imsave skimage.external.tifffile.imsave(filename, data, **kwargs) [source] Write image data to TIFF file. Refer to the TiffWriter class and member functions for documentation. Parameters: filename : str Name of file to write. data : array_like Input image. The last dimensions are assumed to be image depth, height, width, and samples. kwargs : dict Parameters ‘byteorder’, ‘bigtiff’, and ‘software’ are passed to the TiffWriter class. Parameters ‘photometric’, ‘planarconfig’, ‘resolution’

disk skimage.morphology.disk(radius, dtype=) [source] Generates a flat, disk-shaped structuring element. A pixel is within the neighborhood if the euclidean distance between it and the origin is no greater than radius. Parameters: radius : int The radius of the disk-shaped structuring element. Returns: selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. Other Parameters: dtype : data-type The data type of the structuring element.

imread_collection skimage.io.imread_collection(load_pattern, conserve_memory=True, plugin=None, **plugin_args) [source] Load a collection of images. Parameters: load_pattern : str or list List of objects to load. These are usually filenames, but may vary depending on the currently active plugin. See the docstring for ImageCollection for the default behaviour of this parameter. conserve_memory : bool, optional If True, never keep more than one in memory at a specific time. Otherwise, imag

sum_bilateral skimage.filters.rank.sum_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [source] Apply a flat kernel bilateral filter. This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity. Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element (selem). Radiometric similarity is defined by the greylevel interva

rgb2xyz skimage.color.rgb2xyz(rgb) [source] RGB to XYZ color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Returns: out : ndarray The image in XYZ format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Raises: ValueError If rgb is not a 3- or 4-D array of shape (.., ..,[ ..,] 3). Notes The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts from sRGB. Ref

threshold_li skimage.filters.threshold_li(image) [source] Return threshold value based on adaptation of Li’s Minimum Cross Entropy method. Parameters: image : array Input image. Returns: threshold : float Upper threshold value. All pixels intensities more than this value are assumed to be foreground. References [R202] Li C.H. and Lee C.K. (1993) “Minimum Cross Entropy Thresholding” Pattern Recognition, 26(4): 617-625 [R203] Li C.H. and Tam P.K.S. (1998) “An Iterative Algorithm for Mi

noise_filter skimage.filters.rank.noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Noise feature. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (defaul

deltaE_ciede2000 skimage.color.deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1) [source] Color difference as given by the CIEDE 2000 standard. CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces. Parameters: lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) kL : float (range), optional lightness scale factor, 1 for “acceptably close”; 2 for “imperc

lab2lch skimage.color.lab2lch(lab) [source] CIE-LAB to CIE-LCH color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters: lab : array_like The N-D image in CIE-LAB format. The last (N+1-th) dimension must have at least 3 elements, corresponding to the L, a, and b color channels. Subsequent elements are copied. Returns: out : ndarray The image in LCH format, in a N-D array with same shape as input lab. Raises: ValueError If lch does n

ellipse_perimeter skimage.draw.ellipse_perimeter() Generate ellipse perimeter coordinates. Parameters: cy, cx : int Centre coordinate of ellipse. yradius, xradius : int Minor and major semi-axes. (x/xradius)**2 + (y/yradius)**2 = 1. orientation : double, optional (default 0) Major axis orientation in clockwise direction as radians. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses which exceed th