Question

我有一个要解析的tfrecords数据集。
我正在使用以下代码对其进行解析：

image_size = [224,224]
def read_tfrecord(tf_record):
    features = {
        "filename": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring
        "fun": tf.io.FixedLenFeature([], tf.string),  
        "label": tf.io.VarLenFeature(tf.int64),
    }
    tf_record = tf.parse_single_example(tf_record, features)

    filename = tf.image.decode_jpeg(tf_record['filename'], channels=3)
    filename = tf.cast(filename, tf.float32) / 255.0  # convert image to floats in [0, 1] range
    filename = tf.reshape(filename, [*image_size, 3]) # explicit size will be needed for TPU

    label = tf.cast(tf_record['label'],tf.float32)

    return filename, label

def load_dataset(filenames):
    option_no_order = tf.data.Options()
    option_no_order.experimental_deterministic = False

    dataset = tf.data.Dataset.from_tensor_slices(filenames)
    dataset = dataset.with_options(option_no_order)
    #dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=16)
    dataset = dataset.interleave(tf.data.TFRecordDataset, cycle_length=32, num_parallel_calls=AUTO) # faster
    dataset = dataset.map(read_tfrecord, num_parallel_calls=AUTO)
    return dataset

train_data=load_dataset(train_filenames)
val_data=load_dataset(val_filenames)
test_data=load_dataset(test_filenames)

运行此代码后，我得到： train_data <DatasetV1Adapter shapes: ((224, 224, 3), (?,)), types: (tf.float32, tf.float32)> 我试图通过以下方式查看数据集中的图像：

def display_9_images_from_dataset(dataset):
    subplot=331
    plt.figure(figsize=(13,13))
    images, labels = dataset_to_numpy_util(dataset, 9)
    for i, image in enumerate(images):
        title = CLASSES[np.argmax(labels[i], axis=-1)]
        subplot = display_one_flower(image, title, subplot)
        if i >= 8:
            break;

    plt.tight_layout()
    plt.subplots_adjust(wspace=0.1, hspace=0.1)
    plt.show()



def dataset_to_numpy_util(dataset, N):
    dataset = dataset.batch(N)

    if tf.executing_eagerly():
    # In eager mode, iterate in the Datset directly.
        for images, labels in dataset:
            numpy_images = images.numpy()
            numpy_labels = labels.numpy()
            break;

    else: # In non-eager mode, must get the TF note that 
        # yields the nextitem and run it in a tf.Session.
        get_next_item = dataset.make_one_shot_iterator().get_next()
        with tf.Session() as ses:
            numpy_images, numpy_labels = ses.run(get_next_item)

    return numpy_images, numpy_labels


display_9_images_from_dataset(train_data)

但是我得到了错误： InvalidArgumentError: Expected image (JPEG, PNG, or GIF), got unknown format starting with 'B2.jpg' [[{{node DecodeJpeg}}]] [[IteratorGetNext_3]]

我有点困惑，因为它说文件是jpg格式，并要求jpeg，据我所知是相同的。
还有一个原因是我不确定如何查看图像，甚至不确定我是否正确解析了图像。

Answer 1

扩展名“ .jpg”和“ .jpeg”在使用它的API进行的验证检查方面有所不同。

tf.image.decode_jpeg拍摄带有“ .jpeg”扩展名的图像。

尝试使用.jpeg扩展名重命名.jpg图像，它应该开始起作用。

InvalidArgumentError：预期的图像（JPEG，PNG或GIF），格式以'B2.jpg'开头

1 个答案: