def plot_sp_graph(image, superpixels, edges, colors='black'):
plt.figure(figsize=(10, 10))
bounary_image = mark_boundaries(image, superpixels)
plt.imshow(bounary_image)
centers = get_superpixel_centers(superpixels)
for i, edge in enumerate(edges):
e0, e1 = edge
if len(colors) == len(edges):
color = colors[i]
else:
color = colors
plt.plot([centers[e0][0], centers[e1][0]],
[centers[e0][1], centers[e1][1]],
c=color)
plt.scatter(centers[:, 0], centers[:, 1], s=100)
plt.tight_layout()
plt.xlim(0, superpixels.shape[1])
plt.ylim(superpixels.shape[0], 0)
plt.axis("off")
def plot_sp_graph(image, superpixels, edges, colors='black'):
plt.figure(figsize=(10, 10))
bounary_image = mark_boundaries(image, superpixels)
plt.imshow(bounary_image)
centers = get_superpixel_centers(superpixels)
for i, edge in enumerate(edges):
e0, e1 = edge
if len(colors) == len(edges):
color = colors[i]
else:
color = colors
plt.plot([centers[e0][0], centers[e1][0]],
[centers[e0][1], centers[e1][1]],
c=color)
plt.scatter(centers[:, 0], centers[:, 1], s=100)
plt.tight_layout()
plt.xlim(0, superpixels.shape[1])
plt.ylim(superpixels.shape[0], 0)
plt.axis("off")
def crazy_visual():
dataset = NYUSegmentation()
# load training data
data = load_nyu(n_sp=500)
data = add_edges(data)
for x, image_name, superpixels, y in zip(data.X, data.file_names,
data.superpixels, data.Y):
print(image_name)
if int(image_name) != 11:
continue
image = dataset.get_image(image_name)
plt.figure(figsize=(20, 20))
bounary_image = mark_boundaries(image, superpixels)
plt.imshow(bounary_image)
gridx, gridy = np.mgrid[:superpixels.shape[0], :superpixels.shape[1]]
edges = x[1]
points_normals = dataset.get_pointcloud_normals(image_name)
centers2d = get_superpixel_centers(superpixels)
centers3d = [
np.bincount(superpixels.ravel(), weights=c.ravel())
for c in points_normals[:, :, :3].reshape(-1, 3).T
]
centers3d = (np.vstack(centers3d) / np.bincount(superpixels.ravel())).T
sp_normals = get_sp_normals(points_normals[:, :, 3:], superpixels)
offset = centers3d[edges[:, 0]] - centers3d[edges[:, 1]]
offset = offset / np.sqrt(np.sum(offset**2, axis=1))[:, np.newaxis]
#mean_normal = (sp_normals[edges[:, 0]] + sp_normals[edges[:, 1]]) / 2.
mean_normal = sp_normals[edges[:, 0]]
#edge_features = np.arccos(np.abs((offset * mean_normal).sum(axis=1))) * 2. / np.pi
edge_features = 1 - np.abs((offset * mean_normal).sum(axis=1))
no_normals = (np.all(sp_normals[edges[:, 0]] == 0, axis=1) +
np.all(sp_normals[edges[:, 1]] == 0, axis=1))
edge_features[no_normals] = 0 # nan normals
if True:
coords = points_normals[:, :, :3].reshape(-1, 3)
perm = np.random.permutation(superpixels.max() + 1)
mv.points3d(coords[:, 0],
coords[:, 1],
coords[:, 2],
perm[superpixels.ravel()],
mode='point')
#mv.points3d(centers3d[:, 0], centers3d[:, 1], centers3d[:, 2], scale_factor=.04)
mv.quiver3d(centers3d[:, 0], centers3d[:, 1], centers3d[:, 2],
sp_normals[:, 0], sp_normals[:, 1], sp_normals[:, 2])
mv.show()
from IPython.core.debugger import Tracer
Tracer()()
for i, edge in enumerate(edges):
e0, e1 = edge
#color = (dataset.colors[y[e0]] + dataset.colors[y[e1]]) / (2. * 255.)
#f = edge_features[i]
#if f < 0:
#e0, e1 = e1, e0
#f = -f
#plt.arrow(centers[e0][0], centers[e0][1],
#centers[e1][0] - centers[e0][0], centers[e1][1] - centers[e0][1],
#width=f * 5
#)
color = "black"
plt.plot([centers2d[e0][0], centers2d[e1][0]],
[centers2d[e0][1], centers2d[e1][1]],
c=color,
linewidth=edge_features[i] * 5)
plt.scatter(centers2d[:, 0], centers2d[:, 1], s=100)
plt.tight_layout()
plt.xlim(0, superpixels.shape[1])
plt.ylim(superpixels.shape[0], 0)
plt.axis("off")
plt.savefig("figures/normal_relative/%s.png" % image_name,
bbox_inches="tight")
plt.close()
def crazy_visual():
dataset = NYUSegmentation()
# load training data
data = load_nyu(n_sp=500)
data = add_edges(data)
for x, image_name, superpixels, y in zip(data.X, data.file_names,
data.superpixels, data.Y):
print(image_name)
if int(image_name) != 11:
continue
image = dataset.get_image(image_name)
plt.figure(figsize=(20, 20))
bounary_image = mark_boundaries(image, superpixels)
plt.imshow(bounary_image)
gridx, gridy = np.mgrid[:superpixels.shape[0], :superpixels.shape[1]]
edges = x[1]
points_normals = dataset.get_pointcloud_normals(image_name)
centers2d = get_superpixel_centers(superpixels)
centers3d = [np.bincount(superpixels.ravel(), weights=c.ravel())
for c in points_normals[:, :, :3].reshape(-1, 3).T]
centers3d = (np.vstack(centers3d) / np.bincount(superpixels.ravel())).T
sp_normals = get_sp_normals(points_normals[:, :, 3:], superpixels)
offset = centers3d[edges[:, 0]] - centers3d[edges[:, 1]]
offset = offset / np.sqrt(np.sum(offset ** 2, axis=1))[:, np.newaxis]
#mean_normal = (sp_normals[edges[:, 0]] + sp_normals[edges[:, 1]]) / 2.
mean_normal = sp_normals[edges[:, 0]]
#edge_features = np.arccos(np.abs((offset * mean_normal).sum(axis=1))) * 2. / np.pi
edge_features = 1 - np.abs((offset * mean_normal).sum(axis=1))
no_normals = (np.all(sp_normals[edges[:, 0]] == 0, axis=1)
+ np.all(sp_normals[edges[:, 1]] == 0, axis=1))
edge_features[no_normals] = 0 # nan normals
if True:
coords = points_normals[:, :, :3].reshape(-1, 3)
perm = np.random.permutation(superpixels.max()+1)
mv.points3d(coords[:,0], coords[:, 1], coords[:, 2], perm[superpixels.ravel()], mode='point')
#mv.points3d(centers3d[:, 0], centers3d[:, 1], centers3d[:, 2], scale_factor=.04)
mv.quiver3d(centers3d[:, 0], centers3d[:, 1], centers3d[:, 2], sp_normals[:, 0], sp_normals[:, 1], sp_normals[:, 2])
mv.show()
from IPython.core.debugger import Tracer
Tracer()()
for i, edge in enumerate(edges):
e0, e1 = edge
#color = (dataset.colors[y[e0]] + dataset.colors[y[e1]]) / (2. * 255.)
#f = edge_features[i]
#if f < 0:
#e0, e1 = e1, e0
#f = -f
#plt.arrow(centers[e0][0], centers[e0][1],
#centers[e1][0] - centers[e0][0], centers[e1][1] - centers[e0][1],
#width=f * 5
#)
color = "black"
plt.plot([centers2d[e0][0], centers2d[e1][0]],
[centers2d[e0][1], centers2d[e1][1]],
c=color,
linewidth=edge_features[i] * 5
)
plt.scatter(centers2d[:, 0], centers2d[:, 1], s=100)
plt.tight_layout()
plt.xlim(0, superpixels.shape[1])
plt.ylim(superpixels.shape[0], 0)
plt.axis("off")
plt.savefig("figures/normal_relative/%s.png" % image_name,
bbox_inches="tight")
plt.close()
