def test():
device = torch.device("cpu")
xml = "particle/nn/config/dcgan.xml"
net_G = Generator(xml).to(device)
# 以下为查看当前生成效果
checkpoint = torch.load(
'/home/chuan/soil/output/dcgan/iterD:iterG=1:2/nLatent64/state_dict-25.tar',
map_location='cpu')
net_G.load_state_dict(checkpoint['generator_state_dict'])
torch.manual_seed(3.14)
vec = torch.randn(3000, net_G.nLatent, device=device)
print(vec.shape)
cubes = generate(net_G, vec)
print(cubes.shape, cubes.dtype)
np.save('output/geometry/gan.npy', cubes)
for cube in cubes:
sand = Sand(cube)
# sand.visualize(voxel=True, glyph='sphere', scale_mode='scalar')
# sand.visualize(vivid=False)
sand.visualize(vivid=True)
mlab.outline()
mlab.axes()
mlab.show()
def build():
num = 1000
fakeParticles = np.load(
"/home/chuan/soil/output/tvsnet/vae/通道加多/no-dropout, rotation60, 1*1ConvT-no-bn/state_dict.pt")[:num]
fakeParticlesTmp = []
balls = []
clumps = []
for particle in tqdm(fakeParticles):
labeled = measure.label(particle, background=0)
props = measure.regionprops(labeled)
props.sort(key=lambda prop: prop.area)
particle = props[-1].image
fakeParticlesTmp.append(particle)
# 外接球
sphere = Sand(particle).circumscribedSphere()
balls.append([*sphere[1], sphere[0]])
# 计算clump
clump = distTrans.build(particle, 0.4, 160)
clump = np.hstack([clump[0], clump[1][:, np.newaxis]])
clumps.append(clump)
fakeParticles = fakeParticlesTmp
del fakeParticlesTmp
fakeParticles = np.array([Sand(particle).toCoords()
for particle in fakeParticles], dtype=object)
np.save("output/clump/pipeline/fakeParticles.npy", fakeParticles)
np.save("output/clump/pipeline/balls.npy", balls)
np.save("output/clump/pipeline/clumps.npy", clumps)
return
def testGenerate(src, model, **kwargs):
particle = generateOneParticle(model, src)
sand = Sand(particle)
from skimage.util import random_noise
sand.cube = random_noise(sand.cube, mode="gaussian", var=0.02, seed=314)
print("已添加噪声！")
fig = sand.visualize(**kwargs)
return fig
def test():
device = torch.device("cpu")
xml = "particle/nn/config/vae.xml"
vae = Vae(xml).to(device)
ckpt = torch.load("output/vae/nLatent64/state_dict.pt", map_location="cpu")
vae.load_state_dict(ckpt)
torch.manual_seed(3.14)
vec = torch.randn(3000, vae.decoder.nLatent, device=device)
print(vec.shape)
cubes = vae.generate(vec)
print(cubes.shape, cubes.dtype)
np.save("output/geometry/vae.npy", cubes)
for cube in cubes:
sand = Sand(cube)
sand.visualize(vivid=True)
mlab.outline()
mlab.axes()
mlab.show()
def display(dataFile, savePath, thrd=0.5):
data = np.load(dataFile)
for i in trange(len(data)):
cube = data[i]
cube[cube > thrd] = 1
cube[cube <= thrd] = 0
Sand(cube).visualize(vivid=True)
# mlab.outline()
# mlab.axes()
mlab.savefig(os.path.join(savePath, f"{i+1:04d}.png"))
mlab.close()
return
def testSimpleGeometry(ind, model=None):
cwd = os.getcwd()
os.chdir('./data/简单几何图形')
filenames = sorted(os.listdir())
filename = filenames[ind]
print(filename)
image = skio.imread(filename, as_gray=True)
image[image == 255] = 1
print(image.max())
os.chdir(cwd)
x = torch.empty((3, 64, 64), dtype=torch.uint8)
for i in range(3):
x[i] = torch.from_numpy(image)
cube = generateOneParticle(model, x)
Sand(cube).visualize()
mlab.outline()
mlab.axes()
mlab.show()
mlab.savefig(filename)
return
def plotParticles(saveObj=True):
fakeParticles = np.load("output/clump/pipeline/fakeParticles-move.npy",
allow_pickle=True)
fakeParticles = np.vstack(fakeParticles)
print(fakeParticles.shape)
bigCubes = np.zeros((
fakeParticles[:, 0].max() - fakeParticles[:, 0].min() + 1,
fakeParticles[:, 1].max() - fakeParticles[:, 1].min() + 1,
fakeParticles[:, 2].max() - fakeParticles[:, 2].min() + 1
), dtype=np.uint8)
bigCubes[tuple(fakeParticles.T)] = 1
outermostLayerThickness = 4
newBigCubes = np.zeros(np.array(bigCubes.shape)+outermostLayerThickness*2)
newBigCubes[outermostLayerThickness:-outermostLayerThickness,
outermostLayerThickness:-outermostLayerThickness,
outermostLayerThickness:-outermostLayerThickness] = bigCubes
bigCubes = newBigCubes
del newBigCubes
print(bigCubes.shape)
fig = Sand(bigCubes).visualize()
if saveObj:
mlab.savefig("output/clump/pipeline/fakeParticles.obj")
return fig
def interpolate(z1, z2, xml="particle/nn/config/dcgan.xml"):
net_G = Generator(xml)
ckpt = torch.load(
"output/dcgan/iterD:iterG=1:2/nLatent64/state_dict-25.tar",
map_location="cpu")
net_G.load_state_dict(ckpt['generator_state_dict'])
betas = [0, 0.2, 0.4, 0.6, 0.8, 1]
inters = [(1 - beta) * z1 + beta * z2 for beta in betas]
cubes = []
for beta, z in zip(betas, inters):
cubes.append(generate(net_G, z))
sand = Sand(cubes[-1])
sand.cube = sand.poseNormalization()
sand.visualize(figure=f"{1-beta:.1f}-{beta:.1f}")
# mlab.outline()
# mlab.axes()
mlab.savefig(f"/home/chuan/{1-beta:.1f}-{beta:.1f}.png")
mlab.show()
def surround(z, xml="particle/nn/config/dcgan.xml", angleInd=1):
net_G = Generator(xml)
ckpt = torch.load(
"output/dcgan/iterD:iterG=1:2/nLatent64/state_dict-25.tar",
map_location="cpu")
net_G.load_state_dict(ckpt['generator_state_dict'])
sigmas = [0.1, 0.2, 0.3, 0.4, 0.5]
noises = [torch.normal(0, sigma, size=z.size()) for sigma in sigmas]
zs = [z + noise for noise in noises]
cubes = []
sigmas = [0] + sigmas
zs = [z] + zs
for sigma, z in zip(sigmas, zs):
cubes.append(generate(net_G, z))
sand = Sand(cubes[-1])
# sand.cube = sand.poseNormalization()
if len(cubes) == 1:
Sand(cubes[0]).visualize(figure=f"{sigma:.1f}",
color=(0.9, 0.9, 0.9),
opacity=1)
elif len(cubes) > 1:
sand.visualize(figure=f"{sigma:.1f}",
color=(0, 0.6, 0.85),
opacity=0.9)
Sand(cubes[0]).visualize(figure=f"{sigma:.1f}",
color=(1, 1, 1),
opacity=1)
views = mlab.view()
mlab.gcf().scene.camera.zoom(0.8)
if angleInd == 1:
mlab.view(azimuth=views[0], elevation=views[1])
elif angleInd == 2:
mlab.view(azimuth=views[0] + 120, elevation=views[1] + 60)
elif angleInd == 3:
mlab.view(azimuth=views[0] + 240, elevation=views[1] + 120)
else:
raise ValueError
mlab.savefig(f"/home/chuan/{angleInd}-{sigma:.1f}.png",
magnification=2)
data = np.load("data/liutao/v1/particles.npz")["testSet"]
print(data.shape)
for i in range(10, 11):
cube = data[i, 0]
lamdas = [0.1, 0.2, 0.3, 0.4, 0.5]
phis = [90, 105, 130, 145, 160, 175]
for lamda in lamdas:
for phi in tqdm(phis):
c, r = build(cube, lamda, phi)
# 剔除最小的颗粒
# mask = r != r.min()
# c = c[mask]
# r = r[mask]
# print(f"Total {len(r)} spheres.")
fig = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
mlab.points3d(*c.T, r*2, scale_factor=1, opacity=1,
resolution=30, mode="sphere", color=(0.65,)*3)
sand = Sand(cube)
sand.visualize(vivid=True, figure=fig,
color=(0, 0.6, 0.85), opacity=0.5)
# mlab.title(f"Total {len(r)} spheres.")
# mlab.outline()
# mlab.axes()
mlab.savefig(f"../myClump/{lamda}-{phi}-{len(r)}.png")
mlab.close(all=True)
# mlab.show()
import numpy as np
from particle.pipeline import Sand
from mayavi import mlab
# data = np.load("output/geometry/generatedParticles/vae.npy")
# initInd = 281
# for i, particle in enumerate(data[initInd:initInd+30]):
# Sand(particle).visualize()
# mlab.savefig(f"/home/chuan/obj/{i+1:02d}.obj")
# mlab.close(all=True)
data = np.load("data/liutao/v1/particles.npz")["trainSet"]
print(data.shape)
initInd = 114
for i, particle in enumerate(data[initInd:initInd+30]):
Sand(particle[0]).visualize()
mlab.savefig(f"/home/chuan/obj/{i+1:02d}.obj")
mlab.savefig(f"/home/chuan/obj/{i+1:02d}.png")
mlab.close(all=True)