Python Library Reference

The Python library provide two functions that run classification on an input image and write data to an output location.

API

kelp_o_matic.find_kelp(source, dest, species=False, crop_size=512, padding=256, batch_size=1, use_gpu=True)

Detect kelp in image at path source and output the resulting classification raster to file at path dest.

Parameters:

Name	Type	Description	Default
source	str	Input image with Byte data type.	required
dest	str	File path location to save output to.	required
species	bool	Do species classification instead of presence/absence.	False
crop_size	int	The size of cropped image square run through the segmentation model.	512
padding	int	The number of context pixels added to each side of the cropped image.	256
batch_size	int	The batch size of cropped image sections to process together.	1
use_gpu	bool	Disable Cuda GPU usage and run on CPU only.	True

Source code in kelp_o_matic/lib.py

def find_kelp(
    source: str,
    dest: str,
    species: bool = False,
    crop_size: int = 512,
    padding: int = 256,
    batch_size: int = 1,
    use_gpu: bool = True,
):
    """
    Detect kelp in image at path `source` and output the resulting classification raster
    to file at path `dest`.

    Args:
        source: Input image with Byte data type.
        dest: File path location to save output to.
        species: Do species classification instead of presence/absence.
        crop_size: The size of cropped image square run through the segmentation model.
        padding: The number of context pixels added to each side of the cropped image.
        batch_size: The batch size of cropped image sections to process together.
        use_gpu: Disable Cuda GPU usage and run on CPU only.
    """
    if species:
        model = KelpSpeciesSegmentationModel(use_gpu=use_gpu)
    else:
        model = KelpPresenceSegmentationModel(use_gpu=use_gpu)
    RichSegmentationManager(
        model, source, dest, crop_size=crop_size, padding=padding, batch_size=batch_size
    )()

Example

import kelp_o_matic
kelp_o_matic.find_kelp("./path/to/kelp_image.tif", "./path/to/output_file_to_write.tif", crop_size=3200)

---

kelp_o_matic.find_mussels(source, dest, crop_size=512, padding=256, batch_size=1, use_gpu=True)

Detect mussels in image at path source and output the resulting classification raster to file at path dest.

Parameters:

Name	Type	Description	Default
source	str	Input image with Byte data type.	required
dest	str	File path location to save output to.	required
crop_size	int	The size of cropped image square run through the segmentation model.	512
padding	int	The number of context pixels added to each side of the cropped image.	256
batch_size	int	The batch size of cropped image sections to process together.	1
use_gpu	bool	Disable Cuda GPU usage and run on CPU only.	True

Source code in kelp_o_matic/lib.py

def find_mussels(
    source: str,
    dest: str,
    crop_size: int = 512,
    padding: int = 256,
    batch_size: int = 1,
    use_gpu: bool = True,
):
    """
    Detect mussels in image at path `source` and output the resulting classification
    raster to file at path `dest`.

    Args:
        source: Input image with Byte data type.
        dest: File path location to save output to.
        crop_size: The size of cropped image square run through the segmentation model.
        padding: The number of context pixels added to each side of the cropped image.
        batch_size: The batch size of cropped image sections to process together.
        use_gpu: Disable Cuda GPU usage and run on CPU only.
    """
    model = MusselPresenceSegmentationModel(use_gpu=use_gpu)
    RichSegmentationManager(
        model, source, dest, crop_size=crop_size, padding=padding, batch_size=batch_size
    )()

Example

import kelp_o_matic
kelp_o_matic.find_mussels("./path/to/mussel_image.tif", "./path/to/output_file_to_write.tif", crop_size=3200)