def volumina_n_layer(data, labels=None):
"""
BASIC FUNCTION
:param data:
:param labels:
:return:
"""
app = QApplication(sys.argv)
import volumina
from volumina.api import Viewer
v = Viewer()
v.title = " Volumina Demo "
v.showNormal()
for ind, d in enumerate(data):
layer_name = "layer_" + str(ind)
if labels is not None:
layer_name = labels[ind]
# get data type of the elements d, to determine
# if we use a grayscale overlay (float32) or a randomcolors overlay (uint) for batchLabels
data_type = d.dtype
if data_type is FloatType or data_type == np.float32 or data_type == np.float64:
v.addGrayscaleLayer(d, name=layer_name)
else:
v.addRandomColorsLayer(d.astype(np.uint32), name=layer_name)
app.exec_()
def streaming_n_layer(files, keys, labels=None, block_shape=[100, 100, 100]):
from volumina.api import Viewer
from volumina.pixelpipeline.datasources import LazyflowSource
from lazyflow.graph import Graph
from lazyflow.operators.ioOperators.opStreamingHdf5Reader import OpStreamingHdf5Reader
from lazyflow.operators import OpCompressedCache
app = QApplication(sys.argv)
v = Viewer()
graph = Graph()
def mkH5source(fname, gname):
h5file = h5py.File(fname)
dtype = h5file[gname].dtype
source = OpStreamingHdf5Reader(graph=graph)
source.Hdf5File.setValue(h5file)
source.InternalPath.setValue(gname)
op = OpCompressedCache(parent=None, graph=graph)
op.BlockShape.setValue(block_shape)
op.Input.connect(source.OutputImage)
return op.Output, dtype
#rawSource = mkH5source(data[0], keys[0])
#v.addGrayscaleLayer(rawSource, name = 'raw')
for i, f in enumerate(files):
if labels is not None:
layer_name = labels[i]
else:
layer_name = "layer_%i" % (i)
source, dtype = mkH5source(f, keys[i])
if np.dtype(dtype) in (np.dtype('uint8'), np.dtype('float32'),
np.dtype('float64')):
v.addGrayscaleLayer(source, name=layer_name)
else:
v.addRandomColorsLayer(source, name=layer_name)
v.setWindowTitle("Streaming Viewer")
v.showNormal()
app.exec_()
