def __init__(self, testing_database_file_f, testing_database_file_m, opt, filename):
'''Opens the specified pickle files to get the combined dataset:
This dataset is a dictionary of pressure maps with the corresponding
3d position and orientation of the markers associated with it.'''
# change this to 'direct' when you are doing baseline methods
self.CTRL_PNL = {}
self.CTRL_PNL['batch_size'] = 64
self.CTRL_PNL['loss_vector_type'] = opt.losstype
self.CTRL_PNL['verbose'] = opt.verbose
self.opt = opt
self.CTRL_PNL['num_epochs'] = 101
self.CTRL_PNL['incl_inter'] = True
self.CTRL_PNL['shuffle'] = False
self.CTRL_PNL['incl_ht_wt_channels'] = True
self.CTRL_PNL['incl_pmat_cntct_input'] = True
self.CTRL_PNL['lock_root'] = False
self.CTRL_PNL['num_input_channels'] = 3
self.CTRL_PNL['GPU'] = GPU
self.CTRL_PNL['dtype'] = dtype
self.CTRL_PNL['repeat_real_data_ct'] = 1
self.CTRL_PNL['regr_angles'] = 1
self.CTRL_PNL['depth_map_labels'] = False
self.CTRL_PNL['dropout'] = False
self.CTRL_PNL['depth_map_labels_test'] = True #can only be true is we have 100% synth for testing
self.CTRL_PNL['depth_map_output'] = True
self.CTRL_PNL['depth_map_input_est'] = False #do this if we're working in a two-part regression
self.CTRL_PNL['adjust_ang_from_est'] = self.CTRL_PNL['depth_map_input_est'] #holds betas and root same as prior estimate
self.CTRL_PNL['clip_sobel'] = True
self.CTRL_PNL['clip_betas'] = True
self.CTRL_PNL['mesh_bottom_dist'] = True
self.CTRL_PNL['full_body_rot'] = True
self.CTRL_PNL['all_tanh_activ'] = True
self.CTRL_PNL['normalize_input'] = True
self.CTRL_PNL['L2_contact'] = True
self.CTRL_PNL['pmat_mult'] = int(5)
self.CTRL_PNL['cal_noise'] = True
self.CTRL_PNL['double_network_size'] = False
self.CTRL_PNL['first_pass'] = True
self.filename = filename
if opt.losstype == 'direct':
self.CTRL_PNL['depth_map_labels'] = False
self.CTRL_PNL['depth_map_output'] = False
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL['incl_pmat_cntct_input'] = False #if there's calibration noise we need to recompute this every batch
self.CTRL_PNL['clip_sobel'] = False
if self.CTRL_PNL['incl_pmat_cntct_input'] == True:
self.CTRL_PNL['num_input_channels'] += 1
if self.CTRL_PNL['depth_map_input_est'] == True: #for a two part regression
self.CTRL_PNL['num_input_channels'] += 3
self.CTRL_PNL['num_input_channels_batch0'] = np.copy(self.CTRL_PNL['num_input_channels'])
if self.CTRL_PNL['incl_ht_wt_channels'] == True:
self.CTRL_PNL['num_input_channels'] += 2
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL['num_input_channels'] += 1
pmat_std_from_mult = ['N/A', 11.70153502792190, 19.90905848383454, 23.07018866032369, 0.0, 25.50538629767412]
if self.CTRL_PNL['cal_noise'] == False:
sobel_std_from_mult = ['N/A', 29.80360490415032, 33.33532963163579, 34.14427844692501, 0.0, 34.86393494050921]
else:
sobel_std_from_mult = ['N/A', 45.61635847182483, 77.74920396659292, 88.89398421073700, 0.0, 97.90075708182506]
self.CTRL_PNL['norm_std_coeffs'] = [1./41.80684362163343, #contact
1./16.69545796387731, #pos est depth
1./45.08513083167194, #neg est depth
1./43.55800622930469, #cm est
1./pmat_std_from_mult[int(self.CTRL_PNL['pmat_mult'])], #pmat x5
1./sobel_std_from_mult[int(self.CTRL_PNL['pmat_mult'])], #pmat sobel
1./1.0, #bed height mat
1./1.0, #OUTPUT DO NOTHING
1./1.0, #OUTPUT DO NOTHING
1. / 30.216647403350, #weight
1. / 14.629298141231] #height
if self.opt.aws == True:
self.CTRL_PNL['filepath_prefix'] = '/home/ubuntu/'
else:
self.CTRL_PNL['filepath_prefix'] = '/home/henry/'
#self.CTRL_PNL['filepath_prefix'] = '/media/henry/multimodal_data_2/'
if self.CTRL_PNL['depth_map_output'] == True: #we need all the vertices if we're going to regress the depth maps
self.verts_list = "all"
else:
self.verts_list = [1325, 336, 1032, 4515, 1374, 4848, 1739, 5209, 1960, 5423]
print self.CTRL_PNL['num_epochs'], 'NUM EPOCHS!'
# Entire pressure dataset with coordinates in world frame
self.save_name = '_' + opt.losstype + \
'_synth_32000' + \
'_' + str(self.CTRL_PNL['batch_size']) + 'b' + \
'_' + str(self.CTRL_PNL['num_epochs']) + 'e' + \
'_x' + str(self.CTRL_PNL['pmat_mult']) + 'pmult'
if self.CTRL_PNL['depth_map_labels'] == True:
self.save_name += '_' + str(self.opt.j_d_ratio) + 'rtojtdpth'
if self.CTRL_PNL['depth_map_input_est'] == True:
self.save_name += '_depthestin'
if self.CTRL_PNL['adjust_ang_from_est'] == True:
self.save_name += '_angleadj'
if self.CTRL_PNL['all_tanh_activ'] == True:
self.save_name += '_alltanh'
if self.CTRL_PNL['L2_contact'] == True:
self.save_name += '_l2cnt'
if self.CTRL_PNL['cal_noise'] == True:
self.save_name += '_calnoise'
# self.save_name = '_' + opt.losstype+'_real_s9_alltest_' + str(self.CTRL_PNL['batch_size']) + 'b_'# + str(self.CTRL_PNL['num_epochs']) + 'e'
print 'appending to', 'train' + self.save_name
self.train_val_losses = {}
self.train_val_losses['train' + self.save_name] = []
self.train_val_losses['val' + self.save_name] = []
self.train_val_losses['epoch' + self.save_name] = []
self.mat_size = (NUMOFTAXELS_X, NUMOFTAXELS_Y)
self.output_size_train = (NUMOFOUTPUTNODES_TRAIN, NUMOFOUTPUTDIMS)
self.output_size_val = (NUMOFOUTPUTNODES_TEST, NUMOFOUTPUTDIMS)
self.parents = np.array([4294967295, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21]).astype(np.int32)
#################################### PREP TESTING DATA ##########################################
# load in the test file
test_dat_f_synth = TensorPrepLib().load_files_to_database(testing_database_file_f, 'synth')
test_dat_m_synth = TensorPrepLib().load_files_to_database(testing_database_file_m, 'synth')
test_dat_f_real = TensorPrepLib().load_files_to_database(testing_database_file_f, 'real')
test_dat_m_real = TensorPrepLib().load_files_to_database(testing_database_file_m, 'real')
for possible_dat in [test_dat_f_synth, test_dat_m_synth, test_dat_f_real, test_dat_m_real]:
if possible_dat is not None:
self.dat = possible_dat
self.dat['mdm_est'] = []
self.dat['cm_est'] = []
self.dat['angles_est'] = []
self.dat['root_xyz_est'] = []
self.dat['betas_est'] = []
self.dat['root_atan2_est'] = []
self.test_x_flat = [] # Initialize the testing pressure mat list
self.test_x_flat = TensorPrepLib().prep_images(self.test_x_flat, test_dat_f_synth, test_dat_m_synth, num_repeats = 1)
self.test_x_flat = list(np.clip(np.array(self.test_x_flat) * float(self.CTRL_PNL['pmat_mult']), a_min=0, a_max=100))
self.test_x_flat = TensorPrepLib().prep_images(self.test_x_flat, test_dat_f_real, test_dat_m_real, num_repeats = 1)
if self.CTRL_PNL['cal_noise'] == False:
self.test_x_flat = PreprocessingLib().preprocessing_blur_images(self.test_x_flat, self.mat_size, sigma=0.5)
if len(self.test_x_flat) == 0: print("NO TESTING DATA INCLUDED")
self.test_a_flat = [] # Initialize the testing pressure mat angle listhave
self.test_a_flat = TensorPrepLib().prep_angles(self.test_a_flat, test_dat_f_synth, test_dat_m_synth, num_repeats = 1)
self.test_a_flat = TensorPrepLib().prep_angles(self.test_a_flat, test_dat_f_real, test_dat_m_real, num_repeats = 1)
if self.CTRL_PNL['depth_map_labels_test'] == True:
self.depth_contact_maps = [] #Initialize the precomputed depth and contact maps. only synth has this label.
self.depth_contact_maps = TensorPrepLib().prep_depth_contact(self.depth_contact_maps, test_dat_f_synth, test_dat_m_synth, num_repeats = 1)
else:
self.depth_contact_maps = None
if self.CTRL_PNL['depth_map_input_est'] == True:
self.depth_contact_maps_input_est = [] #Initialize the precomputed depth and contact map input estimates
self.depth_contact_maps_input_est = TensorPrepLib().prep_depth_contact_input_est(self.depth_contact_maps_input_est,
test_dat_f_synth, test_dat_m_synth, num_repeats = 1)
self.depth_contact_maps_input_est = TensorPrepLib().prep_depth_contact_input_est(self.depth_contact_maps_input_est,
test_dat_f_real, test_dat_m_real, num_repeats = 1)
else:
self.depth_contact_maps_input_est = None
print np.shape(self.test_x_flat), np.shape(self.test_a_flat)
test_xa = PreprocessingLib().preprocessing_create_pressure_angle_stack(self.test_x_flat,
self.test_a_flat,
self.mat_size,
self.CTRL_PNL)
test_xa = TensorPrepLib().append_input_depth_contact(np.array(test_xa),
CTRL_PNL = self.CTRL_PNL,
mesh_depth_contact_maps_input_est = self.depth_contact_maps_input_est,
mesh_depth_contact_maps = self.depth_contact_maps)
#normalize the input
if self.CTRL_PNL['normalize_input'] == True:
test_xa = TensorPrepLib().normalize_network_input(test_xa, self.CTRL_PNL)
self.test_x_tensor = torch.Tensor(test_xa)
test_y_flat = [] # Initialize the ground truth listhave
test_y_flat = TensorPrepLib().prep_labels(test_y_flat, test_dat_f_synth, num_repeats = 1,
z_adj = -0.075, gender = "f", is_synth = True,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot = self.CTRL_PNL['full_body_rot'])
test_y_flat = TensorPrepLib().prep_labels(test_y_flat, test_dat_m_synth, num_repeats = 1,
z_adj = -0.075, gender = "m", is_synth = True,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot = self.CTRL_PNL['full_body_rot'])
test_y_flat = TensorPrepLib().prep_labels(test_y_flat, test_dat_f_real, num_repeats = 1,
z_adj = 0.0, gender = "f", is_synth = False,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot = self.CTRL_PNL['full_body_rot'])
test_y_flat = TensorPrepLib().prep_labels(test_y_flat, test_dat_m_real, num_repeats = 1,
z_adj = 0.0, gender = "m", is_synth = False,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot = self.CTRL_PNL['full_body_rot'])
if self.CTRL_PNL['normalize_input'] == True:
test_y_flat = TensorPrepLib().normalize_wt_ht(test_y_flat, self.CTRL_PNL)
self.test_y_tensor = torch.Tensor(test_y_flat)
print self.test_x_tensor.shape, 'Input testing tensor shape'
print self.test_y_tensor.shape, 'Output testing tensor shape'
def __init__(self, testing_database_file_f, testing_database_file_m, opt):
'''Opens the specified pickle files to get the combined dataset:
This dataset is a dictionary of pressure maps with the corresponding
3d position and orientation of the markers associated with it.'''
# change this to 'direct' when you are doing baseline methods
self.CTRL_PNL = {}
self.CTRL_PNL['batch_size'] = 1
self.CTRL_PNL['loss_vector_type'] = opt.losstype
self.CTRL_PNL['verbose'] = opt.verbose
self.opt = opt
self.CTRL_PNL['num_epochs'] = 101
self.CTRL_PNL['incl_inter'] = True
self.CTRL_PNL['shuffle'] = False
self.CTRL_PNL['incl_ht_wt_channels'] = True
self.CTRL_PNL['incl_pmat_cntct_input'] = True
self.CTRL_PNL['num_input_channels'] = 3
self.CTRL_PNL['GPU'] = GPU
self.CTRL_PNL['dtype'] = dtype
self.CTRL_PNL['repeat_real_data_ct'] = 1
self.CTRL_PNL['regr_angles'] = 1
self.CTRL_PNL['depth_map_labels'] = False
self.CTRL_PNL['depth_map_labels_test'] = False
self.CTRL_PNL['depth_map_output'] = True
self.CTRL_PNL['depth_map_input_est'] = False #do this if we're working in a two-part regression
self.CTRL_PNL['precomp_net1'] = False
if self.CTRL_PNL['precomp_net1'] == True:
self.CTRL_PNL['depth_map_input_est'] = True
self.CTRL_PNL['adjust_ang_from_est'] = self.CTRL_PNL['depth_map_input_est'] #holds betas and root same as prior estimate
self.CTRL_PNL['clip_sobel'] = True
self.CTRL_PNL['clip_betas'] = True
self.CTRL_PNL['mesh_bottom_dist'] = True
if opt.losstype == 'direct':
self.CTRL_PNL['depth_map_labels'] = False
self.CTRL_PNL['depth_map_output'] = False
if self.CTRL_PNL['incl_pmat_cntct_input'] == True:
self.CTRL_PNL['num_input_channels'] += 1
if self.CTRL_PNL['depth_map_input_est'] == True: #for a two part regression
self.CTRL_PNL['num_input_channels'] += 3
self.CTRL_PNL['num_input_channels_batch0'] = np.copy(self.CTRL_PNL['num_input_channels'])
if self.CTRL_PNL['incl_ht_wt_channels'] == True:
self.CTRL_PNL['num_input_channels'] += 2
self.CTRL_PNL['filepath_prefix'] = '/home/henry/'
self.CTRL_PNL['aws'] = False
self.CTRL_PNL['lock_root'] = False
# change this to 'direct' when you are doing baseline methods
self.CTRL_PNL_COR = self.CTRL_PNL.copy()
self.CTRL_PNL_COR['depth_map_output'] = True
self.CTRL_PNL_COR['depth_map_input_est'] = True
self.CTRL_PNL_COR['adjust_ang_from_est'] = True
self.CTRL_PNL_COR['incl_ht_wt_channels'] = True
self.CTRL_PNL_COR['incl_pmat_cntct_input'] = True
self.CTRL_PNL_COR['num_input_channels'] = 3
if self.CTRL_PNL_COR['incl_pmat_cntct_input'] == True:
self.CTRL_PNL_COR['num_input_channels'] += 1
if self.CTRL_PNL_COR['depth_map_input_est'] == True: #for a two part regression
self.CTRL_PNL_COR['num_input_channels'] += 3
self.CTRL_PNL_COR['num_input_channels_batch0'] = np.copy(self.CTRL_PNL['num_input_channels'])
if self.CTRL_PNL_COR['incl_ht_wt_channels'] == True:
self.CTRL_PNL_COR['num_input_channels'] += 2
self.mat_size = (NUMOFTAXELS_X, NUMOFTAXELS_Y)
self.output_size_train = (NUMOFOUTPUTNODES_TRAIN, NUMOFOUTPUTDIMS)
self.output_size_val = (NUMOFOUTPUTNODES_TEST, NUMOFOUTPUTDIMS)
self.parents = np.array([4294967295, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21]).astype(np.int32)
#################################### PREP TESTING ##########################################
#load testing synth data
dat_f_synth = TensorPrepLib().load_files_to_database(testing_database_file_f, 'synth')
dat_m_synth = TensorPrepLib().load_files_to_database(testing_database_file_m, 'synth')
dat_f_real = TensorPrepLib().load_files_to_database(testing_database_file_f, 'real')
dat_m_real = TensorPrepLib().load_files_to_database(testing_database_file_m, 'real')
self.test_x_flat = [] # Initialize the testing pressure mat list
self.test_x_flat = TensorPrepLib().prep_images(self.test_x_flat, dat_f_synth, dat_m_synth, num_repeats = 1)
self.test_x_flat = list(np.clip(np.array(self.test_x_flat) * 5.0, a_min=0, a_max=100))
self.test_x_flat = TensorPrepLib().prep_images(self.test_x_flat, dat_f_real, dat_m_real, num_repeats = self.CTRL_PNL['repeat_real_data_ct'])
self.test_x_flat = PreprocessingLib().preprocessing_blur_images(self.test_x_flat, self.mat_size, sigma=0.5)
if len(self.test_x_flat) == 0: print("NO testing DATA INCLUDED")
self.test_a_flat = [] # Initialize the testing pressure mat angle list
self.test_a_flat = TensorPrepLib().prep_angles(self.test_a_flat, dat_f_synth, dat_m_real, num_repeats = 1)
self.test_a_flat = TensorPrepLib().prep_angles(self.test_a_flat, dat_f_real, dat_m_real, num_repeats = self.CTRL_PNL['repeat_real_data_ct'])
if self.CTRL_PNL['depth_map_labels'] == True:
self.depth_contact_maps = [] #Initialize the precomputed depth and contact maps
self.depth_contact_maps = TensorPrepLib().prep_depth_contact(self.depth_contact_maps, dat_f_synth, dat_m_synth, num_repeats = 1)
else:
self.depth_contact_maps = None
test_xa = PreprocessingLib().preprocessing_create_pressure_angle_stack(self.test_x_flat,
self.test_a_flat,
self.CTRL_PNL['incl_inter'], self.mat_size,
self.CTRL_PNL['clip_sobel'],
self.CTRL_PNL['verbose'])
test_xa = TensorPrepLib().append_input_depth_contact(np.array(test_xa),
include_pmat_contact = self.CTRL_PNL_COR['incl_pmat_cntct_input'],
mesh_depth_contact_maps = self.depth_contact_maps,
include_mesh_depth_contact = self.CTRL_PNL['depth_map_labels'])
self.test_x_tensor = torch.Tensor(test_xa)
self.test_y_flat = [] # Initialize the testing ground truth list
self.test_y_flat = TensorPrepLib().prep_labels(self.test_y_flat, dat_f_synth, num_repeats = 1,
z_adj = -0.075, gender = "f", is_synth = True,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'])
self.test_y_flat = TensorPrepLib().prep_labels(self.test_y_flat, dat_m_synth, num_repeats = 1,
z_adj = -0.075, gender = "m", is_synth = True,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'])
self.test_y_flat = TensorPrepLib().prep_labels(self.test_y_flat, dat_f_real, num_repeats = self.CTRL_PNL['repeat_real_data_ct'],
z_adj = 0.0, gender = "m", is_synth = False,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'])
self.test_y_flat = TensorPrepLib().prep_labels(self.test_y_flat, dat_m_real, num_repeats = self.CTRL_PNL['repeat_real_data_ct'],
z_adj = 0.0, gender = "m", is_synth = False,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'])
self.test_y_tensor = torch.Tensor(self.test_y_flat)
print self.test_x_tensor.shape, 'Input testing tensor shape'
print self.test_y_tensor.shape, 'Output testing tensor shape'
def __init__(self, filepath_prefix = '/home/henry'):
## Load SMPL model
self.filepath_prefix = filepath_prefix
self.index_queue = []
if GENDER == "m":
model_path = filepath_prefix+'/git/SMPL_python_v.1.0.0/smpl/models/basicModel_m_lbs_10_207_0_v1.0.0.pkl'
else:
model_path = filepath_prefix+'/git/SMPL_python_v.1.0.0/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl'
self.reset_pose = False
self.m = load_model(model_path)
self.marker0, self.marker1, self.marker2, self.marker3 = None, None, None, None
self.pressure = None
self.markers = [self.marker0, self.marker1, self.marker2, self.marker3]
rospy.Subscriber("/multi_pose/ar_pose_marker", AlvarMarkers, self.callback_bed_tags)
rospy.Subscriber("/multi_pose/kinect2/qhd/points", PointCloud2, self.callback_points)
rospy.Subscriber("/multi_pose/fsascan", FloatArrayBare, self.callback_pressure)
rospy.Subscriber("/abdout0", FloatArrayBare, self.bed_config_callback)
#rospy.Subscriber("/abdout0", FloatArrayBare, self.callback_bed_state)
print "init subscriber"
rospy.init_node('vol_3d_listener', anonymous=False)
self.point_cloud_array = np.array([[0., 0., 0.]])
self.pc_isnew = False
self.CTRL_PNL = {}
self.CTRL_PNL['batch_size'] = 1
self.CTRL_PNL['loss_vector_type'] = 'anglesDC' #'anglesEU'
self.CTRL_PNL['loss_vector_type'] = 'anglesDC' #'anglesEU'
self.CTRL_PNL['verbose'] = False
self.CTRL_PNL['num_epochs'] = 101
self.CTRL_PNL['incl_inter'] = True
self.CTRL_PNL['shuffle'] = False
self.CTRL_PNL['incl_ht_wt_channels'] = False
self.CTRL_PNL['incl_pmat_cntct_input'] = True
self.CTRL_PNL['num_input_channels'] = 3
self.CTRL_PNL['lock_root'] = False
self.CTRL_PNL['GPU'] = True
self.CTRL_PNL['dtype'] = torch.cuda.FloatTensor
self.CTRL_PNL['repeat_real_data_ct'] = 1
self.CTRL_PNL['regr_angles'] = 1
self.CTRL_PNL['dropout'] = False
self.CTRL_PNL['depth_map_labels'] = False
self.CTRL_PNL['depth_map_output'] = True
self.CTRL_PNL['depth_map_input_est'] = False#rue #do this if we're working in a two-part regression
self.CTRL_PNL['adjust_ang_from_est'] = self.CTRL_PNL['depth_map_input_est'] #holds betas and root same as prior estimate
self.CTRL_PNL['clip_sobel'] = True
self.CTRL_PNL['clip_betas'] = True
self.CTRL_PNL['mesh_bottom_dist'] = True
self.CTRL_PNL['full_body_rot'] = True#False
self.CTRL_PNL['normalize_input'] = True#False
self.CTRL_PNL['all_tanh_activ'] = True#False
self.CTRL_PNL['L2_contact'] = True#False
self.CTRL_PNL['pmat_mult'] = int(5)
self.CTRL_PNL['cal_noise'] = False
self.CTRL_PNL['double_network_size'] = False
self.CTRL_PNL['first_pass'] = True
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL['incl_pmat_cntct_input'] = False #if there's calibration noise we need to recompute this every batch
self.CTRL_PNL['clip_sobel'] = False
if self.CTRL_PNL['incl_pmat_cntct_input'] == True:
self.CTRL_PNL['num_input_channels'] += 1
if self.CTRL_PNL['depth_map_input_est'] == True: #for a two part regression
self.CTRL_PNL['num_input_channels'] += 3
self.CTRL_PNL['num_input_channels_batch0'] = np.copy(self.CTRL_PNL['num_input_channels'])
if self.CTRL_PNL['incl_ht_wt_channels'] == True:
self.CTRL_PNL['num_input_channels'] += 2
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL['num_input_channels'] += 1
pmat_std_from_mult = ['N/A', 11.70153502792190, 19.90905848383454, 23.07018866032369, 0.0, 25.50538629767412]
if self.CTRL_PNL['cal_noise'] == False:
sobel_std_from_mult = ['N/A', 29.80360490415032, 33.33532963163579, 34.14427844692501, 0.0, 34.86393494050921]
else:
sobel_std_from_mult = ['N/A', 45.61635847182483, 77.74920396659292, 88.89398421073700, 0.0, 97.90075708182506]
self.CTRL_PNL['norm_std_coeffs'] = [1./41.80684362163343, #contact
1./16.69545796387731, #pos est depth
1./45.08513083167194, #neg est depth
1./43.55800622930469, #cm est
1./pmat_std_from_mult[int(self.CTRL_PNL['pmat_mult'])], #pmat x5
1./sobel_std_from_mult[int(self.CTRL_PNL['pmat_mult'])], #pmat sobel
1./1.0, #bed height mat
1./1.0, #OUTPUT DO NOTHING
1./1.0, #OUTPUT DO NOTHING
1. / 30.216647403350, #weight
1. / 14.629298141231] #height
self.CTRL_PNL['filepath_prefix'] = '/home/henry/'
if self.CTRL_PNL['depth_map_output'] == True: # we need all the vertices if we're going to regress the depth maps
self.verts_list = "all"
self.TPL = TensorPrepLib()
def __init__(self, training_database_file_f, training_database_file_m):
#if this is your first time looking at the code don't pay much attention to this __init__ function.
#it's all just boilerplate for loading in the synthetic data
self.CTRL_PNL = {}
self.CTRL_PNL['loss_vector_type'] = 'anglesDC'
self.CTRL_PNL['verbose'] = False
self.CTRL_PNL['batch_size'] = 1
self.CTRL_PNL['num_epochs'] = 100
self.CTRL_PNL['incl_inter'] = True
self.CTRL_PNL['shuffle'] = False
self.CTRL_PNL['incl_ht_wt_channels'] = False
self.CTRL_PNL['incl_pmat_cntct_input'] = True
self.CTRL_PNL['dropout'] = False
self.CTRL_PNL['lock_root'] = False
self.CTRL_PNL['num_input_channels'] = 3
self.CTRL_PNL['GPU'] = False
repeat_real_data_ct = 3
self.CTRL_PNL['regr_angles'] = False
self.CTRL_PNL['aws'] = False
self.CTRL_PNL[
'depth_map_labels'] = True #can only be true if we have 100% synthetic data for training
self.CTRL_PNL[
'depth_map_labels_test'] = True #can only be true is we have 100% synth for testing
self.CTRL_PNL['depth_map_output'] = self.CTRL_PNL['depth_map_labels']
self.CTRL_PNL[
'depth_map_input_est'] = False #do this if we're working in a two-part regression
self.CTRL_PNL['adjust_ang_from_est'] = self.CTRL_PNL[
'depth_map_input_est'] #holds betas and root same as prior estimate
self.CTRL_PNL['clip_sobel'] = True
self.CTRL_PNL['clip_betas'] = True
self.CTRL_PNL['mesh_bottom_dist'] = True
self.CTRL_PNL['full_body_rot'] = True
self.CTRL_PNL['normalize_input'] = True
self.CTRL_PNL['all_tanh_activ'] = True
self.CTRL_PNL['L2_contact'] = True
self.CTRL_PNL['pmat_mult'] = int(1)
self.CTRL_PNL['cal_noise'] = False
self.CTRL_PNL['double_network_size'] = False
self.CTRL_PNL['first_pass'] = True
self.weight_joints = 1.0 #self.opt.j_d_ratio*2
self.weight_depth_planes = (1 - 0.5) #*2
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL[
'incl_pmat_cntct_input'] = False #if there's calibration noise we need to recompute this every batch
self.CTRL_PNL['clip_sobel'] = False
if self.CTRL_PNL['incl_pmat_cntct_input'] == True:
self.CTRL_PNL['num_input_channels'] += 1
if self.CTRL_PNL[
'depth_map_input_est'] == True: #for a two part regression
self.CTRL_PNL['num_input_channels'] += 3
self.CTRL_PNL['num_input_channels_batch0'] = np.copy(
self.CTRL_PNL['num_input_channels'])
if self.CTRL_PNL['incl_ht_wt_channels'] == True:
self.CTRL_PNL['num_input_channels'] += 2
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL['num_input_channels'] += 1
pmat_std_from_mult = [
'N/A', 11.70153502792190, 19.90905848383454, 23.07018866032369,
0.0, 25.50538629767412
]
if self.CTRL_PNL['cal_noise'] == False:
sobel_std_from_mult = [
'N/A', 29.80360490415032, 33.33532963163579, 34.14427844692501,
0.0, 34.86393494050921
]
else:
sobel_std_from_mult = [
'N/A', 45.61635847182483, 77.74920396659292, 88.89398421073700,
0.0, 97.90075708182506
]
self.CTRL_PNL['norm_std_coeffs'] = [
1. / 41.80684362163343, #contact
1. / 16.69545796387731, #pos est depth
1. / 45.08513083167194, #neg est depth
1. / 43.55800622930469, #cm est
1. / pmat_std_from_mult[int(self.CTRL_PNL['pmat_mult'])], #pmat x5
1. /
sobel_std_from_mult[int(self.CTRL_PNL['pmat_mult'])], #pmat sobel
1. / 1.0, #bed height mat
1. / 1.0, #OUTPUT DO NOTHING
1. / 1.0, #OUTPUT DO NOTHING
1. / 30.216647403350, #weight
1. / 14.629298141231
] #height
self.CTRL_PNL['filepath_prefix'] = '/home/henry/'
#self.CTRL_PNL['filepath_prefix'] = '/media/henry/multimodal_data_2/'
if self.CTRL_PNL[
'depth_map_output'] == True: #we need all the vertices if we're going to regress the depth maps
self.verts_list = "all"
else:
self.verts_list = [
1325, 336, 1032, 4515, 1374, 4848, 1739, 5209, 1960, 5423
]
print self.CTRL_PNL['num_epochs'], 'NUM EPOCHS!'
# Entire pressure dataset with coordinates in world frame
self.mat_size = (NUMOFTAXELS_X, NUMOFTAXELS_Y)
#################################### PREP TRAINING DATA ##########################################
#load training ysnth data
dat_f_synth = TensorPrepLib().load_files_to_database(
training_database_file_f, 'synth')
dat_m_synth = TensorPrepLib().load_files_to_database(
training_database_file_m, 'synth')
self.train_x_flat = [] # Initialize the testing pressure mat list
self.train_x_flat = TensorPrepLib().prep_images(self.train_x_flat,
dat_f_synth,
dat_m_synth,
num_repeats=1)
self.train_x_flat = list(
np.clip(np.array(self.train_x_flat) *
float(self.CTRL_PNL['pmat_mult']),
a_min=0,
a_max=100))
if self.CTRL_PNL['cal_noise'] == False:
self.train_x_flat = PreprocessingLib().preprocessing_blur_images(
self.train_x_flat, self.mat_size, sigma=0.5)
if len(self.train_x_flat) == 0: print("NO TRAINING DATA INCLUDED")
self.train_a_flat = [
] # Initialize the training pressure mat angle list
self.train_a_flat = TensorPrepLib().prep_angles(self.train_a_flat,
dat_f_synth,
dat_m_synth,
num_repeats=1)
if self.CTRL_PNL['depth_map_labels'] == True:
self.depth_contact_maps = [
] #Initialize the precomputed depth and contact maps. only synth has this label.
self.depth_contact_maps = TensorPrepLib().prep_depth_contact(
self.depth_contact_maps,
dat_f_synth,
dat_m_synth,
num_repeats=1)
else:
self.depth_contact_maps = None
if self.CTRL_PNL['depth_map_input_est'] == True:
self.depth_contact_maps_input_est = [
] #Initialize the precomputed depth and contact map input estimates
self.depth_contact_maps_input_est = TensorPrepLib(
).prep_depth_contact_input_est(self.depth_contact_maps_input_est,
dat_f_synth,
dat_m_synth,
num_repeats=1)
else:
self.depth_contact_maps_input_est = None
#stack the bed height array on the pressure image as well as a sobel filtered image
train_xa = PreprocessingLib(
).preprocessing_create_pressure_angle_stack(self.train_x_flat,
self.train_a_flat,
self.mat_size,
self.CTRL_PNL)
#stack the depth and contact mesh images (and possibly a pmat contact image) together
train_xa = TensorPrepLib().append_input_depth_contact(
np.array(train_xa),
CTRL_PNL=self.CTRL_PNL,
mesh_depth_contact_maps_input_est=self.
depth_contact_maps_input_est,
mesh_depth_contact_maps=self.depth_contact_maps)
#normalize the input
if self.CTRL_PNL['normalize_input'] == True:
train_xa = TensorPrepLib().normalize_network_input(
train_xa, self.CTRL_PNL)
self.train_x_tensor = torch.Tensor(train_xa)
train_y_flat = [] # Initialize the training ground truth list
train_y_flat = TensorPrepLib().prep_labels(
train_y_flat,
dat_f_synth,
num_repeats=1,
z_adj=-0.075,
gender="f",
is_synth=True,
loss_vector_type=self.CTRL_PNL['loss_vector_type'],
initial_angle_est=self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot=self.CTRL_PNL['full_body_rot'])
train_y_flat = TensorPrepLib().prep_labels(
train_y_flat,
dat_m_synth,
num_repeats=1,
z_adj=-0.075,
gender="m",
is_synth=True,
loss_vector_type=self.CTRL_PNL['loss_vector_type'],
initial_angle_est=self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot=self.CTRL_PNL['full_body_rot'])
# normalize the height and weight
if self.CTRL_PNL['normalize_input'] == True:
train_y_flat = TensorPrepLib().normalize_wt_ht(
train_y_flat, self.CTRL_PNL)
self.train_y_tensor = torch.Tensor(train_y_flat)
self.train_dataset = torch.utils.data.TensorDataset(
self.train_x_tensor, self.train_y_tensor)
self.train_loader = torch.utils.data.DataLoader(
self.train_dataset,
self.CTRL_PNL['batch_size'],
shuffle=self.CTRL_PNL['shuffle'])
def __init__(self, filepath_prefix, participant_directory):
##load participant info
#if False:
participant_info = load_pickle("/home/henry/Desktop/CVPR2020_study/P136/participant_info.p")
print "participant directory: ", participant_directory
for entry in participant_info:
print entry, participant_info[entry]
self.gender = participant_info['gender']
self.height_in = participant_info['height_in']
self.weight_lbs = participant_info['weight_lbs']
self.calibration_optim_values = participant_info['cal_func']
self.tf_corners = participant_info['corners']
#except:
## Load SMPL model
self.filepath_prefix = filepath_prefix
self.index_queue = []
if self.gender == "m":
model_path = filepath_prefix+'/git/SMPL_python_v.1.0.0/smpl/models/basicModel_m_lbs_10_207_0_v1.0.0.pkl'
else:
model_path = filepath_prefix+'/git/SMPL_python_v.1.0.0/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl'
self.reset_pose = False
self.m = load_model(model_path)
self.marker0, self.marker1, self.marker2, self.marker3 = None, None, None, None
self.pressure = None
self.markers = [self.marker0, self.marker1, self.marker2, self.marker3]
self.point_cloud_array = np.array([[0., 0., 0.]])
self.pc_isnew = False
self.CTRL_PNL = {}
self.CTRL_PNL['batch_size'] = 1
self.CTRL_PNL['loss_vector_type'] = 'anglesDC'
self.CTRL_PNL['verbose'] = False
self.CTRL_PNL['num_epochs'] = 101
self.CTRL_PNL['incl_inter'] = True
self.CTRL_PNL['shuffle'] = False
self.CTRL_PNL['incl_ht_wt_channels'] = True
self.CTRL_PNL['incl_pmat_cntct_input'] = True
self.CTRL_PNL['num_input_channels'] = 3
self.CTRL_PNL['GPU'] = GPU
self.CTRL_PNL['dtype'] = dtype
self.CTRL_PNL['repeat_real_data_ct'] = 1
self.CTRL_PNL['regr_angles'] = 1
self.CTRL_PNL['dropout'] = DROPOUT
self.CTRL_PNL['depth_map_labels'] = False
self.CTRL_PNL['depth_map_output'] = True
self.CTRL_PNL['depth_map_input_est'] = False#rue #do this if we're working in a two-part regression
self.CTRL_PNL['adjust_ang_from_est'] = self.CTRL_PNL['depth_map_input_est'] #holds betas and root same as prior estimate
self.CTRL_PNL['clip_sobel'] = True
self.CTRL_PNL['clip_betas'] = True
self.CTRL_PNL['mesh_bottom_dist'] = True
self.CTRL_PNL['full_body_rot'] = True#False
self.CTRL_PNL['normalize_input'] = True#False
self.CTRL_PNL['all_tanh_activ'] = True#False
self.CTRL_PNL['L2_contact'] = True#False
self.CTRL_PNL['pmat_mult'] = int(5)
self.CTRL_PNL['cal_noise'] = False
self.CTRL_PNL['output_only_prev_est'] = False
self.CTRL_PNL['double_network_size'] = False
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL['incl_pmat_cntct_input'] = False #if there's calibration noise we need to recompute this every batch
self.CTRL_PNL['clip_sobel'] = False
if self.CTRL_PNL['incl_pmat_cntct_input'] == True:
self.CTRL_PNL['num_input_channels'] += 1
if self.CTRL_PNL['depth_map_input_est'] == True: #for a two part regression
self.CTRL_PNL['num_input_channels'] += 3
self.CTRL_PNL['num_input_channels_batch0'] = np.copy(self.CTRL_PNL['num_input_channels'])
if self.CTRL_PNL['incl_ht_wt_channels'] == True:
self.CTRL_PNL['num_input_channels'] += 2
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL['num_input_channels'] += 1
pmat_std_from_mult = ['N/A', 11.70153502792190, 19.90905848383454, 23.07018866032369, 0.0, 25.50538629767412]
if self.CTRL_PNL['cal_noise'] == False:
sobel_std_from_mult = ['N/A', 29.80360490415032, 33.33532963163579, 34.14427844692501, 0.0, 34.86393494050921]
else:
sobel_std_from_mult = ['N/A', 45.61635847182483, 77.74920396659292, 88.89398421073700, 0.0, 97.90075708182506]
self.CTRL_PNL['norm_std_coeffs'] = [1./41.80684362163343, #contact
1./16.69545796387731, #pos est depth
1./45.08513083167194, #neg est depth
1./43.55800622930469, #cm est
1./pmat_std_from_mult[int(self.CTRL_PNL['pmat_mult'])], #pmat x5
1./sobel_std_from_mult[int(self.CTRL_PNL['pmat_mult'])], #pmat sobel
1./1.0, #bed height mat
1./1.0, #OUTPUT DO NOTHING
1./1.0, #OUTPUT DO NOTHING
1. / 30.216647403350, #weight
1. / 14.629298141231] #height
self.CTRL_PNL['filepath_prefix'] = '/home/henry/'
if self.CTRL_PNL['depth_map_output'] == True: # we need all the vertices if we're going to regress the depth maps
self.verts_list = "all"
self.TPL = TensorPrepLib()
self.count = 0
self.CTRL_PNL['filepath_prefix'] = '/home/henry/'
self.CTRL_PNL['aws'] = False
self.CTRL_PNL['lock_root'] = False
self.bridge = CvBridge()
self.color, self.depth_r, self.pressure = 0, 0, 0
self.kinect_im_size = (960, 540)
self.pressure_im_size = (64, 27)
self.pressure_im_size_required = (64, 27)
# initialization of kinect and thermal cam calibrations from YAML files
dist_model = 'rational_polynomial'
self.kcam = Camera('kinect', self.kinect_im_size, dist_model)
self.kcam.init_from_yaml(osp.expanduser('~/catkin_ws/src/multimodal_pose/calibrations/kinect.yaml'))
# we are at qhd not hd so need to cut the focal lengths and centers in half
self.kcam.K[0:2, 0:3] = self.kcam.K[0:2, 0:3] / 2
print self.kcam.K
self.new_K_kin, roi = cv2.getOptimalNewCameraMatrix(self.kcam.K, self.kcam.D, self.kinect_im_size, 1,
self.kinect_im_size)
print self.new_K_kin
self.drawing = False # true if mouse is pressed
self.mode = True # if True, draw rectangle. Press 'm' to toggle to curve
self.ix, self.iy = -1, -1
self.label_index = 0
self.coords_from_top_left = [0, 0]
self.overall_image_scale_amount = 0.85
self.depthcam_midpixel = [0, 0]
self.select_new_calib_corners = {}
self.select_new_calib_corners["lay"] = True
self.select_new_calib_corners["sit"] = True
self.calib_corners = {}
self.calib_corners["lay"] = 8 * [[0, 0]]
self.calib_corners["sit"] = 8 * [[0, 0]]
self.final_dataset = {}
self.filler_taxels = []
for i in range(28):
for j in range(65):
self.filler_taxels.append([i - 1, j - 1, 20000])
self.filler_taxels = np.array(self.filler_taxels).astype(int)
def __init__(self, training_database_file_f, training_database_file_m, opt):
'''Opens the specified pickle files to get the combined dataset:
This dataset is a dictionary of pressure maps with the corresponding
3d position and orientation of the markers associated with it.'''
# change this to 'direct' when you are doing baseline methods
self.CTRL_PNL = {}
self.CTRL_PNL['loss_vector_type'] = opt.losstype
self.CTRL_PNL['verbose'] = opt.verbose
self.opt = opt
self.CTRL_PNL['batch_size'] = 128
self.CTRL_PNL['num_epochs'] = 201
self.CTRL_PNL['incl_inter'] = True
self.CTRL_PNL['shuffle'] = True
self.CTRL_PNL['incl_ht_wt_channels'] = True
self.CTRL_PNL['incl_pmat_cntct_input'] = True
self.CTRL_PNL['lock_root'] = False
self.CTRL_PNL['num_input_channels'] = 3
self.CTRL_PNL['GPU'] = GPU
self.CTRL_PNL['dtype'] = dtype
repeat_real_data_ct = 3
self.CTRL_PNL['regr_angles'] = False
self.CTRL_PNL['aws'] = False
self.CTRL_PNL['depth_map_labels'] = True #can only be true if we have 100% synthetic data for training
self.CTRL_PNL['depth_map_labels_test'] = True #can only be true is we have 100% synth for testing
self.CTRL_PNL['depth_map_output'] = self.CTRL_PNL['depth_map_labels']
self.CTRL_PNL['depth_map_input_est'] = False #do this if we're working in a two-part regression
self.CTRL_PNL['adjust_ang_from_est'] = self.CTRL_PNL['depth_map_input_est'] #holds betas and root same as prior estimate
self.CTRL_PNL['clip_sobel'] = True
self.CTRL_PNL['clip_betas'] = True
self.CTRL_PNL['mesh_bottom_dist'] = True
self.CTRL_PNL['full_body_rot'] = True
self.CTRL_PNL['normalize_input'] = True
self.CTRL_PNL['all_tanh_activ'] = False
self.CTRL_PNL['L2_contact'] = False
self.CTRL_PNL['pmat_mult'] = int(3)
self.CTRL_PNL['cal_noise'] = True
if opt.losstype == 'direct':
self.CTRL_PNL['depth_map_labels'] = False
self.CTRL_PNL['depth_map_output'] = False
if self.CTRL_PNL['cal_noise'] == True:
#self.CTRL_PNL['pmat_mult'] = int(1)
#self.CTRL_PNL['incl_pmat_cntct_input'] = False #if there's calibration noise we need to recompute this every batch
self.CTRL_PNL['clip_sobel'] = False
if self.CTRL_PNL['incl_pmat_cntct_input'] == True:
self.CTRL_PNL['num_input_channels'] += 1
if self.CTRL_PNL['depth_map_input_est'] == True: #for a two part regression
self.CTRL_PNL['num_input_channels'] += 3
self.CTRL_PNL['num_input_channels_batch0'] = np.copy(self.CTRL_PNL['num_input_channels'])
if self.CTRL_PNL['incl_ht_wt_channels'] == True:
self.CTRL_PNL['num_input_channels'] += 2
if self.CTRL_PNL['cal_noise'] == True:
self.CTRL_PNL['num_input_channels'] += 1
self.CTRL_PNL['filepath_prefix'] = '/home/henry/'
if self.CTRL_PNL['depth_map_output'] == True: #we need all the vertices if we're going to regress the depth maps
self.verts_list = "all"
else:
self.verts_list = [1325, 336, 1032, 4515, 1374, 4848, 1739, 5209, 1960, 5423]
print self.CTRL_PNL['num_epochs'], 'NUM EPOCHS!'
# Entire pressure dataset with coordinates in world frame
self.save_name = '_' + opt.losstype + \
'_synth_32000' + \
'_' + str(self.CTRL_PNL['batch_size']) + 'b' + \
'_' + str(self.CTRL_PNL['num_epochs']) + 'e'
self.mat_size = (NUMOFTAXELS_X, NUMOFTAXELS_Y)
self.output_size_train = (NUMOFOUTPUTNODES_TRAIN, NUMOFOUTPUTDIMS)
self.output_size_val = (NUMOFOUTPUTNODES_TEST, NUMOFOUTPUTDIMS)
self.parents = np.array([4294967295, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21]).astype(np.int32)
#################################### PREP TRAINING DATA ##########################################
#load training ysnth data
dat_f_synth = TensorPrepLib().load_files_to_database(training_database_file_f, 'synth')
dat_m_synth = TensorPrepLib().load_files_to_database(training_database_file_m, 'synth')
dat_f_real = TensorPrepLib().load_files_to_database(training_database_file_f, 'real')
dat_m_real = TensorPrepLib().load_files_to_database(training_database_file_m, 'real')
self.train_x_flat = [] # Initialize the testing pressure mat list
self.train_x_flat = TensorPrepLib().prep_images(self.train_x_flat, dat_f_synth, dat_m_synth, num_repeats = 1)
self.train_x_flat = list(np.clip(np.array(self.train_x_flat) * float(self.CTRL_PNL['pmat_mult']), a_min=0, a_max=100))
self.train_x_flat = TensorPrepLib().prep_images(self.train_x_flat, dat_f_real, dat_m_real, num_repeats = repeat_real_data_ct)
if self.CTRL_PNL['cal_noise'] == False:
self.train_x_flat = PreprocessingLib().preprocessing_blur_images(self.train_x_flat, self.mat_size, sigma=0.5)
if len(self.train_x_flat) == 0: print("NO TRAINING DATA INCLUDED")
self.train_a_flat = [] # Initialize the training pressure mat angle list
self.train_a_flat = TensorPrepLib().prep_angles(self.train_a_flat, dat_f_synth, dat_m_synth, num_repeats = 1)
self.train_a_flat = TensorPrepLib().prep_angles(self.train_a_flat, dat_f_real, dat_m_real, num_repeats = repeat_real_data_ct)
if self.CTRL_PNL['depth_map_labels'] == True:
self.depth_contact_maps = [] #Initialize the precomputed depth and contact maps. only synth has this label.
self.depth_contact_maps = TensorPrepLib().prep_depth_contact(self.depth_contact_maps, dat_f_synth, dat_m_synth, num_repeats = 1)
else:
self.depth_contact_maps = None
if self.CTRL_PNL['depth_map_input_est'] == True:
self.depth_contact_maps_input_est = [] #Initialize the precomputed depth and contact map input estimates
self.depth_contact_maps_input_est = TensorPrepLib().prep_depth_contact_input_est(self.depth_contact_maps_input_est,
dat_f_synth, dat_m_synth, num_repeats = 1)
self.depth_contact_maps_input_est = TensorPrepLib().prep_depth_contact_input_est(self.depth_contact_maps_input_est,
dat_f_real, dat_m_real, num_repeats = repeat_real_data_ct)
else:
self.depth_contact_maps_input_est = None
#self.CTRL_PNL['clip_sobel'] = False
#stack the bed height array on the pressure image as well as a sobel filtered image
train_xa = PreprocessingLib().preprocessing_create_pressure_angle_stack(self.train_x_flat,
self.train_a_flat,
self.mat_size,
self.CTRL_PNL)
#print np.shape(train_xa), 'shape@'
train_xa = np.array(train_xa)
if self.CTRL_PNL['cal_noise'] == True:
train_xa[:, 0, :, :] = np.array(PreprocessingLib().preprocessing_blur_images(list(np.array(train_xa)[:, 0, :, :]), self.mat_size, sigma=0.5)).reshape(-1, self.mat_size[0], self.mat_size[1])
train_xa[:, 1, :, :] = np.array(PreprocessingLib().preprocessing_blur_images(list(np.array(train_xa)[:, 1, :, :]), self.mat_size, sigma=0.5)).reshape(-1, self.mat_size[0], self.mat_size[1])
#stack the depth and contact mesh images (and possibly a pmat contact image) together
train_xa = TensorPrepLib().append_input_depth_contact(np.array(train_xa),
CTRL_PNL = self.CTRL_PNL,
mesh_depth_contact_maps_input_est = self.depth_contact_maps_input_est,
mesh_depth_contact_maps = self.depth_contact_maps)
self.train_x = train_xa
self.train_y_flat = [] # Initialize the training ground truth list
self.train_y_flat = TensorPrepLib().prep_labels(self.train_y_flat, dat_f_synth, num_repeats = 1,
z_adj = -0.075, gender = "f", is_synth = True,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot=self.CTRL_PNL['full_body_rot'])
self.train_y_flat = TensorPrepLib().prep_labels(self.train_y_flat, dat_m_synth, num_repeats = 1,
z_adj = -0.075, gender = "m", is_synth = True,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot=self.CTRL_PNL['full_body_rot'])
self.train_y_flat = TensorPrepLib().prep_labels(self.train_y_flat, dat_f_real, num_repeats = repeat_real_data_ct,
z_adj = 0.0, gender = "m", is_synth = False,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot=self.CTRL_PNL['full_body_rot'])
self.train_y_flat = TensorPrepLib().prep_labels(self.train_y_flat, dat_m_real, num_repeats = repeat_real_data_ct,
z_adj = 0.0, gender = "m", is_synth = False,
loss_vector_type = self.CTRL_PNL['loss_vector_type'],
initial_angle_est = self.CTRL_PNL['adjust_ang_from_est'],
full_body_rot=self.CTRL_PNL['full_body_rot'])