def generate_images(self, salient_edge_set, n_samples=1):
"""Generate depth image, normal image, and binary edge mask tuples.
Parameters
----------
salient_edge_set : SalientEdgeSet
A salient edge set to generate images of.
n_samples : int
The number of samples to generate.
Returns
-------
depth_ims : (n,) list of perception.DepthImage
Randomly-rendered depth images of object.
normal_ims : (n,) list of perception.PointCloudImage
Normals for the given image
edge_masks : (n,) list of perception.BinaryImage
Masks for pixels on the salient edges of the object.
"""
# Compute stable poses of mesh
mesh = salient_edge_set.mesh
stp_pose_tfs, probs = mesh.compute_stable_poses()
probs = probs / sum(probs)
# Generate n renders
depth_ims, normal_ims, edge_masks = [], [], []
scene = Scene()
so = SceneObject(mesh, RigidTransform(from_frame='obj', to_frame='world'))
scene.add_object('object', so)
for i in range(n_samples):
# Sample random stable pose.
tf_id = np.random.choice(np.arange(len(probs)), p=probs)
tf = stp_pose_tfs[tf_id]
T_obj_world = RigidTransform(tf[:3,:3], tf[:3,3], from_frame='obj', to_frame='world')
so.T_obj_world = T_obj_world
# Create the random uniform workspace sampler
ws_cfg = self._config['worksurface_rv_config']
uvs = UniformPlanarWorksurfaceImageRandomVariable('object', scene, [RenderMode.DEPTH], frame='camera', config=ws_cfg)
# Sample and extract the depth image, camera intrinsics, and T_obj_camera
sample = uvs.sample()
depth_im = sample.renders[RenderMode.DEPTH]
cs = sample.camera
ci = CameraIntrinsics(frame='camera', fx=cs.focal, fy=cs.focal, cx=cs.cx, cy=cs.cy,
skew=0.0, height=ws_cfg['im_height'], width=ws_cfg['im_width'])
T_obj_camera = cs.T_camera_world.inverse().dot(T_obj_world)
edge_mask = self._compute_edge_mask(salient_edge_set, depth_im, ci, T_obj_camera)
point_cloud_im = ci.deproject_to_image(depth_im)
normal_im = point_cloud_im.normal_cloud_im()
depth_ims.append(depth_im)
normal_ims.append(normal_im)
edge_masks.append(edge_mask)
return depth_ims, normal_ims, edge_masks
'elevation': {
'min': 0.10,
'max': 10.0,
},
'roll': {
'min': -0.2,
'max': 0.2,
},
'x': {
'min': -0.01,
'max': 0.01,
},
'y': {
'min': -0.01,
'max': 0.01,
},
'im_width': 600,
'im_height': 600
}
urv = UniformPlanarWorksurfaceImageRandomVariable('pawn', scene,
[RenderMode.COLOR], 'camera',
cfg)
renders = urv.sample(10, front_and_back=True)
for i, render in enumerate(renders):
color = render.renders[RenderMode.COLOR]
color.save('output/random_{}.jpg'.format(i))
v = SceneViewer(scene, raymond_lighting=True)
def generate_examples(self, salient_edge_set_filename, n_samples=1):
"""Generate RegistrationExamples for evaluating the algorithm.
Parameters
----------
salient_edge_set_filename : str
A file containing the salient edge set to generate images of.
n_samples : int
The number of samples to generate.
Returns
-------
list of RegistrationExample
A list of RegistrationExamples.
"""
# Compute stable poses of mesh
salient_edge_set = SalientEdgeSet.load(salient_edge_set_filename)
mesh = salient_edge_set.mesh
stp_pose_tfs, probs = mesh.compute_stable_poses()
probs = probs / sum(probs)
# Generate n renders
examples = []
scene = Scene()
so = SceneObject(mesh,
RigidTransform(from_frame='obj', to_frame='world'))
scene.add_object('object', so)
for i in range(n_samples):
# Sample random stable pose.
tf_id = np.random.choice(np.arange(len(probs)), p=probs)
tf = stp_pose_tfs[tf_id]
T_obj_world = RigidTransform(tf[:3, :3],
tf[:3, 3],
from_frame='obj',
to_frame='world')
so.T_obj_world = T_obj_world
# Create the random uniform workspace sampler
ws_cfg = self._config['worksurface_rv_config']
uvs = UniformPlanarWorksurfaceImageRandomVariable(
'object',
scene, [RenderMode.DEPTH],
frame='camera',
config=ws_cfg)
# Sample and extract the depth image, camera intrinsics, and T_obj_camera
sample = uvs.sample()
depth_im = sample.renders[RenderMode.DEPTH]
cs = sample.camera
ci = CameraIntrinsics(frame='camera',
fx=cs.focal,
fy=cs.focal,
cx=cs.cx,
cy=cs.cy,
skew=0.0,
height=ws_cfg['im_height'],
width=ws_cfg['im_width'])
T_obj_camera = cs.T_camera_world.inverse().dot(T_obj_world)
examples.append(
RegistrationExample(salient_edge_set_filename, depth_im, ci,
T_obj_camera))
return examples
# },
# 'x': {
# 'min' : -0.01,
# 'max' : -0.01,
# },
# 'y': {
# 'min' : -0.01,
# 'max' : 0.01,
# },
# 'im_width': 640,
# 'im_height': 480
# }
# num_images: 874 for test dataset, 3730 for train dataset
urv = UniformPlanarWorksurfaceImageRandomVariable(
'to_render', scene, [RenderMode.COLOR, RenderMode.DEPTH], 'camera', cfg)
renders = urv.sample(args.num_images, front_and_back=True)
# v = SceneViewer(scene, raymond_lighting=True)
camera_intr = None
camera_poses = {}
for i, render in enumerate(renders):
color = render.renders[RenderMode.COLOR]
depth = render.renders[RenderMode.DEPTH]
# print(np.max(depth.data))
# print(np.min(depth.data))
# print(1/0)
# depth_scaled = depth.data/0.2 * 255.
# depth_to_grayscale = np.stack([depth_scaled]*3, axis=2)