############ below code is required if we want to create video - it saves each frame ina folder ######
# shutil.rmtree('/data/Guha/GR/Output/GT/')
# shutil.rmtree('/data/Guha/GR/Output/Prediction/')
# os.mkdir('/data/Guha/GR/Output/GT/')
# os.mkdir('/data/Guha/GR/Output/Prediction/')
with open(path, 'rb') as file:
print (path)
data_dict = dict(np.load(file))
gt = data_dict['target']
pred = data_dict['predictions']
# for SMPL 15 to 24 joints
gt_full = myUtil.smpl_reduced_to_full(gt.reshape(-1,15*4))
pred_full = myUtil.smpl_reduced_to_full(pred.reshape(-1,15*4))
#from quat to axis-angle
gt_aa = myUtil.quat_to_aa_representation(gt_full,24)
pred_aa = myUtil.quat_to_aa_representation(pred_full,24)
seq_len = pred_aa.shape[0]
print('seq len:', seq_len)
######### loop through the Sequence
for seq_num in range(seq_len):
pose1 = gt_aa[seq_num]
pose2 = pred_aa[seq_num]
############ update SMPL model with ground truth pose parameters
'lankle', 'rankle'
]
SENSORS = ['lknee', 'rknee', 'lelbow', 'relbow', 'head', 'belly']
SMPL_SENSOR = ['L_Knee', 'R_Knee', 'L_Elbow', 'R_Elbow', 'Head', 'Pelvis']
sensor_idx = [11, 12, 7, 8, 0, 2]
############# calirated file
SMPL_MAJOR_JOINTS = [1, 2, 3, 4, 5, 6, 9, 12, 13, 14, 15, 16, 17, 18, 19]
file_path1 = '/data/Guha/GR/code/dip18/train_and_eval/data/dipIMU/imu_own_validation.npz'
file_path2 = '/data/Guha/GR/DIPIMUandOthers/DIP_IMU_and_Others/DIP_IMU/s_10'
fileList = os.listdir(file_path2)
dippath = os.path.join(file_path2, fileList[4])
with open(file_path1, 'rb') as file1, open(dippath, 'rb') as file2:
data_dict_nn = dict(np.load(file1))
smpl_nn = data_dict_nn['smpl_pose'][1]
smpl_nn_full = myUtil.smpl_reduced_to_full(smpl_nn)
aa_nn = myUtil.rot_matrix_to_aa(smpl_nn_full)
data_dict_imu = dict(np.load(file2))
smpl_imu = data_dict_imu['gt']
seq_len = aa_nn.shape[0]
print(aa_nn - smpl_imu)
for seq_num in range(seq_len):
pose1 = aa_nn[seq_num]
pose2 = smpl_imu[seq_num]
m1.pose[:] = (pose1).reshape(72)
m1.pose[0] = np.pi
def getGlobalBoneOriFromPose(pose, boneName):
pose = pose.reshape(1, 15 * 3 * 3)
fullPose = myUtil.smpl_reduced_to_full(pose).reshape(24, 3, 3)
return fullPose[myUtil.boneSMPLDict[boneName]]
path2 = '/data/Guha/GR/synthetic60FPS/H36/S8_WalkDog.pkl'
with open(path1, 'rb') as file1, open(path2, 'rb') as file2:
print(path1)
data_dict1 = np.load(file1, encoding='latin1')
sample_pose1 = data_dict1['poses'].reshape(-1, 135)
sample_ori1 = data_dict1['ori'].reshape(-1, 5, 3, 3)
sample_acc1 = data_dict1['acc'].reshape(-1, 5, 3)
print(path2)
data_dict2 = np.load(file2, encoding='latin1')
sample_pose2 = data_dict2['poses'].reshape(-1, 135)
sample_ori2 = data_dict2['ori'].reshape(-1, 5, 3, 3)
sample_acc2 = data_dict2['acc'].reshape(-1, 5, 3)
# for SMPL 15 to 24 joints
dip = myUtil.smpl_reduced_to_full(sample_pose1)
syn = myUtil.smpl_reduced_to_full(sample_pose2)
#from quat to axis-angle
dip_aa = myUtil.rot_matrix_to_aa(dip)
syn_aa = myUtil.rot_matrix_to_aa(syn)
seq_len = dip_aa.shape[0]
print('seq len:', seq_len)
for seq_num in range(seq_len):
pose1 = dip_aa[seq_num]
pose2 = syn_aa[seq_num]
m1.pose[:] = pose1
m1.pose[0] = np.pi