def main():
argv = sys.argv
print("loading %s ..." % argv[1])
ssvm1 = SaveLogger(file_name=argv[1]).load()
ssvm2 = SaveLogger(file_name=argv[2]).load()
data_str = 'val'
if len(argv) <= 3:
raise ValueError("Need a folder name for plotting.")
print("loading data...")
data = load_nyu(data_str, n_sp=500)
dataset = NYUSegmentation()
print("done")
data1 = add_edges(data, kind="pairwise")
data2 = add_edges(data, kind="pairwise")
data1 = add_edge_features(dataset, data1)
data2 = add_edge_features(dataset, data2, depth_diff=True)
Y_pred1 = ssvm1.predict(data1.X)
Y_pred2 = ssvm2.predict(data2.X)
folder = argv[3]
if not os.path.exists(folder):
os.mkdir(folder)
np.random.seed(0)
for image_name, superpixels, y_pred1, y_pred2 in zip(data.file_names,
data.superpixels,
Y_pred1, Y_pred2):
if np.all(y_pred1 == y_pred2):
continue
gt = dataset.get_ground_truth(image_name)
perf1 = eval_on_pixels(dataset, [gt], [y_pred1[superpixels]],
print_results=False)[0]
perf1 = np.mean(perf1[np.isfinite(perf1)])
perf2 = eval_on_pixels(dataset, [gt], [y_pred2[superpixels]],
print_results=False)[0]
perf2 = np.mean(perf2[np.isfinite(perf2)])
if np.abs(perf1 - perf2) < 2:
continue
image = dataset.get_image(image_name)
fig, axes = plt.subplots(2, 3, figsize=(12, 6))
axes[0, 0].imshow(image)
axes[0, 0].imshow((y_pred1 != y_pred2)[superpixels], vmin=0, vmax=1,
alpha=.7)
axes[0, 1].set_title("ground truth")
axes[0, 1].imshow(image)
axes[0, 1].imshow(gt, alpha=.7, cmap=dataset.cmap, vmin=0,
vmax=dataset.cmap.N)
axes[1, 0].set_title("%.2f" % perf1)
axes[1, 0].imshow(image)
axes[1, 0].imshow(y_pred1[superpixels], vmin=0, vmax=dataset.cmap.N,
alpha=.7, cmap=dataset.cmap)
axes[1, 1].set_title("%.2f" % perf2)
axes[1, 1].imshow(image)
axes[1, 1].imshow(y_pred2[superpixels], alpha=.7, cmap=dataset.cmap,
vmin=0, vmax=dataset.cmap.N)
present_y = np.unique(np.hstack([y_pred1, y_pred2, np.unique(gt)]))
present_y = np.array([y_ for y_ in present_y if y_ !=
dataset.void_label])
axes[0, 2].imshow(present_y[:, np.newaxis], interpolation='nearest',
cmap=dataset.cmap, vmin=0, vmax=dataset.cmap.N)
for i, c in enumerate(present_y):
axes[0, 2].text(1, i, dataset.classes[c])
for ax in axes.ravel():
ax.set_xticks(())
ax.set_yticks(())
axes[1, 2].set_visible(False)
fig.savefig(folder + "/%s.png" % image_name, bbox_inches="tight")
plt.close(fig)
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()
