You can get a nice global image feature!
Project description
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
Project details
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