recognize_3d.py File Reference

Go to the source code of this file.

Classes
class	hai_sandbox.recognize_3d.DataScale
class	hai_sandbox.recognize_3d.FiducialPicker
class	hai_sandbox.recognize_3d.ImagePublisher
class	hai_sandbox.recognize_3d.InterestPointDataset
class	hai_sandbox.recognize_3d.NarrowTextureFeatureExtractor
class	hai_sandbox.recognize_3d.PCAIntensities
class	hai_sandbox.recognize_3d.Recognize3DParam
class	hai_sandbox.recognize_3d.ScanLabeler
class	hai_sandbox.recognize_3d.SVM
class	hai_sandbox.recognize_3d.SVMPCA_ActiveLearner
Namespaces
namespace	hai_sandbox::recognize_3d
Functions
def	hai_sandbox::recognize_3d.confusion_matrix
def	hai_sandbox::recognize_3d.dataset_to_libsvm
def	hai_sandbox::recognize_3d.draw_dataset
def	hai_sandbox::recognize_3d.draw_labeled_points
def	hai_sandbox::recognize_3d.draw_points
def	hai_sandbox::recognize_3d.find_max_in_density
def	hai_sandbox::recognize_3d.insert_folder_name
def	hai_sandbox::recognize_3d.instance_to_image
def	hai_sandbox::recognize_3d.instances_to_image
def	hai_sandbox::recognize_3d.inverse_indices
def	hai_sandbox::recognize_3d.load_data_from_file2
def	hai_sandbox::recognize_3d.make_point_exclusion_test_set
def	hai_sandbox::recognize_3d.preprocess_data_in_dir
def	hai_sandbox::recognize_3d.preprocess_scan_extract_features
Variables
tuple	hai_sandbox::recognize_3d.current_scan_pred = InterestPointDataset(xs, results, locs2d, locs3d, None)
tuple	hai_sandbox::recognize_3d.dataset = ut.load_pickle(fname)
string	hai_sandbox::recognize_3d.dest = 'mode'
list	hai_sandbox::recognize_3d.dset = locations['data']
tuple	hai_sandbox::recognize_3d.fname = raw_input('pick a file name')
tuple	hai_sandbox::recognize_3d.fp = FiducialPicker(args[0])
tuple	hai_sandbox::recognize_3d.fpfh = rospy.ServiceProxy('fpfh', fsrv.FPFHCalc)
string	hai_sandbox::recognize_3d.help = 'fiducialpicker, preprocess, or label'
tuple	hai_sandbox::recognize_3d.histogram = np.matrix(res.hist.histograms)
tuple	hai_sandbox::recognize_3d.img = cv.CloneMat(cdisp['cv'])
tuple	hai_sandbox::recognize_3d.ip = ImagePublisher('active_learn')
list	hai_sandbox::recognize_3d.keys = locations['data']
tuple	hai_sandbox::recognize_3d.kfe = KinectFeatureExtractor()
tuple	hai_sandbox::recognize_3d.learner = SVMPCA_ActiveLearner(use_pca=True)
tuple	hai_sandbox::recognize_3d.locations = ut.load_pickle(opt.locations)
	hai_sandbox::recognize_3d.mode = opt.mode
tuple	hai_sandbox::recognize_3d.neg_to_pos_ratio = float(nneg)
int	hai_sandbox::recognize_3d.NEGATIVE = 0
tuple	hai_sandbox::recognize_3d.nneg = np.sum(dataset.outputs == NEGATIVE)
tuple	hai_sandbox::recognize_3d.npos = np.sum(dataset.outputs == POSITIVE)
tuple	hai_sandbox::recognize_3d.p = optparse.OptionParser()
tuple	hai_sandbox::recognize_3d.picked_i = int(raw_input('pick a key to use'))
tuple	hai_sandbox::recognize_3d.points3d = np.matrix(res.hist.points3d)
float	hai_sandbox::recognize_3d.POSITIVE = 1.0
tuple	hai_sandbox::recognize_3d.req = fsrv.FPFHCalcRequest()
tuple	hai_sandbox::recognize_3d.res = fpfh(req)
tuple	hai_sandbox::recognize_3d.results = np.matrix(learner.classify(sdset))
tuple	hai_sandbox::recognize_3d.s
list	hai_sandbox::recognize_3d.seed_dset = keys[i]
	hai_sandbox::recognize_3d.trained = False
float	hai_sandbox::recognize_3d.UNLABELED = 2.0
string	hai_sandbox::recognize_3d.weight_balance = ' -w0 1 -w1 %.2f'