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]))}
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]))}
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 {}
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 {}