Example #1
0
    def process(self, input_images, connected_outs):
        if 'Input' not in input_images:
            return FAIL
        if self.getParamContent("Log") == "Yes": do_log = True
        else: do_log = False
        if self.getParamContent("Normalize") == "Yes": normalize = True
        else: normalize = False
        if self.hist_fig is None:
            self.hist_fig = plt.figure(figsize=(4, 2), dpi=100)
            self.hist_axis = self.hist_fig.add_subplot(111)
            self.hist_axis.tick_params(labelsize=8)
            self.hist_axis.grid(True)
        width = self.getParamContent('Display width (pixels)')
        height = self.getParamContent('Display height (pixels)')
        if width != self.last_width:
            self.hist_fig.set_figwidth(width / 100)
            self.last_width = width
        if height != self.last_height:
            self.hist_fig.set_figheight(height / 100)
            self.last_height = height
        src = input_images['Input']
        no_of_bins = int(self.getParamContent("# of bins"))

        if src.flags['C_CONTIGUOUS'] == False:
            src = numpy.ascontiguousarray(src, 'f')
        if src.dtype != 'f' and src.dtype != 'uint8':
            src = src.astype('f')
        mini = float(numpy.amin(src))
        maxi = float(numpy.amax(src))
        hist_vals = cv2.calcHist([src], [0], None, [no_of_bins], [mini, maxi])
        x_vals = numpy.arange(mini, maxi, (maxi - mini) / no_of_bins)
        if do_log: hist_vals = numpy.log(hist_vals)
        if normalize: hist_vals = hist_vals / numpy.amax(hist_vals)
        if self.getParamContent("Plot histogram") == "Yes":
            x2plt, hist2plt = histOutline(hist_vals, x_vals)
            self.abortQuery()
            #            self.hist_axis.clear()
            if self.hist_curve is None:
                self.hist_curve = self.hist_axis.plot(x2plt, hist2plt, 'k')[0]
            else:
                self.hist_curve.set_data(x2plt, hist2plt)
            self.hist_axis.set_xlim(x2plt.min(), x2plt.max())
            self.hist_axis.set_ylim(hist2plt.min(), hist2plt.max())
            #            self.setParamContent("Histogram plot",self.hist_fig, emitSignal=True)
            self.abortQuery()
            self.pixmap = arr2pixmap(plotfig2image(self.hist_fig))
        return {'Histogram': hist_vals, 'Bins': numpy.hstack((x_vals, [maxi]))}
Example #2
0
    def process(self, input_images, connected_outs):
        if 'Input' not in input_images:
            return FAIL
        if self.getParamContent("Log") == "Yes": do_log = True
        else: do_log = False
        if self.getParamContent("Normalize") == "Yes": normalize = True
        else: normalize = False
        if self.hist_fig is None:
            self.hist_fig = plt.figure(figsize=(4, 2), dpi=100)
            self.hist_axis = self.hist_fig.add_subplot(111)
            self.hist_axis.tick_params(labelsize=8)
            self.hist_axis.grid(True)        
        width = self.getParamContent('Display width (pixels)')
        height = self.getParamContent('Display height (pixels)')
        if width!=self.last_width:
            self.hist_fig.set_figwidth(width/100)
            self.last_width=width
        if height!=self.last_height:
            self.hist_fig.set_figheight(height/100)
            self.last_height=height
        src = input_images['Input']
        no_of_bins = int(self.getParamContent("# of bins"))
        
        if src.flags['C_CONTIGUOUS'] == False:
            src = numpy.ascontiguousarray(src, 'f')
        if src.dtype != 'f' and src.dtype != 'uint8':
            src = src.astype('f')
        mini = float(numpy.amin(src))
        maxi = float(numpy.amax(src))
        hist_vals = cv2.calcHist([src],[0],None,[no_of_bins],[mini,maxi])
        x_vals = numpy.arange(mini,maxi,(maxi-mini)/no_of_bins)
        if do_log: hist_vals = numpy.log(hist_vals)
        if normalize: hist_vals = hist_vals/numpy.amax(hist_vals)
        if self.getParamContent("Plot histogram") == "Yes":
            x2plt,hist2plt = histOutline(hist_vals,x_vals)
            self.abortQuery()
#            self.hist_axis.clear()
            if self.hist_curve is None:
                self.hist_curve = self.hist_axis.plot(x2plt,hist2plt,'k')[0]
            else:
                self.hist_curve.set_data(x2plt,hist2plt)
            self.hist_axis.set_xlim(x2plt.min(),x2plt.max())
            self.hist_axis.set_ylim(hist2plt.min(), hist2plt.max())
#            self.setParamContent("Histogram plot",self.hist_fig, emitSignal=True)
            self.abortQuery()
            self.pixmap = arr2pixmap(plotfig2image(self.hist_fig))
        return {'Histogram':hist_vals, 'Bins':numpy.hstack((x_vals,[maxi]))}
Example #3
0
    def process(self, input_images, connected_outs):
        """Return a black&white image if something is connected to the B&W input, 
        otherwise return an RGB-image with unconnected channels zeroed (pure black).
        """
        names = self.input_names
        if len(input_images) == 0:
            self.pixmap = None
            return {}

        processed_images = {}

        if names[0] in input_images:
            bw_rgb = input_images[names[0]]
            if not (bw_rgb.ndim == 3
                    and bw_rgb.shape[2] == 3) and bw_rgb.ndim > 2:
                print "In '%s': Multichannel image must have 3 channels to display." % self.name
                return FAIL
            else:
                processed_images[names[0]] = bw_rgb
        else:
            for i, channel in enumerate([self.i_ch1, self.i_ch2, self.i_ch3]):
                if channel in input_images:
                    ch_im = input_images[channel]
                    if ch_im.ndim == 3 and ch_im.shape[2] == 3:
                        try:
                            processed_images[channel] = ch_im[:, :, i]
                        except ValueError:
                            print "Can not display image in '%s' unless all channels have the same dimensions." % (
                                self.name)
                            return FAIL
                    elif ch_im.ndim > 2:
                        print "In '%s': Multichannel image must have 3 channels to display." % self.name
                        return FAIL
                    else:
                        processed_images[channel] = input_images[channel]

        res_images = self.resizeQuery(processed_images)
        disp_im = self.constructFinal(res_images)
        try:
            self.pixmap = arr2pixmap(disp_im)
        except Exception as e:
            print e
        return {}
Example #4
0
 def process(self, input_images, connected_outs):
     """Return a black&white image if something is connected to the B&W input, 
     otherwise return an RGB-image with unconnected channels zeroed (pure black).
     """
     names = self.input_names
     if len(input_images)==0:
         self.pixmap = None
         return {}
     
     processed_images = {}
     
     if names[0] in input_images: 
         bw_rgb = input_images[names[0]]
         if not (bw_rgb.ndim == 3 and bw_rgb.shape[2] == 3) and bw_rgb.ndim > 2:
             print "In '%s': Multichannel image must have 3 channels to display." %self.name
             return FAIL
         else:
             processed_images[names[0]] = bw_rgb
     else:     
         for i,channel in enumerate([self.i_ch1, self.i_ch2, self.i_ch3]):
             if channel in input_images:
                 ch_im = input_images[channel] 
                 if ch_im.ndim == 3 and ch_im.shape[2] == 3: 
                     try:
                         processed_images[channel] = ch_im[:,:,i]
                     except ValueError:
                         print "Can not display image in '%s' unless all channels have the same dimensions." %(self.name)
                         return FAIL
                 elif ch_im.ndim > 2:
                     print "In '%s': Multichannel image must have 3 channels to display." %self.name
                     return FAIL
                 else:
                     processed_images[channel] = input_images[channel]
                  
     res_images = self.resizeQuery(processed_images)
     disp_im = self.constructFinal(res_images)
     try: self.pixmap = arr2pixmap(disp_im)
     except Exception as e: print e
     return {}