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emmanuelle
83 lines
2.5 KiB
Plaintext
83 lines
2.5 KiB
Plaintext
A crash course on Numpy for images
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----------------------------------
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Images manipulated by ``scikit-image`` are simply NumPy arrays. Hence, a
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large fraction of operations on images will just consist in using NumPy::
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>>> from skimage import data
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>>> camera = data.camera()
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Retrieving the geometry of the image and the number of pixels: ::
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>>> camera.shape
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(512, 512)
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>>> camera.size
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262144
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Retrieving statistical information about gray values: ::
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>>> camera.min(), camera.max()
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(0, 255)
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>>> camera.mean()
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118.31400299072266
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Numpy arrays representing images can be of different integer of float
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numerical types. See :ref:`data_types` for more information about data
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types.
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Numpy indexing can be used both for looking at pixel values, and to
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modify pixel values: ::
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>>> # Value of pixel on 10th line and 20th column
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>>> camera[10, 20]
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153
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>>> # Turn to black pixel on 3rd line and 10th column
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>>> camera[3, 10] = 0
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Be careful that the first dimension (``camera.shape[0]``) corresponds to
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lines, while the second dimension (``camera.shape[1]``) stands for
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columns.
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Beyond individual pixels, it is possible to access / modify values of
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whole sets of pixels, using the different indexing possibilities of
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NumPy.
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Slicing::
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>>> # Set to black the ten first lines
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>>> camera[:10] = 0
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Masking (indexing with masks of booleans)::
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>>> mask = camera < 87
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>>> # Set to "white" (255) pixels where mask is True
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>>> camera[mask] = 255
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Fancy indexing (indexing with sets of indices) ::
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>>> inds_x = np.arange(len(camera))
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>>> inds_y = 4 * inds_x % len(camera)
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>>> camera[inds_x, inds_y] = 0
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Using masks, especially, is very useful to select a set of pixels on
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which to perform further manipulations. The mask can be any boolean array
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of same shape as the image (or at least a shape broadcastable to the
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image shape). This can be useful to define a region of interest, as a
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disk: ::
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>>> l_x, l_y = camera.shape[0], camera.shape[1]
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>>> X, Y = np.ogrid[:l_x, :l_y]
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>>> outer_disk_mask = (X - l_x / 2)**2 + (Y - l_y / 2)**2 < (l_x / 2)**2
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>>> camera[outer_disk_mask] = 0
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.. image:: ../../_images/plot_camera_numpy_1.png
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:width: 45%
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:target: ../auto_examples/plot_camera_numpy.html
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Boolean arithmetics can be used to define more complex masks: ::
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>>> lower_half = X > l_x / 2
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>>> lower_half_disk = np.logical_and(lower_half, outer_disk_mask)
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>>> camera = data.camera()
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>>> camera[lower_half_disk] = 0
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