def test_lut_simple():
lut = np.zeros(2**9, dtype='uint8')
lut[0] = 1
res = np.zeros((64, 64), dtype='uint8')
res = lut2d.binary_lut_filter(res, lut)
assert (res == 1).all()
res = lut2d.binary_lut_filter(res, lut)
assert (res == 0).all()
res = load_img('test_input.png')
lut[:] = 1
res = lut2d.binary_lut_filter(res, lut)
assert (res == 1).all()
def gen_walls_and_food(map_seed, size):
rnd = np.random.RandomState(map_seed)
lut_fn = join(dirname(__file__), 'blobgen_lut2d.dat')
lut = np.loadtxt(lut_fn, dtype='uint8')
walls = rnd.randint(0, 2, (size, size), dtype='uint8')
for i in range(11):
walls = lut2d.binary_lut_filter(walls, lut)
food = rnd.randint(0, 2, (size, size), dtype='uint8')
for i in range(9):
food = lut2d.binary_lut_filter(food, lut)
food[walls > 0] = 0
return walls, food
def generate_image(lut, count=filter_steps):
# generate binary noise image
img = np.random.randint(0, 2, (size, size), dtype='uint8')
# apply the lut as filter several times
for j in range(count):
img = lut2d.binary_lut_filter(img, lut)
return img
def test_lut_result():
res = load_img('test_input.png')
res = lut2d.binary_lut_filter(res, test_data.test_lut)
# imageio.imwrite('test_actual.png', res*255)
expected = load_img('test_expected.png')
assert (res == expected)[1:-1,
1:-1].all() # test ignoring border conditions
#!/usr/bin/env python3
import numpy as np
import lut2d
import sys
import imageio
if len(sys.argv) != 2:
print('usage:', sys.argv[0], 'some-lut.txt')
filename = sys.argv[1]
lut = np.loadtxt(filename, dtype='uint8')
size = 128
img = np.random.randint(0, 2, (size, size), dtype='uint8')
steps = 70
for i in range(steps):
imageio.imwrite('lut-step%05d.png' % i, img * 255, compress_level=6)
img = lut2d.binary_lut_filter(img, lut)
def test_numba(benchmark):
res = load_img('test_input.png')
lut2d.binary_lut_filter(res, test_data.test_lut) # compile aka warm-up
benchmark(lut2d.binary_lut_filter, res, test_data.test_lut)