os.path.split(__file__)[1], eval_prefix), "wb"),
protocol=cPickle.HIGHEST_PROTOCOL)
print "Testing baseline"
#################################
# BASELINE
# Load the evaluation
data_baseline = di.loadBaseline('../data/ICVL/LRF_Results_seq_1.txt')
hpe_base = ICVLHandposeEvaluation(gt3D, data_baseline)
hpe_base.subfolder += '/' + eval_prefix + '/'
print("Mean error: {}mm".format(hpe_base.getMeanError()))
hpe.plotEvaluation(eval_prefix,
methodName='Our regr',
baseline=[('Tang et al.', hpe_base)])
ind = 0
for i in testSeqs[0].data:
if ind % 20 != 0:
ind += 1
continue
jt = joints[ind]
jtI = di.joints3DToImg(jt)
for joint in range(jt.shape[0]):
t = transformPoint2D(jtI[joint], i.T)
jtI[joint, 0] = t[0]
jtI[joint, 1] = t[1]
hpe.plotResult(i.dpt, i.gtcrop, jtI, "{}_{}".format(eval_prefix, ind))
ind += 1
print("{}".format([hpe[ind].getJointMeanError(j) for j in range(joints[0].shape[0])]))
print("{}".format([hpe[ind].getJointMaxError(j) for j in range(joints[0].shape[0])]))
print "Testing baseline"
#################################
# BASELINE
# Load the evaluation
di = ICVLImporter('../dataset/ICVL/', cacheDir='../dataset/cache/')
data_baseline = di.loadBaseline('../dataset/ICVL/Results/LRF_Results_seq_1.txt')
hpe_base = ICVLHandposeEvaluation(gt3D, data_baseline)
hpe_base.subfolder += eval_prefix[0]+'/'
print("Mean error: {}mm".format(hpe_base.getMeanError()))
plot_list = zip(model, hpe)
hpe_base.plotEvaluation(eval_prefix[0], methodName='Tang et al.', baseline=plot_list)
Seq2_1 = di.loadSequence('test_seq_1')
Seq2_2 = di.loadSequence('test_seq_2')
testSeqs = [Seq2_1, Seq2_2]
for index in xrange(len(hpe)):
ind = 0
for i in testSeqs[0].data:
if ind % 20 != 0:
ind += 1
continue
jt = pred_joints[index][ind]
jtI = di.joints3DToImg(jt)
for joint in range(jt.shape[0]):
t=transformPoint2D(jtI[joint], i.T)