labeled_file_in = data_root + r'/{}/Labeled/trainingdata.txt'.format(data_tag) test_file_in = data_root + r'/{}/Test/test.txt'.format(data_tag) rendered_labeled_out = out_root + r'/{}/Labeled'.format(data_tag) rendered_test_out = out_root + r'/{}/Test'.format(data_tag) specular_file = data_root + r'/{}/Labeled/specroughness.txt'.format(data_tag) lightpool_file = params_global['envMapFolder'] + r'/lightPool_{}.dat'.format(data_tag) AugmentRender = FastRenderEngine(gpuid) AugmentRender.SetGeometry('Plane') AugmentRender.PreLoadAllLight(r'{}/light.txt'.format(params_global['envMapFolder'])) AugmentRender.SetSampleCount(128, 1024) fovRadian = 60.0 / 180.0 * math.pi cameraDist = 1.0 / (math.tan(fovRadian / 2.0)) AugmentRender.SetCamera(0, 0, cameraDist, 0, 0, 0, 0, 1, 0, fovRadian, 0.01, 100, 384, 384) specList_final = {} roughnessList_final = {} with open(specular_file, 'r') as f: rawList = f.read().strip().split('\n') for t in rawList: mid = int(t.split(',')[0]) spec = float(t.split(',')[1]) roughness = float(t.split(',')[2]) specList_final[mid] = spec roughnessList_final[mid] = roughness lightPool = pickle.load(open(lightpool_file, 'rb')) lightNormPool = {}
resultfolder, modelfile = os.path.split(savedNet) #Load test params test_params = {} test_params = loadParams(testParamPath) outputFolder = resultfolder + r'/test_{}'.format(test_params['outtag']) if (os.path.exists(outputFolder) == False): os.makedirs(outputFolder) OnlineRender = FastRenderEngine(0) OnlineRender.SetGeometry('Sphere') #params['geometryPath'], True, '') OnlineRender.PreLoadAllLight(r'{}/light.txt'.format( params['envMapFolder'])) fovRadian = 60.0 / 180.0 * math.pi cameraDist = 1.5 / (math.tan(fovRadian / 2.0)) OnlineRender.SetCamera(0, 0, cameraDist, 0, 0, 0, 0, 1, 0, fovRadian, 0.01, 100, 128, 128) OnlineRender.SetSampleCount(128, 512) renderContext['experimentTag'] = savedNet random.seed(23333) np.random.seed(23333) caffe.set_random_seed(23333) caffe.set_mode_gpu() caffe.set_device(gpuid) logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) fh = logging.FileHandler(outputFolder + '/test_log_text.log') fh.setLevel(logging.DEBUG)
lightIDToEnumerateID[lid] = id if __name__ == '__main__': modelFile = sys.argv[1] testSetPath = sys.argv[2] gpuid = int(sys.argv[3]) imgw = 256 fovRadian = 60.0 / 180.0 * math.pi cameraDist = 1.0 / (math.tan(fovRadian / 2.0)) cameraDist_1 = 1.5 / (math.tan(fovRadian / 2.0)) RelightingRender = FastRenderEngine(gpuid) RelightingRender.SetGeometry('Plane') RelightingRender.SetCamera(0, 0, cameraDist, 0, 0, 0, 0, 1, 0, fovRadian, 0.01, 100, 256, 256) RelightingRender.SetSampleCount(128, 512) RelightingRender.PreLoadAllLight(r'{}/light.txt'.format( params_global['envMapFolder'])) caffe.set_mode_gpu() caffe.set_device(gpuid) path, file = os.path.split(modelFile) modelFolder = path testnet = caffe.Net(path + r'/net_test.prototxt', caffe.TEST) testnet.copy_from(modelFile) path, file = os.path.split(testSetPath) with open(testSetPath, 'r') as f: filenames = f.read().strip().split('\n')