def paste_mask_in_image_old(mask, box, img_h, img_w, threshold):
    """
    Paste a single mask in an image.
    This is a per-box implementation of :func:`paste_masks_in_image`.
    This function has larger quantization error due to incorrect pixel
    modeling and is not used any more.

    Args:
        mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single
            object instance. Values are in [0, 1].
        box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners
            of the object instance.
        img_h, img_w (int): Image height and width.
        threshold (float): Mask binarization threshold in [0, 1].

    Returns:
        im_mask (Tensor):
            The resized and binarized object mask pasted into the original
            image plane (a tensor of shape (img_h, img_w)).
    """
    # Conversion from continuous box coordinates to discrete pixel coordinates
    # via truncation (cast to int32). This determines which pixels to paste the
    # mask onto.
    box = box.to(dtype=torch.int32)  # Continuous to discrete coordinate conversion
    # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to
    # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1
    # pixels (not x1 - x0 pixels).
    samples_w = box[2] - box[0] + 1  # Number of pixel samples, *not* geometric width
    samples_h = box[3] - box[1] + 1  # Number of pixel samples, *not* geometric height

    # Resample the mask from it's original grid to the new samples_w x samples_h grid
    mask = Image.fromarray(mask.cpu().numpy())
    mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR)
    mask = np.array(mask, copy=False)

    if threshold >= 0:
        mask = np.array(mask > threshold, dtype=np.uint8)
        mask = torch.from_numpy(mask)
    else:
        # for visualization and debugging, we also
        # allow it to return an unmodified mask
        mask = torch.from_numpy(mask * 255).to(torch.uint8)

    im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8)
    x_0 = max(box[0], 0)
    x_1 = min(box[2] + 1, img_w)
    y_0 = max(box[1], 0)
    y_1 = min(box[3] + 1, img_h)

    im_mask[y_0:y_1, x_0:x_1] = mask[
        (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
    ]
    return im_mask

    Paste a single mask in an image.
    This is a per-box implementation of :func:`paste_masks_in_image`.
    This function has larger quantization error due to incorrect pixel
    modeling and is not used any more.

    Args:
        mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single
            object instance. Values are in [0, 1].
        box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners
            of the object instance.
        img_h, img_w (int): Image height and width.
        threshold (float): Mask binarization threshold in [0, 1].

    Returns:
        im_mask (Tensor):
            The resized and binarized object mask pasted into the original
            image plane (a tensor of shape (img_h, img_w)).
    
ControlNet-v1-1-nightly/annotator/oneformer/detectron2/layers/mask_ops.py