def addHocViewer(grayData=None, segData=None, title="viewer", visu=True):
if visu:
app = QApp.Instance().app
v = Viewer()
if grayData is not None:
for name in grayData.keys():
data = grayData[name]
if hasVigra:
if isinstance(data, vigra.arraytypes.VigraArray):
v.addGrayscaleLayer(data.view(numpy.ndarray), name=name)
else:
v.addGrayscaleLayer(data, name=name)
else:
v.addGrayscaleLayer(data, name=name)
if segData is not None:
for name in segData.keys():
data = segData[name]
if hasVigra:
if isinstance(data, vigra.arraytypes.VigraArray):
v.addColorTableLayer(data.view(numpy.ndarray), name=name)
else:
v.addColorTableLayer(data, name=name)
else:
v.addGrayscaleLayer(data, name=name)
v.setWindowTitle(title)
v.showMaximized()
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 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 view(data, labels=None, layer_types=None):
"""
"""
if labels is not None:
assert len(labels) == len(data)
if layer_types is not None:
assert len(layer_types) == len(data)
assert all(ltype in ('Grayscale', 'RandomColors', 'Red', 'Green', 'Blue')
for ltype in layer_types)
app = QApplication(sys.argv)
from volumina.api import Viewer
v = Viewer()
v.title = "Volumina Viewer"
v.showMaximized()
for i, d in enumerate(data):
layer_name = layer_name = "layer_" + str(i) if labels is None else labels[i]
if layer_types is None:
_dtype_to_layer(v, d, layer_name)
else:
_name_to_layer(v, d, layer_types[i], layer_name)
app.exec_()
def addHocViewer(grayData=None, segData=None, title="viewer", visu=True):
if visu:
app = QApp.Instance().app
v = Viewer()
if grayData is not None:
for name in grayData.keys():
data = grayData[name]
if _hasVigra:
if isinstance(data, vigra.arraytypes.VigraArray):
v.addGrayscaleLayer(data.view(numpy.ndarray),
name=name)
else:
v.addGrayscaleLayer(data, name=name)
else:
v.addGrayscaleLayer(data, name=name)
if segData is not None:
for name in segData.keys():
data = segData[name]
if _hasVigra:
if isinstance(data, vigra.arraytypes.VigraArray):
v.addColorTableLayer(data.view(numpy.ndarray),
name=name)
else:
v.addColorTableLayer(data, name=name)
else:
v.addGrayscaleLayer(data, name=name)
v.setWindowTitle(title)
v.showMaximized()
app.exec_()
def volumina_single_layer(data): app = QApplication (sys.argv) from volumina.api import Viewer v = Viewer () v.title = " Volumina Demo " v.showMaximized () v.addGrayscaleLayer (data , name =" raw data ") 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.showMaximized()
app.exec_()
def view(dset):
from volumina.api import Viewer
from PyQt4.QtGui import QApplication
app = QApplication([])
v = Viewer()
if isinstance(dset, str):
f = h5py.File(dset, 'r')
d = f["volume/data"].value
f.close()
v.setWindowTitle(dset)
v.addGrayscaleLayer(d, name="raw")
elif isinstance(dset, numpy.ndarray):
v.addGrayscaleLayer(dset, name="raw")
else:
raise RuntimeError("%r" % dset)
v.showMaximized()
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_ ()
print "seg "
labels = vigra.readHDF5(lPath, 'data')
labelsS = vigra.readHDF5(lsPath, 'data')
#labelsSS = vigra.readHDF5(lssPath, 'data')
labelsPS = vigra.readHDF5(
"/mnt/CLAWS1/tbeier/data/stack_with_holes/lps.h5", 'data')
print ew.shape
print "datashape", data.shape
print "resshape", smoothedT.shape
app = QApplication(sys.argv)
v = Viewer()
v.addGrayscaleLayer(data, name="raw")
v.addGrayscaleLayer(smoothedT, name="smoothedT")
v.addGrayscaleLayer(ew, name="hessian ew")
v.addGrayscaleLayer(ews, name="hessian ews")
v.addGrayscaleLayer(ewss, name="hessian ewss")
v.addGrayscaleLayer(pEws, name="hessian p80ews")
#v.addGrayscaleLayer(dd, name="diff ews")
v.addColorTableLayer(labels, name="labels")
v.addColorTableLayer(labelsS, name="labelsS")
#v.addColorTableLayer(labelsSS, name="labelsSS")
v.addColorTableLayer(labelsPS, name="labelsPS")
v.setWindowTitle("stack with holes")
v.showMaximized()
app.exec_()
from volumina.api import Viewer from PyQt4.QtGui import QApplication import numpy app = QApplication([]) v = Viewer() a = (255*numpy.random.random((1, 50,60,70,10) )).astype(numpy.uint8) v.addGrayscaleLayer(a, name="raw") v.showMaximized() app.exec_()
def showStuff(raw_name, pred_viewer1, pred_viewer2, cutout_name, one_extra = None):
# display the raw and annotations for cremi challenge data
raw = vigra.impex.readHDF5(indir+datasets[raw_name], "data", order = 'C')
# raw_old = vigra.readHDF5(indir+datasets["raw_bad"], "data", order = 'C')
defect_prediction_128 = vigra.impex.readHDF5(indir+datasets[pred_viewer2], "data", order = 'C')
defect_prediction_150 = vigra.impex.readHDF5(indir+datasets[pred_viewer1], "data", order = 'C')
cutout_from_150_pred = vigra.impex.readHDF5(indir+datasets[cutout_name], "data", order = 'C')
####################################################################################################################
# only used for fast testing stuff
#change_one = vigra.readHDF5(indir+datasets["segmentation_on_equalized_image"], "data", order = 'C')
#pdb.set_trace()
#defect_prediction_150[1,:,:] = change_one[0,:,:,0]
####################################################################################################################
# defect_prediction_150 = gt[..., 0]
cutout = numpy.asarray(cutout_from_150_pred)
rawdata = numpy.asarray(raw)
# rawdata_old = numpy.asarray(raw_old)
# op5ify
# shape5d = rawdata.shape
shape5d = (1,)+rawdata.shape+(1,)
print shape5d, rawdata.shape, rawdata.dtype
app = QApplication([])
v = Viewer()
direct = False
# layer for raw data
rawdata = numpy.reshape(rawdata, shape5d)
rawsource = ArraySource(rawdata)
v.dataShape = shape5d
lraw = GrayscaleLayer(rawsource, direct=direct)
lraw.visible = True
lraw.name = "raw"
v.layerstack.append(lraw)
# layer for cutout regions from raw data
cutout = numpy.reshape(cutout, shape5d)
cutoutsource = ArraySource(cutout)
lcutout = GrayscaleLayer(cutoutsource, direct = direct)
lcutout.visible = False
lcutout.name = "cut_out"
v.layerstack.append(lcutout)
# layer for first prediction result
defect_prediction_128 = numpy.reshape(defect_prediction_128, shape5d)
synsource = ArraySource(defect_prediction_128)
ct = create_random_16bit()
ct[0] = 0
lsyn = ColortableLayer(synsource, ct)
lsyn.name = pred_viewer2
lsyn.visible = False
v.layerstack.append(lsyn)
# layer for second prediction result
segm = numpy.reshape(defect_prediction_150, shape5d)
segsource = ArraySource(segm)
ct = create_random_16bit()
ct[0] = 0
lseg = ColortableLayer(segsource, ct)
lseg.name = pred_viewer1
lseg.visible = False
v.layerstack.append(lseg)
if one_extra is None:
v.showMaximized()
app.exec_()
if one_extra is not None:
# layer for third prediction result
extra_prediction = vigra.readHDF5(indir+datasets[one_extra], "data", order = 'C')
extra_pred_reshaped = numpy.reshape(extra_prediction, shape5d)
segsource = ArraySource(extra_pred_reshaped)
ct = create_random_16bit()
ct[0] = 0
# ct = create_default_16bit()
lseg = ColortableLayer(segsource, ct)
lseg.name = one_extra
lseg.visible = False
v.layerstack.append(lseg)
v.showMaximized()
app.exec_()
def showStuff(raw_name,
pred_viewer1,
pred_viewer2,
cutout_name,
one_extra=None):
# display the raw and annotations for cremi challenge data
raw = vigra.impex.readHDF5(indir + datasets[raw_name], "data", order='C')
# raw_old = vigra.readHDF5(indir+datasets["raw_bad"], "data", order = 'C')
defect_prediction_128 = vigra.impex.readHDF5(indir +
datasets[pred_viewer2],
"data",
order='C')
defect_prediction_150 = vigra.impex.readHDF5(indir +
datasets[pred_viewer1],
"data",
order='C')
cutout_from_150_pred = vigra.impex.readHDF5(indir + datasets[cutout_name],
"data",
order='C')
####################################################################################################################
# only used for fast testing stuff
#change_one = vigra.readHDF5(indir+datasets["segmentation_on_equalized_image"], "data", order = 'C')
#pdb.set_trace()
#defect_prediction_150[1,:,:] = change_one[0,:,:,0]
####################################################################################################################
# defect_prediction_150 = gt[..., 0]
cutout = numpy.asarray(cutout_from_150_pred)
rawdata = numpy.asarray(raw)
# rawdata_old = numpy.asarray(raw_old)
# op5ify
# shape5d = rawdata.shape
shape5d = (1, ) + rawdata.shape + (1, )
print shape5d, rawdata.shape, rawdata.dtype
app = QApplication([])
v = Viewer()
direct = False
# layer for raw data
rawdata = numpy.reshape(rawdata, shape5d)
rawsource = ArraySource(rawdata)
v.dataShape = shape5d
lraw = GrayscaleLayer(rawsource, direct=direct)
lraw.visible = True
lraw.name = "raw"
v.layerstack.append(lraw)
# layer for cutout regions from raw data
cutout = numpy.reshape(cutout, shape5d)
cutoutsource = ArraySource(cutout)
lcutout = GrayscaleLayer(cutoutsource, direct=direct)
lcutout.visible = False
lcutout.name = "cut_out"
v.layerstack.append(lcutout)
# layer for first prediction result
defect_prediction_128 = numpy.reshape(defect_prediction_128, shape5d)
synsource = ArraySource(defect_prediction_128)
ct = create_random_16bit()
ct[0] = 0
lsyn = ColortableLayer(synsource, ct)
lsyn.name = pred_viewer2
lsyn.visible = False
v.layerstack.append(lsyn)
# layer for second prediction result
segm = numpy.reshape(defect_prediction_150, shape5d)
segsource = ArraySource(segm)
ct = create_random_16bit()
ct[0] = 0
lseg = ColortableLayer(segsource, ct)
lseg.name = pred_viewer1
lseg.visible = False
v.layerstack.append(lseg)
if one_extra is None:
v.showMaximized()
app.exec_()
if one_extra is not None:
# layer for third prediction result
extra_prediction = vigra.readHDF5(indir + datasets[one_extra],
"data",
order='C')
extra_pred_reshaped = numpy.reshape(extra_prediction, shape5d)
segsource = ArraySource(extra_pred_reshaped)
ct = create_random_16bit()
ct[0] = 0
# ct = create_default_16bit()
lseg = ColortableLayer(segsource, ct)
lseg.name = one_extra
lseg.visible = False
v.layerstack.append(lseg)
v.showMaximized()
app.exec_()
