| ## ScalpPipeline (`pipeline.py`) | |
| Refactored pipeline combining all steps into a single class. | |
| ### Usage | |
| ```bash | |
| python pipeline.py --pixel_ratio 2.54 | |
| ``` | |
| Arguments: | |
| - `--root_dir`: Root directory of the project (default: `.`) | |
| - `--pixel_ratio`: Pixel to micrometer ratio (default: `2.54`) | |
| ### Dependencies (Files) | |
| The pipeline requires the following files and directories to exist: | |
| 1. **Input Images**: | |
| - `datasets/data/` : Directory containing input images (`.jpg`, `.jpeg`, `.png`). | |
| 2. **Model Weights**: | |
| - `segmentation/model/U2NET.pth` : Pre-trained U2NET model. | |
| - `sam_vit_h_4b8939.pth` : SAM (Segment Anything Model) checkpoint (ViT-H). | |
| 3. **Code Modules**: | |
| - `segmentation/data_loader.py` : Data loading utilities for U2NET. | |
| ### Output | |
| Results are saved in: | |
| - `datasets/seg_train/` (U2NET masks) | |
| - `prediction/sam_result/sam_val/` (SAM masks) | |
| - `prediction/ensemble_result/ensemble_val/` (Ensemble masks) | |
| - `alopecia/thickness_result/` (Thickness data & visualization) | |
| - `alopecia/count_result/` (Hair count CSV & visualization) | |