Mussel Gooseneck Rgb

The Mussel-Gooseneck-RGB model is a deep learning semantic segmentation model specifically trained for detecting and differentiating between mussels and gooseneck barnacles in RGB drone imagery. This model enables detailed intertidal monitoring and research by providing species-specific segmentation maps for marine habitat assessment.

Key Features:

• Multi-species classification (mussels vs. gooseneck barnacles)
• Optimized for standard RGB imagery from drones
• ImageNet-pretrained normalization statistics
• Efficient ONNX format for cross-platform deployment
• Designed for intertidal zone monitoring

Model Details

• Version: 20250725
• Input Channels: 3 (RGB)
• Input Size: Dynamic tiling (recommended: 2048x2048 tiles)
• Normalization: Standard (ImageNet statistics)
• Output: Multi-class segmentation (0: background, 1: mussels, 2: gooseneck barnacles)
• Format: ONNX

Normalization Parameters

The model expects input images to be normalized using ImageNet statistics:

{
  "mean": [0.485, 0.456, 0.406],
  "std": [0.229, 0.224, 0.225],
  "max_pixel_value": 255.0
}

Usage

1. Using kelp-o-matic CLI (recommended)

For command-line usage:

# Install kelp-o-matic
pip install git+https://github.com/HakaiInstitute/kelp-o-matic@dev

# List available models
kom list-models

# Run segmentation on intertidal RGB drone imagery
kom segment \
    --model mussel-gooseneck-rgb \
    --input /path/to/intertidal_rgb_image.tif \
    --output /path/to/species_segmentation.tif \
    --batch-size 8 \
    --crop-size 2048 \
    --blur-kernel 3 \
    --morph-kernel 3

# Use specific model version
kom segment \
    --model mussel-gooseneck-rgb \
    --version 20250725 \
    --input image.tif \
    --output result.tif

# For high-resolution intertidal surveys
kom segment \
    --model mussel-gooseneck-rgb \
    --input high_res_intertidal_survey.tif \
    --output result.tif \
    --batch-size 4 \
    --crop-size 1024

2. Using kelp-o-matic Python API

The easiest way to use this model is through the kelp-o-matic package:

from kelp_o_matic import model_registry

# Load the model (automatically downloads if needed)
model = model_registry["mussel-gooseneck-rgb"]

# Process a large intertidal survey image with automatic tiling
model.process(
    input_path="path/to/your/intertidal_rgb_image.tif",
    output_path="path/to/output/species_segmentation.tif",
    batch_size=8,  # Higher batch size for RGB
    crop_size=2048,
    blur_kernel_size=3,  # Gentle post-processing
    morph_kernel_size=3,  # Morphological operations
)

# For more control, use the predict method directly
import rasterio
import numpy as np

with rasterio.open("intertidal_image.tif") as src:
    # Read a 2048x2048 tile (3 bands: RGB)
    tile = src.read(window=((0, 2048), (0, 2048)))  # Shape: (3, 2048, 2048)
    tile = np.transpose(tile, (1, 2, 0))  # Convert to HWC
    
    # Add batch dimension and predict
    batch = np.expand_dims(tile, axis=0)  # Shape: (1, 2048, 2048, 3)
    batch = np.transpose(batch, (0, 3, 1, 2))  # Convert to BCHW
    
    # Run inference (preprocessing handled automatically)
    predictions = model.predict(batch)
    
    # Post-process to get final segmentation
    segmentation = model.postprocess(predictions)
    # Result contains class labels: 0=background, 1=mussels, 2=gooseneck barnacles

3. Direct ONNX Runtime Usage

import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
from PIL import Image

# Download the model
model_path = hf_hub_download(repo_id="HakaiInstitute/mussel-gooseneck-rgb", filename="model.onnx")

# Load the model
session = ort.InferenceSession(model_path)

# ImageNet normalization parameters
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])

# Preprocess your RGB image
def preprocess(image):
    """
    Preprocess RGB image for model input
    image: numpy array of shape [height, width, 3] with pixel values 0-255
    """
    # Normalize to 0-1
    image = image.astype(np.float32) / 255.0
    
    # Apply ImageNet normalization
    image = (image - mean) / std
    
    # Reshape to model input format [batch, channels, height, width]
    image = np.transpose(image, (2, 0, 1))  # HWC to CHW
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    
    return image

# Load and preprocess image
image = np.array(Image.open("intertidal_drone_image.jpg"))
preprocessed = preprocess(image)

# Run inference
input_name = session.get_inputs()[0].name
output = session.run(None, {input_name: preprocessed})

# Postprocess to get multi-class mask
logits = output[0]
prediction = np.argmax(logits, axis=1).squeeze(0).astype(np.uint8)
# Result: 0=background, 1=mussels, 2=gooseneck barnacles

4. Using HuggingFace Hub Integration

from huggingface_hub import hf_hub_download
import onnxruntime as ort

# Download and load model
model_path = hf_hub_download(
    repo_id="HakaiInstitute/mussel-gooseneck-rgb",
    filename="model.onnx",
    cache_dir="./models"
)

session = ort.InferenceSession(model_path)
# ... continue with preprocessing and inference as above

Installation

For kelp-o-matic usage:

# Via pip
pip install git+https://github.com/HakaiInstitute/kelp-o-matic@dev

For direct ONNX usage:

pip install onnxruntime huggingface-hub numpy pillow
# For GPU support:
pip install onnxruntime-gpu

Input Requirements

• Image Format: 3-band RGB raster (JPEG, PNG, GeoTIFF)
• Band Order: Red, Green, Blue
• Pixel Values: Standard 8-bit (0-255 range)
• Spatial Resolution: Optimized for high-resolution drone imagery (cm-level)
• Scene Type: Intertidal zones with exposed mussel beds and barnacle colonies

Output Format

• Type: Single-band raster with class labels
• Values:
  • 0: Background (water, rocks, other substrates)
  • 1: Mussels (typically blue mussels, Mytilus spp.)
  • 2: Gooseneck barnacles (Pollicipes spp.)
• Format: Matches input raster format and projection
• Spatial Resolution: Same as input

Note: The model outputs class probabilities, but kelp-o-matic automatically applies argmax to convert these to discrete class labels.

Performance Notes

• Dynamic Tile Size: Supports flexible tile sizes (recommended: 2048x2048 or 1024x1024)
• Batch Size: Start with 4, adjust based on available GPU memory

Large Image Processing

For processing large geospatial images, the kelp-o-matic package handles:

• Automatic Tiling: Splits large images into manageable tiles
• Overlap Handling: Uses overlapping tiles to avoid edge artifacts
• Memory Management: Processes tiles in batches to manage memory usage
• Geospatial Metadata: Preserves coordinate reference system and geotransforms
• Post-processing: Optional median filtering and morphological operations

Citation

If you use this model in your research, please cite:

@software{Denouden_Kelp-O-Matic,
  author = {Denouden, Taylor and Reshitnyk, Luba},
  doi = {10.5281/zenodo.7672166},
  title = {{Kelp-O-Matic}},
  url = {https://github.com/HakaiInstitute/kelp-o-matic}
}

License

MIT License - see the kelp-o-matic repository for details.

Related Resources

• Documentation: kelp-o-matic.readthedocs.io
• Source Code: github.com/HakaiInstitute/kelp-o-matic
• Other Models: Check the Hakai Institute HuggingFace organization for additional marine segmentation models

Contact

For questions or issues:

• Open an issue on the GitHub repository
• Contact: Hakai Institute