def demo_data_image():
ids = h5load(
'/home/mfk/axon/mkilling/devel/data/BirdGT/j0126_bird_new/j0126 cubeSegmentor category-volume_gt__016-kkiesl-20150913-205217-final.h5')
ids, raw = ids[1], ids[0]
ids = np.transpose(ids, (2, 0, 1))
raw = np.transpose(raw, (2, 0, 1))
ids2 = np.unique(ids, return_inverse=True)[1].reshape(ids.shape)
barrier = image.ids2barriers(ids, dilute=[0, 0, 0], ecs_as_barr=True)
barrier, raw = image.center_cubes(barrier, raw, crop=True)
barrier2 = image.smearbarriers(barrier, kernel=(3, 5, 5)) # (z,x,y)
# fig, scroller = _scroll_plot2([barrier2, (0.75*raw+0.25*255*(1-barrier))], ['ids', 'raw'])
# fig, scroller = _scroll_plot2([barrier2.T, (0.75*raw.T+0.25*255*(1-barrier.T))], ['ids', 'raw'])
# fig, scroller = _scroll_plot2([barrier2.T, barrier.T], ['ids', 'raw'])
# fig, scroller = _scroll_plot2([barrier2, barrier], ['ids', 'raw'])
fig, scroller = _scroll_plot4(
[barrier2, barrier, (barrier2 > 0.5) - barrier, barrier2 > 0.5],
['bar1', 'bar2', 'ids', 'raw'])
fig.show()
seg = image.seg_old(barrier2 * 255)
fig, scroller = _scroll_plot4([barrier2, seg, ids2, raw],
['bar1', 'seg', 'ids', 'raw'])
fig.show()
ri = timeit(image.rand_index)
print(ri(ids2, seg)) # ~ 0.99
fig, scroller = scroll_plot(raw.T, ['raw'])
fig.show()
def demo_quiver():
data = h5py.File(
"/home/mfk/lustre/mkilling/BirdGT/skeleton/rel_direc+branch+barr+obj_zyx.h5"
)['combi']
stack = data[:, 50:50 + 50, 50:300 + 50, 50:300 + 50]
# z_i =5
# barr = stack[4,z_i]
# x = stack[2,z_i]
# y = stack[1,z_i]
# z = stack[0,z_i]
# my_quiver(x, y, barr, z)
# stack has spatial shape (f,z,y,x)
# stack[0]: how the vec field changes along z, i.e. 0 spatial dim
# stack[1]: how the vec field changes along y, i.e. 1 spatial dim
# stack[2]: how the vec field changes along x, i.e. 2 spatial dim
direction_iso = [1, 0, 0]
direction_iso /= np.linalg.norm(direction_iso)
gamma = 0 * np.pi / 180
img_new, target_new, M = transformations.get_tracing_slice(
stack[4:5], [10, 80, 80], [25, 150, 150],
direction_iso=direction_iso,
gamma=gamma,
target=stack,
target_ps=[10, 80, 80],
target_vec_ix=[[0, 1, 2]],
target_discrete_ix=[3, 4, 5, 6, 7, 8])
z_i = 2
# barr = target_new[4,z_i]
# x = target_new[2,z_i]
# y = target_new[1,z_i]
# z = target_new[0,z_i]
# my_quiver(x, y, barr, z)
#
# zz,yy,xx = np.mgrid[:5:1,:40:1,:40:1]
# mlab.quiver3d(zz,yy,xx, target_new[0, ::2, ::2, ::2], target_new[1, ::2, ::2, ::2], target_new[2, ::2, ::2, ::2])
fig = scroll_plot(target_new[:, z_i], 'channels')
#R[3,1] = 0.002
ps = [31, 131, 131]
z_shift = 0
aniso_factor = 2
sample_aniso = True
dest_center = np.array(ps, dtype=np.float32) / 2
dest_center[0] -= z_shift # sign?
T_src = translate(-pos[0], -pos[1], -pos[2])
S_src = scale(aniso_factor, 1, 1)
if sample_aniso:
S_dest = scale(1.0 / aniso_factor, 1, 1)
else:
S_dest = identity()
T_dest = translate(dest_center[0], dest_center[1], dest_center[2])
M = chain_matrices([T_dest, S_dest, R, S_src, T_src])
for i in range(5):
img_new, targ_new = warp_time(img,
ps,
M,
target=targ,
target_ps=[11, 121, 121],
target_discrete_ix=[0, 1, 2])
#f0 = scroll_plot(img_new[0])
i, t = center_cubes(img_new, targ_new)
f1 = scroll_plot([i[0], t[0]])
# a0 = arr[0:100, 0:400, 0:200]
# a1 = arr[0:100, 100:400+100, 100:200+100]
# b = arr[0:640, 0:640, 0:384]
# c = arr[500:600, 500:800, 500:800]
#
kds = KnossosDataset()
kds.initialize_from_knossos_path(path)
get = utils.timeit(kds.from_raw_cubes_to_matrix)
# a0 = arr.cut_slice(sh, off)
# a1 = get(sh[[2,1,0]], off[[2,1,0]])
# a2 = (a1.T).astype(np.float32)/255
# print(np.allclose(a0, a2))
# fig = scroll_plot2([a0, a2], 'ab')
t = 0
out = np.empty(sh, dtype=np.float32)
N = 500
for i in range(N):
t0 = time.time()
a = arr.cut_slice(sh, off, out=out)
t += time.time() - t0
time.sleep(0.2)
off += np.random.randint(0, 10, size=3)
print("%.3f slices/s" % (float(N) / t))
arr._clear_q(None)
fig = scroll_plot(out, 'a')
# #stuff.append([img, pred, fig])
#
#[59+32,385+32,385+32] = 0.27
#[59+32,385+32+32,385+32+32] = 0.28
#[59+32,385+3*32,385+3*32] = 0.29
#import numpy as np
#from elektronn2.utils import gpu
#gpu.initgpu('auto')
#from elektronn2 import neuromancer
#model_path = "~/axon/mkilling/CNN_Training/3D/DualScale/DS-2-1-2xConv/Backup/DS-2-1-2xConv-88.0h.mdl"
#model = neuromancer.model.modelload(model_path, imposed_patch_size=[59+32,385+3*32,385+3*32])
#x = np.random.rand(*model.input_node.shape.shape[1:]).astype(np.float32)
#y = model.predict_dense(x)
#y = model.predict_dense(x)
#y = model.predict_dense(x)
model = neuromancer.model.modelload(
"/home/mfk/CNN_Training/3D/DS-3-0/Backup/DS-3-0-400k.mdl",
imposed_patch_size=[59 - 16, 385 - 32, 385 - 32])
pred, img = predict_centered(knossos_raw,
model, [6092, 5551, 3135][::-1],
excess=[0, 4, 4])
barr = pred[1] + pred[2]
pc = colorize_10ch(pred)
fig = scroll_plot(pc, '1234')
img = pc[3, 407:-408, 407:-408]
plt.imsave(
"/home/mfk/axon/mkilling/investigation/MA-TEX/figures/sXY_6092_5551_3135_dscnn2.png",
img)