我可以对一批数据使用cv2.cvtColor(image,cv2.COLOR_GRAY2RGB)吗?

时间:2019-04-21 01:16:17

标签: python opencv tensorflow mnist cv2

我正在使用Inception v3,尝试将MNIST JPG图像用作预测数据集。在将培训批次输入模型时,我遇到了一个问题。错误是由于形状。 X_batch的形状为(?,299,299),其中第一层需要的形状为(?,299,299,3)。在代码的不同部分中,我替换了一些示例,我得以使用example_image = cv2.cvtColor(example_image,cv2.COLOR_GRAY2RGB)将示例转换为RGB,从而得到example_image.shape为(299,299,3)。我的问题是,我是否可以使用cv2将X_batch转换为RGB或代码的一部分,以使X_batch的形状为(?,299,299,3)?

这是我需要进行转换的代码的一部分:

from random import sample

def prepare_batch(MNIST_paths_and_classes, batch_size):
    batch_paths_and_classes = sample(MNIST_paths_and_classes, batch_size)
    images = [mpimg.imread(path)[:, :] for path, labels in batch_paths_and_classes]
    prepared_images = [prepare_image(image) for image in images]
    X_batch = 2 * np.stack(prepared_images) - 1 # Inception expects colors ranging from -1 to 1
    y_batch = np.array([labels for path, labels in batch_paths_and_classes], dtype=np.int32)
    return X_batch, y_batch

X_batch, y_batch = prepare_batch(MNIST_paths_and_classes_train, batch_size=4)

X_batch =(4,299,299) y_batch =(4,)

  X_test, y_test = prepare_batch(MNIST_paths_and_classes_test, batch_size=len(MNIST_paths_and_classes_test))

X_test =(12000,299,299)

此部分中的错误:

ValueError:无法为形状为((,, 299,299,3)'的张量'X:0'输入形状(40,299,299)的值

n_epochs = 10
batch_size = 40
n_iterations_per_epoch = len(MNIST_paths_and_classes_train) // batch_size

with tf.Session() as sess:
    init.run()
    inception_saver.restore(sess, INCEPTION_V3_CHECKPOINT_PATH)

    for epoch in range(n_epochs):
        print("Epoch", epoch, end="")
        for iteration in range(n_iterations_per_epoch):
            print(".", end="")
            X_batch, y_batch = prepare_batch(MNIST_paths_and_classes_train, batch_size)
            sess.run(training_op, feed_dict={X: X_batch, y: y_batch, training: True})

        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})
        print("  Train accuracy:", acc_train)

        save_path = saver.save(sess, "./my_MNIST_model")

1 个答案:

答案 0 :(得分:0)

我不明白是什么使您感到困惑。正如您所说的,cv2.cvtColor的形状正确,因此只需将X_batch中的图像一张一张地转换。

X_batch_rgb = np.copy(X_batch)
for i in len(X_batch):
    X_batch_rgb[i, ...] = cv2.cvtColor(X_batch[i, ...],cv2.COLOR_GRAY2RGB)

现在X_batch_rgb数组具有所需的形状。