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
