def test_rgb2grey(): f = mahotas.imread("mahotas/demos/data/luispedro.jpg") fg = rgb2grey(f) fg8 = rgb2grey(f, dtype=np.uint8) assert f.ndim == 3 assert fg.ndim == 2 assert fg8.ndim == 2 assert fg.shape[0] == f.shape[0] assert fg.shape[1] == f.shape[1] assert fg.shape == fg8.shape assert fg8.dtype == np.uint8
def test_rgb2grey(): f = luispedro_jpg() fg = rgb2grey(f) fg8 = rgb2grey(f, dtype=np.uint8) assert f.ndim == 3 assert fg.ndim == 2 assert fg8.ndim == 2 assert fg.shape[0] == f.shape[0] assert fg.shape[1] == f.shape[1] assert fg.shape == fg8.shape assert fg8.dtype == np.uint8
def load_image(filename): # returns image as ndarray in grayscale and negative image = colors.rgb2grey(misc.imread(filename)) image = 255 - image # creating a negative return image
# This code is supporting material for the book # Building Machine Learning Systems with Python # by Willi Richert and Luis Pedro Coelho # published by PACKT Publishing # # It is made available under the MIT License import mahotas as mh from mahotas.colors import rgb2grey import numpy as np im = mh.imread('lenna.jpg') im = rgb2grey(im) salt = np.random.random(im.shape) > .975 pepper = np.random.random(im.shape) > .975 im = np.maximum(salt * 170, mh.stretch(im)) im = np.minimum(pepper * 30 + im * (~pepper), im) mh.imsave('../1400OS_10_13+.jpg', im.astype(np.uint8))