Image Viewer

ImageCollection class skimage.io.ImageCollection(load_pattern, conserve_memory=True, load_func=None, **load_func_kwargs) [source] Bases: object Load and manage a collection of image files. Note that files are always stored in alphabetical order. Also note that slicing returns a new ImageCollection, not a view into the data. Parameters: load_pattern : str or list Pattern glob or filenames to load. The path can be absolute or relative. Multiple patterns should be separated by os.pathsep, e.g

Image Segmentation Image segmentation is the task of labeling the pixels of objects of interest in an image. In this tutorial, we will see how to segment objects from a background. We use the coins image from skimage.data. This image shows several coins outlined against a darker background. The segmentation of the coins cannot be done directly from the histogram of grey values, because the background shares enough grey levels with the coins that a thresholding segmentation is not sufficient.

Image adjustment: transforming image content

Image data types and what they mean In skimage, images are simply numpy arrays, which support a variety of data types [1], i.e. “dtypes”. To avoid distorting image intensities (see Rescaling intensity values), we assume that images use the following dtype ranges: Data type Range uint8 0 to 255 uint16 0 to 65535 uint32 0 to 232 float -1 to 1 or 0 to 1 int8 -128 to 127 int16 -32768 to 32767 int32 -231 to 231 - 1 Note that float images should be restricted to the range -1 to 1 even though the da

I/O Plugin Infrastructure A plugin consists of two files, the source and the descriptor .ini. Let’s say we’d like to provide a plugin for imshow using matplotlib. We’ll call our plugin mpl: skimage/io/_plugins/mpl.py skimage/io/_plugins/mpl.ini The name of the .py and .ini files must correspond. Inside the .ini file, we give the plugin meta-data: [mpl] <-- name of the plugin, may be anything description = Matplotlib image I/O plugin provides = imshow <-- a comma-separated list, one or mo

ifrt2 skimage.transform.ifrt2(a) [source] Compute the 2-dimensional inverse finite radon transform (iFRT) for an (n+1) x n integer array. Parameters: a : array_like A 2-D (n+1) row x n column integer array. Returns: iFRT : 2-D n x n ndarray Inverse Finite Radon Transform array of n x n integer coefficients. See also frt2 The two-dimensional FRT Notes The FRT has a unique inverse if and only if n is prime. See [R368] for an overview. The idea for this algorithm is due to Vlad Neg

hubble_deep_field skimage.data.hubble_deep_field() [source] Hubble eXtreme Deep Field. This photograph contains the Hubble Telescope’s farthest ever view of the universe. It can be useful as an example for multi-scale detection. Notes This image was downloaded from HubbleSite. The image was captured by NASA and may be freely used in the public domain.

hsobel skimage.filters.hsobel(*args, **kwargs) [source] Deprecated function. Use skimage.filters.sobel_h instead. Find the horizontal edges of an image using the Sobel transform. Parameters: image : 2-D array Image to process. mask : 2-D array, optional An optional mask to limit the application to a certain area. Note that pixels surrounding masked regions are also masked to prevent masked regions from affecting the result. Returns: output : 2-D array The absolute Sobel edge map. N

hsv2rgb skimage.color.hsv2rgb(hsv) [source] HSV to RGB color space conversion. Parameters: hsv : array_like The image in HSV format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If hsv is not a 3-D array of shape (.., .., 3). Notes The conversion assumes an input data range of [0, 1] for all color components. Conversion between RGB and HSV color spaces results in some loss of precision