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Description
Examples tend to be more compelling and easier to understand when the developer can compare input and output images, and sometimes even interact with the data. A "good first issue" for a contributor could be to choose one of the following examples, read the available code snippet to understand its purpose, and then add one or two visuals to aid in learning.
When selecting visuals to add, consider the following:
- Static examples such as still 2D images can be particularly useful for developers that are briefly visiting an example. Consider adding at a minimum a 2D input image and 2D output image to load in the user's browser alongside the example.
- In many cases example images may be more compelling when they represent simplified real-world data rather than contrived instances. Consider using existing scientific data samples such as BrainProtonDensitySliceBorder20.png, cthead1, or HeadMRVolume.mha to illustrate image filter effects, or even add your own public scientific image data.
- In cases where it is reasonable to generate data, such as visualizing kernels, consider using a library such as
matplotlib.pyplot.imshowto construct output visualizations. - For 3D cases consider generating screenshots with 3D Slicer, itkwidgets, or ParaView Glance.
- Please upload data accompanying examples to its respective ITKExamples folder at data.kitware.com (https://data.kitware.com/#collection/57b5c9e58d777f126827f5a1/folder/58bcc0998d777f0aef5d111c). Ask maintainers such as @thewtex or @tbirdso for help if you encounter trouble in creating the appropriate example folder.
Existing example source files such as https://github.com/InsightSoftwareConsortium/ITKSphinxExamples/tree/master/src/Core/Common/AddNoiseToBinaryImage can be referenced for learning how to add new figures to .rst documentation.
The following is a non-exhaustive list of examples that could benefit from new or updated visuals:
- Apply A Filter Only To A Specified Region Of An Image (please see
itk::CropImageFilterfor how to crop to the requested region before writing out the image) (duplicates at https://examples.itk.org/src/filtering/imagefeature/applyafilteronlytoaspecifiedimageregion/documentation and https://examples.itk.org/src/filtering/imagefeature/applyafiltertoaspecifiedregionofanimage/documentation) - Convert Array To Image
- Apply Custom Operation To Each Pixel In Image
- Bounding Box Of A Point Set
- Bresenham Line
- Create Derivative Kernel
- Create Gaussian Kernel
- Create Vector Image
- Crop Image By Specifying Region
- Image Region Intersection
- Make Part of an Image Transparent
- Mini Pipeline
- Produce Image Programmatically
- Read a PointSet
- Extract Channel of Image with Multiple Components
- View Component Image As Scalar Image
- Compute Median of Image At Pixel
- Convert Spatial Object To An Image
- Normalized Correlation of Masked Image
- Normalized Correlation Using FFT
- Normalized Correlation Using FFT With Mask Images for Input Images
- Approximate Distance Map of Binary Image
- Maurer Distance Map of Binary Image
- Signed Distance Map Of Binary Image
- Create Distance Map From Seeds
- Compute Forward FFT
- Compute Inverse FFT Of Image
- Absolute Value of Difference Between Two Images
- Squared Difference of Two Images
- Join Images
- Extract Contours From An Image
- Sharpen an Image
- Apply Kernel To Every Pixel
- Cast An Image To Another Type
- Compute And Display Gradient of Image
- Gradient of Vector Image
- Add Constant To Every Pixel
- Add Two Images Together
- Compute PCA Shape from Training Sample
- Label Binary Regions and Get Properties
- Blurring an Image Using a Binomial Kernel
- Resample DICOM Series
- Create 3D Volume from Series of 2D Images
- Read Transform From File
- Amoeba Optimizer
- Compute Texture Features
- Compute Mean Squares Metric Between Two Images
- Global Registration of Two Images
- Match Feature Points
- Multiresolution Pyramid From Image
- Register Two Point Sets
- K-Means Clustering
- Connect Components In Image