mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 02:47:01 +08:00
Tony S Yu
112 lines
3.2 KiB
Python
112 lines
3.2 KiB
Python
"""
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=========================================
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Adapting gray-scale filters to RGB images
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=========================================
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There are many filters that are designed work with gray-scale images but not
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color images. To simplify the process of creating functions that can adapt to
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RGB images, scikit-image provides the ``adapt_rgb`` decorator.
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To actually use the ``adapt_rgb`` decorator, you have to decide how you want to
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adapt the RGB image for use with the gray-scale filter. There are two
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pre-defined handlers:
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``each_channel``
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Pass each of the RGB channels to the filter one-by-one, and stitch the
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results back into an RGB image.
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``hsv_value``
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Convert the RGB image to HSV and pass the value channel to the filter.
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The filtered result is inserted back into the HSV image and converted
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back to RGB.
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Below, we demonstrate the use of ``adapt_rgb`` on a couple of gray-scale
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filters:
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"""
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from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value
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from skimage import filters
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@adapt_rgb(each_channel)
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def sobel_each(image):
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return filters.sobel(image)
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@adapt_rgb(hsv_value)
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def sobel_hsv(image):
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return filters.sobel(image)
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"""
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We can use these functions as we would normally use them, but now they work
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with both gray-scale and color images. Let's plot the results with a color
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image:
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"""
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from skimage import data
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import matplotlib.pyplot as plt
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image = data.lena()
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fig, (ax_each, ax_hsv) = plt.subplots(ncols=2)
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ax_each.imshow(sobel_each(image))
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ax_hsv.imshow(sobel_hsv(image))
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"""
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.. image:: PLOT2RST.current_figure
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Notice that the result for the value-filtered image preserves the color of the
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original image, but channel filtered image combines in a more surprising way.
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In other common cases, smoothing for example, the channel filtered image will
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produce a better result than the value-filtered image.
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You can also create your own handler functions for ``adapt_rgb``. To do so,
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just create a function with the following signature::
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def handler(image_filter, image, *args, **kwargs):
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# Manipulate RGB image here...
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image = image_filter(image, *args, **kwargs)
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# Manipulate filtered image here...
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return image
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Note that ``adapt_rgb`` handlers are written for filters where the image is the
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first argument.
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As a very simple example, we can just convert any RGB image to grayscale and
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then return the filtered result:
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"""
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from skimage.color import rgb2gray
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def as_gray(image_filter, image, *args, **kwargs):
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gray_image = rgb2gray(image)
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return image_filter(gray_image, *args, **kwargs)
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"""
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It's important to create a signature that uses ``*args`` and ``**kwargs`` to
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pass arguments along to the filter so that the decorated function is allowed to
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have any number of positional and keyword arguments.
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Finally, we can use this handler with ``adapt_rgb`` just as before:
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"""
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@adapt_rgb(as_gray)
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def sobel_gray(image):
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return filters.sobel(image)
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fig, ax = plt.subplots()
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ax.imshow(sobel_gray(image), cmap=plt.cm.gray)
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plt.show()
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"""
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.. image:: PLOT2RST.current_figure
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.. note::
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A very simple check of the array shape is used for detecting RGB images, so
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``adapt_rgb`` is not recommended for functions that support 3D volumes or
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color images in non-RGB spaces.
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"""
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