Seq2 = di.loadSequence('P0')
testSeqs = [Seq2]
# di = ICVLImporter('../data/ICVL/')
# Seq2 = di.loadSequence('test_seq_1')
# testSeqs = [Seq2]
#di = NYUImporter('../data/NYU/')
#Seq2 = di.loadSequence('test_1')
#testSeqs = [Seq2]
# load trained network
poseNetParams = ResNetParams(type=1, nChan=1, wIn=128, hIn=128, batchSize=1, numJoints=14, nDims=3)
poseNetParams.loadFile = "/content/deep-prior-pp/src/eval/MSRA_network_prior_0.pkl"
comrefNetParams = ScaleNetParams(type=1, nChan=1, wIn=128, hIn=128, batchSize=1, resizeFactor=2, numJoints=1, nDims=3)
comrefNetParams.loadFile = "/content/deep-prior-pp/src/eval/net_MSRA15_COM_AUGMENT.pkl"
config = {'fx': 588., 'fy': 587., 'cube': (300, 300, 300)}
# config = {'fx': 241.42, 'fy': 241.42, 'cube': (250, 250, 250)}
# config = {'fx': 224.5, 'fy': 230.5, 'cube': (300, 300, 300)} # Creative Gesture Camera
rtp = RealtimeHandposePipeline(poseNetParams, config, di, verbose=False, comrefNet=comrefNetParams)
# use filenames
filenames = []
for i in testSeqs[0].data:
filenames.append(i.fileName)
dev = FileDevice(filenames, di)
# use depth camera
# dev = CreativeCameraDevice(mirror=True)
rtp.processVideoThreaded(dev)
nChan=1,
wIn=128,
hIn=128,
batchSize=1,
numJoints=14,
nDims=3)
poseNetParams.loadFile = "./eval/NYU_network_prior.pkl"
comrefNetParams = ScaleNetParams(type=1,
nChan=1,
wIn=128,
hIn=128,
batchSize=1,
resizeFactor=2,
numJoints=1,
nDims=3)
comrefNetParams.loadFile = "./eval/net_NYU_COM_AUGMENT.pkl"
config = {'fx': 588., 'fy': 587., 'cube': (300, 300, 300)}
# config = {'fx': 241.42, 'fy': 241.42, 'cube': (250, 250, 250)}
# config = {'fx': 224.5, 'fy': 230.5, 'cube': (300, 300, 300)} # Creative Gesture Camera
rtp = RealtimeHandposePipeline(poseNetParams,
config,
di,
verbose=False,
comrefNet=comrefNetParams)
# use filenames
filenames = []
for i in testSeqs[0].data:
filenames.append(i.fileName)
dev = FileDevice(filenames, di)
dsize = (int(train_data.shape[2] // 4), int(train_data.shape[3] // 4))
xstart = int(train_data.shape[2] / 2 - dsize[0] / 2)
xend = xstart + dsize[0]
ystart = int(train_data.shape[3] / 2 - dsize[1] / 2)
yend = ystart + dsize[1]
train_data4 = train_data[:, :, ystart:yend, xstart:xend]
comrefNetParams = ScaleNetParams(type=1,
nChan=1,
wIn=96,
hIn=96,
batchSize=1,
resizeFactor=2,
numJoints=1,
nDims=3)
comrefNetParams.loadFile = "../../ptm/net_MSRA15_COM_AUGMENT.pkl"
poseNet = ScaleNet(numpy.random.RandomState(23455), cfgParams=comrefNetParams)
train_data = numpy.ndarray.astype(train_data, dtype='float64')
train_data2 = numpy.ndarray.astype(train_data2, dtype='float64')
train_data4 = numpy.ndarray.astype(train_data4, dtype='float64')
#Seq_all list of sequence data
gt3D = []
for i in xrange(len(Seq_all)):
gt3D_temp = [
j.gt3Dorig[di.crop_joint_idx].reshape(1, 3) for j in Seq_all[i].data
]
gt3D.extend(gt3D_temp)
jts = poseNet.computeOutput([train_data, train_data2, train_data4])
joints = []
for i in xrange(train_data.shape[0]):
joints.append(jts[i].reshape(1, 3) * (Seq_all[0].config['cube'][2] / 2.) +