caffe_functions.py 文件源码

def compute_mean(input_list, plot_mean=False):
    # If no data supplied, use mean supplied with pretrained model
    if len(input_list) == 0:
        net_root = '.'
        net_dir = 'VGG_S_rgb'
        mean_filename=os.path.join(net_root, net_dir, 'mean.binaryproto')
        proto_data = open(mean_filename, "rb").read()
        a = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
        mean  = caffe.io.blobproto_to_array(a)[0]
    else:
        x,y,c = 256,256,3
        mean = np.zeros((c, x, y))
        for img_file in input_list:
            img = caffe.io.load_image(img_file)
            img = mod_dim(img, x, y, c)
            mean += img
        mean /= len(input_list)

        # Plot the mean image if desired:
        if plot_mean:
            plt.imshow(np.swapaxes(np.swapaxes(mean, 0, 1), 1, 2))
            plt.show()
    return mean

# Return VGG_S_Net from mean image and optional network type