def display_image_sequence(image_stack, cmap_name):
stack_len = len(image_stack)
#print(image_stack.shape)
img = None
for i in range(stack_len):
im = image_stack[i]
if img is None:
img = pylab.figimage(im, cmap=cmap_name)
else:
img.set_data(im)
pylab.pause(.1)
pylab.draw()
plt.close()
def plainImage2(var, imageFile):
M.clf()
M.figimage(var)
M.savefig(imageFile)
with open('tmp/' + name + '_etf.pickle', 'wb') as f:
pickle.dump(t, f)
texture = np.random.rand(size[0], size[1]).astype(np.float32)
kernellen = 31
kernel = np.sin(np.arange(kernellen) * np.pi / kernellen)
kernel = kernel.astype(np.float32)
for h in range(size[0]):
for w in range(size[1]):
if (t[h][w][0] == 0):
t[h][w][0] = np.random.rand() * 0.01
if (t[h][w][1] == 0):
t[h][w][1] = np.random.rand() * 0.01
tnorm = np.linalg.norm(t, axis=2)
np.place(tnorm, tnorm == 0, [1])
t = np.divide(t, np.stack([tnorm, tnorm], axis=2))
etf_lic = lic_internal.line_integral_convolution(t.astype(np.float32),
texture, kernel)
dpi = 100
plt.bone()
plt.clf()
plt.axis('off')
plt.figimage(etf_lic)
plt.gcf().set_size_inches((size[0] / float(dpi), size[1] / float(dpi)))
plt.savefig("output/" + name + "_flow.png", dpi=dpi)