import glob # Load PyGreentea # Relative path to where PyGreentea resides pygt_path = '../..' sys.path.append(pygt_path) import pygreentea.pygreentea as pygt from pygreentea.pygreentea import malis # Load the datasets - individual tiff files in a directory raw_dir = '../../../project_data/dataset_01/train/raw' raw_path = sorted(glob.glob(raw_dir+'/*.tif')) num_images = len(raw_path) raw_ds = [np.expand_dims(pygt.normalize(np.array(Image.open(raw_path[i]).convert('L'), 'f')),0) for i in range(0, num_images)] datasets = [] for i in range(0,len(raw_ds)): dataset = {} dataset['data'] = raw_ds[i] datasets += [dataset] test_net_file = 'net.prototxt' test_device = 0 pygt.caffe.set_devices((test_device,)) caffemodels = pygt.get_caffe_models('net') test_net = pygt.init_testnet(test_net_file, trained_model=caffemodels[-1][1], test_device=test_device)
import pygreentea.pygreentea as pygt from pygreentea.pygreentea import malis # Load the datasets - individual tiff files in a directory raw_dir = '../../../project_data/dataset_01/train/raw' raw_path = sorted(glob.glob(raw_dir+'/*.tif')) num_images = len(raw_path) raw_ds = [np.array(Image.open(raw_path[i]).convert('L'), 'f') for i in range(0, num_images)] datasets = [] test_datasets = [] for i in range(0,1): dataset = {} dataset['data'] = np.expand_dims(pygt.normalize(np.asarray(raw_ds, float32)), 0) datasets += [dataset] test_net_file = 'net.prototxt' test_device = 0 pygt.caffe.set_devices((test_device,)) caffemodels = pygt.get_caffe_models('net') test_net = pygt.init_testnet(test_net_file, trained_model=caffemodels[-1][1], test_device=test_device) def process_data_slice_callback(input_specs, batch_size, dataset_indexes, offsets, dataset_combined_sizes, data_arrays, slices): # Nothing to process here pass