def tensor2images()

in tensorflow_similarity/visualization/projector.py [0:0]

• 20 lines of code
• 7 McCabe index (conditional complexity)

def tensor2images(tensor: Tensor, size: Optional[int] = 64) -> List[str]:
    """Convert tensor images back to in memory images
    encoded in base 64.

    Args:
        tensor: 4D tensor that represent an image list.
        size: Image size to output in pixels. Defaults to 64.

    Returns:
        list of images encoded as base64 strings
    """

    # casting as iterating over a Tensor is slow.
    data = np.array(tensor)

    # if image provided are scaled between [0,1] then rescale
    if np.max(data) <= 1:
        data = data * 255

    # cast as int so PIL accepts its
    data = data.astype(np.uint8)

    imgs_b64 = []
    for a in tqdm(data, desc="generating diplayabe images"):
        # if single channel, treat it as black and white
        if a.shape[-1] == 1:
            a = np.reshape(a, (a.shape[0], a.shape[1]))
            img = PIL.Image.fromarray(a, 'L')
        else:
            img = PIL.Image.fromarray(a)

        img_resized = img.resize((size, size))
        buffer = io.BytesIO()
        img_resized.save(buffer, format='JPEG')
        img_bytes = buffer.getvalue()
        img64 = 'data:image/png;base64,%s' % str(
            base64.b64encode(img_bytes))[2:-1]
        imgs_b64.append(img64)

    return imgs_b64