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Minor corrections
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@@ -108,7 +108,7 @@ values over a larger range.
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A first class of methods compute a nonlinear function of the intensity,
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that is independent of the pixel values of a specific image. Such methods
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are often used for correcting a known non-linearity of sensors, or
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receptors such as the human eye. A well-known example is the `Gamma
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receptors such as the human eye. A well-known example is `Gamma
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correction <http://en.wikipedia.org/wiki/Gamma_correction>`_, implemented
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in :func:`adjust_gamma`.
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@@ -120,14 +120,14 @@ the image. The histogram of pixel values is computed with
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>>> exposure.histogram(image)
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(array([3, 0, 1]), array([1, 2, 3]))
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that returns the number of pixels for each value bin, and the centers of
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the bins. The behavior of :func:`histogram` is therefore slightly
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different from the one of :func:`np.histogram`, which returns the
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boundaries of the bins.
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:func:`histogram` returns the number of pixels for each value bin, and
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the centers of the bins. The behavior of :func:`histogram` is therefore
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slightly different from the one of :func:`np.histogram`, which returns
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the boundaries of the bins.
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The simplest contrast enhancement :func:`rescale_intensity` consists in
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stretching pixel values to the whole allowed range, using a linear
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transformation.::
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transformation::
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>>> from skimage import exposure
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>>> text = data.text()
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@@ -141,14 +141,14 @@ Even if an image uses the whole value range, sometimes there is very
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little weight at the ends of the value range. In such a case, clipping
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pixel values using percentiles of the image improves the contrast (at the
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expense of some loss of information, because some pixels are saturated by
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this operation).::
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this operation)::
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>>> moon = data.moon()
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>>> v_min, v_max = np.percentile(moon, (0.2, 99.8))
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>>> v_min, v_max
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(10.0, 186.0)
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>>> better_contrast = exposure.rescale_intensity(moon,
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... in_range=(v_min, v_max))
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>>> better_contrast = exposure.rescale_intensity(
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... moon, in_range=(v_min, v_max))
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The function :func:`equalize_hist` maps the cumulative distribution
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function (cdf) of pixel values onto a linear cdf, ensuring that all parts
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