def convert_output_type(self, frame, output_choice):
"""Convert 'frame' to 'output_choice' if necessary and return it."""
if output_choice == 'as float, range 0-1':
if frame.min()<0 or frame.max()>1: return filter_base.make_normfloat32(frame)
elif frame.dtype == numpy.dtype('f'): return frame
else:
return filter_base.make_normfloat32(frame) #frame.astype('f')
# return frame.astype('f')
elif output_choice == 'original': return frame
else: raise Exception("In '%s': An error (unhandled case) occurred when converting between input and output data types." %self.name)
def process(self, input_images, connected_outs):
if len(input_images) == 0:
return FAIL
all_weights = [self.getParamContent(input_name+' weight') for input_name in self.input_names]
imgs = []
weights = []
all_inputs_8bit = True
for i,input_name in enumerate(self.input_names):
if input_name in input_images.keys():
imgs.append(input_images[input_name])
if input_images[input_name].dtype != 'uint8': all_inputs_8bit = False
weights.append(all_weights[i])
summation_img = numpy.zeros_like(imgs[0], dtype='f')
for i,img in enumerate(imgs):
summation_img = summation_img+img*weights[i]
if all_inputs_8bit:
out_im = filter_base.make_uint8(summation_img)
else:
out_im = filter_base.make_normfloat32(summation_img)
return {self.o_output:out_im}
