Question

我将一些图像编码为TFRecords，然后尝试对其进行解码。但是，解码过程中存在一个错误，我真的无法修复。

InvalidArgumentError：预期的图像（JPEG，PNG或GIF），格式从'\ 257 \ 222 \ 244 \ 257 \ 222 \ 244 \ 260 \ 223 \ 245 \ 260 \ 223 \ 245 \ 262 \ 225开始\ 247 \ 263' [[{{node DecodeJpeg}}]] [Op：IteratorGetNextSync]

编码： def _bytes_feature（值）： “”“从字符串/字节返回bytes_list。”“” 返回tf.train.Feature（bytes_list = tf.train.BytesList（value = [value]））

def _float_feature(value):
  """Returns a float_list from a float / double."""
  return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))

def _int64_feature(value):
  """Returns an int64_list from a bool / enum / int / uint."""
  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

src_path = r"E:\data\example"
record_path = r"E:\data\data"
sum_per_file = 4
num = 0
key = 3

for img_name in os.listdir(src_path):
    recordFileName = "trainPrecipitate.tfrecords"
    writer = tf.io.TFRecordWriter(record_path + recordFileName)
    img_path = os.path.join(src_path, img_name)
    img = Image.open(img_path, "r")
    height = np.array(img).shape[0]
    width = np.array(img).shape[1]
    img_raw = img.tobytes()
    example = tf.train.Example(features = tf.train.Features(feature={
        'image/encoded': _bytes_feature(img_raw),
        'image/class/label': _int64_feature(key),
        'image/height': _int64_feature(height),
        'image/width': _int64_feature(width)
    }))
    writer.write(example.SerializeToString())
writer.close()

解码：导入IPython.display作为显示

train_files = tf.data.Dataset.list_files(r"E:\data\datatrainPrecipitate.tfrecords")
train_files = train_files.interleave(tf.data.TFRecordDataset)

def decode_example(example_proto):
    image_feature_description = {
    'image/height': tf.io.FixedLenFeature([], tf.int64),
    'image/width': tf.io.FixedLenFeature([], tf.int64),
    'image/class/label': tf.io.FixedLenFeature([], tf.int64, default_value=3),
    'image/encoded': tf.io.FixedLenFeature([], tf.string)
}
    parsed_features = tf.io.parse_single_example(example_proto, image_feature_description)
    height = tf.cast(parsed_features['image/height'], tf.int32)
    width = tf.cast(parsed_features['image/width'], tf.int32)
    label = tf.cast(parsed_features['image/class/label'], tf.int32)
    image_buffer = parsed_features['image/encoded']
    image = tf.io.decode_jpeg(image_buffer, channels=3)
    image = tf.cast(image, tf.float32)
    return image, label

def processed_dataset(dataset):
    dataset = dataset.repeat()
    dataset = dataset.batch(1)
    dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
#     print(dataset)
    return dataset

train_dataset = train_files.map(decode_example)
# train_dataset = processed_dataset(train_dataset)
print(train_dataset)
for (image, label) in train_dataset:
    print(repr(image))

InvalidArgumentError：预期的图像（JPEG，PNG或GIF），格式从'\ 257 \ 222 \ 244 \ 257 \ 222 \ 244 \ 260 \ 223 \ 245 \ 260 \ 223 \ 245 \ 262 \ 225开始\ 247 \ 263' [[{{node DecodeJpeg}}]] [Op：IteratorGetNextSync]

Answer 1

我可以使用tf.io.decode_raw（）解码TFRecords，然后使用tf.reshape（）获得原始图像。虽然仍然不知道何时使用tf.io.decode_raw（）和何时使用tf.io.decode_jpeg（）。

解码TFRecord失败。预期的图像（JPEG，PNG或GIF）以'\ 257 \

1 个答案: