def main(argv=None):
print('Hello! This is PointNet-Segmentation and Surface-Matching Program')
opt = parser.parse_args()
num_points = opt.np
pretrained_model = opt.ptn
num_classes = 3
'''
Load PointNet Model (model for point-wise classification)
'''
classifier = PointNetPointWise(num_points=num_points,
num_classes=num_classes)
classifier.load_state_dict(torch.load(pretrained_model))
classifier.eval()
'''
Setup camera
'''
fps = 30
# w = 1280 #640
# h = 960 #480
# fps = 30
# # config crop area [h1:h2, w1:w2]
# h1 = 240 #140
# h2 = 720 #340
# w1 = 320 #200
# w2 = 960 #440
# w, h, h1, h2, w1, w2 = (np.array([640, 480, 140, 340, 200, 440])).astype(int)
w, h, h1, h2, w1, w2 = (np.array([640, 480, 140, 340, 200,
440])).astype(int)
rgb_stream, depth_stream = setup_camera(w=w, h=h, fps=fps)
from config import CAMERA_CONFIG
'''
Record
'''
done = False
while not done:
key = cv2.waitKey(1) & 255
## Read keystrokes
if key == 27: # terminate
print("\tESC key detected!")
done = True
elif chr(key) == 's': # screen capture
# print("\ts key detected. Saving image {}".format(s))
'''
Get data
'''
# rgb = rgb[h1:h2, w1:w2, :]
# dmap = dmap[h1:h2, w1:w2]
# pc_scene = rgbd2pc(rgb=rgb, depth=dmap, center_x=(w1+w2)/2, center_y=(h1+h2)/2, focal_length=889)
# pc_scene = sample_data(point_cloud=pc_scene, num_points=num_points)
# rgbd2pc(rgb, depth=dmap, center_x=325.5, center_y=255.5, focal_length=572, scale=2000)
xyzrgbrc = rgbd2xyzrgbrc(rgb=rgb, depth=dmap, scale=1000)
xyzrgbrc = xyzrgbrc[h1:h2, w1:w2, :] # crop the interested area
pc_scene = xyzrgb2pc(xyzrgb=xyzrgbrc)
# ply = generate_ply(pc_scene)
# with open('./tmp/abc' + '.ply', 'w') as output:
# output.write(ply)
pc_scene = sample_data(point_cloud=pc_scene, num_points=num_points)
# pc_scene = (np.array(pc_scene)[:, :3]).astype(np.float32)
'''
Predict and Segment objects
'''
pred_labels = predict_segmentation(classifier=classifier,
input_points=pc_scene)
visualize(x=pc_scene[:, 0],
y=pc_scene[:, 1],
z=pc_scene[:, 2],
label=pred_labels,
point_radius=0.0008) # visualize predicted results
pipe_points, wrench_points = visualize_segmented_objects(
scene_points=pc_scene,
labels=pred_labels) # visualize segmented objects
'''
Surface-Matching
'''
# Convert numpy array to point cloud type
pc_model = load_ply(path='./models/1.ply', num_points=-1)
# pc_scene = wrench_points
visualize(x=pc_model[:, 0],
y=pc_model[:, 1],
z=pc_model[:, 2],
label=np.ones(len(pc_model)),
point_radius=0.0008)
visualize(x=pipe_points[:, 0],
y=pipe_points[:, 1],
z=pipe_points[:, 2],
label=np.ones(len(pipe_points)),
point_radius=0.0008)
result_icp = match_surface(model_points=pc_model,
object_points=pipe_points)
# Transformation(Rotation angles)
print('Theta x, Theta y, Theta z:(in Degree) ')
print(
rotationMatrixToEulerAngles(result_icp.transformation[:3, :3])
/ np.pi * 180)
rgb, dmap = display_stream(rgb_stream=rgb_stream,
depth_stream=depth_stream,
h=h,
w=w,
crop=((w1, h1), (w2, h2)))
# end while
## Release resources
cv2.destroyAllWindows()
rgb_stream.stop()
depth_stream.stop()
openni2.unload()
print("Terminated")
def main(argv=None):
print('Hello! This is PointNet-Segmentation and Surface-Matching Program')
opt = parser.parse_args()
num_points = opt.np
pretrained_model = opt.ptn
num_classes = 3
'''
Load PointNet Model (model for point-wise classification)
'''
classifier = PointNetPointWise(num_points=num_points,
num_classes=num_classes)
classifier.load_state_dict(torch.load(pretrained_model))
classifier.eval()
'''
Setup camera
'''
fps = 30
w, h, h1, h2, w1, w2 = (np.array([640, 480, 150, 330, 210,
430])).astype(int)
rgb_stream, depth_stream = setup_camera(w=w, h=h, fps=fps)
# Load model
pc_model = load_ply(path='./models/model13_1.ply', num_points=-1)
'''
Record
'''
done = False
while not done:
key = cv2.waitKey(1) & 255
## Read keystrokes
if key == 27: # terminate
print("\tESC key detected!")
done = True
elif chr(key) == 's': # screen capture
'''
Get data
'''
# sTime = time.time()
# xyzrgbrc1 = rgbd2xyzrgbrc(rgb=rgb, depth=dmap, scale=1000)
# xyzrgbrc1 = xyzrgbrc1[h1:h2, w1:w2, :] # crop the interested area
# print(time.time() - sTime)
sTime = time.time()
xyzrgbrc2 = rgbd2xyzrgbrc(rgb=rgb[h1:h2, w1:w2, :],
depth=dmap[h1:h2, w1:w2],
scale=1000)
print(time.time() - sTime)
pc_scene = xyzrgb2pc(xyzrgb=xyzrgbrc)
pc_scene = sample_data(point_cloud=pc_scene, num_points=num_points)
'''
Predict and Segment objects
'''
pred_labels = predict_segmentation(classifier=classifier,
input_points=pc_scene)
visualize(x=pc_scene[:, 0],
y=pc_scene[:, 1],
z=pc_scene[:, 2],
label=pred_labels,
point_radius=0.0008) # visualize predicted results
pipe_points, wrench_points = visualize_segmented_objects(
scene_points=pc_scene,
labels=pred_labels) # visualize segmented objects
'''
Surface-Matching
'''
# pc_scene = wrench_points
visualize(x=pc_model[:, 0],
y=pc_model[:, 1],
z=pc_model[:, 2],
label=np.ones(len(pc_model)),
point_radius=0.0008)
visualize(x=pipe_points[:, 0],
y=pipe_points[:, 1],
z=pipe_points[:, 2],
label=np.ones(len(pipe_points)),
point_radius=0.0008)
result_icp = match_surface(model_points=pc_model,
object_points=pipe_points)
# Transformation(Rotation angles)
print('Theta x, Theta y, Theta z:(in Degree) ')
print(
rotationMatrixToEulerAngles(result_icp.transformation[:3, :3])
/ np.pi * 180)
rgb, dmap = display_stream(rgb_stream=rgb_stream,
depth_stream=depth_stream,
h=h,
w=w,
crop=((w1, h1), (w2, h2)))
# end while
## Release resources
cv2.destroyAllWindows()
rgb_stream.stop()
depth_stream.stop()
openni2.unload()
print("Terminated")
def main(argv=None):
print('Hello! This is PointNet-Segmentation and Surface-Matching Program')
opt = parser.parse_args()
num_points = opt.np
pretrained_model = opt.ptn
num_classes = 4
'''
Load PointNet Model (model for point-wise classification)
'''
classifier = PointNet(num_points=num_points, num_classes=num_classes)
classifier.load_weights(filepath='./tmp/model.acc.53.hdf5')
'''
Setup camera
'''
fps = 30
w, h, h1, h2, w1, w2 = (np.array([640, 480, 150, 330, 210, 430])).astype(int)
rgb_stream, depth_stream = setup_camera(w=w, h=h, fps=fps)
# Load model
pc_models = []
centroid_models = []
files = os.listdir('./models/pipe/pc/')
for i, file in enumerate(files):
pc = load_ply(path='./models/pipe/pc/' + file, num_points=-1)
centroid = np.mean(pc, axis=0)
pc_models.append(pc)
centroid_models.append(centroid)
'''
Record
'''
done = False
while not done:
key = cv2.waitKey(1) & 255
## Read keystrokes
if key == 27: # terminate
print("\tESC key detected!")
done = True
elif chr(key) == 's': # screen capture
'''
Get data
'''
# sTime = time.time()
# xyzrgbrc1 = rgbd2xyzrgbrc(rgb=rgb, depth=dmap, scale=1000)
# xyzrgbrc1 = xyzrgbrc1[h1:h2, w1:w2, :] # crop the interested area
# print(time.time() - sTime)
sTime = time.time()
xyzrgbrc = rgbd2xyzrgbrc(rgb=rgb[h1:h2, w1:w2, :], depth=dmap[h1:h2, w1:w2], scale=1000)
print('Converting time from rgb-d to xyz: ', time.time() - sTime)
sTime = time.time()
pc_scene = xyzrgb2pc(xyzrgb=xyzrgbrc)
print('Converting time from xyz to point cloud: ', time.time() - sTime)
pipe = []
for i in range(2):
pc_scene = sample_data(point_cloud=pc_scene, num_points=num_points)
'''
Predict and Segment objects
'''
pred_labels = predict_segmentation(classifier=classifier, input_points=pc_scene)
# visualize(x=pc_scene[:, 0], y=pc_scene[:, 1], z=pc_scene[:, 2], label=pred_labels,
# point_radius=0.0008) # visualize predicted results
pipe_points, wrench_points = visualize_segmented_objects(scene_points=pc_scene,
labels=pred_labels) # visualize segmented objects
if pipe_points != []:
pipe.append(pipe_points)
pipe = np.concatenate((pipe[0], pipe[1]), axis=0)
visualize(x=pipe[:, 0], y=pipe[:, 1], z=pipe[:, 2], label=np.ones(len(pipe)), point_radius=0.0008)
if len(pipe) < 100: continue
result = []
for i in range(len(centroid_models)):
print('Attempt fitting model: ', i)
pc_model = pc_models[i]
centroid_model = centroid_models[i]
'''
Surface-Matching
'''
# pc_scene = wrench_points
# visualize(x=pc_model[:, 0], y=pc_model[:, 1], z=pc_model[:, 2], label=np.ones(len(pc_model)), point_radius=0.0008)
# visualize(x=pipe[:, 0], y=pipe[:, 1], z=pipe[:, 2], label=np.ones(len(pipe)), point_radius=0.0008)
result_icp, result_ransac = match_surface(model_points=pc_model, object_points=pipe)
if (result==[]) or (result_ransac.fitness> result[0].fitness):
result = [result_ransac, result_icp, centroid_model, i]
if result == []: continue
# Transformation(Rotation angles)
print('Best fitness: ')
print(result[0].fitness, result[3])
print('Theta x, Theta y, Theta z:(in Degree) ')
print(rotationMatrixToEulerAngles(result[1].transformation[:3, :3]) / np.pi * 180)
print('Translation:(in Meters) ')
print(result[1].transformation[:3, 3], result[2])
source = PointCloud()
source.points = Vector3dVector(pc_models[result[3]])
target = PointCloud()
target.points = Vector3dVector(pipe)
draw_registration_result(source, target, result[1].transformation)
trans_vector = centroid_models[result[3]] - centroid_models[10] + result[1].transformation[:3, 3]
grasp_pose = np.array([171.14, 271.31, -16]) + trans_vector
rgb, dmap = display_stream(rgb_stream=rgb_stream, depth_stream=depth_stream, h=h, w=w, crop=((w1, h1), (w2, h2)))
# end while
## Release resources
cv2.destroyAllWindows()
rgb_stream.stop()
depth_stream.stop()
openni2.unload()
print("Terminated")
def main(argv=None):
print('Hello! This is PointNet-Segmentation and Surface-Matching Program')
opt = parser.parse_args()
num_points = opt.np
pretrained_model = opt.ptn
num_classes = 4
'''
Load PointNet Model (model for point-wise classification)
'''
classifier = PointNet(num_points=num_points, num_classes=num_classes)
classifier.load_weights(filepath='./tmp/model.acc.53.hdf5')
'''
Setup camera
'''
fps = 30
w, h, h1, h2, w1, w2 = (np.array([640, 480, 150, 330, 210,
430])).astype(int)
rgb_stream, depth_stream = setup_camera(w=w, h=h, fps=fps)
from threefinger_command_sender_v0200 import ROBOTIQ, ThreeFingerModes
from robot_command_sender_v0200 import TX60
tx60 = TX60()
tx60.enable()
tx60.move_point(x=-400, y=40, z=60, rx=-180, ry=0, rz=0)
tx60.move_dimension(z=20)
robotiq = ROBOTIQ()
robotiq.enable()
robotiq.set_mode(1)
# Load model
pc_models = []
centroid_models = []
grasping_poses = []
files = os.listdir('./models/pipe/pc/')
for i, file in enumerate(files):
filename, ext = os.path.splitext(file)
pc_model = load_ply(path='./models/pipe/pc/' + file, num_points=-1)
centroid, grasping_pose = np.load('./models/pipe/info/' + filename +
'.npy')
pc_models.append(pc_model)
centroid_models.append(centroid)
grasping_poses.append(grasping_pose)
'''
Record
'''
done = False
while not done:
key = cv2.waitKey(1) & 255
## Read keystrokes
if key == 27: # terminate
print("\tESC key detected!")
done = True
elif chr(key) == 's': # screen capture
'''
Get data
'''
# sTime = time.time()
# xyzrgbrc1 = rgbd2xyzrgbrc(rgb=rgb, depth=dmap, scale=1000)
# xyzrgbrc1 = xyzrgbrc1[h1:h2, w1:w2, :] # crop the interested area
# print(time.time() - sTime)
sTime = time.time()
xyzrgbrc = rgbd2xyzrgbrc(rgb=rgb[h1:h2, w1:w2, :],
depth=dmap[h1:h2, w1:w2],
depthStream=depth_stream,
scale=1000)
# xyzrgbrc = rgbd2xyzrgbrc(rgb=rgb, depth=dmap, depthStream=depth_stream, scale=1000)
print('Converting time from rgb-d to xyz: ', time.time() - sTime)
sTime = time.time()
pc_scene = xyzrgb2pc(xyzrgb=xyzrgbrc)
print('Converting time from xyz to point cloud: ',
time.time() - sTime)
pipe = []
for i in range(2):
pc_scene = sample_data(point_cloud=pc_scene,
num_points=num_points)
'''
Predict and Segment objects
'''
pred_labels = predict_segmentation(classifier=classifier,
input_points=pc_scene)
# visualize(x=pc_scene[:, 0], y=pc_scene[:, 1], z=pc_scene[:, 2], label=pred_labels,
# point_radius=0.0008) # visualize predicted results
pipe_points, wrench_points = visualize_segmented_objects(
scene_points=pc_scene,
labels=pred_labels) # visualize segmented objects
if pipe_points != []:
pipe.append(pipe_points)
pipe = np.concatenate((pipe[0], pipe[1]), axis=0)
visualize(x=pipe[:, 0],
y=pipe[:, 1],
z=pipe[:, 2],
label=np.ones(len(pipe)),
point_radius=0.0008)
if len(pipe) < 100: continue
result = []
# for i in range(len(centroid_models)):
for i in range(1):
i = 10
print('Attempt fitting model: ', i)
pc_model = pc_models[i]
centroid_model = centroid_models[i]
'''
Surface-Matching
'''
# pc_scene = wrench_points
# visualize(x=pc_model[:, 0], y=pc_model[:, 1], z=pc_model[:, 2], label=np.ones(len(pc_model)), point_radius=0.0008)
# visualize(x=pipe[:, 0], y=pipe[:, 1], z=pipe[:, 2], label=np.ones(len(pipe)), point_radius=0.0008)
result_icp, result_ransac = match_surface(
model_points=pc_model, object_points=pipe)
# if (result==[]) or (result_ransac.fitness > result[0].fitness):
if (result == []) or (length_vector(
result_icp.transformation[:3, 3]) < result[4]):
result = [
result_ransac, result_icp, centroid_model, i,
length_vector(result_icp.transformation[:3, 3])
]
if result == []: continue
# Transformation(Rotation angles)
print('Best fitness: ')
print(result[0].fitness)
print('Theta x, Theta y, Theta z:(in Degree) ')
print(
rotationMatrixToEulerAngles(result[1].transformation[:3, :3]) /
np.pi * 180)
print('Best fitted model: ', result[3], ' Model centroid: ',
result[2] * 1000)
print('Translation:(in miliMeters) ',
result[1].transformation[:3, 3] * 1000)
source = PointCloud()
source.points = Vector3dVector(pc_models[result[3]])
target = PointCloud()
target.points = Vector3dVector(pipe)
# draw_registration_result(source, target, result[1].transformation)
translation = result[1].transformation[:2, 3] * 1000
translation = translation[::-1]
grasping_pose = grasping_poses[result[3]][0, :2] + translation
if grasping_pose[0] > 370: continue
if grasping_pose[1] > 370: continue
# grasping_pose = grasping_poses[result[3]][0, :3].astype(np.float64)
rotate_angle = rotationMatrixToEulerAngles(
result[1].transformation[:3, :3]) / np.pi * 180
tx60.enable()
tx60.move_dimension(z=60)
tx60.move_dimension(rz=rotate_angle[2])
tx60.move_point(x=grasping_pose[0],
y=grasping_pose[1],
z=60,
rx=-180,
ry=0,
rz=rotate_angle[2])
tx60.move_dimension(z=-20)
robotiq.move(200)
tx60.move_dimension(z=60)
tx60.move_point(x=-400, y=40, z=60, rx=-180, ry=0, rz=0)
tx60.move_dimension(z=10)
robotiq.move(40)
tx60.disable()
rgb, dmap = display_stream(rgb_stream=rgb_stream,
depth_stream=depth_stream,
h=h,
w=w,
crop=((w1, h1), (w2, h2)))
# end while
## Release resources
robotiq.disable()
tx60.disable()
cv2.destroyAllWindows()
rgb_stream.stop()
depth_stream.stop()
openni2.unload()
print("Terminated")
def main(argv=None):
print('Hello! This is PointNet-Segmentation and Surface-Matching Program')
opt = parser.parse_args()
num_points = opt.np
pretrained_model = opt.ptn
idx = opt.idx
root = 'E:/PROJECTS/NTUT/PointNet/pointnet1_pytorch/DATA/Shapenet/shapenetcore_partanno_segmentation_benchmark_v0'
num_classes = 3
'''
Load PointNet Model (model for point-wise classification)
'''
classifier = PointNetPointWise(num_points=num_points,
num_classes=num_classes)
classifier.load_state_dict(torch.load(pretrained_model))
classifier.eval()
'''
Load data
'''
# data_tes = PartDataset(root=root, num_points=num_points, categories=['tools'], training=True, balanced=True,
# shuffle=True, seed=0, offset=0)
#
# points, labels = data_tes[idx] # Load data points and ground truth labels
# visualize(x=points[:, 0], y=points[:, 1], z=points[:, 2], label=labels, point_radius=0.0008) # visualize ground truth
pc_scene = load_ply(path='./models/model5/PC/pipe/1001.ply',
num_points=4096)
pc_scene = pc_scene.astype(np.float32)
'''
Predict and Segment objects
'''
pred_labels = predict_segmentation(classifier=classifier,
input_points=pc_scene)
visualize(x=pc_scene[:, 0],
y=pc_scene[:, 1],
z=pc_scene[:, 2],
label=pred_labels,
point_radius=0.0008) # visualize predicted results
pipe_points, wrench_points = visualize_segmented_objects(
scene_points=pc_scene, labels=pred_labels)
'''
Surface-Matching
'''
# Convert numpy array to point cloud type
pc_model = load_ply(path='./models/model5/PC/pipe/1002.ply', num_points=-1)
# pc_scene = wrench_points
visualize(x=pc_model[:, 0],
y=pc_model[:, 1],
z=pc_model[:, 2],
label=np.ones(len(pc_model)),
point_radius=0.0008)
visualize(x=pipe_points[:, 0],
y=pipe_points[:, 1],
z=pipe_points[:, 2],
label=np.ones(len(pipe_points)),
point_radius=0.0008)
# result_icp = match_surface(source_points=pc_model, target_points=pipe_points); print(result_icp.transformation[:3, 3])
result_icp = match_surface(source_points=pc_model, target_points=pc_scene)
print(result_icp.transformation[:3, 3])
# Transformation(Rotation angles)
print('Theta x, Theta y, Theta z:(in Degree) ')
print(
rotationMatrixToEulerAngles(result_icp.transformation[:3, :3]) /
np.pi * 180)