hscharr skimage.filters.hscharr(*args, **kwargs) [source] Deprecated function. Use skimage.filters.scharr_h instead. Find the horizontal edges of an image using the Scharr 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 Scharr edge map

hprewitt skimage.filters.hprewitt(*args, **kwargs) [source] Deprecated function. Use skimage.filters.prewitt_h instead. Find the horizontal edges of an image using the Prewitt 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 Prewitt edg

hough_line_peaks skimage.transform.hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10, threshold=None, num_peaks=inf) [source] Return peaks in hough transform. Identifies most prominent lines separated by a certain angle and distance in a hough transform. Non-maximum suppression with different sizes is applied separately in the first (distances) and second (angles) dimension of the hough space to identify peaks. Parameters: hspace : (N, M) array Hough space returned by th

How to parallelize loops In image processing, we frequently apply the same algorithm on a large batch of images. In this paragraph, we propose to use joblib to parallelize loops. Here is an example of such repetitive tasks: from skimage import data, color, util from skimage.restoration import denoise_tv_chambolle from skimage.feature import hog def task(image): """ Apply some functions and return an image. """ image = denoise_tv_chambolle(image[0][0], weight=0.1, multichannel=T

hough_ellipse skimage.transform.hough_ellipse(img, threshold=4, accuracy=1, min_size=4, max_size=None) [source] Perform an elliptical Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. threshold: int, optional (default 4) Accumulator threshold value. accuracy : double, optional (default 1) Bin size on the minor axis used in the accumulator. min_size : int, optional (default 4) Minimal major axis length. max_size : int, optional Max

hough_line skimage.transform.hough_line(img, theta=None) [source] Perform a straight line Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. theta : 1D ndarray of double Angles at which to compute the transform, in radians. Defaults to -pi/2 .. pi/2 Returns: H : 2-D ndarray of uint64 Hough transform accumulator. theta : ndarray Angles at which the transform was computed, in radians. distances : ndarray Distance values. Notes Th

hough_circle skimage.transform.hough_circle(image, radius, normalize=True, full_output=False) [source] Perform a circular Hough transform. Parameters: image : (M, N) ndarray Input image with nonzero values representing edges. radius : scalar or sequence of scalars Radii at which to compute the Hough transform. Floats are converted to integers. normalize : boolean, optional (default True) Normalize the accumulator with the number of pixels used to draw the radius. full_output : boolean

horse skimage.data.horse() [source] Black and white silhouette of a horse. This image was downloaded from openclipart <http://openclipart.org/detail/158377/horse-by-marauder> Released into public domain and drawn and uploaded by Andreas Preuss (marauder).

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

histogram skimage.exposure.histogram(image, nbins=256) [source] Return histogram of image. Unlike numpy.histogram, this function returns the centers of bins and does not rebin integer arrays. For integer arrays, each integer value has its own bin, which improves speed and intensity-resolution. The histogram is computed on the flattened image: for color images, the function should be used separately on each channel to obtain a histogram for each color channel. Parameters: image : array Inpu