def __init__(self, N, lb=-0.5, rb=0.5, perturb=0.02):
self.name = 'SparseGrid2D'
points_per_axis = int(N**0.5)
data = gen_grid(2, points_per_axis, lb=lb, rb=rb)
# Add noise
data += np.random.normal(loc=np.zeros_like(data), scale=perturb, size=data.shape)
self.data = data
def __init__(self, n_modes=9, lb=-0.5, rb=0.5):
self.name = 'MixGrid'
self.dim = 2
self.n_modes = n_modes
points_per_axis = int(n_modes**0.5)
self.sigma = (rb - lb) / points_per_axis / 4
self.centers = gen_grid(2,
points_per_axis,
lb=lb + 2 * self.sigma,
rb=rb - 2 * self.sigma)
def plot_grid(points_per_axis,
gen_func,
d=2,
bounds=(0.0, 1.0),
scale=.8,
save_path=None):
''' Uniformly plots synthesized shapes in the latent space
K : number of samples for each point in the latent space '''
scale *= (bounds[1] - bounds[0]) / points_per_axis
Z = gen_grid(2, points_per_axis, bounds[0], bounds[1]) # Generate a grid
plot_synthesized(Z, gen_func, d=d, scale=scale, save_path=save_path)
def plot_samples(Z, X_list, alphas=None, scale=None, scatter=True,
mirror=False, annotate=False, fname=None, **kwargs):
''' Plot shapes given design sapce and latent space coordinates '''
plt.rc("font", size=12)
if Z is None or Z.shape[1] != 2:
N = X_list[0].shape[0]
points_per_axis = int(N**.5)
bounds = (-1., 1.)
Z = gen_grid(2, points_per_axis, bounds[0], bounds[1]) # Generate a grid
scale = 0.8*2.0/points_per_axis
if scale is None:
r0 = np.max(Z[:,0]) - np.min(Z[:,0])
r1 = np.max(Z[:,1]) - np.min(Z[:,1])
scale = 0.8*np.minimum(r0, r1)/10
# Create a 2D plot
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111)
kwargs2 = kwargs.copy()
if 'samples' not in fname and 'assemblies' not in fname:
kwargs2['c'] = 'gray'
for (i, z) in enumerate(Z):
if alphas is not None:
kwargs['alpha'] = alphas[i]
kwargs2['alpha'] = alphas[i]
for (j, X) in enumerate(X_list):
if j == 0:
plot_shape(X[i], z[0], z[1], ax, scale, scatter, mirror, **kwargs)
else:
plot_shape(X[i], z[0], z[1], ax, scale, scatter, mirror, **kwargs2)
if annotate:
label = '{0}'.format(i+1)
plt.annotate(label, xy = (z[0], z[1]), size=10)
plt.xticks([])
plt.yticks([])
plt.axis('off')
plt.axis('equal')
plt.tight_layout()
plt.savefig(fname+'.svg', dpi=300)
# plt.savefig(fname+'.png', dpi=300)
plt.close()
def black_box_function(winSize, winSizeNCC, target="ATA"):
# optimizer.probe(
# params={"pointsInGrid": 10,
# "winSize": 3,
# "maxLevel": 5,
# "termCriteria_maxCount": 20,
# "termCriteria_epsilon": 2.9999999999999999e-01,
# "winSizeNCC": 30,
# "maxMedianLengthOfDisplacementDifference": 10.0},
# lazy=True,
# )
grid = [{
'maxLevel': [5],
'maxMedianLengthOfDisplacementDifference': [10.0],
'pointsInGrid': [10],
'termCriteria_epsilon': [2.9999999999999999e-01],
'termCriteria_maxCount': [20],
'tracker': ['MEDIANFLOW'],
'video': vid,
'winSize': [[winSize, winSize]],
'winSizeNCC': [[winSizeNCC, winSizeNCC]]
}]
results = utils.gen_grid(grid)
pool = ThreadPool(8)
# print(results[1])
# Analise(results[1])
# print(results[1])
# pbar = tqdm(total=len(results))
res = [0.0, 0]
for result in results:
pool.apply_async(utils.Analise, args=[result], callback=update1)
# pool.map(lambda p: Analise(vid, p), results)
pool.close()
pool.join()
# [print(i["ATA"]) for i in results]
for result in results:
try:
res[0] += result[target]
res[1] += 1
except:
return -1
res[0] /= res[1]
return res[0]
def plot_samples(Z,
X,
scale=0.8,
points_per_axis=None,
scatter=True,
symm_axis=None,
annotate=False,
fname=None,
**kwargs):
''' Plot shapes given design sapce and latent space coordinates '''
plt.rc("font", size=12)
if Z is None or Z.shape[1] != 2 or points_per_axis is None:
N = X.shape[0]
points_per_axis = int(N**.5)
bounds = (0., 1.)
Z = gen_grid(2, points_per_axis, bounds[0],
bounds[1]) # Generate a grid
scale /= points_per_axis
# Create a 2D plot
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111)
for (i, z) in enumerate(Z):
plot_shape(X[i], z[0], .3 * z[1], ax, scale, scatter, symm_axis,
**kwargs)
if annotate:
label = '{0}'.format(i + 1)
plt.annotate(label, xy=(z[0], z[1]), size=10)
# plt.xlabel('c1')
# plt.ylabel('c2')
plt.xticks([])
plt.yticks([])
plt.axis('off')
plt.axis('equal')
plt.tight_layout()
plt.savefig(fname + '.svg', dpi=600)
plt.savefig(fname + '.png', dpi=600)
plt.close()
def plot_samples(Z,
X,
P=None,
W=None,
scale=0.8,
scatter=True,
symm_axis=None,
annotate=False,
fname=None,
**kwargs):
''' Plot shapes given design sapce and latent space coordinates '''
plt.rc("font", size=12)
if Z is None or Z.shape[1] != 2:
N = X.shape[0]
points_per_axis = int(N**.5)
bounds = (-1., 1.)
Z = gen_grid(2, points_per_axis, bounds[0],
bounds[1]) # Generate a grid
r0 = np.max(Z[:, 0]) - np.min(Z[:, 0])
r1 = np.max(Z[:, 1]) - np.min(Z[:, 1])
scale *= np.minimum(r0, r1) / points_per_axis
# Create a 2D plot
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111)
for (i, z) in enumerate(Z):
if P is not None and W is not None:
ind = W[i] >= 0
p = P[i][ind]
w = W[i][ind]
w /= np.max(w)
plot_shape(p,
z[0],
.3 * z[1],
ax,
scale,
scatter,
symm_axis,
c='g',
s=10 * w,
marker='P',
alpha=.5)
plot_shape(p,
z[0],
.3 * z[1],
ax,
scale,
False,
symm_axis,
lw=.5,
alpha=.5,
c='g')
for j in range(X.shape[1]):
plot_shape(X[i, j], z[0], .3 * z[1], ax, scale, scatter, symm_axis,
**kwargs)
if annotate:
label = '{0}'.format(i + 1)
plt.annotate(label, xy=(z[0], z[1]), size=10)
# plt.xlabel('c1')
# plt.ylabel('c2')
plt.xticks([])
plt.yticks([])
plt.axis('off')
plt.axis('equal')
plt.tight_layout()
plt.savefig(fname + '.svg', dpi=600)
plt.close()
def plot_samples(Z, X1, X2=None, scale=.05, annotate=False, save_path=None):
''' Plot shapes given design sapce and latent space coordinates '''
plt.rc("font", size=12)
if Z is None:
N = X1.shape[0]
points_per_axis = int(N**.5)
bounds = (-1., 1.)
Z = gen_grid(2, points_per_axis, bounds[0],
bounds[1]) # Generate a grid
scale = 0.8 * 2.0 / points_per_axis
# Create a 2D plot
fig = plt.figure()
ax = fig.add_subplot(111)
for (i, z) in enumerate(Z):
if annotate:
label = '{0}'.format(i + 1)
plt.annotate(label, xy=(z[0], z[1]), size=10)
if X2 is None:
plot_shape(X1[i],
z[0],
z[1],
ax,
scale=scale,
lw=1.0,
ls='-',
c='k',
alpha=1)
else:
plot_shape(X1[i],
z[0],
z[1],
ax,
scale=scale,
lw=1.0,
ls=':',
c='k',
alpha=1)
plot_shape(X2[i],
z[0],
z[1],
ax,
scale=scale,
lw=1.0,
ls='-',
c='k',
alpha=1)
# plt.xlabel('c1')
# plt.ylabel('c2')
plt.xticks([])
plt.yticks([])
plt.axis('off')
plt.tight_layout()
plt.savefig(save_path, dpi=600)
plt.close()