173 lines
5.9 KiB
Python
173 lines
5.9 KiB
Python
from itertools import combinations_with_replacement
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import itertools
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import numpy as np
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from skimage import filters, feature
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from skimage import img_as_float32
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from concurrent.futures import ThreadPoolExecutor
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def _texture_filter(gaussian_filtered):
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H_elems = [
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np.gradient(np.gradient(gaussian_filtered)[ax0], axis=ax1)
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for ax0, ax1 in combinations_with_replacement(range(gaussian_filtered.ndim), 2)
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]
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eigvals = feature.hessian_matrix_eigvals(H_elems)
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return eigvals
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def _singlescale_basic_features_singlechannel(
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img, sigma, intensity=True, edges=True, texture=True
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):
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results = ()
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gaussian_filtered = filters.gaussian(img, sigma)
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if intensity:
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results += (gaussian_filtered,)
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if edges:
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results += (filters.sobel(gaussian_filtered),)
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if texture:
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results += (*_texture_filter(gaussian_filtered),)
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return results
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def _mutiscale_basic_features_singlechannel(
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img,
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intensity=True,
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edges=True,
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texture=True,
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sigma_min=0.5,
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sigma_max=16,
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num_sigma=None,
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num_workers=None,
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):
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"""Features for a single channel nd image.
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Parameters
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----------
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img : ndarray
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Input image, which can be grayscale or multichannel.
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intensity : bool, default True
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If True, pixel intensities averaged over the different scales
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are added to the feature set.
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edges : bool, default True
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If True, intensities of local gradients averaged over the different
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scales are added to the feature set.
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texture : bool, default True
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If True, eigenvalues of the Hessian matrix after Gaussian blurring
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at different scales are added to the feature set.
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sigma_min : float, optional
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Smallest value of the Gaussian kernel used to average local
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neighbourhoods before extracting features.
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sigma_max : float, optional
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Largest value of the Gaussian kernel used to average local
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neighbourhoods before extracting features.
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num_sigma : int, optional
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Number of values of the Gaussian kernel between sigma_min and sigma_max.
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If None, sigma_min multiplied by powers of 2 are used.
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num_workers : int or None, optional
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The number of parallel threads to use. If set to ``None``, the full
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set of available cores are used.
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Returns
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-------
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features : list
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List of features, each element of the list is an array of shape as img.
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"""
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# computations are faster as float32
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img = np.ascontiguousarray(img_as_float32(img))
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if num_sigma is None:
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num_sigma = int(np.log2(sigma_max) - np.log2(sigma_min) + 1)
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sigmas = np.logspace(
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np.log2(sigma_min),
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np.log2(sigma_max),
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num=num_sigma,
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base=2,
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endpoint=True,
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)
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with ThreadPoolExecutor(max_workers=num_workers) as ex:
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out_sigmas = list(
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ex.map(
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lambda s: _singlescale_basic_features_singlechannel(
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img, s, intensity=intensity, edges=edges, texture=texture
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),
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sigmas,
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)
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)
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features = itertools.chain.from_iterable(out_sigmas)
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return features
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def multiscale_basic_features(
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image,
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multichannel=False,
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intensity=True,
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edges=True,
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texture=True,
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sigma_min=0.5,
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sigma_max=16,
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num_sigma=None,
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num_workers=None,
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):
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"""Local features for a single- or multi-channel nd image.
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Intensity, gradient intensity and local structure are computed at
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different scales thanks to Gaussian blurring.
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Parameters
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----------
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image : ndarray
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Input image, which can be grayscale or multichannel.
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multichannel : bool, default False
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True if the last dimension corresponds to color channels.
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intensity : bool, default True
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If True, pixel intensities averaged over the different scales
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are added to the feature set.
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edges : bool, default True
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If True, intensities of local gradients averaged over the different
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scales are added to the feature set.
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texture : bool, default True
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If True, eigenvalues of the Hessian matrix after Gaussian blurring
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at different scales are added to the feature set.
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sigma_min : float, optional
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Smallest value of the Gaussian kernel used to average local
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neighbourhoods before extracting features.
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sigma_max : float, optional
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Largest value of the Gaussian kernel used to average local
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neighbourhoods before extracting features.
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num_sigma : int, optional
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Number of values of the Gaussian kernel between sigma_min and sigma_max.
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If None, sigma_min multiplied by powers of 2 are used.
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num_workers : int or None, optional
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The number of parallel threads to use. If set to ``None``, the full
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set of available cores are used.
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Returns
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-------
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features : np.ndarray
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Array of shape ``image.shape + (n_features,)``
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"""
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if not any([intensity, edges, texture]):
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raise ValueError(
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"At least one of ``intensity``, ``edges`` or ``textures``"
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"must be True for features to be computed."
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)
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if image.ndim < 3:
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multichannel = False
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if not multichannel:
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image = image[..., np.newaxis]
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all_results = (
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_mutiscale_basic_features_singlechannel(
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image[..., dim],
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intensity=intensity,
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edges=edges,
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texture=texture,
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sigma_min=sigma_min,
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sigma_max=sigma_max,
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num_sigma=num_sigma,
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num_workers=num_workers,
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)
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for dim in range(image.shape[-1])
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)
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features = list(itertools.chain.from_iterable(all_results))
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return np.stack(features, axis=-1)
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