Installation

Install and update using pip:

$ pip install melon

Supported in Python >= 3.4.0

Examples

Images

With default options:

from melon import ImageReader

def train():
    source_dir = "resources/images"
    reader = ImageReader(source_dir)
    X, Y = reader.read()
    ...
    with tf.Session() as s:
        s.run(..., feed_dict = {X_placeholder: X, Y_placeholder: Y})

Since number of images may be too large to fit into memory the tool supports batch-processing.

from melon import ImageReader

def train():
    source_dir = "resources/images"
    options = { "batch_size": 32 }
    reader = ImageReader(source_dir, options)
    while reader.has_next():
        X, Y = reader.read()
        ...

With custom options:

from melon import ImageReader

def train():
    source_dir = "resources/images"
    options = { "data_format": "channels_last", "normalize": False }
    reader = ImageReader(source_dir, options)
    ...

Options

Images

width

Width of the output (pixels). default: 255

height

Height of the output (pixels). default: 255

batch_size

Batch size of each read. default: All images in a directory

data_format

Format of the images data

channels_first - Channel x Height x Width (default)

channels_last - Height x Width x Channel

label_format

Format of the labels data

one_hot - as a matrix, with one-hot vector per image (default)

label - as a vector, with a single label per image

normalize

Normalize data. default: True

num_threads - number of threads for parallel processing

default: Number of cores of the machine

Labeling

Generating labels file

$ melon generate
> Source dir:

#legend
pedestrian:0
cat:1
parrot:2
car:3
apple tree:4

#map
img275.jpg:1
img324.jpg:2
img551.jpg:3
img928.jpg:1
img999.png:0
img736.png:4

Format of the labels

Label’s output format can be specified in Custom options. It defaults to one-hot format.

Roadmap

• Support for video data (Q1 2019)

• Support for reading from AWS S3 (Q2 2019)