# jts = diaboloNet.computeOutputFromEmbedding(jts_embed) # calculate pose from codes
# jts = pca.inverse_transform(jts_embed)
jts = jts_embed
for i in range(test_data.shape[0]):
joints.append(jts[i].reshape((-1, 3)) *
(testSeqs[0].config['cube'][2] / 2.) +
testSeqs[0].data[i].com)
joints = numpy.array(joints)
hpe = MSRAHandposeEvaluation(gt3D, joints)
hpe.subfolder += '/' + eval_prefix + '/'
print("Train samples: {}, test samples: {}".format(
train_data.shape[0], len(gt3D)))
print("Mean error: {}mm, max error: {}mm".format(
hpe.getMeanError(), hpe.getMaxError()))
print("{}".format(
[hpe.getJointMeanError(j) for j in range(joints[0].shape[0])]))
print("{}".format(
[hpe.getJointMaxError(j) for j in range(joints[0].shape[0])]))
# save results
cPickle.dump(gt3D,
open("./eval/{}/gt_{}.pkl".format(eval_prefix, icv),
"wb"),
protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(joints,
open("./eval/{}/result_{}.pkl".format(eval_prefix, icv),
"wb"),
protocol=cPickle.HIGHEST_PROTOCOL)
# save results
poseNet.save("./eval/{}/net_{}.pkl".format(eval_prefix, eval_prefix))
# poseNet.load("./eval/{}/net_{}.pkl".format(eval_prefix,eval_prefix))
####################################################
# TEST
print("Testing ...")
gt3D = [j.gt3Dorig[di.crop_joint_idx].reshape(1, 3) for j in testSeqs[0].data]
jts = poseNet.computeOutput([test_data, test_data2, test_data4])
joints = []
for i in range(test_data.shape[0]):
joints.append(jts[i].reshape(1, 3)*(testSeqs[0].config['cube'][2]/2.) + testSeqs[0].data[i].com)
hpe = MSRAHandposeEvaluation(gt3D, joints)
hpe.subfolder += '/'+eval_prefix+'/'
print(("Mean error: {}mm, max error: {}mm".format(hpe.getMeanError(), hpe.getMaxError())))
# save results
pickle.dump(joints, open("./eval/{}/result_{}_{}.pkl".format(eval_prefix,os.path.split(__file__)[1],eval_prefix), "wb"), protocol=pickle.HIGHEST_PROTOCOL)
print("Testing baseline")
#################################
# BASELINE
com = [j.com for j in testSeqs[0].data]
hpe_com = MSRAHandposeEvaluation(gt3D, numpy.asarray(com).reshape((len(gt3D), 1, 3)))
hpe_com.subfolder += '/'+eval_prefix+'/'
print(("Mean error: {}mm".format(hpe_com.getMeanError())))
hpe.plotEvaluation(eval_prefix, methodName='Our regr', baseline=[('CoM', hpe_com)])
# TEST
print("Testing ...")
gt3D = [
j.gt3Dorig[di.crop_joint_idx].reshape(1, 3) for j in testSeqs[0].data
]
jts = poseNet.computeOutput([test_data, test_data2, test_data4])
joints = []
for i in xrange(test_data.shape[0]):
joints.append(jts[i].reshape(1, 3) *
(testSeqs[0].config['cube'][2] / 2.) +
testSeqs[0].data[i].com)
hpe = MSRAHandposeEvaluation(gt3D, joints)
hpe.subfolder += '/' + eval_prefix + '/'
print("Mean error: {}mm, max error: {}mm".format(hpe.getMeanError(),
hpe.getMaxError()))
# save results
cPickle.dump(joints,
open(
"./eval/{}/result_{}_{}.pkl".format(
eval_prefix,
os.path.split(__file__)[1], eval_prefix), "wb"),
protocol=cPickle.HIGHEST_PROTOCOL)
print "Testing baseline"
#################################
# BASELINE
com = [j.com for j in testSeqs[0].data]
hpe_com = MSRAHandposeEvaluation(