How to use the spacepy.pycdf function in spacepy
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nkolot / SPIN / datasets / preprocess / h36m_train.py View on Github 
# go over all the sequences of each user
seq_list = glob.glob(os.path.join(pose_path, '*.cdf'))
seq_list.sort()
for seq_i in seq_list:
# sequence info
seq_name = seq_i.split('/')[-1]
action, camera, _ = seq_name.split('.')
action = action.replace(' ', '_')
# irrelevant sequences
if action == '_ALL':
continue
# 3D pose file
poses_3d = pycdf.CDF(seq_i)['Pose'][0]
# 2D pose file
pose2d_file = os.path.join(pose2d_path, seq_name)
poses_2d = pycdf.CDF(pose2d_file)['Pose'][0]
# bbox file
bbox_file = os.path.join(bbox_path, seq_name.replace('cdf', 'mat'))
bbox_h5py = h5py.File(bbox_file)
# video file
if extract_img:
vid_file = os.path.join(vid_path, seq_name.replace('cdf', 'mp4'))
imgs_path = os.path.join(dataset_path, 'images')
vidcap = cv2.VideoCapture(vid_file)
# go over each frame of the sequencedef load_targets(target_path, file_name, subject):
target_prefix = "/MyPoseFeatures/D2_Positions"
target_path = os.path.join(target_path, subject + target_prefix)
file_meta = file_name.split("_")
activity = file_meta[0]
cdf = pycdf.CDF(os.path.join(target_path, activity + ".cdf"))
targets = cdf[0]
targets = targets[0, :, :]
cdf.close()
return targetsfor seq_i in seq_list:
# sequence info
seq_name = seq_i.split('/')[-1]
action, camera, _ = seq_name.split('.')
action = action.replace(' ', '_')
# irrelevant sequences
if action == '_ALL':
continue
# 3D pose file
poses_3d = pycdf.CDF(seq_i)['Pose'][0]
# 2D pose file
pose2d_file = os.path.join(pose2d_path, seq_name)
poses_2d = pycdf.CDF(pose2d_file)['Pose'][0]
# bbox file
bbox_file = os.path.join(bbox_path, seq_name.replace('cdf', 'mat'))
bbox_h5py = h5py.File(bbox_file)
# video file
if extract_img:
vid_file = os.path.join(vid_path, seq_name.replace('cdf', 'mp4'))
imgs_path = os.path.join(dataset_path, 'images')
vidcap = cv2.VideoCapture(vid_file)
# go over each frame of the sequence
for frame_i in range(poses_3d.shape[0]):
# read video frame
if extract_img:
success, image = vidcap.read()ls = ftp.nlst( fl0 )
fl = ls[-1]
fl_path = os.path.join( self.path, fl )
ftp.retrbinary( "RETR " + fl,
open( fl_path, 'wb' ).write )
except :
self.mesg_txt( 'fail', date_str )
return
# If the file now exists, try to load it; otherwise, abort.
self.mesg_txt( 'load', date_str )
if ( os.path.isfile( fl_path ) ) :
try :
cdf = pycdf.CDF( fl_path )
except :
self.mesg_txt( 'fail', date_str )
return
else :
self.mesg_txt( 'fail', date_str )
return
# Add the CDF object and tags for each spectrum to the arrays.
c = len( self.arr_cdf )
self.arr_cdf = self.arr_cdf + [ cdf ] # arr_cdf and arr_date of same size
self.arr_date = self.arr_date + [ date_str ]
n_spec = len( cdf['Epoch'] )
self.arr_tag = self.arr_tag + [ fc_tag( c=c, s=s,except :
self.mesg_txt( 'fail', date_str )
return
# If the file now exists locally, try to load it; otherwise,
# abort.
self.mesg_txt( 'load', date_str )
if ( os.path.isfile( fl_path ) ) :
try :
cdf = pycdf.CDF( fl_path )
except :
self.mesg_txt( 'fail', date_str )
return
else :
self.mesg_txt( 'fail', date_str )
return
# Append the requested date to the list of dates loaded.
ind = len( self.arr_date )#!/usr/bin/python
import sys
import os
os.putenv("CDF_LIB", "/usr/local/cdf/lib")
import spacepy.pycdf as cdf
#cdf_file = cdf.CDF('~/Download/QinDenton_1min_merged_20101229-v2.cdf')
cdf_file = cdf.CDF('~/Download/QinDenton_hour_merged_20120206-v5.cdf')
Epoch = cdf_file['Epoch']
Year = cdf_file['Year']
DOY = cdf_file['DOY']
hour = cdf_file['hour']
min = cdf_file['min']
ByIMF = cdf_file['ByIMF']
BzIMF = cdf_file['BzIMF']
V_SW = cdf_file['V_SW']
Den_P = cdf_file['Den_P']
Pdyn = cdf_file['Pdyn']
G1 = cdf_file['G1']
G2 = cdf_file['G2']
G3 = cdf_file['G3']
ByIMF_status = cdf_file['ByIMF_status']
BzIMF_status = cdf_file['BzIMF_status']
V_SW_status = cdf_file['V_SW_status']fl = ls[-1]
fl_path = os.path.join( self.path, fl )
ftp.retrbinary( "RETR " + fl,
open( fl_path, 'wb' ).write )
except :
self.mesg_txt( 'fail', date_str )
return
# If the file now exists, try to load it; otherwise,
# abort.
self.mesg_txt( 'load', date_str )
if ( os.path.isfile( fl_path ) ) :
try :
cdf = pycdf.CDF( fl_path )
except :
self.mesg_txt( 'fail', date_str )
return
else :
self.mesg_txt( 'fail', date_str )
return
# Extract the data from the loaded file and select those data
# which seem to have valid (versus fill) values.
if ( self.use_h2 ) :
# Extract the data from the loaded file.
sub_t = cdf['Epoch'][:,0]
sub_b_x = cdf['BGSE'][:,0]camera_dict['k'] = camera[4]
camera_dict['p'] = camera[5]
subdir_format = 's_{:02d}_act_{:02d}_subact_{:02d}_ca_{:02d}'
subdir = subdir_format.format(s, a, sa, c)
basename = metadata.get_base_filename('S{:d}'.format(s), '{:d}'.format(a), '{:d}'.format(sa), metadata.camera_ids[c-1])
annotname = basename + '.cdf'
subject = 'S' + str(s)
annofile3d = osp.join('extracted', subject, 'Poses_D3_Positions_mono_universal', annotname)
annofile3d_camera = osp.join('extracted', subject, 'Poses_D3_Positions_mono', annotname)
annofile2d = osp.join('extracted', subject, 'Poses_D2_Positions', annotname)
with pycdf.CDF(annofile3d) as data:
pose3d = np.array(data['Pose'])
pose3d = np.reshape(pose3d, (-1, 32, 3))
with pycdf.CDF(annofile3d_camera) as data:
pose3d_camera = np.array(data['Pose'])
pose3d_camera = np.reshape(pose3d_camera, (-1, 32, 3))
with pycdf.CDF(annofile2d) as data:
pose2d = np.array(data['Pose'])
pose2d = np.reshape(pose2d, (-1, 32, 2))
nposes = min(pose3d.shape[0], pose2d.shape[0])
image_format = 's_{:02d}_act_{:02d}_subact_{:02d}_ca_{:02d}_{:06d}.jpg'
for i in range(nposes):
datum = {}subdir_format = 's_{:02d}_act_{:02d}_subact_{:02d}_ca_{:02d}'
subdir = subdir_format.format(s, a, sa, c)
basename = metadata.get_base_filename('S{:d}'.format(s), '{:d}'.format(a), '{:d}'.format(sa), metadata.camera_ids[c-1])
annotname = basename + '.cdf'
subject = 'S' + str(s)
annofile3d = osp.join('extracted', subject, 'Poses_D3_Positions_mono_universal', annotname)
annofile3d_camera = osp.join('extracted', subject, 'Poses_D3_Positions_mono', annotname)
annofile2d = osp.join('extracted', subject, 'Poses_D2_Positions', annotname)
with pycdf.CDF(annofile3d) as data:
pose3d = np.array(data['Pose'])
pose3d = np.reshape(pose3d, (-1, 32, 3))
with pycdf.CDF(annofile3d_camera) as data:
pose3d_camera = np.array(data['Pose'])
pose3d_camera = np.reshape(pose3d_camera, (-1, 32, 3))
with pycdf.CDF(annofile2d) as data:
pose2d = np.array(data['Pose'])
pose2d = np.reshape(pose2d, (-1, 32, 2))
nposes = min(pose3d.shape[0], pose2d.shape[0])
image_format = 's_{:02d}_act_{:02d}_subact_{:02d}_ca_{:02d}_{:06d}.jpg'
for i in range(nposes):
datum = {}
imagename = image_format.format(s, a, sa, c, i+1)
imagepath = osp.join(subdir, imagename)
if not osp.isfile(osp.join('images', imagepath)):
print(osp.join('images', imagepath))def get_raw_angles(self, actor, action, sub_action=0):
"""
:param actor:
:param action:
:param sub_action:
:return:
"""
cdf_file = self.get_cdf_file('RawAngles',
actor, action, sub_action)
cdf = pycdf.CDF(cdf_file)
angles3d = np.squeeze(cdf['Pose'])
return angles3dspacepy
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