Stream selected channels

[robmarkcole]

🚀 Feature

Streaming subsets of channels

Motivation

My geotiff data is typically multispectral and I do experiments using subsets of the channels. I would like to stream only the required channels in order to save bandwidth

Pitch

Select e.g. channels 1,3,5 to stream

Alternatives

I can list the channels as separate files, and then access only those I require

Additional context

The equivalent using Rasterio:

import rasterio

# URL to an S3 bucket raster file
s3_url = 's3://your-bucket-name/path-to-your-raster-file.tif'

# Open the raster file
with rasterio.open(s3_url) as src:
    # Read a specific band, for example, band 1
    band1 = src.read(1)  # Reading only the first band

    # You can also read multiple specific bands by passing a tuple
    band1, band3 = src.read((1, 3))  # Reading bands 1 and 3

    # Process or analyze the bands as needed
    print(band1, band3)

My current solution:

class SegmentationStreamingDataset(StreamingDataset):
    """
    Segmentation dataset with streaming.

    Args:
        input_dir (str): Local directory or S3 location of the dataset
        transforms (Optional[Callable]): A transform that takes in an image and returns a transformed version.
        band_indices (Optional[List[int]]): List of band indices to read from the dataset.
    """

    def __init__(self, *args, transforms: Optional[Callable] = None, band_indices: Optional[List[int]] = None, **kwargs):
        super().__init__(*args, **kwargs)
        self.transforms = transforms
        self.band_indices = band_indices

    def __getitem__(self, index) -> dict:
        data = super().__getitem__(index)
        image_name = data["name"]
        image = data["image"]
        mask = data["mask"]

        with MemoryFile(image) as memfile:
            with memfile.open() as dataset:
                image = torch.from_numpy(dataset.read()).float()
                if self.band_indices:
                    image = image[self.band_indices]

        with MemoryFile(mask) as memfile:
            with memfile.open() as dataset:
                mask = torch.from_numpy(dataset.read()).long()                    

        sample = {"image": image, "mask": mask, "image_name": image_name}
        if self.transforms is not None:
            sample = self.transforms(sample)
        return sample

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