specList_final[mid] = spec
roughnessList_final[mid] = roughness
lightPool = pickle.load(open(lightpool_file, 'rb'))
lightNormPool = {}
# precompute auto-exposure factor
normal_one = np.dstack((np.ones((256,256)), np.ones((256,256)), np.ones((256,256))))
for m in specList_final.keys():
lightNormPool[m] = np.zeros((len(lightID), 10, 3))
for id, lid in enumerate(lightID):
for v in range(0, lightPool[m].shape[1]):
AugmentRender.SetEnvLightByID(lid + 1, lightPool[m][id, v, 0], lightPool[m][id, v, 1])
AugmentRender.SetAlbedoValue([1.0, 1.0, 1.0])
AugmentRender.SetSpecValue([0.0, 0.0, 0.0])
AugmentRender.SetRoughnessValue(0)
AugmentRender.SetNormalMap(normal_one)
img_diffuse = AugmentRender.Render()
norm = np.mean(img_diffuse, axis = (0,1))
lightNormPool[m][id, v] = norm
with open(params_global['envMapFolder'] + r'/lightNormPool_{}.dat'.format(data_tag), 'wb') as f:
pickle.dump(lightNormPool, f)
print('Factor done.\n')
#render training
if(renderTag == 'train' or renderTag == 'all'):
make_dir(rendered_labeled_out)
path, file = os.path.split(labeled_file_in)
dataset = DataLoaderSVBRDF(path, file, 384, 384, True)
print('Rendering Training data...\n')
ftrain = open(out_root + r'/train_envlight/train_full.txt', 'w')
for aid, a in enumerate(trainAlbedo):
for sid, s in enumerate(trainSpec):
for rid, r in enumerate(trainRoughness):
lightMatrix = np.zeros((len(lightID), 9, 2))
print('...{}_{}_{}\n'.format(aid, sid, rid))
trainCube[aid, sid, rid] = [a, s, r]
brdfFolder = out_root + r'/train_envlight/{}_{}_{}'.format(
aid, sid, rid)
make_dir(brdfFolder)
OnlineRender.SetAlbedoValue([a, a, a])
OnlineRender.SetSpecValue([s, s, s])
OnlineRender.SetRoughnessValue(r)
for lid, l in enumerate(lightID):
OnlineRender.SetEnvLightByID(l + 1)
lightView, lightX, lightY = getLightTransList(3, 3)
for vid, v in enumerate(lightView):
OnlineRender.SetLightXform(lightX[vid], lightY[vid])
img = OnlineRender.Render()
save_pfm(brdfFolder + r'/{}_{}.pfm'.format(lid, vid),
img)
ftrain.write('{}_{}_{}_{}_{}\n'.format(
aid, sid, rid, lid, vid))
lightMatrix[lid, vid, 0] = lightX[vid]
lightMatrix[lid, vid, 1] = lightY[vid]
np.savetxt(