# 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)[:, 13, :].reshape((len(gt3D), 1, 3)))
hpe_base.subfolder += '/' + eval_prefix + '/'
print("Mean error: {}mm".format(hpe_base.getMeanError()))
com = [j.com for j in testSeqs[0].data]
com.extend([j.com for j in testSeqs[1].data])
hpe_com = NYUHandposeEvaluation(
gt3D,
numpy.asarray(com).reshape((len(gt3D), 1, 3)))
hpe_com.subfolder += '/' + eval_prefix + '/'
[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"
#################################
# BASELINE
# Load the evaluation
data_baseline = di.loadBaseline('../data/NYU/test/test_predictions.mat',
numpy.asarray(gt3D))
hpe_base = NYUHandposeEvaluation(gt3D, data_baseline)
hpe_base.subfolder += '/' + eval_prefix + '/'
print("Mean error: {}mm".format(hpe_base.getMeanError()))
hpe.plotEvaluation(eval_prefix,
methodName='Our regr',
baseline=[('Tompson et al.', hpe_base)])
ind = 0
for i in testSeqs[0].data:
if ind % 20 != 0:
ind += 1
continue
jtI = transformPoints2D(di.joints3DToImg(joints[ind]), i.T)
hpe.plotResult(i.dpt, i.gtcrop, jtI, "{}_{}".format(eval_prefix, ind))
if ABLATION:
# plot list
plot_list = zip(['Basic', 'Spatial', 'Temporal', 'Final'], hpe)
# plot ablation result
# NOTE: figure is in mix_100000 folder
hpe[-1].plotEvaluation('ablation',
methodName='Final',
baseline=plot_list[:-1])
else:
# plot list
plot_list = []
# Comparison
# siggraph
data_siggrpah = di.loadBaseline(
'../dataset/NYU/test/test_predictions.mat', np.asarray(gt3D))
hpe_siggrpah = NYUHandposeEvaluation(gt3D, data_siggrpah)
hpe_siggrpah.subfolder += 'comparison/'
print("Tompson et al. Siggraph 2014")
print("Mean error: {}mm".format(hpe_siggrpah.getMeanError()))
plot_list.append(('HeatMap', hpe_siggrpah))
# cvww15 deeppriori
data_deeppriori = di.loadBaseline(
'../result/CVWW15/CVWW15_NYU_Prior-Refinement.txt')
hpe_deeppriori = NYUHandposeEvaluation(gt3D, data_deeppriori)
hpe_deeppriori.subfolder += 'comparison/'
print("Oberweger et al. CVWW 2015")
print("Mean error: {}mm".format(hpe_deeppriori.getMeanError()))
plot_list.append(('DeepPrior', hpe_deeppriori))