fiftyone-dataset-import

What this skill does

# Universal Dataset Import for FiftyOne

## Key Directives

**ALWAYS follow these rules:**

### 1. Scan folder FIRST
Before any import, deeply scan the directory to understand its structure:
```bash
# Use bash to explore
find /path/to/data -type f | head -50
ls -la /path/to/data
```

### 2. Auto-detect everything
Detect media types, label formats, and grouping patterns automatically. Never ask the user to specify format if it can be inferred.

### 3. Detect multimodal groups
Look for patterns that indicate grouped data:
- Scene folders containing multiple media files
- Filename patterns with common prefixes (e.g., `scene_001_left.jpg`, `scene_001_right.jpg`)
- Mixed media types that should be grouped (images + point clouds)

### 4. Detect and install required packages
Many specialized dataset formats require external Python packages. After detecting the format:

1. **Identify required packages** based on the detected format
2. **Check if packages are installed** using `pip show <package>`
3. **Search for installation instructions** if needed (use web search or FiftyOne docs)
4. **Ask user for permission** before installing any packages
5. **Install required packages** (see installation methods below)
6. **Verify installation** before proceeding

**Common format-to-package mappings:**

| Dataset Format | Package Name | Install Command |
|---------------|--------------|-----------------|
| PandaSet | `pandaset` | `pip install "git+https://github.com/scaleapi/pandaset-devkit.git#subdirectory=python"` |
| nuScenes | `nuscenes-devkit` | `pip install nuscenes-devkit` |
| Waymo Open | `waymo-open-dataset-tf` | See Waymo docs (requires TensorFlow) |
| Argoverse 2 | `av2` | `pip install av2` |
| KITTI 3D | `pykitti` | `pip install pykitti` |
| Lyft L5 | `l5kit` | `pip install l5kit` |
| A2D2 | `a2d2` | See Audi A2D2 docs |

**Additional packages for 3D processing:**

| Purpose | Package Name | Install Command |
|---------|--------------|-----------------|
| Point cloud conversion to PCD | `open3d` | `pip install open3d` |
| Point cloud processing | `pyntcloud` | `pip install pyntcloud` |
| LAS/LAZ point clouds | `laspy` | `pip install laspy` |

**Additional packages for Hugging Face Hub:**

| Purpose | Package Name | Install Command |
|---------|--------------|-----------------|
| HF Hub API | `huggingface_hub` | `pip install huggingface_hub` |
| Parquet file reading | `pyarrow` | `pip install pyarrow` |
| Image processing | `Pillow` | `pip install Pillow` |

**Installation methods (in order of preference):**

1. **PyPI** - Standard pip install:
   ```bash
   pip install <package-name>
   ```

2. **GitHub URL** - When package is not on PyPI:
   ```bash
   # Standard GitHub install
   pip install "git+https://github.com/<org>/<repo>.git"

   # With subdirectory (for monorepos)
   pip install "git+https://github.com/<org>/<repo>.git#subdirectory=python"

   # Specific branch or tag
   pip install "git+https://github.com/<org>/<repo>[email protected]"
   ```

3. **Clone and install** - For complex builds:
   ```bash
   git clone https://github.com/<org>/<repo>.git
   cd <repo>
   pip install .
   ```

**Dynamic package discovery workflow:**

If the format is not in the table above:
1. **Search PyPI** for `<format-name>`, `<format-name>-devkit`, or `<format-name>-sdk`
2. **Search GitHub** for `<format-name> devkit` or `<format-name> python`
3. **Search web** for "FiftyOne import <format-name>" or "<format-name> python tutorial"
4. **Check the dataset's official website** for developer tools/SDK
5. **Present findings to user** with installation options

**After installation:**
1. **Verify** the package is installed: `pip show <package-name>`
2. **Test import** in Python: `python -c "from <package> import ..."`
3. **Search for FiftyOne integration** examples or write custom import code

### 5. Confirm before importing
Present findings to user and **explicitly ask for confirmation** before creating the dataset.
Always end your scan summary with a clear question like:
- "Proceed with import?"
- "Should I create the dataset with these settings?"

**Wait for user response before proceeding.** Do not create the dataset until the user confirms.

### 6. Check for existing datasets and generate names
Before creating a dataset, check if the proposed name already exists:
```python
list_datasets()
```
If the dataset name exists, ask the user:
- **Overwrite**: Delete existing and create new
- **Rename**: Use a different name (suggest alternatives like `dataset-name-v2`)
- **Abort**: Cancel the import

**If no dataset name was provided by the user**, generate a fun unique name using the pattern `{ADJECTIVE}_{NOUN}_{VERB}`:
- `squiggly_sky_floats`
- `droopy_horse_slams`
- `smooth_grass_grumbles`
- `nervous_hamburger_yodels`

Cross-check against `list_datasets()` to ensure uniqueness. **Always inform the user of the generated name before proceeding.**

### 7. Validate after import
Compare imported sample count with source file count. Report any discrepancies.

### 8. Report errors minimally to user
Keep error messages simple for the user. Use detailed error info internally to diagnose issues.

## Complete Workflow

### Step 1: Deep Folder Scan

Scan the target directory to understand its structure:

```bash
# Count files by extension
find /path/to/data -type f | sed 's/.*\.//' | sort | uniq -c | sort -rn

# List directory structure (2 levels deep)
find /path/to/data -maxdepth 2 -type d

# Sample some files
ls -la /path/to/data/* | head -20

# IMPORTANT: Scan for ALL annotation/label directories
ls -la /path/to/data/annotations/ 2>/dev/null || ls -la /path/to/data/labels/ 2>/dev/null
```

Build an inventory of:
- Media files by type (images, videos, point clouds, 3D)
- Label files by format (JSON, XML, TXT, YAML, PKL)
- Directory structure (flat vs nested vs scene-based)
- **ALL annotation types present** (cuboids, segmentation, tracking, etc.)

**For 3D/Autonomous Driving datasets, specifically check:**
```bash
# List all annotation subdirectories
find /path/to/data -type d -name "annotations" -o -name "labels" | xargs -I {} ls -la {}

# Sample an annotation file to understand its structure
python3 -c "import pickle, gzip; print(pickle.load(gzip.open('path/to/annotation.pkl.gz', 'rb'))[:2])"
```

### Step 2: Identify Media Types

Classify files by extension:

| Extensions | Media Type | FiftyOne Type |
|------------|------------|---------------|
| `.jpg`, `.jpeg`, `.png`, `.gif`, `.bmp`, `.webp`, `.tiff` | Image | `image` |
| `.mp4`, `.avi`, `.mov`, `.mkv`, `.webm` | Video | `video` |
| `.pcd`, `.ply`, `.las`, `.laz` | Point Cloud | `point-cloud` |
| `.fo3d`, `.obj`, `.gltf`, `.glb` | 3D Scene | `3d` |

### Step 3: Detect Label Format

Identify label format from file patterns:

| Pattern | Format | Dataset Type |
|---------|--------|--------------|
| `annotations.json` or `instances*.json` with COCO structure | COCO | `COCO` |
| `*.xml` files with Pascal VOC structure | VOC | `VOC` |
| `*.txt` per image + `classes.txt` | YOLOv4 | `YOLOv4` |
| `data.yaml` + `labels/*.txt` | YOLOv5 | `YOLOv5` |
| `*.txt` per image (KITTI format) | KITTI | `KITTI` |
| Single `annotations.xml` (CVAT format) | CVAT | `CVAT Image` |
| `*.json` with OpenLABEL structure | OpenLABEL | `OpenLABEL Image` |
| Folder-per-class structure | Classification | `Image Classification Directory Tree` |
| `*.csv` with filepath column | CSV | `CSV` |
| `*.json` with GeoJSON structure | GeoJSON | `GeoJSON` |
| `.dcm` DICOM files | DICOM | `DICOM` |
| `.tiff` with geo metadata | GeoTIFF | `GeoTIFF` |

**Specialized Autonomous Driving Formats (require external packages):**

| Directory Pattern | Format | Required Package |
|------------------|--------|------------------|
| `camera/`, `lidar/`, `annotations/cuboids/` with `.pkl.gz` | PandaSet | `pandaset-devkit` |
| `samples/`, `sweeps/`, `v1.0-*` folders | nuScenes | `nuscenes-devkit` |
| `segment-*` with `.tfrecord` files | Waymo Open | `waymo-open-dataset-tf` |
| `argovers