CODEWITHSUNDEEP
tensorflowtensorflow-datasetstfrecord
Uchiha Madara
Uchiha Madara

Reputation: 1043

Reading Images from TFrecord using Dataset API and showing them on Jupyter notebook

I created a tfrecord from a folder of images, now I want to iterate over entries in TFrecord file using Dataset API and show them on Jupyter notebook. However I'm facing problems with reading tfrecord file.

Code I used to create TFRecord

def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))

def generate_tfr(image_list):
    with tf.python_io.TFRecordWriter(output_path) as writer:
        for image in images:
            image_bytes = open(image,'rb').read()
            image_array = imread(image)
            image_shape = image_array.shape
            image_x, image_y, image_z = image_shape[0],image_shape[1], image_shape[2]

            data = {

              'image/bytes':_bytes_feature(image_bytes),
              'image/x':_int64_feature(image_x),
              'image/y':_int64_feature(image_y),
              'image/z':_int64_feature(image_z)
            }

            features = tf.train.Features(feature=data)
            example = tf.train.Example(features=features)
            serialized = example.SerializeToString()
            writer.write(serialized)

Code to read TFRecord

#This code is incomplete and has many flaws. 
#Please give some suggestions in correcting this code if you can

def parse(serialized):
    features = \
    {
        'image/bytes': tf.FixedLenFeature([], tf.string),
        'image/x': tf.FixedLenFeature([], tf.int64),
        'image/y': tf.FixedLenFeature([], tf.int64),
        'image/z': tf.FixedLenFeature([], tf.int64)
    }

    parsed_example = tf.parse_single_example(serialized=serialized,features=features)
    image = parsed_example['image/bytes']
    image = tf.decode_raw(image,tf.uint8)
    x = parsed_example['image/x'] # breadth
    y = parsed_example['image/y'] # height
    z = parsed_example['image/z'] # depth
    image = tf.cast(image,tf.float32)

    # how can I reshape image tensor here? tf.reshape throwing some weird errors.

    return {'image':image,'x':x,'y':y,'z':z}



dataset = tf.data.TFRecordDataset([output_path])
dataset.map(parse)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
epoch = 1

with tf.Session() as sess:
    for _ in range(epoch):
    img = next_element.eval()
    print(img)
    # when I print image, it shows byte code.
    # How can I convert it to numpy array and then show image on my jupyter notebook ?

I've never worked with any of this before and I'm stuck at reading TFRecords. Please answer how to iterate over the contents of TFrecords and show them on Jupyter notebook. Feel free to correct/optimize both pieces of code. That would help me a lot.

Upvotes: 1

Views: 6165

Answers (1)

Mahesh
Mahesh

Reputation: 1683

Is this what you may be looking for? I think once u convert to numpy array you can show in jupyter notebook using PIL.Image.

convert tf records to numpy => How can I convert TFRecords into numpy arrays?

show numpy array as image https://gist.github.com/kylemcdonald/2f1b9a255993bf9b2629

Upvotes: 1

Related Questions