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Description:

ife 0.0.3

Image Feature Extractor(IFE)

What is this
IFE is a package to get an image feature more easily for Python. It contains many kinds of feature extract algorithms.
Insatall
For the latest version are available using pip install.
pip install ife

1. Features
Color Moment

Mean, Median, Variance, Skewness, Kurtosis of RGB, HSV, HSL, CMY

Colourfulness

Colourfulness measure of the image

2. Examples
Import the basic image reader of IFE.
from ife.io.io import ImageReader

2.1 Get Moment
Add a image file path to read_from_single_file(). This will return basic features class.
And now! You can get a RGB color moment feature from image!!
Sample
>>> features = ImageReader.read_from_single_file("ife/data/small_rgb.jpg")
>>> features.moment()
array([[ 0.57745098, 0.52156863, 0.55980392],
[ 0.58823529, 0.48823529, 0.54901961],
[ 0.15220588, 0.12136101, 0.12380911],
[-0.01944425, 0.18416571, 0.04508015],
[-1.94196824, -1.55209335, -1.75586748]])

Also, you can get an flatten vector, dictionary, or pandas
>>> features.moment(output_type="one_col")
array([ 0.57745098, 0.52156863, 0.55980392, 0.58823529, 0.48823529,
0.54901961, 0.15220588, 0.12136101, 0.12380911, -0.01944425,
0.18416571, 0.04508015, -1.94196824, -1.55209335, -1.75586748])

>>> features.moment(output_type="dict")
defaultdict(<class 'dict'>, {'mean': {'R': 0.57745098039215681, 'G': 0.52156862745098043, 'B': 0.55980392156862746}, 'median': {'R': 0.58823529411764708, 'G': 0.48823529411764705, 'B': 0.5490196078431373}, 'var': {'R': 0.15220588235294119, 'G': 0.12136101499423299, 'B': 0.12380911188004615}, 'skew': {'R': -0.019444250980856902, 'G': 0.18416570783012232, 'B': 0.045080152334687214}, 'kurtosis': {'R': -1.9419682406751135, 'G': -1.5520933544103905, 'B': -1.7558674751807395}})

>>> features.moment(output_type="pandas")
mean median var skew kurtosis
R 0.577451 0.588235 0.152206 -0.019444 -1.941968
G 0.521569 0.488235 0.121361 0.184166 -1.552093
B 0.559804 0.549020 0.123809 0.045080 -1.755867

No! I want a HSV Color space feature :(

It can set another color space! Default will be RGB.
>>> features.moment(output_type="one_col", color_space="CMY")
array([ 0.42254902, 0.47843137, 0.44019608, 0.41176471, 0.51176471,
0.45098039, 0.15220588, 0.12136101, 0.12380911, 0.01944425,
-0.18416571, -0.04508015, -1.94196824, -1.55209335, -1.75586748])

>>> features.moment(output_type="dict", color_space="HSL")
defaultdict(<class 'dict'>, {'mean': {'H': 0.50798329143793874, 'S': 0.52775831413836383, 'L': 0.61421568627450984}, 'median': {'H': 0.51915637553935423, 'S': 0.62898601603182969, 'L': 0.52156862745098043}, 'var': {'H': 0.13290200013401141, 'S': 0.10239897927552907, 'L': 0.051550124951941563}, 'skew': {'H': -0.078898095002588917, 'S': -0.83203104238315984, 'L': 1.0202366337483093}, 'kurtosis': {'H': -1.2599104562470791, 'S': -0.87111810912637022, 'L': -0.7502836585891588}})

>>> features.moment(output_type="pandas", color_space="HSV")
mean median var skew kurtosis
H 0.507983 0.519156 0.132902 -0.078898 -1.259910
S 0.595236 0.749543 0.122723 -1.028366 -0.768867
V 0.855882 0.864706 0.013867 -0.155656 -1.498179

2.2 Colourfulness
Reference
D. Hasler and S.E.Suesstrunk, ``Measuring colorfulness in natural images," Human
Vision andElectronicImagingVIII, Proceedings of the SPIE, 5007:87-95, 2003.
Sample
>>> features = ImageReader.read_from_single_file("ife/data/strawberry.jpg")
>>> features.colourfulness()
0.18441700366624714

3. Future work
IO

Read from URL links
Read from Base64
Sliding window
Video files

Color space

CMYK
CIE Lab
XYZ

Features

Value normalize
Average Gradient
LBP
Histogram
Color harmony
Entropy
Brightness measure
Contrast measure
Saturation measure
Naturalness
Color fidelity metric
Saliency map
Fisher vector
VGG16, 19 layer feature
and more...

4. Author
@Collonville
5. Licence
BSD-3-Clause