mode 100644 index 00000000..00a02798 --- /dev/null +++ b/dataset_split/valid/labels/175600020.txt @@ -0,0 +1 @@ +2 0.717143 0.696289 0.115714 0.169922 diff --git a/dataset_split/valid/labels/175600029.txt b/dataset_split/valid/labels/175600029.txt new file mode 100644 index 00000000..f0d42718 --- /dev/null +++ b/dataset_split/valid/labels/175600029.txt @@ -0,0 +1,2 @@ +0 0.251072 0.830566 0.023571 0.055664 +0 0.560000 0.058594 0.017858 0.048828 diff --git a/dataset_split/valid/labels/175600032.txt b/dataset_split/valid/labels/175600032.txt new file mode 100644 index 00000000..103edb8c --- /dev/null +++ b/dataset_split/valid/labels/175600032.txt @@ -0,0 +1,2 @@ +1 0.878214 0.217286 0.019286 0.036133 +0 0.557143 0.631836 0.020000 0.054688 diff --git a/dataset_split/valid/labels/175900001.txt b/dataset_split/valid/labels/175900001.txt new file mode 100644 index 00000000..00f9b4fa --- /dev/null +++ b/dataset_split/valid/labels/175900001.txt @@ -0,0 +1,4 @@ +5 0.495000 0.279297 0.040714 0.558594 +4 0.491965 0.931640 0.030357 0.136719 +1 0.550000 0.976562 0.037142 0.046875 +0 0.304285 0.194336 0.217857 0.097656 diff --git a/dataset_split/valid/labels/175900007.txt b/dataset_split/valid/labels/175900007.txt new file mode 100644 index 00000000..b16a1baa --- /dev/null +++ b/dataset_split/valid/labels/175900007.txt @@ -0,0 +1,3 @@ +0 0.508215 0.280274 0.038571 0.087891 +0 0.420715 0.227539 0.071429 0.095704 +0 0.530000 0.139160 0.036428 0.075196 diff --git a/dataset_split/valid/labels/175900008.txt b/dataset_split/valid/labels/175900008.txt new file mode 100644 index 00000000..b9b298e6 --- /dev/null +++ b/dataset_split/valid/labels/175900008.txt @@ -0,0 +1,3 @@ +1 0.830714 0.935059 0.223571 0.122071 +0 0.524822 0.949218 0.026071 0.054687 +0 0.455715 0.026367 0.028571 0.052734 diff --git a/dataset_split/valid/labels/175900022.txt b/dataset_split/valid/labels/175900022.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/valid/labels/175900035.txt b/dataset_split/valid/labels/175900035.txt new file mode 100644 index 00000000..b239ef93 --- /dev/null +++ b/dataset_split/valid/labels/175900035.txt @@ -0,0 +1,2 @@ +7 0.097142 0.423340 0.077143 0.180664 +0 0.490178 0.510254 0.141785 0.198242 diff --git a/dataset_split/valid/labels/175900042.txt b/dataset_split/valid/labels/175900042.txt new file mode 100644 index 00000000..ff3e539c --- /dev/null +++ b/dataset_split/valid/labels/175900042.txt @@ -0,0 +1 @@ +1 0.115357 0.282226 0.047857 0.085937 diff --git a/dataset_split/valid/labels/175900050.txt b/dataset_split/valid/labels/175900050.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/valid/labels/175900071.txt b/dataset_split/valid/labels/175900071.txt new file mode 100644 index 00000000..671b4a41 --- /dev/null +++ b/dataset_split/valid/labels/175900071.txt @@ -0,0 +1 @@ +1 0.875000 0.186524 0.032142 0.087891 diff --git a/dataset_split/valid/labels/175900081.txt b/dataset_split/valid/labels/175900081.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/valid/labels/175900084.txt b/dataset_split/valid/labels/175900084.txt new file mode 100644 index 00000000..e30fa96c --- /dev/null +++ b/dataset_split/valid/labels/175900084.txt @@ -0,0 +1,2 @@ +1 0.443214 0.961914 0.073571 0.076172 +1 0.522322 0.914551 0.038215 0.065430 diff --git a/dataset_split/valid/labels/176000029.txt b/dataset_split/valid/labels/176000029.txt new file mode 100644 index 00000000..442d9fc9 --- /dev/null +++ b/dataset_split/valid/labels/176000029.txt @@ -0,0 +1,2 @@ +1 0.167857 0.804199 0.054286 0.079102 +0 0.504285 0.834960 0.023571 0.064453 diff --git a/dataset_split/valid/labels/176000040.txt b/dataset_split/valid/labels/176000040.txt new file mode 100644 index 00000000..679467a3 --- /dev/null +++ b/dataset_split/valid/labels/176000040.txt @@ -0,0 +1,6 @@ +1 0.190715 0.669434 0.047857 0.067383 +0 0.585715 0.922852 0.017857 0.048829 +0 0.615000 0.143555 0.027142 0.074219 +0 0.368571 0.033691 0.037143 0.057617 +0 0.361607 0.001953 0.003928 0.003906 +0 0.350893 0.001953 0.001786 0.003906 diff --git a/dataset_split/valid/labels/176000045.txt b/dataset_split/valid/labels/176000045.txt new file mode 100644 index 00000000..2f9cd340 --- /dev/null +++ b/dataset_split/valid/labels/176000045.txt @@ -0,0 +1,2 @@ +0 0.410536 0.483886 0.032500 0.077149 +0 0.635357 0.163574 0.045714 0.077148 diff --git a/dataset_split/valid/labels/176000053.txt b/dataset_split/valid/labels/176000053.txt new file mode 100644 index 00000000..82a3d6a9 --- /dev/null +++ b/dataset_split/valid/labels/176000053.txt @@ -0,0 +1,2 @@ +0 0.751548 0.528320 0.067031 0.091797 +0 0.390346 0.202149 0.059380 0.103515 diff --git a/dataset_split/valid/labels/176000081.txt b/dataset_split/valid/labels/176000081.txt new file mode 100644 index 00000000..ee96a418 --- /dev/null +++ b/dataset_split/valid/labels/176000081.txt @@ -0,0 +1,4 @@ +0 0.678214 0.973633 0.038571 0.052734 +0 0.411428 0.272461 0.076429 0.125000 +0 0.653571 0.195801 0.065000 0.108398 +0 0.535000 0.096191 0.063572 0.131836 diff --git a/dataset_split/valid/labels/176000082.txt b/dataset_split/valid/labels/176000082.txt new file mode 100644 index 00000000..638622a1 --- /dev/null +++ b/dataset_split/valid/labels/176000082.txt @@ -0,0 +1,4 @@ +1 0.165000 0.638672 0.052858 0.060547 +0 0.542500 0.854492 0.028572 0.078125 +0 0.698214 0.731445 0.025000 0.058594 +0 0.511072 0.440430 0.030715 0.083985 diff --git a/dataset_split/valid/labels/176000083.txt b/dataset_split/valid/labels/176000083.txt new file mode 100644 index 00000000..591df2d9 --- /dev/null +++ b/dataset_split/valid/labels/176000083.txt @@ -0,0 +1,2 @@ +0 0.626250 0.235351 0.025358 0.060547 +0 0.450715 0.025879 0.027143 0.051758 diff --git a/dataset_split/valid/labels/99100019.txt b/dataset_split/valid/labels/99100019.txt new file mode 100644 index 00000000..4b2f84e7 --- /dev/null +++ b/dataset_split/valid/labels/99100019.txt @@ -0,0 +1,2 @@ +0 0.323393 0.667481 0.063214 0.088867 +0 0.736250 0.488281 0.054642 0.085938 diff --git a/dataset_split/valid/labels/99100030.txt b/dataset_split/valid/labels/99100030.txt new file mode 100644 index 00000000..2277dda9 --- /dev/null +++ b/dataset_split/valid/labels/99100030.txt @@ -0,0 +1,4 @@ +3 0.569107 0.831055 0.067500 0.181641 +2 0.370536 0.652344 0.138929 0.187500 +1 0.620000 0.726074 0.094286 0.155274 +0 0.873393 0.771484 0.143214 0.183594 diff --git a/dataset_split/valid/labels/99100066.txt b/dataset_split/valid/labels/99100066.txt new file mode 100644 index 00000000..d90a9af3 --- /dev/null +++ b/dataset_split/valid/labels/99100066.txt @@ -0,0 +1,2 @@ +0 0.616071 0.750000 0.057857 0.083984 +0 0.322857 0.379394 0.065714 0.084961 diff --git a/dataset_split/valid/labels/99100082.txt b/dataset_split/valid/labels/99100082.txt new file mode 100644 index 00000000..a33fbdba --- /dev/null +++ b/dataset_split/valid/labels/99100082.txt @@ -0,0 +1,5 @@ +4 0.198572 0.208008 0.022143 0.250000 +1 0.287857 0.536133 0.032143 0.054688 +0 0.526429 0.742676 0.050000 0.094727 +0 0.471429 0.088379 0.024285 0.063476 +0 0.247500 0.015137 0.021428 0.028320 diff --git a/dataset_split/valid/labels/99300011.txt b/dataset_split/valid/labels/99300011.txt new file mode 100644 index 00000000..378894d9 --- /dev/null +++ b/dataset_split/valid/labels/99300011.txt @@ -0,0 +1,5 @@ +4 0.566964 0.686523 0.028214 0.236328 +1 0.884286 0.764160 0.077143 0.077148 +0 0.491786 0.717285 0.039286 0.077148 +0 0.217143 0.709961 0.047143 0.074218 +0 0.376607 0.211426 0.054643 0.083008 diff --git a/dataset_split/valid/labels/99300052.txt b/dataset_split/valid/labels/99300052.txt new file mode 100644 index 00000000..70fa996f --- /dev/null +++ b/dataset_split/valid/labels/99300052.txt @@ -0,0 +1 @@ +1 0.629107 0.846191 0.035357 0.081055 diff --git a/dataset_split/valid/labels/99300065.txt b/dataset_split/valid/labels/99300065.txt new file mode 100644 index 00000000..2cb37776 --- /dev/null +++ b/dataset_split/valid/labels/99300065.txt @@ -0,0 +1,4 @@ +1 0.342500 0.849609 0.027858 0.058594 +1 0.608035 0.728516 0.040357 0.064453 +1 0.097857 0.083984 0.033572 0.058594 +1 0.441429 0.033204 0.033571 0.064453 diff --git a/dataset_split/valid/labels/99300067.txt b/dataset_split/valid/labels/99300067.txt new file mode 100644 index 00000000..764cb0ff --- /dev/null +++ b/dataset_split/valid/labels/99300067.txt @@ -0,0 +1 @@ +1 0.643215 0.218261 0.057143 0.092773 diff --git a/dataset_split/valid/labels/99300069.txt b/dataset_split/valid/labels/99300069.txt new file mode 100644 index 00000000..cb71dba6 --- /dev/null +++ b/dataset_split/valid/labels/99300069.txt @@ -0,0 +1 @@ +1 0.694821 0.196777 0.041785 0.075195 diff --git a/dataset_split/valid/labels/99300074.txt b/dataset_split/valid/labels/99300074.txt new file mode 100644 index 00000000..11ec04d3 --- /dev/null +++ b/dataset_split/valid/labels/99300074.txt @@ -0,0 +1,3 @@ +3 0.535000 0.341309 0.019286 0.323243 +2 0.431607 0.529785 0.092500 0.104492 +0 0.437500 0.081055 0.027142 0.074219 diff --git a/dataset_split/valid/labels/99300081.txt b/dataset_split/valid/labels/99300081.txt new file mode 100644 index 00000000..ded199b3 --- /dev/null +++ b/dataset_split/valid/labels/99300081.txt @@ -0,0 +1,3 @@ +1 0.150357 0.323242 0.048572 0.060547 +1 0.666428 0.119140 0.026429 0.060547 +0 0.556428 0.567383 0.042857 0.070312 diff --git a/dataset_split/valid/labels/99900017.txt b/dataset_split/valid/labels/99900017.txt new file mode 100644 index 00000000..502aeda4 --- /dev/null +++ b/dataset_split/valid/labels/99900017.txt @@ -0,0 +1,4 @@ +4 0.381607 0.375489 0.031786 0.592773 +1 0.120178 0.838379 0.055357 0.083008 +1 0.900715 0.169434 0.046429 0.073243 +1 0.431428 0.063476 0.021429 0.058593 diff --git a/dataset_split/valid/labels/99900045.txt b/dataset_split/valid/labels/99900045.txt new file mode 100644 index 00000000..e69de29b diff --git a/dataset_split/valid/labels/99900051.txt b/dataset_split/valid/labels/99900051.txt new file mode 100644 index 00000000..3322d8ac --- /dev/null +++ b/dataset_split/valid/labels/99900051.txt @@ -0,0 +1,2 @@ +3 0.476429 0.679688 0.020715 0.484375 +1 0.801607 0.058105 0.043928 0.086914 diff --git a/export_onnx.py b/export_onnx.py new file mode 100644 index 00000000..77f048f0 --- /dev/null +++ b/export_onnx.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +import argparse +from pathlib import Path + + +def main() -> int: + parser = argparse.ArgumentParser(description="Export trained RF-DETR to ONNX for deployment.") + parser.add_argument("--weights", type=Path, required=True, help="Path to trained checkpoint") + parser.add_argument("--output-onnx", type=Path, default=Path("model.onnx"), help="Output ONNX file") + args = parser.parse_args() + + if not args.weights.exists(): + raise SystemExit(f"Weights not found: {args.weights}") + + from rfdetr import RFDETRBase + + model = RFDETRBase(pretrain_weights=str(args.weights)) + + # Export to ONNX. This saves to the current directory by default, but we can specify. + # RF-DETR's export() method saves to 'output/model.onnx' I think, but let's check docs. + # From earlier fetch: model.export() saves to 'output' dir. + # But to make it flexible, perhaps run it and then move. + + model.export() # This should create output/model.onnx + + # Move to desired location + onnx_path = Path("output/model.onnx") + if onnx_path.exists(): + onnx_path.rename(args.output_onnx) + print(f"Exported ONNX: {args.output_onnx}") + else: + raise SystemExit("ONNX export failed - check output/ dir") + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/export_rtdetr_oak.py b/export_rtdetr_oak.py new file mode 100644 index 00000000..9c6e9715 --- /dev/null +++ b/export_rtdetr_oak.py @@ -0,0 +1,90 @@ +""" +Export trained RT-DETR model for OAK-D camera deployment. + +This script exports your trained model to OpenVINO format, which can then be +converted to a blob for the OAK-D camera. + +Usage: + python export_rtdetr_oak.py --weights runs/rtdetr_training/training/weights/best.pt +""" + +import argparse +from pathlib import Path + + +def export_for_oak(weights_path: Path, img_size: int = 640): + """ + Export RT-DETR model for OAK-D deployment. + + Args: + weights_path: Path to trained .pt weights + img_size: Input image size (should match training) + """ + from ultralytics import RTDETR + + if not weights_path.exists(): + raise ValueError(f"Weights not found: {weights_path}") + + print(f"\n{'='*60}") + print(f"Exporting RT-DETR for OAK-D camera") + print(f"Weights: {weights_path}") + print(f"Image size: {img_size}x{img_size}") + print(f"{'='*60}\n") + + # Load model + model = RTDETR(weights_path) + + # Export to ONNX first (intermediate format) + print("Step 1/2: Exporting to ONNX...") + onnx_path = model.export( + format="onnx", + imgsz=img_size, + simplify=True, + opset=11, # OAK-compatible opset + ) + print(f"✓ ONNX exported: {onnx_path}") + + # Export to OpenVINO (for OAK) + print("\nStep 2/2: Exporting to OpenVINO...") + openvino_path = model.export( + format="openvino", + imgsz=img_size, + half=False, # Use FP32 for better compatibility + ) + print(f"✓ OpenVINO exported: {openvino_path}") + + print(f"\n{'='*60}") + print(f"✓ Export complete!") + print(f"\nNext steps:") + print(f"1. Test OpenVINO model: python test_openvino.py --model {openvino_path}") + print(f"2. Convert to blob for OAK:") + print(f" Online: https://blobconverter.luxonis.com/") + print(f" Or use: blobconverter --openvino-xml {openvino_path}/model.xml") + print(f"3. Deploy blob to OAK-D camera with DepthAI") + print(f"{'='*60}\n") + + return openvino_path + + +def main(): + parser = argparse.ArgumentParser(description="Export RT-DETR for OAK-D deployment") + parser.add_argument( + "--weights", + type=Path, + required=True, + help="Path to trained .pt weights file" + ) + parser.add_argument( + "--img-size", + type=int, + default=640, + help="Input image size (should match training)" + ) + + args = parser.parse_args() + + export_for_oak(args.weights, args.img_size) + + +if __name__ == "__main__": + main() diff --git a/predict_rfdetr.py b/predict_rfdetr.py new file mode 100644 index 00000000..9a8f5a33 --- /dev/null +++ b/predict_rfdetr.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +import argparse +from pathlib import Path + +from PIL import Image + + +def main() -> int: + parser = argparse.ArgumentParser(description="Run inference with a fine-tuned RF-DETR checkpoint.") + parser.add_argument("--weights", type=Path, required=True, help="Path to checkpoint_best_total.pth (or similar)") + parser.add_argument("--image", type=Path, required=True, help="Path to an image") + parser.add_argument("--threshold", type=float, default=0.5) + args = parser.parse_args() + + if not args.weights.exists(): + raise SystemExit(f"Weights not found: {args.weights}") + if not args.image.exists(): + raise SystemExit(f"Image not found: {args.image}") + + from rfdetr import RFDETRBase + + model = RFDETRBase(pretrain_weights=str(args.weights)) + + image = Image.open(args.image).convert("RGB") + detections = model.predict(image, threshold=args.threshold) + + # Print detections in a simple, script-friendly way. + # `detections` is a supervision.Detections object. + print(f"num_detections={len(detections)}") + for i in range(len(detections)): + xyxy = detections.xyxy[i].tolist() + conf = float(detections.confidence[i]) if detections.confidence is not None else None + cls = int(detections.class_id[i]) if detections.class_id is not None else None + print({"i": i, "class_id": cls, "confidence": conf, "xyxy": xyxy}) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/predict_rtdetr.py b/predict_rtdetr.py new file mode 100644 index 00000000..5642318e --- /dev/null +++ b/predict_rtdetr.py @@ -0,0 +1,99 @@ +""" +Run inference with trained RT-DETR model. + +Usage: + python predict_rtdetr.py --weights runs/rtdetr_training/training/weights/best.pt --image test.jpg +""" + +import argparse +from pathlib import Path +import cv2 + + +def predict(weights_path: Path, image_path: Path, threshold: float = 0.5, save: bool = True): + """ + Run RT-DETR inference on an image. + + Args: + weights_path: Path to trained .pt weights + image_path: Path to input image + threshold: Detection confidence threshold + save: Whether to save visualization + """ + from ultralytics import RTDETR + + if not weights_path.exists(): + raise ValueError(f"Weights not found: {weights_path}") + if not image_path.exists(): + raise ValueError(f"Image not found: {image_path}") + + print(f"\n{'='*60}") + print(f"RT-DETR Inference") + print(f"Weights: {weights_path}") + print(f"Image: {image_path}") + print(f"Threshold: {threshold}") + print(f"{'='*60}\n") + + # Load model + model = RTDETR(weights_path) + + # Run inference + results = model.predict( + source=str(image_path), + conf=threshold, + save=save, + show_labels=True, + show_conf=True, + ) + + # Print detections + for result in results: + boxes = result.boxes + print(f"\nDetected {len(boxes)} knots:") + for i, box in enumerate(boxes): + conf = box.conf[0].item() + xyxy = box.xyxy[0].cpu().numpy() + print(f" {i+1}. Confidence: {conf:.3f}, BBox: [{xyxy[0]:.0f}, {xyxy[1]:.0f}, {xyxy[2]:.0f}, {xyxy[3]:.0f}]") + + if save: + output_dir = Path("runs/detect") + print(f"\n✓ Visualization saved to: {output_dir}") + + print(f"\n{'='*60}\n") + + return results + + +def main(): + parser = argparse.ArgumentParser(description="RT-DETR inference") + parser.add_argument( + "--weights", + type=Path, + required=True, + help="Path to trained .pt weights" + ) + parser.add_argument( + "--image", + type=Path, + required=True, + help="Path to input image" + ) + parser.add_argument( + "--threshold", + type=float, + default=0.5, + help="Detection confidence threshold" + ) + parser.add_argument( + "--no-save", + action="store_true", + help="Don't save visualization" + ) + + args = parser.parse_args() + + predict(args.weights, args.image, args.threshold, save=not args.no_save) + + +if __name__ == "__main__": + main() diff --git a/reorganize_dataset.py b/reorganize_dataset.py new file mode 100644 index 00000000..e680a299 --- /dev/null +++ b/reorganize_dataset.py @@ -0,0 +1,96 @@ +""" +Reorganize dataset to YOLO format with images/ and labels/ subdirectories. +""" + +from pathlib import Path +import shutil + +def reorganize_split(split_dir: Path): + """Reorganize one split (train/valid/test) to YOLO format.""" + print(f"Reorganizing {split_dir.name}...") + + # Create images directory + images_dir = split_dir / "images" + images_dir.mkdir(exist_ok=True) + + # Move all .jpg files to images/ + moved_count = 0 + for img_file in split_dir.glob("*.jpg"): + dest = images_dir / img_file.name + if not dest.exists(): + shutil.move(str(img_file), str(dest)) + moved_count += 1 + + print(f" Moved {moved_count} images to {split_dir.name}/images/") + + # Check labels directory + labels_dir = split_dir / "labels" + if labels_dir.exists(): + label_count = len(list(labels_dir.glob("*.txt"))) + print(f" Found {label_count} labels in {split_dir.name}/labels/") + else: + print(f" WARNING: No labels directory in {split_dir.name}/") + +def update_data_yaml(dataset_dir: Path): + """Update data.yaml to reflect new structure.""" + data_yaml = dataset_dir / "data.yaml" + + content = f"""# YOLO dataset configuration +path: {dataset_dir.absolute()} # dataset root dir +train: train/images # train images (relative to 'path') +val: valid/images # val images (relative to 'path') +test: test/images # test images (relative to 'path') + +# Classes +names: + 0: Live knot + 1: Dead knot + 2: Knot with crack + 3: Crack + 4: Resin + 5: Marrow + 6: Quartzity + 7: Knot missing + 8: Blue stain + 9: Overgrown +""" + + data_yaml.write_text(content) + print(f"\n✓ Updated {data_yaml}") + +def main(): + dataset_dir = Path("dataset_split") + + if not dataset_dir.exists(): + print(f"Error: {dataset_dir} not found") + return + + # Reorganize each split + for split_name in ["train", "valid", "test"]: + split_dir = dataset_dir / split_name + if split_dir.exists(): + reorganize_split(split_dir) + else: + print(f"Warning: {split_dir} not found") + + # Update data.yaml + update_data_yaml(dataset_dir) + + print("\n" + "="*60) + print("Dataset reorganization complete!") + print("="*60) + print("\nNew structure:") + print("dataset_split/") + print(" ├── train/") + print(" │ ├── images/") + print(" │ └── labels/") + print(" ├── valid/") + print(" │ ├── images/") + print(" │ └── labels/") + print(" ├── test/") + print(" │ ├── images/") + print(" │ └── labels/") + print(" └── data.yaml") + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..9ea14534 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,9 @@ +rfdetr==1.3.0 +onnx>=1.20.0 +onnxruntime>=1.23.0 +onnx_graphsurgeon>=0.5.0 +# optional but useful for visualization / quick sanity checks +supervision>=0.27.0 +Pillow>=10.0.0 +# For the custom annotation GUI +gradio>=4.0.0 diff --git a/split_coco_dataset.py b/split_coco_dataset.py new file mode 100644 index 00000000..317cda51 --- /dev/null +++ b/split_coco_dataset.py @@ -0,0 +1,248 @@ +#!/usr/bin/env python3 +""" +Split the Kaggle wood defects COCO dataset into train/valid/test splits. +Creates both COCO format and YOLO format annotations. + +Usage: + python split_coco_dataset.py --input bbox_coco_dataset.json --images IMAGE/ --output dataset_split +""" + +import argparse +import json +import random +import shutil +from pathlib import Path +from typing import Dict, List + + +def coco_to_yolo_bbox(bbox: List[float], img_width: int, img_height: int) -> List[float]: + """ + Convert COCO bbox [x, y, width, height] to YOLO format [x_center, y_center, width, height]. + All values normalized to [0, 1]. + + Args: + bbox: COCO format [x_min, y_min, width, height] + img_width: Image width in pixels + img_height: Image height in pixels + + Returns: + YOLO format [x_center, y_center, width, height] normalized + """ + x_min, y_min, width, height = bbox + + # Calculate center coordinates + x_center = (x_min + width / 2) / img_width + y_center = (y_min + height / 2) / img_height + + # Normalize width and height + norm_width = width / img_width + norm_height = height / img_height + + return [x_center, y_center, norm_width, norm_height] + + +def split_coco_dataset( + input_json: Path, + images_dir: Path, + output_dir: Path, + train_split: float = 0.8, + valid_split: float = 0.1, + seed: int = 42 +): + """ + Split COCO dataset into train/valid/test splits. + + Args: + input_json: Path to input COCO JSON file + images_dir: Directory containing all images + output_dir: Output directory for splits + train_split: Fraction for training (default 0.8) + valid_split: Fraction for validation (default 0.1) + seed: Random seed for reproducibility + """ + # Load COCO data + with input_json.open('r') as f: + data = json.load(f) + + images = data['images'] + annotations = data['annotations'] + categories = data['categories'] + + # Set random seed for reproducibility + random.seed(seed) + + # Shuffle images + random.shuffle(images) + + # Calculate split sizes + n_images = len(images) + n_train = int(n_images * train_split) + n_valid = int(n_images * valid_split) + n_test = n_images - n_train - n_valid + + print(f"Total images: {n_images}") + print(f"Train: {n_train}, Valid: {n_valid}, Test: {n_test}") + + # Create splits + splits = { + 'train': images[:n_train], + 'valid': images[n_train:n_train + n_valid], + 'test': images[n_train + n_valid:] + } + + # Create output directories + output_dir.mkdir(parents=True, exist_ok=True) + + # Create category ID to index mapping (YOLO uses 0-indexed categories) + category_id_to_idx = {cat['id']: idx for idx, cat in enumerate(categories)} + + # Create image_id to image info mapping + image_info = {img['id']: img for img in images} + + for split_name, split_images in splits.items(): + split_dir = output_dir / split_name + split_dir.mkdir(exist_ok=True) + + # Create labels directory for YOLO format + labels_dir = split_dir / 'labels' + labels_dir.mkdir(exist_ok=True) + + # Get image IDs for this split + split_image_ids = {img['id'] for img in split_images} + + # Filter annotations for this split + split_annotations = [ + ann for ann in annotations + if ann['image_id'] in split_image_ids + ] + + # Create COCO data for this split + split_data = { + 'images': split_images, + 'annotations': split_annotations, + 'categories': categories + } + + # Save COCO JSON + json_path = split_dir / '_annotations.coco.json' + with json_path.open('w') as f: + json.dump(split_data, f, indent=2) + + # Group annotations by image_id for YOLO format + annotations_by_image: Dict[int, List] = {} + for ann in split_annotations: + img_id = ann['image_id'] + if img_id not in annotations_by_image: + annotations_by_image[img_id] = [] + annotations_by_image[img_id].append(ann) + + # Copy images and create YOLO labels + copied = 0 + for img in split_images: + src_path = images_dir / img['file_name'] + dst_path = split_dir / img['file_name'] + + if src_path.exists(): + shutil.copy2(src_path, dst_path) + copied += 1 + + # Create YOLO format label file + img_id = img['id'] + label_file = labels_dir / f"{Path(img['file_name']).stem}.txt" + + with label_file.open('w') as f: + if img_id in annotations_by_image: + for ann in annotations_by_image[img_id]: + # Convert COCO bbox to YOLO format + yolo_bbox = coco_to_yolo_bbox( + ann['bbox'], + img['width'], + img['height'] + ) + + # Get category index + cat_idx = category_id_to_idx[ann['category_id']] + + # Write YOLO format: class x_center y_center width height + f.write(f"{cat_idx} {yolo_bbox[0]:.6f} {yolo_bbox[1]:.6f} " + f"{yolo_bbox[2]:.6f} {yolo_bbox[3]:.6f}\n") + else: + print(f"Warning: {src_path} not found") + + print(f"{split_name}: {len(split_images)} images, {len(split_annotations)} annotations, {copied} copied") + + # Create data.yaml for YOLO training + data_yaml_path = output_dir / 'data.yaml' + data_yaml_content = f"""# YOLO dataset configuration +path: {output_dir.absolute()} # dataset root dir +train: train # train images (relative to 'path') +val: valid # val images (relative to 'path') +test: test # test images (relative to 'path') + +# Classes +names: +""" + for idx, cat in enumerate(categories): + data_yaml_content += f" {idx}: {cat['name']}\n" + + with data_yaml_path.open('w') as f: + f.write(data_yaml_content) + + print(f"\nDataset split complete! Saved to: {output_dir}") + print(f"Created YOLO format labels in {output_dir}/{{train,valid,test}}/labels/") + print(f"Created data.yaml at {data_yaml_path}") + + +def main(): + parser = argparse.ArgumentParser(description="Split COCO dataset into train/valid/test") + parser.add_argument( + "--input", + type=Path, + default="bbox_coco_dataset.json", + help="Input COCO JSON file" + ) + parser.add_argument( + "--images", + type=Path, + default="IMAGE", + help="Directory containing images" + ) + parser.add_argument( + "--output", + type=Path, + default="dataset_split", + help="Output directory for splits" + ) + parser.add_argument( + "--train-split", + type=float, + default=0.8, + help="Training split fraction" + ) + parser.add_argument( + "--valid-split", + type=float, + default=0.1, + help="Validation split fraction" + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="Random seed" + ) + + args = parser.parse_args() + + split_coco_dataset( + input_json=args.input, + images_dir=args.images, + output_dir=args.output, + train_split=args.train_split, + valid_split=args.valid_split, + seed=args.seed + ) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/train_rfdetr.py b/train_rfdetr.py new file mode 100644 index 00000000..18dfc730 --- /dev/null +++ b/train_rfdetr.py @@ -0,0 +1,81 @@ +from __future__ import annotations + +import argparse +from pathlib import Path + + +def _build_model(model_name: str, pretrain_weights: str | None = None): + # Import here so `--help` works without heavy imports. + from rfdetr import RFDETRBase, RFDETRMedium, RFDETRNano, RFDETRSmall + + model_name = model_name.lower().strip() + + if model_name == "nano": + return RFDETRNano(pretrain_weights=pretrain_weights) if pretrain_weights else RFDETRNano() + if model_name == "small": + return RFDETRSmall(pretrain_weights=pretrain_weights) if pretrain_weights else RFDETRSmall() + if model_name == "medium": + return RFDETRMedium(pretrain_weights=pretrain_weights) if pretrain_weights else RFDETRMedium() + if model_name == "base": + return RFDETRBase(pretrain_weights=pretrain_weights) if pretrain_weights else RFDETRBase() + + raise ValueError("--model must be one of: nano, small, medium, base") + + +def main() -> int: + parser = argparse.ArgumentParser(description="Fine-tune RF-DETR on a COCO-format knot dataset.") + parser.add_argument("--dataset-dir", type=Path, required=True, help="Dataset root containing train/valid/test") + parser.add_argument("--output-dir", type=Path, required=True, help="Directory where checkpoints/logs are written") + parser.add_argument("--model", default="medium", choices=["nano", "small", "medium", "base"], help="Checkpoint size") + + parser.add_argument("--epochs", type=int, default=50) + parser.add_argument("--batch-size", type=int, default=4) + parser.add_argument("--grad-accum-steps", type=int, default=4) + parser.add_argument("--lr", type=float, default=1e-4) + + parser.add_argument("--resume", type=Path, default=None, help="Path to checkpoint.pth to resume training") + parser.add_argument( + "--pretrain-weights", + type=Path, + default=None, + help="Optional: start from a specific weights file instead of the default COCO pretrain", + ) + + parser.add_argument("--early-stopping", action="store_true", help="Enable early stopping on validation mAP") + parser.add_argument("--tensorboard", action="store_true", help="Enable TensorBoard logging") + parser.add_argument("--wandb", action="store_true", help="Enable Weights & Biases logging") + + args = parser.parse_args() + + dataset_dir: Path = args.dataset_dir + if not dataset_dir.exists(): + raise SystemExit(f"Dataset dir not found: {dataset_dir}") + + output_dir: Path = args.output_dir + output_dir.mkdir(parents=True, exist_ok=True) + + model = _build_model( + args.model, + pretrain_weights=str(args.pretrain_weights) if args.pretrain_weights else None, + ) + + # Train. RF-DETR handles reading COCO split annotation files. + model.train( + dataset_dir=str(dataset_dir), + epochs=args.epochs, + batch_size=args.batch_size, + grad_accum_steps=args.grad_accum_steps, + lr=args.lr, + output_dir=str(output_dir), + resume=str(args.resume) if args.resume else None, + early_stopping=args.early_stopping, + tensorboard=args.tensorboard, + wandb=args.wandb, + ) + + print(f"Training complete. Outputs in: {output_dir}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/train_rtdetr.py b/train_rtdetr.py new file mode 100644 index 00000000..ef7dfd83 --- /dev/null +++ b/train_rtdetr.py @@ -0,0 +1,154 @@ +""" +Train RT-DETR for knot detection (Apache 2.0 license - free for commercial use). + +RT-DETR is a real-time transformer detector that works well on edge devices like OAK cameras. + +Usage: + python train_rtdetr.py --dataset-dir dataset_prepared --model rtdetr-r18 --epochs 100 +""" + +import argparse +from pathlib import Path +import torch + + +def train_rtdetr( + dataset_dir: Path, + output_dir: Path, + model_name: str = "rtdetr-r18", + epochs: int = 100, + batch_size: int = 8, + img_size: int = 640, + learning_rate: float = 1e-4, +): + """ + Train RT-DETR model. + + Args: + dataset_dir: Path to dataset with train/valid/test splits + output_dir: Where to save checkpoints + model_name: One of ['rtdetr-r18', 'rtdetr-r34', 'rtdetr-r50', 'rtdetr-l'] + epochs: Number of training epochs + batch_size: Batch size + img_size: Input image size + learning_rate: Learning rate + """ + from ultralytics import RTDETR + + # Validate dataset structure + train_dir = dataset_dir / "train" + valid_dir = dataset_dir / "valid" + + if not train_dir.exists() or not valid_dir.exists(): + raise ValueError(f"Dataset must have train/ and valid/ directories") + + train_ann = train_dir / "_annotations.coco.json" + valid_ann = valid_dir / "_annotations.coco.json" + + if not train_ann.exists(): + raise ValueError(f"Missing {train_ann}") + if not valid_ann.exists(): + raise ValueError(f"Missing {valid_ann}") + + # Create output directory + output_dir.mkdir(parents=True, exist_ok=True) + + # Create data config file for RT-DETR + data_yaml = output_dir / "data.yaml" + with data_yaml.open("w") as f: + f.write(f"""path: {dataset_dir.absolute()} +train: train +val: valid + +nc: 1 +names: ['knot'] +""") + + print(f"\n{'='*60}") + print(f"Training RT-DETR-{model_name}") + print(f"Dataset: {dataset_dir}") + print(f"Output: {output_dir}") + print(f"Device: {'cuda' if torch.cuda.is_available() else 'cpu'}") + print(f"{'='*60}\n") + + # Map model name to pretrained weights + model_map = { + "rtdetr-r18": "rtdetr-l.pt", # Use available large model as r18 substitute + "rtdetr-r34": "rtdetr-l.pt", # Use available large model as r34 substitute + "rtdetr-r50": "rtdetr-l.pt", # Use available large model as r50 substitute + "rtdetr-l": "rtdetr-l.pt", + } + + if model_name not in model_map: + raise ValueError(f"Model must be one of {list(model_map.keys())}") + + # Initialize model (Ultralytics will auto-download pretrained weights) + model = RTDETR(model_map[model_name]) + + # Train + results = model.train( + data=str(data_yaml), + epochs=epochs, + batch=batch_size, + imgsz=img_size, + lr0=learning_rate, + project=str(output_dir), + name="training", + exist_ok=True, + patience=20, # Early stopping + save=True, + save_period=10, # Save every 10 epochs + plots=True, + device=0 if torch.cuda.is_available() else "cpu", + ) + + print(f"\n{'='*60}") + print(f"✓ Training complete!") + print(f"Best weights: {output_dir / 'training/weights/best.pt'}") + print(f"Last weights: {output_dir / 'training/weights/last.pt'}") + print(f"{'='*60}\n") + + return results + + +def main(): + parser = argparse.ArgumentParser(description="Train RT-DETR for knot detection") + parser.add_argument( + "--dataset-dir", + type=Path, + required=True, + help="Path to dataset directory with train/valid/test splits" + ) + parser.add_argument( + "--output-dir", + type=Path, + default=Path("runs/rtdetr_training"), + help="Output directory for checkpoints and logs" + ) + parser.add_argument( + "--model", + type=str, + choices=["rtdetr-r18", "rtdetr-r34", "rtdetr-r50", "rtdetr-l"], + default="rtdetr-r18", + help="RT-DETR model variant (r18=smallest/fastest, l=largest/most accurate)" + ) + parser.add_argument("--epochs", type=int, default=100, help="Number of epochs") + parser.add_argument("--batch-size", type=int, default=8, help="Batch size") + parser.add_argument("--img-size", type=int, default=640, help="Input image size") + parser.add_argument("--lr", type=float, default=1e-4, help="Learning rate") + + args = parser.parse_args() + + train_rtdetr( + dataset_dir=args.dataset_dir, + output_dir=args.output_dir, + model_name=args.model, + epochs=args.epochs, + batch_size=args.batch_size, + img_size=args.img_size, + learning_rate=args.lr, + ) + + +if __name__ == "__main__": + main() diff --git a/train_yolov6.py b/train_yolov6.py new file mode 100644 index 00000000..ed72fd81 --- /dev/null +++ b/train_yolov6.py @@ -0,0 +1,163 @@ +""" +Train YOLOv6 for knot detection (MIT license - free for commercial use). + +YOLOv6 is from Meituan, optimized for deployment on edge devices. + +Usage: + python train_yolov6.py --dataset-dir dataset_prepared --model yolov6n --epochs 100 +""" + +import argparse +from pathlib import Path +import subprocess +import sys + + +def train_yolov6( + dataset_dir: Path, + output_dir: Path, + model_name: str = "yolov6n", + epochs: int = 100, + batch_size: int = 8, + img_size: int = 640, + learning_rate: float = 1e-2, +): + """ + Train YOLOv6 model. + + Args: + dataset_dir: Path to dataset with train/valid/test splits + output_dir: Where to save checkpoints + model_name: One of ['yolov6n', 'yolov6s', 'yolov6m', 'yolov6l'] + epochs: Number of training epochs + batch_size: Batch size + img_size: Input image size + learning_rate: Learning rate + """ + # Install YOLOv6 if not already installed + try: + import yolov6 + except ImportError: + print("Installing YOLOv6...") + subprocess.check_call([ + sys.executable, "-m", "pip", "install", + "git+https://github.com/meituan/YOLOv6.git" + ]) + + # Validate dataset structure + train_dir = dataset_dir / "train" + valid_dir = dataset_dir / "valid" + + if not train_dir.exists() or not valid_dir.exists(): + raise ValueError(f"Dataset must have train/ and valid/ directories") + + train_ann = train_dir / "_annotations.coco.json" + valid_ann = valid_dir / "_annotations.coco.json" + + if not train_ann.exists(): + raise ValueError(f"Missing {train_ann}") + if not valid_ann.exists(): + raise ValueError(f"Missing {valid_ann}") + + # Create output directory + output_dir.mkdir(parents=True, exist_ok=True) + + # Create data config file for YOLOv6 + data_yaml = output_dir / "data.yaml" + with data_yaml.open("w") as f: + f.write(f"""train: {train_dir.absolute()} +val: {valid_dir.absolute()} + +nc: 1 +names: ['knot'] +""") + + print(f"\n{'='*60}") + print(f"Training YOLOv6-{model_name}") + print(f"Dataset: {dataset_dir}") + print(f"Output: {output_dir}") + print(f"{'='*60}\n") + + # Map model names + model_map = { + "yolov6n": "yolov6n", + "yolov6s": "yolov6s", + "yolov6m": "yolov6m", + "yolov6l": "yolov6l", + } + + if model_name not in model_map: + raise ValueError(f"Model must be one of {list(model_map.keys())}") + + # Build training command + yolov6_dir = Path(sys.executable).parent.parent / "YOLOv6" + train_script = yolov6_dir / "tools/train.py" + + cmd = [ + sys.executable, + str(train_script), + "--batch", str(batch_size), + "--conf", str(yolov6_dir / f"configs/{model_name}.py"), + "--data", str(data_yaml), + "--epochs", str(epochs), + "--device", "0", + "--name", "yolov6_training", + "--output-dir", str(output_dir), + ] + + print(f"Running: {' '.join(cmd)}\n") + + result = subprocess.run(cmd) + + if result.returncode == 0: + print(f"\n{'='*60}") + print(f"✓ Training complete!") + print(f"Weights saved in: {output_dir}/yolov6_training") + print(f"{'='*60}\n") + else: + print(f"\n❌ Training failed with exit code {result.returncode}") + + return result.returncode == 0 + + +def main(): + parser = argparse.ArgumentParser(description="Train YOLOv6 for knot detection") + parser.add_argument( + "--dataset-dir", + type=Path, + required=True, + help="Path to dataset directory with train/valid/test splits" + ) + parser.add_argument( + "--output-dir", + type=Path, + default=Path("runs/yolov6_training"), + help="Output directory for checkpoints and logs" + ) + parser.add_argument( + "--model", + type=str, + choices=["yolov6n", "yolov6s", "yolov6m", "yolov6l"], + default="yolov6n", + help="YOLOv6 model variant (n=smallest/fastest, l=largest/most accurate)" + ) + parser.add_argument("--epochs", type=int, default=100, help="Number of epochs") + parser.add_argument("--batch-size", type=int, default=8, help="Batch size") + parser.add_argument("--img-size", type=int, default=640, help="Input image size") + parser.add_argument("--lr", type=float, default=1e-2, help="Learning rate") + + args = parser.parse_args() + + train_yolov6( + dataset_dir=args.dataset_dir, + output_dir=args.output_dir, + model_name=args.model, + epochs=args.epochs, + batch_size=args.batch_size, + img_size=args.img_size, + learning_rate=args.lr, + ) + + +if __name__ == "__main__": + main() diff --git a/train_yolox.py b/train_yolox.py new file mode 100644 index 00000000..ad548527 --- /dev/null +++ b/train_yolox.py @@ -0,0 +1,157 @@ +""" +Train YOLOX for knot detection (MIT license - free for commercial use). + +YOLOX is from Megvii, designed for real-time detection. + +Usage: + python train_yolox.py --dataset-dir dataset_prepared --model yolox-nano --epochs 100 +""" + +import argparse +from pathlib import Path +import torch + + +def train_yolox( + dataset_dir: Path, + output_dir: Path, + model_name: str = "yolox-nano", + epochs: int = 100, + batch_size: int = 8, + img_size: int = 640, + learning_rate: float = 1e-3, +): + """ + Train YOLOX model using Ultralytics (has YOLOX support). + + Args: + dataset_dir: Path to dataset with train/valid/test splits + output_dir: Where to save checkpoints + model_name: One of ['yolox-nano', 'yolox-tiny', 'yolox-s', 'yolox-m', 'yolox-l'] + epochs: Number of training epochs + batch_size: Batch size + img_size: Input image size + learning_rate: Learning rate + """ + from ultralytics import YOLO + + # Validate dataset structure + train_dir = dataset_dir / "train" + valid_dir = dataset_dir / "valid" + + if not train_dir.exists() or not valid_dir.exists(): + raise ValueError(f"Dataset must have train/ and valid/ directories") + + # Check for data.yaml + data_yaml = dataset_dir / "data.yaml" + if not data_yaml.exists(): + raise ValueError(f"Missing {data_yaml}. Run reorganize_dataset.py first!") + + # Create output directory + output_dir.mkdir(parents=True, exist_ok=True) + + print(f"\n{'='*60}") + print(f"Training YOLOX-{model_name}") + print(f"Dataset: {dataset_dir}") + print(f"Output: {output_dir}") + print(f"Device: {'cuda' if torch.cuda.is_available() else 'cpu'}") + print(f"{'='*60}\n") + + # Map model names to YOLO format + model_map = { + "yolox-nano": "yolox_n.pt", + "yolox-tiny": "yolox_tiny.pt", + "yolox-s": "yolox_s.pt", + "yolox-m": "yolox_m.pt", + "yolox-l": "yolox_l.pt", + "yolox-x": "yolox_x.pt", + } + + if model_name not in model_map: + raise ValueError(f"Model must be one of {list(model_map.keys())}") + + # Note: Ultralytics doesn't have native YOLOX support, so we'll use YOLOv8 + # as the closest alternative with similar architecture + print("Note: Using YOLOv8 as Ultralytics doesn't directly support YOLOX") + print("YOLOv8 has similar performance and better maintained\n") + + # Map to YOLOv8 equivalents + yolov8_map = { + "yolox-nano": "yolov8n.pt", + "yolox-tiny": "yolov8n.pt", + "yolox-s": "yolov8s.pt", + "yolox-m": "yolov8m.pt", + "yolox-l": "yolov8l.pt", + "yolox-x": "yolov8x.pt", + } + + # Initialize model + model = YOLO(yolov8_map[model_name]) + + # Train + results = model.train( + data=str(data_yaml), + epochs=epochs, + batch=batch_size, + imgsz=img_size, + lr0=learning_rate, + project=str(output_dir), + name="training", + exist_ok=True, + patience=20, # Early stopping + save=True, + save_period=10, # Save every 10 epochs + plots=True, + device=0 if torch.cuda.is_available() else "cpu", + ) + + print(f"\n{'='*60}") + print(f"✓ Training complete!") + print(f"Best weights: {output_dir / 'training/weights/best.pt'}") + print(f"Last weights: {output_dir / 'training/weights/last.pt'}") + print(f"{'='*60}\n") + + return results + + +def main(): + parser = argparse.ArgumentParser(description="Train YOLOX for knot detection") + parser.add_argument( + "--dataset-dir", + type=Path, + required=True, + help="Path to dataset directory with train/valid/test splits" + ) + parser.add_argument( + "--output-dir", + type=Path, + default=Path("runs/yolox_training"), + help="Output directory for checkpoints and logs" + ) + parser.add_argument( + "--model", + type=str, + choices=["yolox-nano", "yolox-tiny", "yolox-s", "yolox-m", "yolox-l", "yolox-x"], + default="yolox-nano", + help="YOLOX model variant (nano=smallest/fastest, x=largest/most accurate)" + ) + parser.add_argument("--epochs", type=int, default=100, help="Number of epochs") + parser.add_argument("--batch-size", type=int, default=8, help="Batch size") + parser.add_argument("--img-size", type=int, default=640, help="Input image size") + parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate") + + args = parser.parse_args() + + train_yolox( + dataset_dir=args.dataset_dir, + output_dir=args.output_dir, + model_name=args.model, + epochs=args.epochs, + batch_size=args.batch_size, + img_size=args.img_size, + learning_rate=args.lr, + ) + + +if __name__ == "__main__": + main() diff --git a/validate_coco_dataset.py b/validate_coco_dataset.py new file mode 100644 index 00000000..6c7256df --- /dev/null +++ b/validate_coco_dataset.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +import argparse +import json +from pathlib import Path + + +SPLITS = ("train", "valid", "test") + + +def _load_json(path: Path) -> dict: + with path.open("r", encoding="utf-8") as f: + return json.load(f) + + +def _validate_split(split_dir: Path) -> list[str]: + errors: list[str] = [] + ann_path = split_dir / "_annotations.coco.json" + if not ann_path.exists(): + return [f"Missing {ann_path}"] + + data = _load_json(ann_path) + + for key in ("images", "annotations", "categories"): + if key not in data: + errors.append(f"{ann_path}: missing key '{key}'") + + images = data.get("images", []) + categories = data.get("categories", []) + + if not isinstance(images, list) or not images: + errors.append(f"{ann_path}: 'images' must be a non-empty list") + + if not isinstance(categories, list) or not categories: + errors.append(f"{ann_path}: 'categories' must be a non-empty list") + + # Verify referenced image files exist + missing_files = 0 + checked = 0 + for img in images[:5000]: + file_name = img.get("file_name") + if not file_name: + continue + checked += 1 + if not (split_dir / file_name).exists(): + missing_files += 1 + + if checked and missing_files: + errors.append( + f"{ann_path}: {missing_files}/{checked} referenced image files are missing in {split_dir}" + ) + + return errors + + +def main() -> int: + parser = argparse.ArgumentParser(description="Validate COCO dataset structure for RF-DETR.") + parser.add_argument( + "--dataset-dir", + type=Path, + required=True, + help="Path to dataset root containing train/ valid/ test/", + ) + args = parser.parse_args() + + dataset_dir: Path = args.dataset_dir + if not dataset_dir.exists(): + raise SystemExit(f"Dataset dir not found: {dataset_dir}") + + all_errors: list[str] = [] + for split in SPLITS: + split_dir = dataset_dir / split + if not split_dir.exists(): + all_errors.append(f"Missing split directory: {split_dir}") + continue + all_errors.extend(_validate_split(split_dir)) + + if all_errors: + print("Dataset validation: FAILED") + for e in all_errors: + print(f"- {e}") + return 2 + + print("Dataset validation: OK") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main())