| import numpy as np |
| import scipy.ndimage as ndi |
|
|
| from ..color import rgb2gray |
| from ..util import img_as_float |
|
|
| |
| |
| |
| try: |
| from numpy import AxisError |
| except ImportError: |
| from numpy.exceptions import AxisError |
|
|
|
|
| __all__ = ['blur_effect'] |
|
|
|
|
| _EPSILON = np.spacing(np.float64(1)) |
|
|
|
|
| def blur_effect(image, h_size=11, channel_axis=None, reduce_func=np.max): |
| """Compute a metric that indicates the strength of blur in an image |
| (0 for no blur, 1 for maximal blur). |
| |
| Parameters |
| ---------- |
| image : ndarray |
| RGB or grayscale nD image. The input image is converted to grayscale |
| before computing the blur metric. |
| h_size : int, optional |
| Size of the re-blurring filter. |
| channel_axis : int or None, optional |
| If None, the image is assumed to be grayscale (single-channel). |
| Otherwise, this parameter indicates which axis of the array |
| corresponds to color channels. |
| reduce_func : callable, optional |
| Function used to calculate the aggregation of blur metrics along all |
| axes. If set to None, the entire list is returned, where the i-th |
| element is the blur metric along the i-th axis. |
| |
| Returns |
| ------- |
| blur : float (0 to 1) or list of floats |
| Blur metric: by default, the maximum of blur metrics along all axes. |
| |
| Notes |
| ----- |
| `h_size` must keep the same value in order to compare results between |
| images. Most of the time, the default size (11) is enough. This means that |
| the metric can clearly discriminate blur up to an average 11x11 filter; if |
| blur is higher, the metric still gives good results but its values tend |
| towards an asymptote. |
| |
| References |
| ---------- |
| .. [1] Frederique Crete, Thierry Dolmiere, Patricia Ladret, and Marina |
| Nicolas "The blur effect: perception and estimation with a new |
| no-reference perceptual blur metric" Proc. SPIE 6492, Human Vision and |
| Electronic Imaging XII, 64920I (2007) |
| https://hal.archives-ouvertes.fr/hal-00232709 |
| :DOI:`10.1117/12.702790` |
| """ |
|
|
| if channel_axis is not None: |
| try: |
| |
| image = np.moveaxis(image, channel_axis, -1) |
| except AxisError: |
| print('channel_axis must be one of the image array dimensions') |
| raise |
| except TypeError: |
| print('channel_axis must be an integer') |
| raise |
| image = rgb2gray(image) |
| n_axes = image.ndim |
| image = img_as_float(image) |
| shape = image.shape |
| B = [] |
|
|
| from ..filters import sobel |
|
|
| slices = tuple([slice(2, s - 1) for s in shape]) |
| for ax in range(n_axes): |
| filt_im = ndi.uniform_filter1d(image, h_size, axis=ax) |
| im_sharp = np.abs(sobel(image, axis=ax)) |
| im_blur = np.abs(sobel(filt_im, axis=ax)) |
|
|
| |
| im_sharp = np.maximum(_EPSILON, im_sharp) |
| im_blur = np.maximum(_EPSILON, im_blur) |
|
|
| T = np.maximum(0, im_sharp - im_blur) |
| M1 = np.sum(im_sharp[slices]) |
| M2 = np.sum(T[slices]) |
| B.append(np.abs(M1 - M2) / M1) |
|
|
| return B if reduce_func is None else reduce_func(B) |
|
|
