mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-01 19:32:20 +08:00
Johannes Schönberger
356 lines
10 KiB
Python
356 lines
10 KiB
Python
import warnings
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import numpy as np
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from skimage import img_as_float
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from skimage.util.dtype import dtype_range, dtype_limits
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from skimage._shared.utils import deprecated
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__all__ = ['histogram', 'cumulative_distribution', 'equalize',
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'rescale_intensity', 'adjust_gamma',
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'adjust_log', 'adjust_sigmoid']
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def histogram(image, nbins=256):
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"""Return histogram of image.
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Unlike `numpy.histogram`, this function returns the centers of bins and
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does not rebin integer arrays. For integer arrays, each integer value has
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its own bin, which improves speed and intensity-resolution.
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The histogram is computed on the flattened image: for color images, the
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function should be used separately on each channel to obtain a histogram
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for each color channel.
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Parameters
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----------
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image : array
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Input image.
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nbins : int
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Number of bins used to calculate histogram. This value is ignored for
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integer arrays.
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Returns
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-------
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hist : array
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The values of the histogram.
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bin_centers : array
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The values at the center of the bins.
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Examples
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--------
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>>> from skimage import data, exposure, util
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>>> image = util.img_as_float(data.camera())
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>>> np.histogram(image, bins=2)
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(array([107432, 154712]), array([ 0. , 0.5, 1. ]))
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>>> exposure.histogram(image, nbins=2)
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(array([107432, 154712]), array([ 0.25, 0.75]))
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"""
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sh = image.shape
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if len(sh) == 3 and sh[-1] < 4:
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warnings.warn("This might be a color image. The histogram will be "
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"computed on the flattened image. You can instead "
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"apply this function to each color channel.")
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# For integer types, histogramming with bincount is more efficient.
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if np.issubdtype(image.dtype, np.integer):
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offset = 0
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if np.min(image) < 0:
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offset = np.min(image)
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hist = np.bincount(image.ravel() - offset)
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bin_centers = np.arange(len(hist)) + offset
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# clip histogram to start with a non-zero bin
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idx = np.nonzero(hist)[0][0]
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return hist[idx:], bin_centers[idx:]
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else:
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hist, bin_edges = np.histogram(image.flat, nbins)
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bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.
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return hist, bin_centers
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def cumulative_distribution(image, nbins=256):
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"""Return cumulative distribution function (cdf) for the given image.
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Parameters
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----------
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image : array
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Image array.
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nbins : int
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Number of bins for image histogram.
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Returns
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-------
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img_cdf : array
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Values of cumulative distribution function.
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bin_centers : array
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Centers of bins.
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References
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----------
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.. [1] http://en.wikipedia.org/wiki/Cumulative_distribution_function
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"""
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hist, bin_centers = histogram(image, nbins)
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img_cdf = hist.cumsum()
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img_cdf = img_cdf / float(img_cdf[-1])
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return img_cdf, bin_centers
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@deprecated('equalize_hist')
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def equalize(image, nbins=256):
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return equalize_hist(image, nbins)
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def equalize_hist(image, nbins=256):
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"""Return image after histogram equalization.
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Parameters
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----------
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image : array
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Image array.
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nbins : int
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Number of bins for image histogram.
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Returns
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-------
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out : float array
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Image array after histogram equalization.
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Notes
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-----
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This function is adapted from [1]_ with the author's permission.
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References
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----------
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.. [1] http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html
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.. [2] http://en.wikipedia.org/wiki/Histogram_equalization
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"""
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image = img_as_float(image)
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cdf, bin_centers = cumulative_distribution(image, nbins)
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out = np.interp(image.flat, bin_centers, cdf)
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return out.reshape(image.shape)
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def rescale_intensity(image, in_range=None, out_range=None):
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"""Return image after stretching or shrinking its intensity levels.
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The image intensities are uniformly rescaled such that the minimum and
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maximum values given by `in_range` match those given by `out_range`.
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Parameters
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----------
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image : array
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Image array.
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in_range : 2-tuple (float, float)
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Min and max *allowed* intensity values of input image. If None, the
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*allowed* min/max values are set to the *actual* min/max values in the
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input image.
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out_range : 2-tuple (float, float)
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Min and max intensity values of output image. If None, use the min/max
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intensities of the image data type. See `skimage.util.dtype` for
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details.
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Returns
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-------
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out : array
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Image array after rescaling its intensity. This image is the same dtype
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as the input image.
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Examples
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--------
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By default, intensities are stretched to the limits allowed by the dtype:
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>>> image = np.array([51, 102, 153], dtype=np.uint8)
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>>> rescale_intensity(image)
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array([ 0, 127, 255], dtype=uint8)
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It's easy to accidentally convert an image dtype from uint8 to float:
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>>> 1.0 * image
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array([ 51., 102., 153.])
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Use `rescale_intensity` to rescale to the proper range for float dtypes:
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>>> image_float = 1.0 * image
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>>> rescale_intensity(image_float)
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array([ 0. , 0.5, 1. ])
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To maintain the low contrast of the original, use the `in_range` parameter:
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>>> rescale_intensity(image_float, in_range=(0, 255))
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array([ 0.2, 0.4, 0.6])
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If the min/max value of `in_range` is more/less than the min/max image
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intensity, then the intensity levels are clipped:
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>>> rescale_intensity(image_float, in_range=(0, 102))
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array([ 0.5, 1. , 1. ])
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If you have an image with signed integers but want to rescale the image to
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just the positive range, use the `out_range` parameter:
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>>> image = np.array([-10, 0, 10], dtype=np.int8)
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>>> rescale_intensity(image, out_range=(0, 127))
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array([ 0, 63, 127], dtype=int8)
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"""
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dtype = image.dtype.type
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if in_range is None:
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imin = np.min(image)
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imax = np.max(image)
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else:
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imin, imax = in_range
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if out_range is None:
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omin, omax = dtype_range[dtype]
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if imin >= 0:
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omin = 0
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else:
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omin, omax = out_range
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image = np.clip(image, imin, imax)
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image = (image - imin) / float(imax - imin)
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return dtype(image * (omax - omin) + omin)
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def _assert_non_negative(image):
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if np.any(image < 0):
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raise ValueError('Image Correction methods work correctly only on '
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'images with non-negative values. Use '
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'skimage.exposure.rescale_intensity.')
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def adjust_gamma(image, gamma=1, gain=1):
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"""Performs Gamma Correction on the input image.
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Also known as Power Law Transform.
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This function transforms the input image pixelwise according to the
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equation ``O = I**gamma`` after scaling each pixel to the range 0 to 1.
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Parameters
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----------
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image : ndarray
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Input image.
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gamma : float
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Non negative real number. Default value is 1.
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gain : float
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The constant multiplier. Default value is 1.
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Returns
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-------
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out : ndarray
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Gamma corrected output image.
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Notes
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-----
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For gamma greater than 1, the histogram will shift towards left and
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the output image will be darker than the input image.
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For gamma less than 1, the histogram will shift towards right and
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the output image will be brighter than the input image.
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References
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----------
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.. [1] http://en.wikipedia.org/wiki/Gamma_correction
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"""
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_assert_non_negative(image)
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dtype = image.dtype.type
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if gamma < 0:
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return "Gamma should be a non-negative real number"
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scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
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out = ((image / scale) ** gamma) * scale * gain
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return dtype(out)
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def adjust_log(image, gain=1, inv=False):
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"""Performs Logarithmic correction on the input image.
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This function transforms the input image pixelwise according to the
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equation ``O = gain*log(1 + I)`` after scaling each pixel to the range 0 to 1.
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For inverse logarithmic correction, the equation is ``O = gain*(2**I - 1)``.
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Parameters
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----------
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image : ndarray
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Input image.
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gain : float
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The constant multiplier. Default value is 1.
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inv : float
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If True, it performs inverse logarithmic correction,
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else correction will be logarithmic. Defaults to False.
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Returns
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-------
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out : ndarray
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Logarithm corrected output image.
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References
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----------
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.. [1] http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf
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"""
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_assert_non_negative(image)
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dtype = image.dtype.type
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scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
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if inv:
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out = (2 ** (image / scale) - 1) * scale * gain
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return dtype(out)
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out = np.log2(1 + image / scale) * scale * gain
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return dtype(out)
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def adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False):
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"""Performs Sigmoid Correction on the input image.
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Also known as Contrast Adjustment.
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This function transforms the input image pixelwise according to the
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equation ``O = 1/(1 + exp*(gain*(cutoff - I)))`` after scaling each pixel
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to the range 0 to 1.
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Parameters
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----------
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image : ndarray
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Input image.
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cutoff : float
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Cutoff of the sigmoid function that shifts the characteristic curve
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in horizontal direction. Default value is 0.5.
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gain : float
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The constant multiplier in exponential's power of sigmoid function.
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Default value is 10.
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inv : bool
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If True, returns the negative sigmoid correction. Defaults to False.
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Returns
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-------
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out : ndarray
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Sigmoid corrected output image.
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References
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----------
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.. [1] Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast
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Enhancement Functions",
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http://www.cis.rit.edu/fairchild/PDFs/PAP07.pdf
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"""
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_assert_non_negative(image)
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dtype = image.dtype.type
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scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
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if inv:
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out = (1 - 1 / (1 + np.exp(gain * (cutoff - image/scale)))) * scale
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return dtype(out)
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out = (1 / (1 + np.exp(gain * (cutoff - image/scale)))) * scale
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return dtype(out)
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