def get_current_soln_pic(self, b):
actions = self.get_batched_actions_from_global_graph(
self.sg_current_edge_weights.view(-1))
gt = self.get_batched_actions_from_global_graph(
self.sg_gt_edge_weights.view(-1))
edge_ids = self.edge_ids[:, self.e_offs[b]:self.
e_offs[b + 1]] - self.n_offs[b]
edge_ids = edge_ids.cpu().t().contiguous().numpy()
boundary_input = self.initial_edge_weights[self.e_offs[b]:self.
e_offs[b +
1]].cpu().numpy()
mc_seg1 = general.multicut_from_probas(
self.init_sp_seg[b].squeeze().cpu(), edge_ids,
self.initial_edge_weights[self.e_offs[b]:self.e_offs[b + 1]].cpu(
).numpy(), boundary_input)
mc_seg = general.multicut_from_probas(
self.init_sp_seg[b].squeeze().cpu(), edge_ids,
actions[self.e_offs[b]:self.e_offs[b + 1]].cpu().numpy(),
boundary_input)
gt_mc_seg = general.multicut_from_probas(
self.init_sp_seg[b].squeeze().cpu(), edge_ids,
gt[self.e_offs[b]:self.e_offs[b + 1]].cpu().numpy(),
boundary_input)
mc_seg = cm.prism(mc_seg / mc_seg.max())
mc_seg1 = cm.prism(mc_seg1 / mc_seg1.max())
seg = cm.prism(self.init_sp_seg[b].squeeze().cpu() /
self.init_sp_seg[b].cpu().max())
gt_mc_seg = cm.prism(gt_mc_seg / gt_mc_seg.max())
return np.concatenate((np.concatenate(
(mc_seg1, mc_seg), 0), np.concatenate((gt_mc_seg, seg), 0)), 1)
def show_current_soln(self):
affs = np.expand_dims(self.affinities, axis=1)
boundary_input = np.mean(affs, axis=0)
mc_seg1 = general.multicut_from_probas(
self.init_sp_seg.cpu(),
self.edge_ids.cpu().t().contiguous().numpy(),
self.initial_edge_weights.squeeze().cpu().numpy(), boundary_input)
mc_seg = general.multicut_from_probas(
self.init_sp_seg.cpu(),
self.edge_ids.cpu().t().contiguous().numpy(),
self.current_edge_weights.squeeze().cpu().numpy(), boundary_input)
gt_mc_seg = general.multicut_from_probas(
self.init_sp_seg.cpu(),
self.edge_ids.cpu().t().contiguous().numpy(),
self.gt_edge_weights.squeeze().cpu().numpy(), boundary_input)
mc_seg = cm.prism(mc_seg / mc_seg.max())
mc_seg1 = cm.prism(mc_seg1 / mc_seg1.max())
seg = cm.prism(self.init_sp_seg.cpu() / self.init_sp_seg.cpu().max())
gt_mc_seg = cm.prism(gt_mc_seg / gt_mc_seg.max())
plt.imshow(
np.concatenate((np.concatenate(
(mc_seg1, mc_seg), 0), np.concatenate((gt_mc_seg, seg), 0)),
1))
plt.show()
a = 1
def process_file(i):
fname = fnames[i]
head, tail = os.path.split(fname)
num = tail[4:-3]
# os.rename(os.path.join(graph_dir, "graph_" + str(i) + ".h5"), os.path.join(graph_dir, "graph_" + num + ".h5"))
# os.rename(os.path.join(pix_dir, "pix_" + str(i) + ".h5"), os.path.join(pix_dir, "pix_" + num + ".h5"))
# raw = torch.from_numpy(h5py.File(fname, 'r')['raw'][:].astype(np.float))
# gt = h5py.File(fname, 'r')['label'][:].astype(np.long)
# affs = torch.from_numpy(h5py.File(os.path.join(dir, 'affinities', tail[:-3] + '_predictions' + '.h5'), 'r')['predictions'][:]).squeeze(1)
# raw -= raw.min()
# raw /= raw.max()
#
# node_labeling = run_watershed(gaussian_filter(affs[0] + affs[1] + affs[2] + affs[3], sigma=.2), min_size=4)
#
# # relabel to consecutive ints starting at 0
# node_labeling = torch.from_numpy(node_labeling.astype(np.long))
#
#
# gt = torch.from_numpy(gt.astype(np.long))
# mask = node_labeling[None] == torch.unique(node_labeling)[:, None, None]
# node_labeling = (mask * (torch.arange(len(torch.unique(node_labeling)), device=node_labeling.device)[:, None, None] + 1)).sum(
# 0) - 1
#
# node_labeling += 2
# # bgm
# node_labeling[gt == 0] = 0
# node_labeling[gt == 1] = 1
# plt.imshow(node_labeling);plt.show()
#
# mask = gt[None] == torch.unique(gt)[:, None, None]
# gt = (mask * (torch.arange(len(torch.unique(gt)), device=gt.device)[:, None, None] + 1)).sum(0) - 1
#
# edge_img = get_contour_from_2d_binary(node_labeling[None, None].float())
# edge_img = gauss_kernel(edge_img.float())
# raw = torch.cat([raw[None, None], edge_img], dim=1).squeeze(0).numpy()
# affs = torch.sigmoid(affs).numpy()
#
# edge_feat, edges = get_edge_features_1d(node_labeling.numpy(), offs, affs[:4])
#
# gt_edge_weights = gt_edge_weights.numpy()
# gt = gt.numpy()
# node_labeling = node_labeling.numpy()
# edges = edges.astype(np.long)
#
# affs = affs.astype(np.float32)
# edge_feat = edge_feat.astype(np.float32)
# node_labeling = node_labeling.astype(np.float32)
# gt_edge_weights = gt_edge_weights.astype(np.float32)
# diff_to_gt = np.abs((edge_feat[:, 0] - gt_edge_weights)).sum()
# edges = np.sort(edges, axis=-1)
# edges = edges.T
# #
# #
graph_file = h5py.File(
os.path.join(graph_dir, "graph_" + num + ".h5"), 'r')
pix_file = h5py.File(os.path.join(pix_dir, "pix_" + num + ".h5"),
'r')
raw = pix_file["raw_2chnl"][:]
gt = pix_file["gt"][:]
sp_seg = graph_file["node_labeling"][:]
edges = graph_file["edges"][:]
affs = graph_file["affinities"][:]
graph_file.close()
pix_file.close()
# plt.imshow(multicut_from_probas(sp_seg, edges.T, calculate_gt_edge_costs(torch.from_numpy(edges.T.astype(np.long)).to(dev), torch.from_numpy(sp_seg).to(dev), torch.from_numpy(gt.squeeze()).to(dev), 1.5).cpu()), cmap=random_label_cmap(), interpolation="none");plt.show()
with torch.set_grad_enabled(False):
embeddings = model(
torch.from_numpy(raw).to(device).float()[None])
emb_affs = get_affinities_from_embeddings_2d(
embeddings, offs, 0.4, distance)[0].cpu().numpy()
ew_embedaffs = 1 - get_edge_features_1d(sp_seg, offs,
emb_affs)[0][:, 0]
mc_soln = torch.from_numpy(
multicut_from_probas(sp_seg, edges.T,
ew_embedaffs).astype(np.long)).to(device)
mask = mc_soln[None] == torch.unique(mc_soln)[:, None, None]
mc_soln = (mask *
(torch.arange(len(torch.unique(mc_soln)), device=device)
[:, None, None] + 1)).sum(0) - 1
masses = (
mc_soln[None] == torch.unique(mc_soln)[:, None,
None]).sum(-1).sum(-1)
bg1id = masses.argmax()
masses[bg1id] = 0
bg1_mask = mc_soln == bg1id
bg2_mask = mc_soln == masses.argmax()
sp_seg = torch.from_numpy(sp_seg.astype(np.long)).to(device)
mask = sp_seg[None] == torch.unique(sp_seg)[:, None, None]
sp_seg = (mask *
(torch.arange(len(torch.unique(sp_seg)),
device=sp_seg.device)[:, None, None] +
1)).sum(0) - 1
sp_seg += 2
sp_seg *= (bg1_mask == 0)
sp_seg *= (bg2_mask == 0)
sp_seg += bg2_mask
mask = sp_seg[None] == torch.unique(sp_seg)[:, None, None]
sp_seg = (mask *
(torch.arange(len(torch.unique(sp_seg)),
device=sp_seg.device)[:, None, None] +
1)).sum(0) - 1
sp_seg = sp_seg.cpu()
raw -= raw.min()
raw /= raw.max()
edge_feat, edges = get_edge_features_1d(sp_seg.numpy(), offs[:4],
affs[:4])
edges = edges.astype(np.long)
gt_edge_weights = calculate_gt_edge_costs(
torch.from_numpy(edges).to(device), sp_seg.to(device),
torch.from_numpy(gt).to(device), 0.4)
node_labeling = sp_seg.numpy()
affs = affs.astype(np.float32)
edge_feat = edge_feat.astype(np.float32)
node_labeling = node_labeling.astype(np.float32)
gt_edge_weights = gt_edge_weights.cpu().numpy().astype(np.float32)
# edges = np.sort(edges, axis=-1)
edges = edges.T
# plt.imshow(sp_seg.cpu(), cmap=random_label_cmap(), interpolation="none");plt.show()
# plt.imshow(bg1_mask.cpu());plt.show()
# plt.imshow(bg2_mask.cpu());plt.show()
new_pix_file = h5py.File(
os.path.join(new_pix_dir, "pix_" + num + ".h5"), 'w')
new_graph_file = h5py.File(
os.path.join(new_graph_dir, "graph_" + num + ".h5"), 'w')
# plt.imshow(gt_sp_projection, cmap=random_label_cmap(), interpolation="none");plt.show()
new_pix_file.create_dataset("raw_2chnl", data=raw, chunks=True)
new_pix_file.create_dataset("gt", data=gt, chunks=True)
#
new_graph_file.create_dataset("edges", data=edges, chunks=True)
new_graph_file.create_dataset("edge_feat",
data=edge_feat,
chunks=True)
new_graph_file.create_dataset("gt_edge_weights",
data=gt_edge_weights,
chunks=True)
new_graph_file.create_dataset("node_labeling",
data=node_labeling,
chunks=True)
new_graph_file.create_dataset("affinities", data=affs, chunks=True)
new_graph_file.create_dataset("offsets",
data=np.array([[1, 0], [0,
1], [2, 0],
[0, 2], [4,
0], [0, 4],
[8, 0], [0, 8],
[16, 0], [0, 16]]),
chunks=True)
#
new_graph_file.close()
new_pix_file.close()
def get_current_soln(self):
affs = np.expand_dims(self.affinities, axis=1)
boundary_input = np.mean(affs, axis=0)
mc_seg = general.multicut_from_probas(self.init_sp_seg.squeeze().cpu(), self.edge_ids.cpu().t().contiguous().numpy(),
self.state[0].squeeze().cpu().numpy(), boundary_input)
return torch.from_numpy(mc_seg.astype(np.float))
for i in range(1):
g_file = h5py.File(fnames_graph[i], 'r')
pix_file = h5py.File(fnames_pix[i], 'r')
superpixel_seg = g_file['node_labeling'][:]
gt_seg = pix_file['gt'][:]
superpixel_seg = torch.from_numpy(superpixel_seg.astype(
np.int64)).to(dev)
gt_seg = torch.from_numpy(gt_seg.astype(np.int64)).to(dev)
probas = g_file[
'edge_feat'][:, 0] # take initial edge features as weights
# make sure probas are probas and get a sample prediction
probas -= probas.min()
probas /= (probas.max() + 1e-6)
pred_seg = multicut_from_probas(superpixel_seg.cpu().numpy(),
g_file['edges'][:].T, probas)
pred_seg = torch.from_numpy(pred_seg.astype(np.int64)).to(dev)
# relabel to consecutive integers:
mask = gt_seg[None] == torch.unique(gt_seg)[:, None, None]
gt_seg = (mask * (torch.arange(len(
torch.unique(gt_seg)), device=dev)[:, None, None] + 1)).sum(0) - 1
mask = superpixel_seg[None] == torch.unique(superpixel_seg)[:, None,
None]
superpixel_seg = (
mask * (torch.arange(len(torch.unique(superpixel_seg)),
device=dev)[:, None, None] + 1)).sum(0) - 1
mask = pred_seg[None] == torch.unique(pred_seg)[:, None, None]
pred_seg = (mask *
(torch.arange(len(torch.unique(pred_seg)),
device=dev)[:, None, None] + 1)).sum(0) - 1
fnames_graph = sorted(glob('/g/kreshuk/hilt/projects/data/artificial_cells/graph_data/*.h5'))
gt = torch.from_numpy(h5py.File(fnames_pix[42], 'r')['gt'][:]).to(dev)
# set gt to integer labels
_gt = torch.zeros_like(gt).long()
for _lbl, lbl in enumerate(torch.unique(gt)):
_gt += (gt == lbl).long() * _lbl
gt = _gt
sample_shapes = torch.zeros((int(gt.max()) + 1,) + gt.size(), device=dev).scatter_(0, gt[None], 1)[1:] # 0 should be background
g_file = h5py.File(fnames_graph[42], 'r')
superpixel_seg = g_file['node_labeling'][:]
probas = g_file['edge_feat'][:, 0] # take initial edge features as weights
# make sure probas are probas and get a sample prediction
probas -= probas.min()
probas /= (probas.max() + 1e-6)
pred_seg = multicut_from_probas(superpixel_seg, g_file['edges'][:].T, probas)
pred_seg = torch.from_numpy(pred_seg.astype(np.int64)).to(dev)
superpixel_seg = torch.from_numpy(superpixel_seg.astype(np.int64)).to(dev)
# assert the segmentations are consecutive integers
assert pred_seg.max() == len(torch.unique(pred_seg)) - 1
assert superpixel_seg.max() == len(torch.unique(superpixel_seg)) - 1
# add batch dimension
pred_seg = pred_seg[None]
superpixel_seg = superpixel_seg[None]
f = ArtificialCellsReward(sample_shapes)
rewards = f(pred_seg.long(), superpixel_seg.long())