ProjectiveTransform class skimage.transform.ProjectiveTransform(matrix=None) [source] Bases: skimage.transform._geometric.GeometricTransform Matrix transformation. Apply a projective transformation (homography) on coordinates. For each homogeneous coordinate , its target position is calculated by multiplying with the given matrix, , to give : [[a0 a1 a2] [b0 b1 b2] [c0 c1 1 ]]. E.g., to rotate by theta degrees clockwise, the matrix should be: [[cos(theta) -sin(theta) 0] [sin(theta) cos(

profile_line skimage.measure.profile_line(img, src, dst, linewidth=1, order=1, mode='constant', cval=0.0) [source] Return the intensity profile of an image measured along a scan line. Parameters: img : numeric array, shape (M, N[, C]) The image, either grayscale (2D array) or multichannel (3D array, where the final axis contains the channel information). src : 2-tuple of numeric scalar (float or int) The start point of the scan line. dst : 2-tuple of numeric scalar (float or int) The e

probabilistic_hough_line skimage.transform.probabilistic_hough_line(img, threshold=10, line_length=50, line_gap=10, theta=None) [source] Return lines from a progressive probabilistic line Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. threshold : int, optional (default 10) Threshold line_length : int, optional (default 50) Minimum accepted length of detected lines. Increase the parameter to extract longer lines. line_gap : int, op

prewitt_v skimage.filters.prewitt_v(image, mask=None) [source] Find the vertical 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 Prewitt edge map. Notes We use the following kernel: 1 0 -1 1 0 -1 1

prewitt_h skimage.filters.prewitt_h(image, mask=None) [source] 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 Prewitt edge map. Notes We use the following kernel: 1 1 1 0 0 0

prewitt skimage.filters.prewitt(image, mask=None) [source] Find the edge magnitude 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 Prewitt edge map. See also sobel, scharr Notes Return the square root of the sum of squares

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

pop_bilateral skimage.filters.rank.pop_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10) [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. Additionally pixels must have a greylevel inside the interval [g-s0, g+s1] where g is the greyvalue of the center pixel. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D

pop skimage.filters.rank.pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [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. 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

pop skimage.io.pop() [source] Pop an image from the shared image stack. Returns: img : ndarray Image popped from the stack.