a[ii,jj]=1
a=vigra.filters.discDilation(a,radius=20)
array[:]=a.reshape(shape).view(np.ndarray)*255
op.Input.setDirty()
do()
cron.connect(cron, SIGNAL('timeout()'), do)
ds = LazyflowSource( op.Output )
layer = ColortableLayer(ds,jet())
mainwin=Viewer()
mainwin.layerstack.append(layer)
mainwin.dataShape=(1,h,w,1,1)
print mainwin.centralWidget()
BoxContr=BoxController(mainwin.editor,op.Output,boxListModel)
BoxInt=BoxInterpreter(mainwin.editor.navInterpret,mainwin.editor.posModel,BoxContr,mainwin.centralWidget())
mainwin.editor.setNavigationInterpreter(BoxInt)
# boxListModel.boxRemoved.connect(BoxContr.deleteItem)
LV.show()
mainwin.show()
app.exec_()
a = np.zeros(500 * 500).reshape(500, 500).astype(np.uint8)
ii = np.random.randint(0, 500, 1)
jj = np.random.randint(0, 500, 1)
a[ii, jj] = 1
a = vigra.filters.discDilation(a, radius=20)
array[:] = a.reshape(shape).view(np.ndarray) * 255
op.Input.setDirty()
do()
cron.timeout.connect(do)
ds = createDataSource(op.Output)
layer = ColortableLayer(ds, jet())
mainwin = Viewer()
mainwin.layerstack.append(layer)
mainwin.dataShape = (1, h, w, 1, 1)
print(mainwin.centralWidget())
BoxContr = BoxController(mainwin.editor, op.Output, boxListModel)
BoxInt = BoxInterpreter(mainwin.editor.navInterpret, mainwin.editor.posModel, BoxContr, mainwin.centralWidget())
mainwin.editor.setNavigationInterpreter(BoxInt)
# boxListModel.boxRemoved.connect(BoxContr.deleteItem)
LV.show()
mainwin.show()
app.exec_()
mainwin.editor.brushingModel.erasingNumber = 0
def _handleSelection(int):
if int == 0:
mainwin.editor.brushingModel.setErasing()
else:
# mainwin.editor.brushingModel.disableErasing()
mainwin.editor.brushingModel.setDrawnNumber(int)
if int == 1:
mainwin.editor.brushingModel.setBrushColor(QColor(255, 0, 0))
mainwin.editor.brushingModel.setBrushSize(1)
else:
mainwin.editor.brushingModel.setBrushSize(20)
mainwin.editor.brushingModel.setBrushColor(QColor(0, 255, 0))
labelListModel.elementSelected.connect(_handleSelection)
mainwin.layerstack.append(layer)
mainwin.dataShape = (1, 500, 500, 1, 1)
logger.debug(str(mainwin.centralWidget()))
BoxContr = DotController(mainwin.editor.imageScenes[2], mainwin.editor.brushingController)
BoxInt = DotInterpreter(mainwin.editor.navCtrl, mainwin.editor.brushingController, BoxContr)
mainwin.editor.setNavigationInterpreter(BoxInt)
labelListModel.select(1)
LV.show()
mainwin.show()
app.exec_()
def _handleSelection(int):
if int == 0:
mainwin.editor.brushingModel.setErasing()
else:
#mainwin.editor.brushingModel.disableErasing()
mainwin.editor.brushingModel.setDrawnNumber(int)
if int == 1:
mainwin.editor.brushingModel.setBrushColor(QColor(255, 0, 0))
mainwin.editor.brushingModel.setBrushSize(1)
else:
mainwin.editor.brushingModel.setBrushSize(20)
mainwin.editor.brushingModel.setBrushColor(QColor(0, 255, 0))
labelListModel.elementSelected.connect(_handleSelection)
mainwin.layerstack.append(layer)
mainwin.dataShape = (1, 500, 500, 1, 1)
logger.debug(str(mainwin.centralWidget()))
BoxContr = DotController(mainwin.editor.imageScenes[2],
mainwin.editor.brushingController)
BoxInt = DotInterpreter(mainwin.editor.navCtrl,
mainwin.editor.brushingController, BoxContr)
mainwin.editor.setNavigationInterpreter(BoxInt)
labelListModel.select(1)
LV.show()
mainwin.show()
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_()
label_arr = numpy.zeros(SHAPE, dtype=numpy.uint8)
##-----
app = QApplication(sys.argv)
v = Viewer()
data_src = ArraySource(data_arr)
data_layer = GrayscaleLayer(data_src)
data_layer.name = "Raw"
data_layer.numberOfChannels = 1
label_src = ArraySinkSource(label_arr)
label_layer = ColortableLayer(label_src,
colorTable=default16_new,
direct=False)
label_layer.name = "Labels"
label_layer.ref_object = None
assert SHAPE == label_arr.shape == data_arr.shape
v.dataShape = SHAPE
v.layerstack.append(data_layer)
v.layerstack.append(label_layer)
v.editor.setLabelSink(label_src)
v.editor.setInteractionMode("brushing")
v.setWindowTitle("labeling")
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_()
