scikit-image for image processing

presentation given at PyData Paris, April 3rd 2015
a short introduction to the image processing package scikit-image

Transcript

1. Image processing with scikit-image Emmanuelle Gouillart Surface, Glass and Interfaces, CNRS/Saint-Gobain @EGouillart
2. The revolution of images
3. The revolution of images
4. A flood of images several 108 images uploaded on Facebook each day
5. A flood of images hundreds of terabytes of scientific data for scientific experiment http://sdo.gsfc.nasa.gov/
6. A flood of images hundreds of terabytes of scientific data for scientific experiment http://sdo.gsfc.nasa.gov/ Image processing Manipulating images in order to retrieve new images or image characteristics (features, measurements, ...) Often combined with machine learning
7. Why use scikit-image? Lots of excellent tools available: OpenCV (computer vision), ImageJ (GUI-based software), ITK, ... Native NumPy compatibility Gentle learning curve Variety of features Versatile - 3D-friendly
8. Datasheet Package statistics http://scikit-image.org/ Release 0.11 (1 - 2 release per year) Available in most Scientific Python Distributions: Canopy, Anaconda, . . . Packaged on Ubuntu/Debian Among 1000 best ranked packages on PyPi Development model Mature algorithms Only Python + Cython code for easier maintainability Thorough code review by others: readability, PEP8, efficiency, ... Core team of 5 − 10 persons (close to applications)
9. Manipulating images as numerical (numpy) arrays Pixels are arrays elements import numpy as np image = np. ones ((5, 5)) image [0, 0] = 0 image [2, :] = 0 x
10. Manipulating images as numerical (numpy) arrays Pixels are arrays elements import numpy as np image = np. ones ((5, 5)) image [0, 0] = 0 image [2, :] = 0 x >>> coffee.shape (400, 600, 3) >>> red channel = coffee[..., 0] >>> image 3d = np.ones((100, 100, 100))
11. NumPy-native: images as NumPy arrays NumPy arrays as arguments and outputs >>> from skimage import io , f i l t e r s >>> c a m e r a a r r a y = i o . imread ( ’ camera image . png ’ ) >>> type( c a m e r a a r r a y ) <type ’numpy . ndarray ’ > >>> c a m e r a a r r a y . dtype dtype ( ’ uint8 ’ ) >>> f i l t e r e d a r r a y = f i l t e r s . g a u s s i a n f i l t e r ( camera array , sigma =5) >>> type( f i l t e r e d a r r a y ) <type ’numpy . ndarray ’ > >>> import m a t p l o t l i b . p y p l o t as p l t >>> p l t .imshow( f i l t e r e d a r r a y , cmap= ’ gray ’ ) x
12. An API relying mostly on functions skimage . f i l t e r s . g a u s s i a n f i l t e r (image , sigma , output = None, mode= ’ n e a r e st ’ , c v a l =0, m u l t i c h a n n e l =None) Multi - d i m e n s i o n a l Gaussian filter Parameters ---------- image : array - l i k e input image ( g r a y s c a l e or c o l o r ) to filter. sigma : s c a l a r or sequence of s c a l a r s st and ard d e v i a t i o n f o r Gaussian k e r n e l . The st and ard d e v i a t i o n s of the Gaussian filter are g i v e n f o r each a x i s as a sequence , or as a s i n g l e number , in which case i t i s equal f o r all axes . output : array , o p t i o n a l The ‘‘ output ‘‘ parameter p a s s e s an a r r a y in which to s t o r e the filter output .
13. Getting started: finding documentation
14. Gallery of examples
15. Getting started: finding documentation
16. Filtering: transforming image data skimage.filter, skimage.exposure, skimage.restoration
17. Extracting features skimage.feature, skimage.filter
18. Feature extraction followed by classification Combining scikit-image and scikit-learn Extract features (skimage.feature) Pixels intensity values (R, G, B) Local gradients More advanced descriptors: HOGs, Gabor, ... Train classifier with known regions here, random forest classifier Classify pixels
19. Geometrical transformations skimage.transform scale, zoom, rotate, swirl, warp, ...
20. Segmentation: labelling regions skimage.segmentation
21. Measures on images skimage.measure
22. Versatile use for 2D, 2D-RGB, 3D... >>> from skimage import measure >>> l a b e l s 2 d = measure . l a b e l ( image 2d ) >>> l a b e l s 3 d = measure . l a b e l ( image 3d ) x
23. Try it out! http://scikit-image.org/ Feedback welcome github.com/scikit-image/scikit-image Please cite the paper