Image processing examples with Numpy, Scipy, and Scikit-image

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Image processing examples with Numpy, Scipy, and Scikit-image

Requirements

Python 3.4+
NumPy ($ pip install numpy)
SciPy ($ pip install scipy)
MatplotLib ($ pip install matplotlib)
Scikit-image ($ pip install scikit-image)

Testing after install

Open a Jupyter notebook and execute the following code,

import numpy as np
import matplotlib.pyplot as plt
from skimage import data, io, filters

image = data.coins()  # or any NumPy array!
edges = filters.sobel(image)
io.imshow(edges)

You should see the following output. If you see this, you are all set to go!

Name		Name	Last commit message	Last commit date
Latest commit History 85 Commits
images		images
.gitignore		.gitignore
Active_contour_model.ipynb		Active_contour_model.ipynb
Adapt_RGB_decorator.ipynb		Adapt_RGB_decorator.ipynb
Approximate_subdivide_polygon.ipynb		Approximate_subdivide_polygon.ipynb
Block_view_mean_max_median_sampling.ipynb		Block_view_mean_max_median_sampling.ipynb
Canny_edge_detector.ipynb		Canny_edge_detector.ipynb
Circular_Elliptical_Hough_Transform.ipynb		Circular_Elliptical_Hough_Transform.ipynb
Convex_Hull.ipynb		Convex_Hull.ipynb
Denoise.ipynb		Denoise.ipynb
Edge_operators.ipynb		Edge_operators.ipynb
Entropy.ipynb		Entropy.ipynb
Filtering_regional_maxima.ipynb		Filtering_regional_maxima.ipynb
Finding_contours.ipynb		Finding_contours.ipynb
Gamma_log_contrast_adjustment.ipynb		Gamma_log_contrast_adjustment.ipynb
Histogram_matching.ipynb		Histogram_matching.ipynb
Hough_transform_straight_line.ipynb		Hough_transform_straight_line.ipynb
Hysteresis_thresholding.ipynb		Hysteresis_thresholding.ipynb
Image_deconvolution.ipynb		Image_deconvolution.ipynb
Interpolation - Edge modes.ipynb		Interpolation - Edge modes.ipynb
LICENSE		LICENSE
Local_Histogram_Equalization.ipynb		Local_Histogram_Equalization.ipynb
Marching_cubes.ipynb		Marching_cubes.ipynb
README.md		README.md
RGB2Gray.ipynb		RGB2Gray.ipynb
RGB_to_HSV.ipynb		RGB_to_HSV.ipynb
Random_shapes.ipynb		Random_shapes.ipynb
Rescale_resize_downscale.ipynb		Rescale_resize_downscale.ipynb
Ridge_operators.ipynb		Ridge_operators.ipynb
Scipy_zooming.ipynb		Scipy_zooming.ipynb
Shapes_drawing.ipynb		Shapes_drawing.ipynb
Skeletonize.ipynb		Skeletonize.ipynb
Structural_similarity_index.ipynb		Structural_similarity_index.ipynb
Swirl.ipynb		Swirl.ipynb
Tint_Grayscale.ipynb		Tint_Grayscale.ipynb
Unsharp_mask.ipynb		Unsharp_mask.ipynb
Using_Numpy_image_manipulation.ipynb		Using_Numpy_image_manipulation.ipynb
_config.yml		_config.yml
skimage_getting_started.ipynb		skimage_getting_started.ipynb

License

tirthajyoti/Scikit-image-processing

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Please feel free to connect with me here on LinkedIn if you are interested in data science and machine learning.

Image processing examples with Numpy, Scipy, and Scikit-image

Requirements

Testing after install

Simple NumPy array based operations

Exposure and color channel manipulations

Edges, lines, and contours

Geometrical transformations and registration

Filtering and restoration

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