def volumina_flexible_layer(data, layer_types, labels=None):
assert len(layer_types) == len(data)
app = QApplication (sys.argv)
import volumina
from volumina.api import Viewer
v = Viewer ()
v.title = " Volumina Demo "
v.showMaximized ()
for i, d in enumerate(data):
layer_name = "layer_" + str(i)
if labels is not None:
layer_name = labels[i]
# get data type of the elements d, to determine
# if we use a grayscale overlay (float32) or a randomcolors overlay (uint) for labels
data_type = d.dtype
if layer_types[i] == 'Grayscale':
v.addGrayscaleLayer(d , name = layer_name)
elif layer_types[i] == 'RandomColors':
v.addRandomColorsLayer(d.astype(np.uint32), name=layer_name)
elif layer_types[i] == 'Red':
v.addAlphaModulatedLayer(d , name=layer_name, tintColor=QColor(255,0,0))
elif layer_types[i] == 'Green':
v.addAlphaModulatedLayer(d , name=layer_name, tintColor=QColor(0,255,0))
elif layer_types[i] == 'Blue':
v.addAlphaModulatedLayer(d , name=layer_name, tintColor=QColor(0,0,255))
else:
raise KeyError("Invalid Layer Type, %s!" % layer_types[i])
app.exec_()
def volumina_n_layer(data, labels = None):
app = QApplication(sys.argv)
import volumina
from volumina.api import Viewer
v = Viewer ()
v.title = " Volumina Demo "
v.showMaximized ()
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 labels
data_type = d.dtype
if 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_()
def view_HDF5(inpaths):
app = QApplication(sys.argv)
v = Viewer()
for inpath in inpaths:
if "n_1_" in inpath:
prefix = "n_1_"
elif "n_2_" in inpath:
prefix = "n_2_"
else:
prefix = ""
if "h5" in inpath:
#data = vigra.readHDF5(inpath, "data")
print
print "inpath", inpath
data = read_h5(inpath)
file = inpath.split("/")[-1]
name = prefix + file.split(".")[0]
if "prob_files" in inpath or "seeds" in inpath or "trimap" in inpath:
data = binarize_predict(data)
file = inpath.split("/")[-2] + "_" + inpath.split("/")[-1]
name = prefix + file.split(".")[0]
print "type", type(data)
if "test_data" in inpath or "prob_files" in inpath or "seeds" in inpath or "trimap" in inpath:
v.addGrayscaleLayer(data, name=name)
# if "trimaps" in inpath or "dense" in inpath or "sup_maps" in inpath or "seg_maps" in inpath:
# v.addRandomColorsLayer(255*data, name=name+"_color")
else:
v.addRandomColorsLayer(255*data, name=name+"_color")
if "png" in inpath:
img = vigra.impex.readImage(inpath)
img = np.asarray(img)
file = inpath.split("/")[-1]
name = file.split(".")[0]
print "type",type(img)
v.addGrayscaleLayer(img, name=name)
#v.addRandomColorsLayer(255*img, name=name+"color")
v.showMaximized()
app.exec_()
def volumina_double_layer(data, overlay): # get data type of the elements of overlay, to determine # if we use a grayscale overlay (float32) or a randomcolors overlay (uint) for labels mask = [] for i in range( len(overlay.shape) ): mask.append(0) mask = tuple(mask) data_type = type(overlay[mask]) app = QApplication (sys.argv) from volumina.api import Viewer v = Viewer () v.title = " Volumina Demo " v.showMaximized () v.addGrayscaleLayer(data , name = " raw data ") if data_type == np.float32: v.addGrayscaleLayer(overlay , name = " overlay ") else: v.addRandomColorsLayer(overlay, name = " overlay ") app . exec_ ()
