| import numpy as np |
|
|
| from .._shared.filters import gaussian |
| from .._shared.utils import _supported_float_type |
| from ..color import rgb2lab |
| from ..util import img_as_float |
| from ._quickshift_cy import _quickshift_cython |
|
|
|
|
| def quickshift( |
| image, |
| ratio=1.0, |
| kernel_size=5, |
| max_dist=10, |
| return_tree=False, |
| sigma=0, |
| convert2lab=True, |
| rng=42, |
| *, |
| channel_axis=-1, |
| ): |
| """Segment image using quickshift clustering in Color-(x,y) space. |
| |
| Produces an oversegmentation of the image using the quickshift mode-seeking |
| algorithm. |
| |
| Parameters |
| ---------- |
| image : (M, N, C) ndarray |
| Input image. The axis corresponding to color channels can be specified |
| via the `channel_axis` argument. |
| ratio : float, optional, between 0 and 1 |
| Balances color-space proximity and image-space proximity. |
| Higher values give more weight to color-space. |
| kernel_size : float, optional |
| Width of Gaussian kernel used in smoothing the |
| sample density. Higher means fewer clusters. |
| max_dist : float, optional |
| Cut-off point for data distances. |
| Higher means fewer clusters. |
| return_tree : bool, optional |
| Whether to return the full segmentation hierarchy tree and distances. |
| sigma : float, optional |
| Width for Gaussian smoothing as preprocessing. Zero means no smoothing. |
| convert2lab : bool, optional |
| Whether the input should be converted to Lab colorspace prior to |
| segmentation. For this purpose, the input is assumed to be RGB. |
| rng : {`numpy.random.Generator`, int}, optional |
| Pseudo-random number generator. |
| By default, a PCG64 generator is used (see :func:`numpy.random.default_rng`). |
| If `rng` is an int, it is used to seed the generator. |
| |
| The PRNG is used to break ties, and is seeded with 42 by default. |
| channel_axis : int, optional |
| The axis of `image` corresponding to color channels. Defaults to the |
| last axis. |
| |
| Returns |
| ------- |
| segment_mask : (M, N) ndarray |
| Integer mask indicating segment labels. |
| |
| Notes |
| ----- |
| The authors advocate to convert the image to Lab color space prior to |
| segmentation, though this is not strictly necessary. For this to work, the |
| image must be given in RGB format. |
| |
| References |
| ---------- |
| .. [1] Quick shift and kernel methods for mode seeking, |
| Vedaldi, A. and Soatto, S. |
| European Conference on Computer Vision, 2008 |
| """ |
|
|
| image = img_as_float(np.atleast_3d(image)) |
| float_dtype = _supported_float_type(image.dtype) |
| image = image.astype(float_dtype, copy=False) |
|
|
| if image.ndim > 3: |
| raise ValueError("Only 2D color images are supported") |
|
|
| |
| image = np.moveaxis(image, source=channel_axis, destination=-1) |
|
|
| if convert2lab: |
| if image.shape[-1] != 3: |
| raise ValueError("Only RGB images can be converted to Lab space.") |
| image = rgb2lab(image) |
|
|
| if kernel_size < 1: |
| raise ValueError("`kernel_size` should be >= 1.") |
|
|
| image = gaussian(image, sigma=[sigma, sigma, 0], mode='reflect', channel_axis=-1) |
| image = np.ascontiguousarray(image * ratio) |
|
|
| segment_mask = _quickshift_cython( |
| image, |
| kernel_size=kernel_size, |
| max_dist=max_dist, |
| return_tree=return_tree, |
| rng=rng, |
| ) |
| return segment_mask |
|
|
