DeepDream.py 文件源码

def Deepdream(self, base_img, iter_n=10, octave_n=4, octave_scale=1.4, clip=True):
        # prepare base images for all octaves
        octaves = [self.Preprocess(base_img)]
        for i in xrange(octave_n-1):
            # shrink the image octave[0] so that function always return image size as octave[0]
            octaves.append(nd.zoom(octaves[-1], (1, 1.0/octave_scale,1.0/octave_scale), order=1))

        src = self.net.blobs['data']
        detail = np.zeros_like(octaves[-1]) # allocate image for network-produced details
        for octave, octave_base in enumerate(octaves[::-1]):# from end to 0
            h, w = octave_base.shape[-2:]
            if octave > 0:
                # upscale details from the previous octave
                h1, w1 = detail.shape[-2:]
                detail = nd.zoom(detail, (1, 1.0*h/h1,1.0*w/w1), order=1)
            src.reshape(1,3,h,w) # resize the network's input image size
            src.data[0] = octave_base+detail
            for i in xrange(iter_n):
                self.Make_step()
                # visualization
                '''
                vis = self.deprocess(net, src.data[0])
                if not clip: # adjust image contrast if clipping is disabled
                    vis = vis*(255.0/np.percentile(vis, 99.98))
                showarray(vis)
                print octave, i, end, vis.shape
                clear_output(wait=True)
                '''
            # extract details produced on the current octave
            #print octave, self.end, src.data[0].shape
            detail = src.data[0]-octave_base
        # returning the resulting image
        return self.Deprocess(src.data[0])