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| import cv2 | |
| import numpy as np | |
| from wand.image import Image as WandImage | |
| from scipy.ndimage import zoom as scizoom | |
| from wand.api import library as wandlibrary | |
| class MotionImage(WandImage): | |
| def motion_blur(self, radius=0.0, sigma=0.0, angle=0.0): | |
| wandlibrary.MagickMotionBlurImage(self.wand, radius, sigma, angle) | |
| def clipped_zoom(img, zoom_factor): | |
| h = img.shape[1] | |
| # ceil crop height(= crop width) | |
| ch = int(np.ceil(h / float(zoom_factor))) | |
| top = (h - ch) // 2 | |
| img = scizoom(img[top:top + ch, top:top + ch], (zoom_factor, zoom_factor, 1), order=1) | |
| # trim off any extra pixels | |
| trim_top = (img.shape[0] - h) // 2 | |
| return img[trim_top:trim_top + h, trim_top:trim_top + h] | |
| def disk(radius, alias_blur=0.1, dtype=np.float32): | |
| if radius <= 8: | |
| L = np.arange(-8, 8 + 1) | |
| ksize = (3, 3) | |
| else: | |
| L = np.arange(-radius, radius + 1) | |
| ksize = (5, 5) | |
| X, Y = np.meshgrid(L, L) | |
| aliased_disk = np.array((X ** 2 + Y ** 2) <= radius ** 2, dtype=dtype) | |
| aliased_disk /= np.sum(aliased_disk) | |
| # supersample disk to antialias | |
| return cv2.GaussianBlur(aliased_disk, ksize=ksize, sigmaX=alias_blur) | |
| # modification of https://github.com/FLHerne/mapgen/blob/master/diamondsquare.py | |
| def plasma_fractal(mapsize=256, wibbledecay=3): | |
| """ | |
| Generate a heightmap using diamond-square algorithm. | |
| Return square 2d array, side length 'mapsize', of floats in range 0-255. | |
| 'mapsize' must be a power of two. | |
| """ | |
| assert (mapsize & (mapsize - 1) == 0) | |
| maparray = np.empty((mapsize, mapsize), dtype=np.float_) | |
| maparray[0, 0] = 0 | |
| stepsize = mapsize | |
| wibble = 100 | |
| def wibbledmean(array): | |
| return array / 4 + wibble * np.random.uniform(-wibble, wibble, array.shape) | |
| def fillsquares(): | |
| """For each square of points stepsize apart, | |
| calculate middle value as mean of points + wibble""" | |
| cornerref = maparray[0:mapsize:stepsize, 0:mapsize:stepsize] | |
| squareaccum = cornerref + np.roll(cornerref, shift=-1, axis=0) | |
| squareaccum += np.roll(squareaccum, shift=-1, axis=1) | |
| maparray[stepsize // 2:mapsize:stepsize, | |
| stepsize // 2:mapsize:stepsize] = wibbledmean(squareaccum) | |
| def filldiamonds(): | |
| """For each diamond of points stepsize apart, | |
| calculate middle value as mean of points + wibble""" | |
| mapsize = maparray.shape[0] | |
| drgrid = maparray[stepsize // 2:mapsize:stepsize, stepsize // 2:mapsize:stepsize] | |
| ulgrid = maparray[0:mapsize:stepsize, 0:mapsize:stepsize] | |
| ldrsum = drgrid + np.roll(drgrid, 1, axis=0) | |
| lulsum = ulgrid + np.roll(ulgrid, -1, axis=1) | |
| ltsum = ldrsum + lulsum | |
| maparray[0:mapsize:stepsize, stepsize // 2:mapsize:stepsize] = wibbledmean(ltsum) | |
| tdrsum = drgrid + np.roll(drgrid, 1, axis=1) | |
| tulsum = ulgrid + np.roll(ulgrid, -1, axis=0) | |
| ttsum = tdrsum + tulsum | |
| maparray[stepsize // 2:mapsize:stepsize, 0:mapsize:stepsize] = wibbledmean(ttsum) | |
| while stepsize >= 2: | |
| fillsquares() | |
| filldiamonds() | |
| stepsize //= 2 | |
| wibble /= wibbledecay | |
| maparray -= maparray.min() | |
| return maparray / maparray.max() | |