如何在Google机器学习中将jpeg图像转换为json文件 [英] How convert a jpeg image into json file in Google machine learning
问题描述
我正在研究Google Cloud ML，并且希望获得有关jpeg图像的预测.为此，我想使用:
I'm working on Google cloud ML, and I want to get prediction on jpeg image. To do this, I would like to use:
gcloud beta ml预测--instances = INSTANCES --model = MODEL [--version = VERSION]
gcloud beta ml predict --instances=INSTANCES --model=MODEL [--version=VERSION]
( https://cloud.google.com/ml/reference/commandline/预测)
Instances是包含有关图像的所有信息的json文件的路径. 如何从jpeg图像创建json文件?
Instances is the path to a json file with all info about image. How can I create the json file from my jpeg image?
非常感谢！！
推荐答案
第一步是确保导出的图形具有占位符和可以接受JPEG数据的操作.请注意，CloudML假设您正在发送一批图像.我们必须使用tf.map_fn来解码一批图像并调整其大小.根据模型的不同，可能需要对数据进行额外的预处理以对数据进行规范化，等等.如下所示:
The first step is to make sure that the graph you export has a placeholder and ops that can accept JPEG data. Note that CloudML assumes you are sending a batch of images. We have to use a tf.map_fn to decode and resize a batch of images. Depending on the model, extra preprocessing of the data may be required to normalize the data, etc. This is shown below:
# Number of channels in the input image
CHANNELS = 3
# Dimensions of resized images (input to the neural net)
HEIGHT = 200
WIDTH = 200
# A placeholder for a batch of images
images_placeholder = tf.placeholder(dtype=tf.string, shape=(None,))
# The CloudML Prediction API always "feeds" the Tensorflow graph with
# dynamic batch sizes e.g. (?,). decode_jpeg only processes scalar
# strings because it cannot guarantee a batch of images would have
# the same output size. We use tf.map_fn to give decode_jpeg a scalar
# string from dynamic batches.
def decode_and_resize(image_str_tensor):
"""Decodes jpeg string, resizes it and returns a uint8 tensor."""
image = tf.image.decode_jpeg(image_str_tensor, channels=CHANNELS)
# Note resize expects a batch_size, but tf_map supresses that index,
# thus we have to expand then squeeze. Resize returns float32 in the
# range [0, uint8_max]
image = tf.expand_dims(image, 0)
image = tf.image.resize_bilinear(
image, [HEIGHT, WIDTH], align_corners=False)
image = tf.squeeze(image, squeeze_dims=[0])
image = tf.cast(image, dtype=tf.uint8)
return image
decoded_images = tf.map_fn(
decode_and_resize, images_placeholder, back_prop=False, dtype=tf.uint8)
# convert_image_dtype, also scales [0, uint8_max] -> [0, 1).
images = tf.image.convert_image_dtype(decoded_images, dtype=tf.float32)
# Then shift images to [-1, 1) (useful for some models such as Inception)
images = tf.sub(images, 0.5)
images = tf.mul(images, 2.0)
# ...
此外，我们需要确保正确标记输入，在这种情况下，输入的名称(映射中的键)必须以_bytes结尾.发送base64编码的数据时，它将使CloudML预测服务知道它需要对数据进行解码:
Also, we need to be sure to properly mark the inputs, in this case, it's essential that the name of the input (the key in the map) end in _bytes. When sending base64 encoded data, it will let the CloudML prediction service know it needs to decode the data:
inputs = {"image_bytes": images_placeholder.name}
tf.add_to_collection("inputs", json.dumps(inputs))
gcloud命令期望的数据格式将为以下形式:
The data format that the gcloud command is expecting will be of the form:
{"image_bytes": {"b64": "dGVzdAo="}}
(请注意，如果image_bytes是模型的唯一输入，则可以简化为{"b64": "dGVzdAo="}).
(Note, if image_bytes is the only input to your model you can simplify to just {"b64": "dGVzdAo="}).
例如，要从磁盘上的文件创建此文件，可以尝试以下操作:
For example, to create this from a file on disk, you could try something like:
echo "{\"image_bytes\": {\"b64\": \"`base64 image.jpg`\"}}" > instances
然后将其发送至服务，如下所示:
And then send it to the service like so:
gcloud beta ml predict --instances=instances --model=my_model
请注意，直接将数据发送到服务时，您发送的请求的主体需要包含在实例"列表中.因此，上面的gcloud命令实际上将以下内容发送到HTTP请求正文中的服务:
Please note that when sending data directly to the service, the body of the request you send needs to be wrapped in an "instances" list. So the gcloud command above actually sends the following to the service in the body of the HTTP request:
{"instances" : [{"image_bytes": {"b64": "dGVzdAo="}}]}
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