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"""
Custom image interpolation kernels
==================================
When interpolation is set to 'custom', the convolution kernel provided by
`custom_interpolation_kernel_2d` is used to convolve the image on the gpu.
In this example, we use custom gaussian kernels of arbitrary size, a sharpening
kernel and a ridge detection kernel.
Under the hood, this works by by sampling the image texture with `linear`
interpolation in a regular grid (of size = of the kernel) around each fragment,
and then using the weights in the kernel to add up the final fragment value.
.. tags:: gui, visualization-nD
"""
import numpy as np
from magicgui import magicgui
from scipy.signal.windows import gaussian
from skimage import data
import napari
viewer = napari.Viewer()
layer = viewer.add_image(data.astronaut(), rgb=True, interpolation2d='custom')
def gaussian_kernel(size, sigma):
window = gaussian(size, sigma)
kernel = np.outer(window, window)
return kernel / kernel.sum()
def sharpen_kernel():
return np.array([
[ 0, -1, 0],
[-1, 5, -1],
[ 0, -1, 0],
])
def ridge_detection_kernel():
return np.array([
[-1, -1, -1],
[-1, 9, -1],
[-1, -1, -1],
])
@magicgui(
auto_call=True,
kernel_size={'widget_type': 'Slider', 'min': 1, 'max': 20},
sigma={'widget_type': 'FloatSlider', 'min': 0.1, 'max': 5, 'step': 0.1},
kernel_type={'choices': ['none', 'gaussian', 'sharpen', 'ridge_detection']},
)
def gpu_kernel(image: napari.layers.Image, kernel_type: str = 'gaussian', kernel_size: int = 5, sigma: float = 1):
if kernel_type == 'none':
image.interpolation2d = 'linear'
else:
image.interpolation2d = 'custom'
if kernel_type == 'gaussian':
gpu_kernel.kernel_size.show()
gpu_kernel.sigma.show()
else:
gpu_kernel.kernel_size.hide()
gpu_kernel.sigma.hide()
if kernel_type == 'gaussian':
image.custom_interpolation_kernel_2d = gaussian_kernel(kernel_size, sigma)
elif kernel_type == 'sharpen':
image.custom_interpolation_kernel_2d = sharpen_kernel()
elif kernel_type == 'ridge_detection':
image.custom_interpolation_kernel_2d = ridge_detection_kernel()
viewer.window.add_dock_widget(gpu_kernel)
gpu_kernel()
if __name__ == '__main__':
napari.run()
|
