Merge pull request #19 from LArbys/tmw_multich_dropmean

very nice -- i'll add some labeling, have updated two other image types -- fake color and usual default image.py

mac/pyrgb/__init__.py

@@ -10,5 +10,8 @@
 
 larcv.load_pyutil
 
-import cv2
+try:
+    import cv2
+except:
+    print "NO CV2"
 

mac/pyrgb/display/rgbdisplay.py

@@ -13,9 +13,15 @@
 from ..lib.hoverrect import HoverRect
 from ..lib.roislider import ROISlider
 
-from cv2layout import CV2Layout
-from caffelayout import CaffeLayout
-from ..rgb_caffe.testwrapper import TestWrapper
+try:
+    from cv2layout import CV2Layout
+except:
+    pass
+try:
+    from caffelayout import CaffeLayout
+    from ..rgb_caffe.testwrapper import TestWrapper
+except:
+    pass
 
 class RGBDisplay(QtGui.QWidget) :
 
@@ -91,16 +97,14 @@ def __init__(self,argv):
 
         #Check boxes for drawing plane1/2/3 -- perhaps should
         #become tied to current image being shown (N planes...)
-        self.p0 = QtGui.QCheckBox("Plane 0")
-        self.p0.setChecked(True)
+        # tmw -- changing it so that one can select the channel to show in the RGB channels
+        self.p0 = QtGui.QComboBox()
         self.lay_inputs.addWidget( self.p0, 1, 9 )
 
-        self.p1 = QtGui.QCheckBox("Plane 1")
-        self.p1.setChecked(True)
+        self.p1 = QtGui.QComboBox()
         self.lay_inputs.addWidget( self.p1, 1, 10 )
 
-        self.p2 = QtGui.QCheckBox("Plane 2")
-        self.p2.setChecked(True)
+        self.p2 = QtGui.QComboBox()
         self.lay_inputs.addWidget( self.p2, 1, 11 )
 
         self.planes = [ self.p0, self.p1, self.p2 ]
@@ -143,6 +147,11 @@ def __init__(self,argv):
         #RGBCaffe will open and close bottom of the window
         self.rgbcaffe = QtGui.QPushButton("Enable RGBCaffe")
         self.rgbcv2   = QtGui.QPushButton("Enable OpenCV")
+        try:
+            import cv2
+        except:
+            print "No OpenCV. Disabling."
+            self.rgbcv2.setEnabled(False)
 
         self.rgbcaffe.setFixedWidth(130)
         self.rgbcv2.setFixedWidth(130)
@@ -189,9 +198,15 @@ def __init__(self,argv):
 
         ### Caffe Widgets
         #wrapper for FORWARD function
-        self.caffe_test   = TestWrapper() 
-        #wrapper for the caffe specific layout
-        self.caffe_layout = CaffeLayout(self.caffe_test)
+        try:
+            self.caffe_test   = TestWrapper() 
+            #wrapper for the caffe specific layout
+            self.caffe_layout = CaffeLayout(self.caffe_test)
+            self.has_caffe = True
+        except:
+            print "Caffe Disabled"
+            self.has_caffe = False
+            self.rgbcaffe.setEnabled(False)
 
         ### OpenCV Widgets
         #wrapper for the opencv specific window
@@ -334,8 +349,10 @@ def setViewPlanes(self):
 
         self.views = []
         for ix, p in enumerate( self.planes ):
-            if p.isChecked():
-                self.views.append(ix)
+            if p.currentIndex()!=0:
+                self.views.append( int(p.currentText())  )
+            else:
+                self.views.append( -1 ) # sentinal for don't fill this channel
 
 
     def plotData(self):
@@ -354,20 +371,48 @@ def plotData(self):
         imin  = int( self.imin.text() )
         imax  = int( self.imax.text() )
 
+        # update channel combo boxes
+        nchs = self.dm.get_nchannels(event,self.image_producer)
+        print "Set the RGB drop boxes: ",nchs," current count=",self.p0.count()
+        if self.p0.count()!=(nchs+1):
+            self.p0.clear()
+            self.p1.clear()
+            self.p2.clear()
+            # the none channel must be zero. else we have to know the number of channels
+            self.p0.insertItem(0,"(none)")
+            self.p1.insertItem(0,"(none)")
+            self.p2.insertItem(0,"(none)")
+            for ch in range(0,nchs):
+                self.p0.insertItem( ch+1, "%d"%(ch) )
+                self.p1.insertItem( ch+1, "%d"%(ch) )
+                self.p2.insertItem( ch+1, "%d"%(ch) )
+            if nchs>0:
+                self.p0.setCurrentIndex(1)
+            if nchs>1:
+                self.p1.setCurrentIndex(nchs/3+1)
+            if nchs>2:
+                self.p2.setCurrentIndex(nchs/3*2+1)
+
         self.setViewPlanes()
-        
+
         pimg, self.rois, plotimage = self.dm.get_event_image(event,imin,imax,
                                                              self.image_producer,
                                                              self.roi_producer,
                                                              self.views)
 
+
         self.image = plotimage.imgs
 
         if pimg is None:
             self.image = None
             return
 
-        self.caffe_test.set_image(plotimage.orig_mat)
+        if self.has_caffe:
+            # eventually we need to replace the modified images with the ones here
+            pushit = np.zeros( (plotimage.orig_mat[:,:,0].shape[0],plotimage.orig_mat[:,:,0].shape[1],nchs),dtype=np.float32)
+            for ix,img in enumerate(plotimage.img_v):
+                pushit[:,:,ix] = img
+            self.caffe_test.set_image(pushit)
         self.pimg = pimg
 
         # Emplace the image on the canvas

mac/pyrgb/lib/datamanager.py

@@ -33,6 +33,12 @@ def __init__(self,argv):
 
         self.loaded = {}
 
+    def get_nchannels(self,ii,imgprod) :
+        # Sorry Vic I hacked this
+        self.iom.read_entry(ii)
+        imdata  = self.iom.get_data(larcv.kProductImage2D,imgprod)
+        return imdata.Image2DArray().size()
+
     def get_event_image(self,ii,imin,imax,imgprod,roiprod,planes) :
 
         #Load data in TChain

mac/pyrgb/lib/image_types/ch12image.py

@@ -36,22 +36,17 @@ def __create_mat__(self):
         #compressed images all have the same shape
         self.orig_mat = np.zeros(list(self.img_v[0].shape) + [3])
 
-        try: 
-            ch = int( str(self.channel.text()) )
-        except ValueError:
-            print "\t>> Channel no good defaulting to zero!"
-            ch = 0
-
-        for ix,img in enumerate(split[ch]):
+        for p,fill_ch in enumerate(self.planes):
 
-            if ix not in self.planes: continue
+            if fill_ch==-1:
+                continue
 
-            self.orig_mat[:,:,ix] = img
+            self.orig_mat[:,:,p] = self.img_v[fill_ch]
 
         self.plot_mat = self.orig_mat.copy()
 
         self.plot_mat = self.plot_mat[:,::-1,:]
-        
+
         self.plot_mat[:,:,0][ self.plot_mat[:,:,1] > 0.0 ] = 0.0
         self.plot_mat[:,:,0][ self.plot_mat[:,:,2] > 0.0 ] = 0.0
         self.plot_mat[:,:,1][ self.plot_mat[:,:,2] > 0.0 ] = 0.0

mac/pyrgb/lib/imagefactory.py

@@ -9,6 +9,9 @@ def __init__(self):
     def get(self,imdata,roidata,planes,improd,**kwargs):
 
         if improd == "fake_color": return FakeColorImage(imdata,roidata,planes)
-        if improd == "tpc_12ch":   return Ch12Image(imdata,roidata,planes)
+        if improd == "tpc_12ch_mean":   
+            ch12 = Ch12Image(imdata,roidata,planes)
+            ch12.temp_window.show()
+            return ch12
 
         return DefaultImage(imdata,roidata,planes)

mac/pyrgb/rgb_caffe/testwrapper.py

@@ -54,6 +54,11 @@ def load(self):
             self.caffe.set_mode_gpu()
             self.caffe.set_device(set.config['gpuid'])
 
+        if "channels" in self.config:
+            self.nchannels = self.config["channels"]
+        else:
+            self.nchannels = 3
+
         self.__generate_model__()
         self.__create_net__()
 
@@ -78,8 +83,10 @@ def prep_image(self):
             self.iom.read_entry(0)
             means  = self.iom.get_data(larcv.kProductImage2D,self.config['meanproducer'])
             self.mean_v = [ larcv.as_ndarray(img) for img in means.Image2DArray() ]
+            print "Mean channels=",len(self.mean_v)," mean size=",self.mean_v[0].shape
 
         for ix,mean in enumerate(self.mean_v):
+            print "check mean shape againts: ",im[:,:,ix].shape
             assert mean.shape == im[:,:,ix].shape
             im[:,:,ix] -= mean
 
@@ -97,7 +104,9 @@ def forward_result(self):
 
         im = self.prep_image()
 
-        blob['data'] = np.zeros((1, im.shape[0], im.shape[1], 3),dtype=np.float32)
+        print "FORWARD ON IMAGE: ",im.shape
+
+        blob['data'] = np.zeros((1, im.shape[0], im.shape[1], im.shape[2]),dtype=np.float32)
         print blob['data'].shape
 
         blob['data'][0,:,:,:] = im
@@ -114,22 +123,23 @@ def forward_result(self):
 
         forward_kwargs = {'data': blob['data'] ,'label': blob['label']}
 
+
         blobs_out = self.net.forward(**forward_kwargs)
 
         scores  =  self.net.blobs[ self.config['lastfc'] ].data
-        #softmax =  self.net.blobs[ self.config['loss']   ].data
 
         self.scores = scores
         print "Scores:  {}".format(scores)
-        #print "Softmax: {}".format(softmax)
-
+
 
     def __generate_model__(self):
+
         print "\t>> Got an image of shape: {}".format(self.pimg.shape)
         td = ""
         td += "input: \"data\"\n"
-        td += "input_shape: { dim: 1 dim: 3 dim: %s dim: %s } \n"%(self.pimg.shape[0],
-                                                                   self.pimg.shape[1])
+        td += "input_shape: { dim: 1 dim: %d dim: %s dim: %s } \n"%(self.nchannels,
+                                                                    self.pimg.shape[0],
+                                                                    self.pimg.shape[1])
         td += "input: \"label\"\n"
         td += "input_shape: { dim: 1 }"
 

mac/ubresnet_12ch_768.yml

@@ -0,0 +1,48 @@
+# Where is your caffe?
+cafferoot: /home/taritree/working/larbys/caffe_larbys/python
+
+# CPU or GPU?
+usecpu : True
+
+# GPU number
+gpuid : 0
+
+# Deploy version of the model, please remove ROOTDataLayer or whatever
+modelfile: /home/taritree/working/larbys/resnet-18/training_attempts/ubtri/001/ub_trimese_resnet_headlessdeploy.prototxt
+
+# Pretrained caffe model
+pretrainedmodel: /home/taritree/working/larbys/resnet-18/training_attempts/ubtri/001/snapshot_rmsprop_iter_stage3.caffemodel
+
+# The mean file location on disk
+meanfile: /home/taritree/working/larbys/lmdb2image2d/mean.root
+
+# Producer for the mean file
+meanproducer: tpc_12ch_mean
+
+# Wrapper layer will make a copy of model file (has to sit somewhere on disk...)
+# the will all it's own input layer on top so make sure the first
+# real layer has input "data", we will try to guess the dimensions
+# based on image in the pgplot window
+model: /tmp/test.prototxt
+
+# Number of channels
+channels: 12
+
+# Thresholding parameter iMin and iMax
+imin : 0
+imax : 255
+
+# Can be per plane (per channel...) but is currently unused
+imin_plane0: 0
+imin_plane1: 0
+imin_plane2: 0
+
+imax_plane0: 255
+imax_plane1: 255
+imax_plane2: 255
+
+# This is the name of the last FC layer which computer the score
+lastfc: probt
+
+# This layer doesn't have to be here but that's OK, what is the name of the loss layer
+loss: loss