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First version of sections on color and exposure.
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emmanuelle
@@ -7,6 +7,7 @@ User Guide
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user_guide/getting_started
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user_guide/numpy_images
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user_guide/data_types
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user_guide/transforming_image_data
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user_guide/plugins
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user_guide/tutorials
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user_guide/getting_help
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@@ -0,0 +1,179 @@
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============================================
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Image adjustment: transforming image content
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============================================
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Color manipulation
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------------------
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.. currentmodule:: skimage.color
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Most functions for manipulating color channels are found in the submodule
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:mod:`skimage.color`.
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Conversion between color models
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Color images can be represented using different `color spaces
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<http://en.wikipedia.org/wiki/Color_space>`_. One of the most common
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color spaces is the `RGB space
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<http://en.wikipedia.org/wiki/RGB_color_model>`_, where an image has
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red, blue and green channels. However, other color models are widely
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used, such as the `HSV color model
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<http://en.wikipedia.org/wiki/HSL_and_HSV>`_ (for hue, saturation and
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value), where hue can be changed independently of saturation or value, or
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the `CMYK model <http://en.wikipedia.org/wiki/CMYK_color_model>`_ used
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for printing.
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:mod:`skimage.color` provide utility functions to convert images
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to and from different color spaces. Note that such conversions may change
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the numerical type of the image array::
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>>> # bright saturated red
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>>> red_pixel_rgb = np.array([[[255, 0, 0]]], dtype=np.uint8)
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>>> color.rgb2hsv(red_pixel_rgb)
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array([[[ 0., 1., 1.]]])
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>>> # darker saturated blue
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>>> dark_blue_pixel_rgb = np.array([[[0, 0, 100]]], dtype=np.uint8)
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>>> color.rgb2hsv(dark_blue_pixel_rgb)
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array([[[ 0.66666667, 1. , 0.39215686]]])
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>>> # less saturated pink
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>>> pink_pixel_rgb = np.array([[[255, 100, 255]]], dtype=np.uint8)
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>>> color.rgb2hsv(pink_pixel_rgb)
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array([[[ 0.83333333, 0.60784314, 1. ]]])
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Conversion between color and gray values
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Converting an RGB image to a grayscale image is realized with
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:func:`rgb2gray` ::
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>>> from skimage.color import rgb2gray
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>>> from skimage import data
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>>> img = data.astronaut()
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>>> img_gray = rgb2gray(img)
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:func:`rgb2gray` uses a non-uniform weigthing of color channels, because of the
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different sensivity of the human eye to different colors. ::
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>>> red_pixel = np.array([[[255, 0, 0]]], dtype=np.uint8)
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>>> color.rgb2gray(red_pixel)
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array([[ 0.2125]])
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>>> green_pixel = np.array([[[0, 255, 0]]], dtype=np.uint8)
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>>> color.rgb2gray(green_pixel)
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array([[ 0.7154]])
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Converting a grayscale image to RGB with :func:`gray2rgb``simply
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duplicates the gray values over the three color channels.
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Painting images with labels
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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:func:`label2rgb` can be used to superimpose colors on a grayscale image,
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using an array of labels to encode the regions to be represented with the
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same color.
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.. image:: ../../_images/plot_join_segmentations_1.png
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:target: ../auto_examples/plot_join_segmentations.html
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:align: center
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:width: 80%
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.. topic:: Examples:
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* :ref:`example_plot_tinting_grayscale_images.py`
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* :ref:`example_plot_join_segmentations.py`
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* :ref:`example_plot_rag_mean_color.py`
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Contrast and exposure
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---------------------
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.. currentmodule:: skimage.exposure
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Image values can take values determined by the `dtype` of the image (see
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:ref:`data_types`), such as 0 to 255 for `uint8` images or [-1, 1] for
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floating point images. However, most images either have a narrower range
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of values (because of poor contrast), or have most pixel values
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concentrated in a subrange. :mod:`skimage.exposure` provides functions
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that modify the distribution of pixels values of an image.
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A first class of methods compute a nonlinear function of the luminance,
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which is always the same no matter the pixel values of a specific image.
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Such methods are often used for correcting a known non-linearity of
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sensors, or receptors such as the human eye. A known example is the
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`Gamma correction <http://en.wikipedia.org/wiki/Gamma_correction>`_,
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implemented in :func:`adjust_gamma`.
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Other methods re-distribute pixel values according to the *histogram* of
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the image. The histogram of pixel values is computed with
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:func:`skimage.exposure.histogram`::
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>>> image = np.array([[1, 3], [1, 1]])
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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`, that returns bins'
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boundaries.
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The simplest contrast enhancement :func:`rescale_intensity` consists in
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stretching pixels values to the whole allowed range, using a linear
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transformation.::
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>>> from skimage import exposure
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>>> text = data.text()
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>>> text.min(), text.max()
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(10, 197)
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>>> better_contrast = exposure.rescale_intensity(text)
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>>> better_contrast.min(), better_contrast.max()
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(0, 255)
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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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>>> 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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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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of the value range are equally represented in the image. As a result,
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details are enhanced in large regions with poor contrast. As a further
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refinement, histogram equalization can be performed in subregions of the
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image with :func:`equalize_adapthist`, in order to correct for exposure
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gradients across the image. See the example
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:ref:`example_plot_equalize.py`.
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.. image:: ../../_images/plot_equalize_1.png
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:target: ../auto_examples/plot_equalize.html
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:align: center
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:width: 90%
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.. topic:: Examples:
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* :ref:`example_plot_equalize.py`
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Image filtering
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---------------
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.. currentmodule:: skimage.filters
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Denoising and restoration
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-------------------------
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Mathematical morphology
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-----------------------
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