def CaptureFeaturesxxx():
rospy.init_node('capture_node')
models = [\
'beer',
'bowl',
'create',
'disk_part',
'hammer',
'plastic_cup',
'soda_can']
# Disable gravity and delete the ground plane
initial_setup()
labeled_features = []
numCapturesPerModel = 4
numRetriesPerCapture = 3
for model_name in models:
print("Feature capture on model '{}' ({}) target numAttempts".format(
model_name, numCapturesPerModel))
spawn_model(model_name)
for i in range(numCapturesPerModel):
# make five attempts to get a valid a point cloud then give up
sample_was_good = False
try_count = 0
while not sample_was_good and try_count < numRetriesPerCapture:
sample_cloud = capture_sample()
sample_cloud_arr = ros_to_pcl(sample_cloud).to_array()
# Check for invalid clouds.
if sample_cloud_arr.shape[0] == 0:
print('Invalid cloud detected')
try_count += 1
else:
sample_was_good = True
if (sample_was_good):
# Extract histogram features
chists = pclproc.compute_color_histograms(sample_cloud,
doConvertToHSV=True)
normals = get_normals(sample_cloud)
nhists = pclproc.compute_normal_histograms(normals)
feature = np.concatenate((chists, nhists))
labeled_features.append([feature, model_name])
delete_model()
pickle.dump(labeled_features, open('training_set.sav', 'wb'))
def run(self):
"""
Produces training_set.sav
Formatted as [param, data], where:
- param : {'hsv':bool, 'bin':int}
hsv : hsv colorspace flag (otherwise rgb)
bin : number of histogram bins for feature extraction
- data : [{name : [features]} for name in models]
"""
initial_setup()
# save collection parameters
param = {'hsv': self._as_hsv, 'bin': self._nbins}
data = {}
m_n = len(self._models)
for m_i, model_name in enumerate(self._models):
rospy.loginfo('[{}/{}] Processing Model Name : {}'.format(
m_i, m_n, model_name))
model_data = []
spawn_model(model_name)
for i in range(self._steps):
# get_cloud()
sample_cloud = None
for j in range(self._max_try):
sample_cloud = capture_sample()
sample_cloud_arr = ros_to_pcl(sample_cloud).to_array()
if sample_cloud_arr.shape[0] == 0:
rospy.loginfo('')
print('Invalid cloud detected')
else:
break
# save_data()
if sample_cloud is not None:
if self._as_feature:
# Extract histogram features
normals = get_normals(sample_cloud)
feature = cloud2feature(sample_cloud,
normals,
hsv=self._as_hsv,
bins=self._nbins)
model_data.append(feature)
else:
model_data.append(sample_cloud)
data[model_name] = model_data
delete_model()
# save data with pickle
with open(self._path, 'wb') as f:
pickle.dump([param, data], f)
def CaptureCloud(numRetriesPerCapture=3):
try_count = 0
sample_was_good = False
while not sample_was_good and try_count < numRetriesPerCapture:
sample_cloud = capture_sample()
sample_cloud_arr = pcl_helper.ros_to_pcl(sample_cloud).to_array()
# Check for invalid clouds.
if sample_cloud_arr.shape[0] == 0:
try_count += 1
else:
sample_was_good = True
return try_count, sample_was_good, sample_cloud
def CaptureCloud(numRetriesPerCapture=3):
try_count = 0
sample_was_good = False
while not sample_was_good and try_count < numRetriesPerCapture:
print(str(try_count)),
sys.stdout.flush()
sample_cloud = capture_sample()
sample_cloud_arr = ros_to_pcl(sample_cloud).to_array()
# Check for invalid clouds.
if sample_cloud_arr.shape[0] == 0:
print('*BAD,'),
try_count += 1
else:
print('good,'),
sample_was_good = True
sys.stdout.flush()
return sample_was_good, sample_cloud
"eraser"
]
# Disable gravity and delete the ground plane
initial_setup()
labeled_features = []
for model_name in models:
spawn_model(model_name)
print(model_name)
for i in range(nb_point):
# make five attempts to get a valid a point cloud then give up
sample_was_good = False
try_count = 0
while not sample_was_good and try_count < 5:
sample_cloud = capture_sample()
sample_cloud_arr = ros_to_pcl(sample_cloud).to_array()
# Check for invalid clouds.
if sample_cloud_arr.shape[0] == 0:
print('Invalid cloud detected')
try_count += 1
else:
sample_was_good = True
# Save point cloud
# Extract histogram features
feature = extract_features(sample_cloud, get_normals)
labeled_features.append([feature, model_name])
delete_model()
pitch_ticks = 10
yaw_ticks = 10
for model_name in models:
spawn_model(model_name)
for i in range(pitch_ticks):
pitch = float(i) / pitch_ticks * np.pi
for j in range(yaw_ticks):
yaw = float(j) / yaw_ticks * 2 * np.pi
# make five attempts to get a valid a point cloud then give up
sample_was_good = False
try_count = 0
while not sample_was_good and try_count < 5:
print(0, 0, pitch, yaw)
sample_cloud = capture_sample(0.0, pitch, yaw)
sample_cloud_arr = ros_to_pcl(sample_cloud).to_array()
# Check for invalid clouds.
if sample_cloud_arr.shape[0] == 0:
print('Invalid cloud detected')
try_count += 1
else:
sample_was_good = True
# Extract histogram features
chists = compute_color_histograms(sample_cloud, using_hsv=True)
normals = get_normals(sample_cloud)
nhists = compute_normal_histograms(normals)
feature = np.concatenate((chists, nhists))
labeled_features.append([feature, model_name])
'plastic_cup',
'soda_can']
# Disable gravity and delete the ground plane
initial_setup()
labeled_features = []
for model_name in models:
spawn_model(model_name)
for i in range(5):
# make five attempts to get a valid a point cloud then give up
sample_was_good = False
try_count = 0
while not sample_was_good and try_count < 5:
sample_cloud = capture_sample()
sample_cloud_arr = ros_to_pcl(sample_cloud).to_array()
# Check for invalid clouds.
if sample_cloud_arr.shape[0] == 0:
print('Invalid cloud detected')
try_count += 1
else:
sample_was_good = True
# Extract histogram features
chists = compute_color_histograms(sample_cloud, using_hsv=False)
normals = get_normals(sample_cloud)
nhists = compute_normal_histograms(normals)
feature = np.concatenate((chists, nhists))
labeled_features.append([feature, model_name])
