hog skimage.feature.hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3), visualise=False, transform_sqrt=False, feature_vector=True, normalise=None) [source] Extract Histogram of Oriented Gradients (HOG) for a given image. Compute a Histogram of Oriented Gradients (HOG) by (optional) global image normalisation computing the gradient image in x and y computing gradient histograms normalising across blocks flattening into a feature vector Parameters: image : (M, N) ndar

rocket skimage.data.rocket() [source] Launch photo of DSCOVR on Falcon 9 by SpaceX. This is the launch photo of Falcon 9 carrying DSCOVR lifted off from SpaceX’s Launch Complex 40 at Cape Canaveral Air Force Station, FL. Notes This image was downloaded from SpaceX Photos. The image was captured by SpaceX and released in the public domain.

clear_border skimage.segmentation.clear_border(labels, buffer_size=0, bgval=0, in_place=False) [source] Clear objects connected to the label image border. The changes will be applied directly to the input. Parameters: labels : (N, M) array of int Label or binary image. buffer_size : int, optional The width of the border examined. By default, only objects that touch the outside of the image are removed. bgval : float or int, optional Cleared objects are set to this value. in_place : bo

pop_percentile skimage.filters.rank.pop_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Return the local number (population) of pixels. The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D arr

binary_closing skimage.morphology.binary_closing(image, selem=None, *args, **kwargs) [source] Return fast binary morphological closing of an image. This function returns the same result as greyscale closing but performs faster for binary images. The morphological closing on an image is defined as a dilation followed by an erosion. Closing can remove small dark spots (i.e. “pepper”) and connect small bright cracks. This tends to “close” up (dark) gaps between (bright) features. Parameters: i

register_translation skimage.feature.register_translation(src_image, target_image, upsample_factor=1, space='real') [source] Efficient subpixel image translation registration by cross-correlation. This code gives the same precision as the FFT upsampled cross-correlation in a fraction of the computation time and with reduced memory requirements. It obtains an initial estimate of the cross-correlation peak by an FFT and then refines the shift estimation by upsampling the DFT only in a small ne

points_in_poly skimage.measure.points_in_poly() Test whether points lie inside a polygon. Parameters: points : (N, 2) array Input points, (x, y). verts : (M, 2) array Vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. Returns: mask : (N,) array of bool True if corresponding point is inside the polygon. See also grid_points_in_poly

corner_peaks skimage.feature.corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=0.1, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None) [source] Find corners in corner measure response image. This differs from skimage.feature.peak_local_max in that it suppresses multiple connected peaks with the same accumulator value. Parameters: * : * See skimage.feature.peak_local_max(). Examples >>> from skimage.feature import peak_local_max >

LinearColormap class skimage.viewer.utils.LinearColormap(name, segmented_data, **kwargs) [source] Bases: matplotlib.colors.LinearSegmentedColormap LinearSegmentedColormap in which color varies smoothly. This class is a simplification of LinearSegmentedColormap, which doesn’t support jumps in color intensities. Parameters: name : str Name of colormap. segmented_data : dict Dictionary of ‘red’, ‘green’, ‘blue’, and (optionally) ‘alpha’ values. Each color key contains a list of x, y tuples.

xyz2lab skimage.color.xyz2lab(xyz, illuminant='D65', observer='2') [source] XYZ to CIE-LAB color space conversion. Parameters: xyz : array_like The image in XYZ format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional The name of the illuminant (the function is NOT case sensitive). observer : {“2”, “10”}, optional The aperture angle of the observer. Returns: out : ndarray The image in CIE-LAB format, in a 3- or 4-D array