リモートセンシング意味分割切図予測後再接合

def total_predict(ori_image):
    h_step = ori_image.shape[0] // 256
    w_step = ori_image.shape[1] // 256

    h_rest = -(ori_image.shape[0] - 256 * h_step)
    w_rest = -(ori_image.shape[1] - 256 * w_step)

    image_list = []
    predict_list = []
    #     
    for h in range(h_step):
        for w in range(w_step):
            #     
            image_sample = ori_image[(h * 256):(h * 256 + 256),
                           (w * 256):(w * 256 + 256), :]
            image_list.append(image_sample)
        image_list.append(ori_image[(h * 256):(h * 256 + 256), -256:, :])
    for w in range(w_step - 1):
        image_list.append(ori_image[-256:, (w * 256):(w * 256 + 256), :])
    image_list.append(ori_image[-256:, -256:, :])

    #         
    # predict
    for image in image_list:
        x_batch = image / 255.0
        x_batch = np.expand_dims(x_batch, axis=0)
        feed_dict = {
     img: x_batch
                     }
        pred1 = sess.run(pred, feed_dict=feed_dict)

        predict = np.argmax(pred1, axis=3)
        predict = np.squeeze(predict).astype(np.uint8)
        #        
        predict_list.append(predict)

    #              
    count_temp = 0
    tmp = np.ones([ori_image.shape[0], ori_image.shape[1]])
    #print('tmp shape: ', tmp.shape)
    for h in range(h_step):
        for w in range(w_step):
            tmp[
            h * 256:(h + 1) * 256,
            w * 256:(w + 1) * 256
            ] = predict_list[count_temp]
            count_temp += 1
        tmp[h * 256:(h + 1) * 256, w_rest:] = predict_list[count_temp][:, w_rest:]
        count_temp += 1
    for w in range(w_step - 1):
        tmp[h_rest:, (w * 256):(w * 256 + 256)] = predict_list[count_temp][h_rest:, :]
        count_temp += 1
    # tmp[h_rest:, w_rest:] = predict_list[count_temp][h_rest:, w_rest:]
    tmp[-257:-1, -257:-1] = predict_list[count_temp][:, :]
    return tmp