diff --git a/skimage/draw/_draw.pyx b/skimage/draw/_draw.pyx index babe920f..e95a2366 100644 --- a/skimage/draw/_draw.pyx +++ b/skimage/draw/_draw.pyx @@ -178,7 +178,7 @@ def circle_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t radius, radius: int Radius of circle. method : {'bresenham', 'andres'}, optional - bresenham : Bresenham method + bresenham : Bresenham method (default) andres : Andres method Returns @@ -274,7 +274,7 @@ def ellipse_perimeter(Py_ssize_t cy, Py_ssize_t cx, Py_ssize_t yradius, Centre coordinate of ellipse. yradius, xradius: int Minor and major semi-axes. ``(x/xradius)**2 + (y/yradius)**2 = 1``. - orientation: double, optional + orientation: double, optional (default 0) Major axis orientation in clockwise direction as radians. Returns diff --git a/skimage/feature/corner_cy.pyx b/skimage/feature/corner_cy.pyx index 71c748f8..d4bc5e5a 100644 --- a/skimage/feature/corner_cy.pyx +++ b/skimage/feature/corner_cy.pyx @@ -20,7 +20,7 @@ def corner_moravec(image, Py_ssize_t window_size=1): ---------- image : ndarray Input image. - window_size : int, optional + window_size : int, optional (default 1) Window size. Returns diff --git a/skimage/segmentation/_felzenszwalb_cy.pyx b/skimage/segmentation/_felzenszwalb_cy.pyx index 63d125dc..3babb86e 100644 --- a/skimage/segmentation/_felzenszwalb_cy.pyx +++ b/skimage/segmentation/_felzenszwalb_cy.pyx @@ -25,12 +25,12 @@ def _felzenszwalb_grey(image, double scale=1, sigma=0.8, Py_ssize_t min_size=20) ---------- image: ndarray Input image. - scale: float + scale: float, optional (default 1) Sets the obervation level. Higher means larger clusters. - sigma: float + sigma: float, optional (default 0.8) Width of Gaussian smoothing kernel used in preprocessing. Larger sigma gives smother segment boundaries. - min_size: int + min_size: int, optional (default 20) Minimum component size. Enforced using postprocessing. Returns diff --git a/skimage/segmentation/_quickshift.pyx b/skimage/segmentation/_quickshift.pyx index cc649e8f..b43775b6 100644 --- a/skimage/segmentation/_quickshift.pyx +++ b/skimage/segmentation/_quickshift.pyx @@ -24,23 +24,23 @@ def quickshift(image, ratio=1., float kernel_size=5, max_dist=10, ---------- image : (width, height, channels) ndarray Input image. - ratio : float, between 0 and 1. + ratio : float, optional, between 0 and 1 (default 1). Balances color-space proximity and image-space proximity. Higher values give more weight to color-space. - kernel_size : float + kernel_size : float, optional (default 5) Width of Gaussian kernel used in smoothing the sample density. Higher means fewer clusters. - max_dist : float + max_dist : float, optional (default 10) Cut-off point for data distances. Higher means fewer clusters. - return_tree : bool + return_tree : bool, optional (default False) Whether to return the full segmentation hierarchy tree and distances. - sigma : float + sigma : float, optional (default 0) Width for Gaussian smoothing as preprocessing. Zero means no smoothing. - convert2lab : bool + convert2lab : bool, optional (default True) Whether the input should be converted to Lab colorspace prior to segmentation. For this purpose, the input is assumed to be RGB. - random_seed : None or int + random_seed : None (default) or int, optional Random seed used for breaking ties. Returns diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx index 7db072c0..9a5374d6 100644 --- a/skimage/segmentation/_slic.pyx +++ b/skimage/segmentation/_slic.pyx @@ -20,17 +20,17 @@ def slic(image, n_segments=100, ratio=10., max_iter=10, sigma=1, ---------- image : (width, height [, 3]) ndarray Input image. - n_segments : int + n_segments : int, optional (default 100) The (approximate) number of labels in the segmented output image. - ratio: float + ratio: float, optional (default 10) Balances color-space proximity and image-space proximity. Higher values give more weight to color-space. - max_iter : int + max_iter : int, optional (default 10) Maximum number of iterations of k-means. - sigma : float + sigma : float, optional (default 1) Width of Gaussian smoothing kernel for preprocessing. Zero means no smoothing. - convert2lab : bool + convert2lab : bool, optional (default True) Whether the input should be converted to Lab colorspace prior to segmentation. For this purpose, the input is assumed to be RGB. Highly recommended. diff --git a/skimage/transform/_hough_transform.pyx b/skimage/transform/_hough_transform.pyx index e9a2c575..d8a0b1bf 100644 --- a/skimage/transform/_hough_transform.pyx +++ b/skimage/transform/_hough_transform.pyx @@ -31,10 +31,10 @@ def hough_circle(cnp.ndarray img, Input image with nonzero values representing edges. radius : ndarray Radii at which to compute the Hough transform. - normalize : boolean, optional + normalize : boolean, optional (default True) Normalize the accumulator with the number - of pixels used to draw the radius - full_output : boolean, optional + of pixels used to draw the radius. + full_output : boolean, optional (default False) Extend the output size by twice the largest radius in order to detect centers outside the input picture. diff --git a/skimage/transform/_warps_cy.pyx b/skimage/transform/_warps_cy.pyx index f3a11b45..f51eb8d3 100644 --- a/skimage/transform/_warps_cy.pyx +++ b/skimage/transform/_warps_cy.pyx @@ -67,17 +67,17 @@ def _warp_fast(cnp.ndarray image, cnp.ndarray H, output_shape=None, Input image. H : array of shape ``(3, 3)`` Transformation matrix H that defines the homography. - output_shape : tuple (rows, cols) - Shape of the output image generated. - order : {0, 1} + output_shape : tuple (rows, cols), optional + Shape of the output image generated (default None). + order : {0, 1}, optional Order of interpolation:: * 0: Nearest-neighbour interpolation. * 1: Bilinear interpolation (default). - * 2: Biquadratic interpolation (default). + * 2: Biquadratic interpolation. * 3: Bicubic interpolation. - mode : {'constant', 'reflect', 'wrap', 'nearest'} - How to handle values outside the image borders. - cval : string + mode : {'constant', 'reflect', 'wrap', 'nearest'}, optional + How to handle values outside the image borders (default is constant). + cval : string, optional (default 0) Used in conjunction with mode 'C' (constant), the value outside the image boundaries.