pspnet.py 文件源码

def predict_multi_scale(full_image, net, scales, sliding_evaluation, flip_evaluation):
    """Predict an image by looking at it with different scales."""
    classes = net.model.outputs[0].shape[3]
    full_probs = np.zeros((full_image.shape[0], full_image.shape[1], classes))
    h_ori, w_ori = full_image.shape[:2]
    for scale in scales:
        print("Predicting image scaled by %f" % scale)
        scaled_img = misc.imresize(full_image, size=scale, interp="bilinear")
        if sliding_evaluation:
            scaled_probs = predict_sliding(scaled_img, net, flip_evaluation)
        else:
            scaled_probs = net.predict(scaled_img, flip_evaluation)
        # scale probs up to full size
        h, w = scaled_probs.shape[:2]
        probs = ndimage.zoom(scaled_probs, (1.*h_ori/h, 1.*w_ori/w, 1.),
                             order=1, prefilter=False)
        # visualize_prediction(probs)
        # integrate probs over all scales
        full_probs += probs
    full_probs /= len(scales)
    return full_probs