def test_object_to_world(self):
sim = Simulator(gui=False, use_egl=False)
scene = h5py.File('../data/scenes/200210_1654_manually_generated_scenes.hdf5')['scene'][0]
sim.restore(scene, '../data/3d_models/shapenetsem40')
sim.cam.pos = [0, 0, 1.5]
sim.cam.width = 300
sim.cam.height = 300
cloud = sim.cam.point_cloud()
cloud = ortho_pipeline.transform_world_to_camera(cloud, sim.cam)
net = orthographic.OrthoNet(
model_fn='/Users/mario/Developer/msc-thesis/data/networks/ggcnn_rss/epoch_29_model.hdf5')
points, orientations, angles, widths, scores = net.predict(cloud, net.network_predictor,
debug=False)
best_idx = np.argmax(scores)
p = points[best_idx]
z = orientations[best_idx]
x = angles[best_idx]
w = widths[best_idx]
y = np.cross(z, x)
# Camera to world transformation
p = ortho_pipeline.transform_camera_to_world(p, sim.cam)
R = ortho_pipeline.get_camera_frame(sim.cam)
z = R.dot(z.T).T
x = R.dot(x.T).T
recloud = ortho_pipeline.transform_camera_to_world(cloud, sim.cam)
orthographic.render_pose(PointCloud(recloud), p, z, x, w)
def test_visualize_wrt_camera(self):
cloud = PointCloud(np.load('points.npy'))
eye = np.eye(3)
core.orthographic.render_frame([0, 0, 0],
eye[0],
eye[1],
eye[2],
cloud=cloud)
def test_inverse_transform(self):
cloud = PointCloud(np.load('object_cloud.npy'))
pos = np.array([0, 0, 0])
frame = np.eye(3)
core.orthographic.render_frame(pos,
frame[0],
frame[1],
frame[2],
cloud=cloud)
blue = np.array([[0, 0, 1]])
colors = np.concatenate((np.ones(cloud.cloud.shape), blue))
front = cloud.front_depth()
right = cloud.right_depth()
top = cloud.top_depth()
points = np.concatenate((cloud.cloud, front.to_object(
(21, 110)).reshape(1, 3)))
viewer = pptk.viewer(points)
viewer.attributes(colors)
viewer.set(point_size=0.0005)
points = np.concatenate((cloud.cloud, right.to_object(
(21, 110)).reshape(1, 3)))
viewer = pptk.viewer(points)
viewer.attributes(colors)
viewer.set(point_size=0.0005)
points = np.concatenate((cloud.cloud, top.to_object(
(191, 198)).reshape(1, 3)))
viewer = pptk.viewer(points)
viewer.attributes(colors)
viewer.set(point_size=0.0005)
plt.subplot(131)
plt.imshow(front.img)
plt.scatter([110], [21])
plt.subplot(132)
plt.imshow(right.img)
plt.scatter([110], [21])
plt.subplot(133)
plt.imshow(top.img)
plt.scatter([198], [191])
plt.show()
def test_rotate_points_to_sensible_orientation(self):
cloud = PointCloud.from_npy('points.npy')
plane_cloud = cloud.find_plane(th=0.02)
plane_cloud.pca()
cloud.cloud = plane_cloud.transform(cloud.cloud)
cloud.cloud[:, 0] += 0.7
rotated = cloud.rotate([0, 1, 0], np.radians(-45))
rotated.save('isolated_cloud.npy')
def test_orthographic_projection(self):
cloud = PointCloud(self.pcd)
front, side, top, pca = cloud.orthographic_projection()
ax = self.render_pcd(self.pcd)
ax = self.render_frame(ax, pca.mean_, pca.components_)
plt.figure()
plt.subplot(1, 3, 1)
plt.scatter(front[:, 0], front[:, 1])
plt.title('Front')
plt.subplot(1, 3, 2)
plt.scatter(side[:, 0], side[:, 1])
plt.title('Side')
plt.subplot(1, 3, 3)
plt.scatter(top[:, 0], top[:, 1])
plt.title('Top')
plt.show()
def test_to_depth(self):
cloud = PointCloud(np.load('object_cloud.npy'))
pos = np.array([0, 0, 0])
frame = np.eye(3)
core.orthographic.render_frame(pos,
frame[0],
frame[1],
frame[2],
cloud=cloud)
front, right, top = cloud.orthographic_depth()
plt.subplot(131)
plt.imshow(front.img)
plt.subplot(132)
plt.imshow(right.img)
plt.subplot(133)
plt.imshow(top.img)
plt.colorbar()
plt.show()
def test_full_pipeline(self):
cloud = PointCloud.from_file('pcd0116.txt')
# cloud.filter_roi([-np.inf, np.inf, -np.inf, np.inf, 0, 500])
cloud.filter_roi([0, 1400, -np.inf, np.inf, 0, 500])
cloud.remove_plane()
cloud.render()
cloud.pca()
plt.subplot(1, 3, 1)
plt.imshow(cloud.to_depth(index=0))
plt.subplot(1, 3, 2)
plt.imshow(cloud.to_depth(index=1))
plt.subplot(1, 3, 3)
plt.imshow(cloud.to_depth(index=2))
plt.show()
def test_transform_world_to_camera(self):
sim = Simulator(use_egl=False)
scenes_ds = h5py.File('../data/scenes/shapenetsem40_5.hdf5', 'r')
scene = scenes_ds['scene'][np.random.randint(len(scenes_ds['scene'][:]))]
sim.restore(scene, os.environ['MODELS_PATH'])
sim.cam.pos = [1, 1, 1.5]
sim.cam.width = 600
sim.cam.height = 600
cloud = sim.cam.point_cloud()
print 'cloud was generated'
cloud = ortho_pipeline.transform_world_to_camera(cloud, sim.cam)
eye = np.eye(3)
orthographic.render_frame([0, 0, 0], eye[0], eye[1], eye[2], cloud=PointCloud(cloud))
def test_rotate(self):
pcd = np.load('isolated_cloud.npy')
cloud = PointCloud(pcd)
rotated = cloud.rotate([0, 1, 0], np.radians(45))
rotated.render()
def test_render(self):
pcd = np.load('points.npy')
cloud = PointCloud(pcd)
cloud.render()
cloud.render(use_pptk=False, subsample=0.01)
def test_find_plane(self):
pcd = np.load('points.npy')
cloud = PointCloud(pcd)
mean, normal = cloud.find_plane()
def test_filter_and_ransac(self):
cloud = PointCloud.from_file('pcd0116.txt')
cloud.filter_roi([0, 1400, -np.inf, np.inf, 0, 500])
cloud.remove_plane()
cloud.render()
def test_filter_roi(self):
cloud = PointCloud(self.pcd)
cloud.filter_roi([-0.5, 0.5] * 3)
self.render_pcd(cloud.cloud)
plt.show()
def test_from_file(self):
cloud = PointCloud.from_file('pcd0116.txt')
cloud.render()
def test_remove_plane(self):
cloud = PointCloud(self.pcd)
self.render_pcd(self.pcd)
cloud.remove_plane()
self.render_pcd(cloud.cloud)
plt.show()