def test_conversion_color2():
img = Image.fromGslib("tests/data/test_color.gslib")
img.exportAsPng("tests/data/test_color.png", colored=True)
img = Image.fromGslib("tests/data/test_color.gslib")
img.exportAsPpm("tests/data/test_color.ppm")
img2 = Image.fromPng("tests/data/test_color.png")
img3 = Image.fromPpm("tests/data/test_color.ppm")
assert np.alltrue((img.asArray()-img2.asArray()) < 1e8)
assert img == img3
def test_threshold1(img):
img.threshold(thresholds=[127], values=[0, 255])
expected = Image.fromArray(
np.array([[255, 255, 0],
[0, 0, 255],
[255, 0, 0]]))
assert img == expected
def test_threshold2(img):
img.threshold(thresholds=[80, 210], values=[0, 127, 255])
expected = Image.fromArray(
np.array([[127, 255, 0],
[127, 0, 255],
[255, 127, 0]]))
assert img == expected
def test_categorize2(img):
expected = Image.fromArray(
np.array([[255, 255, 100],
[100, 0, 255],
[255, 100, 0]]))
img.categorize(3, initial_clusters=[2, 99, 254])
assert img == expected
def test_import_gslb2var():
img = Image.fromGslib("tests/data/2var.gslib")
var1 = img.asArray("V0").reshape((3, 3))
var2 = img.asArray("V1").reshape((3, 3))
expected1 = np.array([[1, 1, 1], [1, 1, 1], [0, 0, 0]])
expected2 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
assert np.alltrue(var1 == expected1)
assert np.alltrue(var2 == expected2)
def test_from_list():
data1 = np.array([[0, 255],
[255, 100]])
data2 = np.array([[100, 255],
[255, 0]])
input_data = [data1, data2]
expected = np.array(input_data)
img = Image.fromArray(input_data).asArray()
assert np.alltrue(img == expected)
def test_threshold():
image = Image.fromArray(np.array([[0.3, 3.0], [1.2, 2.4], [2.1, 1.1]]))
categorical_ref = np.array([[0, 2], [1, 2], [2, 1]])
thresholds = np.array([1.0, 2.0])
image.threshold(thresholds)
labels = mpstool.img.labelize(image)
assert np.alltrue(labels == categorical_ref)
def test_categorize1(img):
expected = Image.fromArray(
np.array([[255, 255, 100],
[100, 100, 255],
[255, 100, 100]]))
print(img._data)
img.categorize(2)
print(img._data)
assert img == expected
def generate(epoch, generator, N, args, device, exportCuts=False):
os.makedirs("output/epoch", exist_ok=True)
for i in range(N):
output = generator.generate(1, device).cpu().detach()
if exportCuts:
cuts = extract_3cuts(output)[0].permute(1, 2, 0)
cuts = (255 * cuts.numpy()).astype(np.uint8)
for ind, dim in enumerate(["x", "y", "z"]):
cut_dim = PILImage.fromarray(cuts[..., ind])
cut_dim.save("output/epoch/{}_{}_{}.png".format(
args.name, i, dim))
output = output.numpy()
output = (output * 255).astype(np.uint8)
output = np.squeeze(output)
output = Image.fromArray(output)
output.exportAsVox("output/epoch/{}_{}.vox".format(args.name, i))
def test_from_txt(img):
img1 = Image.fromTxt("tests/data/test_img.txt", (3, 3))
img2 = Image.fromTxt("tests/data/test_img.txt", (3, 3, 1))
assert img == img1
assert img == img2
def cube_image(cube):
return Image.fromArray(cube)
def image():
return Image.fromArray(array())
def test_vtk_pgm():
img = Image.fromVtk("tests/data/test_img.vtk")
img.exportAsPgm("tests/data/test_img.pgm")
img2 = img.fromPgm("tests/data/test_img.pgm")
assert np.alltrue(img == img2)
def test_conversion_txt_gslib():
img = Image.fromTxt("tests/data/test_img.txt", (3, 3))
img.exportAsGslib("tests/data/test_img.gslib")
img_test = Image.fromGslib("tests/data/test_img.gslib")
assert np.alltrue(img_test == img)
def img():
data = np.array([[200, 255, 60],
[100, 10, 255],
[250, 100, 0]])
return Image.fromArray(data)
def test_conversion_png_txt():
img = Image.fromPng("tests/data/test_img.png")
img.exportAsTxt("tests/data/test_img2.txt")
img_test = Image.fromTxt("tests/data/test_img2.txt", (3, 3))
assert np.alltrue(img_test == img)
def test_conversion_vox_png():
img = Image.fromVox("tests/data/test_img.vox")
img.exportAsPng("tests/data/test_img.png")
img_test = Image.fromPng("tests/data/test_img.png")
assert np.alltrue(img_test == img)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-fun", "--fun", type=str, required=True)
parser.add_argument('-model', "--model", type=str, required=True)
parser.add_argument('-ti', "--ti", type=str, required=True)
parser.add_argument('-output', "--output", type=str, default="output.csv")
parser.add_argument("-n-samples", "--n-samples", type=int, default=100)
parser.add_argument("-n-ti", "--n-ti", type=int, default=100)
args = parser.parse_args()
model = GAN(args.model)
if ".vox" in args.ti:
ti = Image.fromVox(args.ti)
elif ".gslib" in args.ti:
ti = Image.fromGslib(args.ti)
ti.threshold(thresholds=[1], values=[0, 1])
ti = ti.asArray()
if args.fun == "conn":
stat_fun = mpstool.connectivity.get_function
elif args.fun == "vario":
stat_fun = mpstool.stats.variogram
else:
raise Exception(
"argument -fun not recognized. Should be 'conn' or 'vario' ")
# Read connectivity for the TI : min and max of values
def test_gslib_to_vtk():
img = Image.fromGslib("tests/data/test_img.gslib")
img.exportAsVtk("tests/data/test_img.vtk")
img2 = img.fromVtk("tests/data/test_img.vtk")
assert np.alltrue(img == img2)
def test_gslib_io():
img = Image.fromGslib("tests/data/test_color_simple.gslib")
img.exportAsGslib("tests/data/test_color_simple2.gslib")
img = Image.fromGslib("tests/data/test_color_simple.gslib")
img2 = Image.fromGslib("tests/data/test_color_simple2.gslib")
assert(img == img2)
def test_pgm_vox():
img = Image.fromPgm("tests/data/test_img.pgm")
img.exportAsVox("tests/data/test_img.vox")
img_test = Image.fromVox("tests/data/test_img.vox")
assert np.alltrue(img_test == img)
parser.add_argument('-ti', "--ti", type=str, required=True)
parser.add_argument('-output', "--output", type=str, default="output.csv")
parser.add_argument("-n-samples", "--n-samples", type=int, default=100)
parser.add_argument("-n-ti", "--n-ti", type=int, default=100)
args = parser.parse_args()
model = Generator(256)
if not torch.cuda.is_available():
model = model.to("cpu")
model.load_state_dict(torch.load(args.model, map_location="cpu"))
else:
model = model.to("cuda")
model.load_state_dict(torch.load(args.model))
if ".vox" in args.ti:
ti = Image.fromVox(args.ti)
elif ".gslib" in args.ti:
ti = Image.fromGslib(args.ti)
ti.threshold(thresholds=[1], values=[0, 1])
ti = ti.asArray()
if args.fun == "conn":
stat_fun = mpstool.connectivity.get_function
elif args.fun == "vario":
stat_fun = mpstool.stats.variogram
else:
raise Exception(
"argument mode not recognized. Should be 'conn' or 'vario' ")
# Read connectivity for the TI : min and max of values
cX_ti = []
