def testtt():
folder = "/opt/visal/tmp/for_sijin/tmp/tmp_saved_tt"
meta = dutils.collect_feature_meta(folder)
meta_folder = "/opt/visal/tmp/for_sijin/Data/H36M/H36MFeatures/2015_02_02_acm_act_14_exp_2_19_graph_0012"
iu.ensure_dir(meta_folder)
meta_path = iu.fullfile(meta_folder, "prediction.meta")
mio.pickle(meta_path, meta)
def pack_01():
"""
input: fc_j0 feature, rel_pose
outputs: rel_pose, fc_j0_feature
"""
source_feature_network_path = '/opt/visal/tmp/for_sijin/Data/saved/theano_models/2015_02_02_acm_act_14_exp_2_19_graph_0012/'
source_meta_path = '/opt/visal/tmp/for_sijin/tmp/tmp_saved'
exp_meta_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_ASM_act_14_exp_2/batches.meta'
save_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_FCJ0_act_14'
feature_name = 'Relative_Y3d_mono_body'
res = dict()
exp_meta = mio.unpickle(exp_meta_path)
source_meta = dutils.collect_feature_meta(source_meta_path)
rel_pose = exp_meta[feature_name]
fc_j0_feature = source_meta['feature_list'][1]
rel_gt = source_meta['feature_list'][0]
diff = rel_gt.reshape((-1, rel_gt.shape[-1]),order='F') * 1200 - rel_pose
print 'diff is {}'.format(diff.flatten().sum())
feature_list = [rel_pose, fc_j0_feature]
feature_dim = [rel_pose.shape[0], fc_j0_feature.shape[0]]
print feature_dim, '<<<feature dim'
res = {'feature_list': feature_list, 'feature_dim':feature_dim,
'info':{'indexes':source_meta['info']['indexes'],
'max_depth': 1200.0}}
indexes = res['info']['indexes']
res['info']['soure_feature_network_path'] = source_feature_network_path
print indexes[:10], min(indexes), max(indexes)
print 'The number of data is {} == {}'.format(indexes.size, feature_list[0].shape[-1])
iu.ensure_dir(save_path)
mio.pickle(iu.fullfile(save_path, 'batches.meta'), res)
def testtt():
folder = '/opt/visal/tmp/for_sijin/tmp/tmp_saved_tt'
meta = dutils.collect_feature_meta(folder)
meta_folder = '/opt/visal/tmp/for_sijin/Data/H36M/H36MFeatures/2015_02_02_acm_act_14_exp_2_19_graph_0012'
iu.ensure_dir(meta_folder)
meta_path = iu.fullfile(meta_folder, 'prediction.meta')
mio.pickle(meta_path, meta)
def collect_feature(op):
save_path = op.get_value('res_save_path')
import dhmlpe_utils as dutils
meta = dutils.collect_feature_meta(save_path)
meta_save_path =iu.fullfile(save_path, 'imgfeatures.meta')
force_collect_type = op.get_value('force_collect_type')
if force_collect_type == 'float32':
print 'Force the feature dtype=float32'
meta['feature_list'] = [np.array(x,dtype=np.float32) for x in meta['feature_list']]
mio.pickle(meta_save_path, meta)
def collect_feature(op):
save_path = op.get_value('res_save_path')
import dhmlpe_utils as dutils
meta = dutils.collect_feature_meta(save_path)
meta_save_path = iu.fullfile(save_path, 'imgfeatures.meta')
force_collect_type = op.get_value('force_collect_type')
if force_collect_type == 'float32':
print 'Force the feature dtype=float32'
meta['feature_list'] = [
np.array(x, dtype=np.float32) for x in meta['feature_list']
]
mio.pickle(meta_save_path, meta)
def test_collect():
folder = "/opt/visal/tmp/for_sijin/tmp/tmp_saved"
allfile = sorted(iu.getfilelist(folder, "feature_batch"), key=lambda x: dutils.extract_batch_num(x))
ntot = 0
for fn in allfile:
d = mio.unpickle(iu.fullfile(folder, fn))
ndata = d["feature_list"][0].shape[-1]
ntot += ndata
print "The total number of data is {}".format(ntot)
meta = dutils.collect_feature_meta(folder)
print meta.keys()
print meta["info"]["indexes"][:10]
print meta["feature_dim"]
def test_collect():
folder = '/opt/visal/tmp/for_sijin/tmp/tmp_saved'
allfile = sorted(iu.getfilelist(folder, 'feature_batch'),
key=lambda x: dutils.extract_batch_num(x))
ntot = 0
for fn in allfile:
d = mio.unpickle(iu.fullfile(folder, fn))
ndata = d['feature_list'][0].shape[-1]
ntot += ndata
print 'The total number of data is {}'.format(ntot)
meta = dutils.collect_feature_meta(folder)
print meta.keys()
print meta['info']['indexes'][:10]
print meta['feature_dim']
def pack_01():
"""
input: fc_j0 feature, rel_pose
outputs: rel_pose, fc_j0_feature
"""
source_feature_network_path = '/opt/visal/tmp/for_sijin/Data/saved/theano_models/2015_02_02_acm_act_14_exp_2_19_graph_0012/'
source_meta_path = '/opt/visal/tmp/for_sijin/tmp/tmp_saved'
exp_meta_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_ASM_act_14_exp_2/batches.meta'
save_path = '/opt/visal/tmp/for_sijin/Data/H36M/H36MExp/folder_FCJ0_act_14'
feature_name = 'Relative_Y3d_mono_body'
res = dict()
exp_meta = mio.unpickle(exp_meta_path)
source_meta = dutils.collect_feature_meta(source_meta_path)
rel_pose = exp_meta[feature_name]
fc_j0_feature = source_meta['feature_list'][1]
rel_gt = source_meta['feature_list'][0]
diff = rel_gt.reshape((-1, rel_gt.shape[-1]), order='F') * 1200 - rel_pose
print 'diff is {}'.format(diff.flatten().sum())
feature_list = [rel_pose, fc_j0_feature]
feature_dim = [rel_pose.shape[0], fc_j0_feature.shape[0]]
print feature_dim, '<<<feature dim'
res = {
'feature_list': feature_list,
'feature_dim': feature_dim,
'info': {
'indexes': source_meta['info']['indexes'],
'max_depth': 1200.0
}
}
indexes = res['info']['indexes']
res['info']['soure_feature_network_path'] = source_feature_network_path
print indexes[:10], min(indexes), max(indexes)
print 'The number of data is {} == {}'.format(indexes.size,
feature_list[0].shape[-1])
iu.ensure_dir(save_path)
mio.pickle(iu.fullfile(save_path, 'batches.meta'), res)
