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A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation.

License: MIT License

Python 99.40% Shell 0.60%
albumentations artificial-intelligence automation computer-vision data-augmentation deep-learning efficiency high-performance-computing image-augmentation image-processing

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albucore's Issues

[Speedup] Check uint8 addition from imgaug

def _add_elementwise_to_uint8(image, values):
    # This special uint8 block is around 60-100% faster than the
    # corresponding non-uint8 function further below (more speedup
    # for smaller images).
    #
    # Also tested to instead compute min/max of image and value
    # and then only convert image/value dtype if actually
    # necessary, but that was like 20-30% slower, even for 224x224
    # images.
    #
    if values.dtype.kind == "f":
        values = np.round(values)

    image = image.astype(np.int16)
    values = np.clip(values, -255, 255).astype(np.int16)

    image_aug = image + values
    return np.clip(image_aug, 0, 255).astype(np.uint8)

[Bug] Add may work incorrectly with negative values

Lines like: value = np.full(img.shape, value, dtype=img.dtype) may give error.

For multuplication we may expect only positive values, but for addition, add_constant, add_vector and add_array negative values are quite valid

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