for i in xrange(test_data1.shape[0]):
joints.append(jts[i].reshape(1, 3) *
(testSeqs[0].config['cube'][2] / 2.) +
testSeqs[0].data[i].com)
gt3D.extend([j.gt3Dorig[13].reshape(1, 3) for j in testSeqs[1].data])
jts = poseNet.computeOutput([test_data2, test_data22, test_data24])
for i in range(test_data2.shape[0]):
joints.append(jts[i].reshape(1, 3) *
(testSeqs[1].config['cube'][2] / 2.) +
testSeqs[1].data[i].com)
hpe = NYUHandposeEvaluation(gt3D, joints)
hpe.subfolder += '/' + eval_prefix + '/'
mean_error = hpe.getMeanError()
max_error = hpe.getMaxError()
print("Mean error: {}mm, max error: {}mm".format(mean_error, max_error))
# 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
# Load the evaluation
jts_embed = poseNet.computeOutput(test_data)
# Backtransform from embedding
# jts = pca.inverse_transform(jts_embed)
jts = jts_embed
for i in xrange(test_data.shape[0]):
joints.append(jts[i].reshape((-1, 3)) *
(seq.config['cube'][2] / 2.) + seq.data[i].com)
joints = numpy.array(joints)
hpe = NYUHandposeEvaluation(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(joints,
open(
"./eval/{}/result_{}_{}.pkl".format(
eval_prefix,
os.path.split(__file__)[1], eval_prefix), "wb"),
protocol=cPickle.HIGHEST_PROTOCOL)
print "Testing baseline"
print("Testing ...")
gt3D = []
joints = []
gt3D.extend([j.gt3Dorig[di.crop_joint_idx].reshape(1, 3) for j in testSeqs[0].data])
jts = poseNet.computeOutput([test_data1, test_data12, test_data14])
for i in xrange(test_data1.shape[0]):
joints.append(jts[i].reshape(1, 3)*(testSeqs[0].config['cube'][2]/2.) + testSeqs[0].data[i].com)
gt3D.extend([j.gt3Dorig[di.crop_joint_idx].reshape(1, 3) for j in testSeqs[1].data])
jts = poseNet.computeOutput([test_data2, test_data22, test_data24])
for i in range(test_data2.shape[0]):
joints.append(jts[i].reshape(1, 3)*(testSeqs[1].config['cube'][2]/2.) + testSeqs[1].data[i].com)
hpe = NYUHandposeEvaluation(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
# Load the evaluation
data_baseline = di.loadBaseline('../data/NYU/test/test_predictions.mat', numpy.concatenate([numpy.asarray([j.gt3Dorig for j in testSeqs[0].data]), numpy.asarray([j.gt3Dorig for j in testSeqs[1].data])]))
hpe_base = NYUHandposeEvaluation(gt3D, numpy.asarray(data_baseline)[:, di.crop_joint_idx, :].reshape((len(gt3D), 1, 3)))
hpe_base.subfolder += '/'+eval_prefix+'/'
print("Mean error: {}mm".format(hpe_base.getMeanError()))