def main():
parser = argparse.ArgumentParser(description="Splinart generator")
parser.add_argument('-f',
'--filename',
type=str,
help="filename where the output is stored",
default='output.png')
parser.add_argument('--path',
type=str,
help="path where to store the results",
default='.')
parser.add_argument('-s',
'--shape',
type=str,
help='apply spline on this shape',
default='circle',
choices=['circle', 'line'])
parser.add_argument('--show',
help='show the result using matplotlib',
action='store_true')
args = parser.parse_args()
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
if args.shape == 'circle':
circle(img)
else:
line(img)
if args.show:
spl.show_img(img)
else:
spl.save_img(img, args.path, args.filename)
def test_case(tmpdir, load_json):
datadir, js = load_json
np.random.seed(js["seed"])
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
theta, path = spl.circle(js["center"], js["radius"])
def xs_func():
nsamples = 500
return (np.random.random() + 2 * np.pi * np.linspace(0, 1, nsamples))%(2*np.pi)
spl.update_img(img, path, xs_func, nrep=js["nrep"], x=theta)
spl.save_img(img, tmpdir.dirname, 'output.png')
assert filecmp.cmp(tmpdir.dirname + '/output.png', datadir + "/" + js["output"])
lo, up = .2, .8
path = []
color_lines = []
npoints = 20
for iy, y in enumerate(np.linspace(lo, up, 10)):
npoints += 5
path.append(spl.line(lo, up, y, npoints))
color = np.random.random(4) * .3
color[-1] = 1.
color_lines.append(color)
def xs_func():
nsamples = 500
return .001 * np.random.random() + np.linspace(lo + .02, up - .02,
nsamples)
for i in range(len(path)):
img1 = np.ones_like(img)
spl.update_img(img1,
path[i],
xs_func,
nrep=1000,
periodic=False,
color=color_lines[i])
mask = img1 < 1.
img[mask] += img1[mask]
spl.save_img(img, "./output", "lines.png")
RADIUS = .3
NREP = 4000
np.random.seed(SEED)
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
theta, path = spl.circle(CENTER, RADIUS)
def xs_func():
nsamples = 500
return (np.random.random() +
2 * np.pi * np.linspace(0, 1, nsamples)) % (2 * np.pi)
spl.update_img(img, path, xs_func, nrep=NREP, x=theta)
spl.save_img(img, '.', 'circle.png')
js = json.dumps(
{
"circle": {
"seed": SEED,
"center": CENTER,
"radius": RADIUS,
"nrep": NREP,
"output": "./circle.png"
}
},
indent=4)
for i in range(nb_circles):
radius = .1 + np.random.random() * .1
center = .2 + np.random.random(2) * .6
theta, path = spl.circle(center, radius, npoints=75)
theta_circles.append(theta)
path_circles.append(path)
color = np.random.random(4) * .3
color[-1] = 1.
color_circles.append(color)
def xs_func():
nsamples = 500
return (np.random.random() +
2 * np.pi * np.linspace(0, 1, nsamples)) % (2 * np.pi)
for i in range(nb_circles):
img1 = np.ones_like(img)
spl.update_img(img1,
path_circles[i],
xs_func,
nrep=1000,
scale_value=.00005,
x=theta_circles[i],
color=color_circles[i])
mask = img1 < 1.
img[mask] += img1[mask]
spl.save_img(img, './output', 'circles.png')
import numpy as np
from six.moves import range
import splinart as spl
np.random.seed(42)
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
theta, path = spl.circle([.5, .5], .3, npoints=40)
def xs_func():
nsamples = 500
return (np.random.random() +
2 * np.pi * np.linspace(0, 1, nsamples)) % (2 * np.pi)
spl.update_img(img, path, xs_func, nrep=4000, x=theta, scale_value=.00005)
spl.save_img(img, 'output', 'circle.png')
import numpy as np
from six.moves import range
import splinart as spl
img_size, channels = 1000, 4
img = np.ones((img_size, img_size, channels), dtype=np.float32)
def xs_func():
nsamples = 500
return .001 * np.random.random() + np.linspace(lo + .1, up - .1, nsamples)
lo, up = .2, .8
path = spl.line(lo, up, .5)
spl.update_img(img, path, xs_func, nrep=1000, periodic=False)
spl.save_img(img, "output", "line.png")
