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))