## @example filter_image_and_display_with_matplotlib.py
# This example show how a FAST image can be converted to a numpy ndarray
# and displayed using matplotlib in python.
import fast
import numpy as np
import matplotlib.pyplot as plt
fast.downloadTestDataIfNotExists(
) # This will download the test data needed to run the example
# Set up FAST pipeline
importer = fast.ImageFileImporter.New()
importer.setFilename(fast.Config.getTestDataPath() +
'US/Heart/ApicalFourChamber/US-2D_0.mhd')
filter = fast.NonLocalMeans.New()
filter.setInputConnection(importer.getOutputPort())
# Execute pipeline and convert images to numpy arrays
input_image = importer.updateAndGetOutputImage()
pixel_spacing = input_image.getSpacing()
input_image = np.asarray(input_image)
filtered_image = np.asarray(filter.updateAndGetOutputImage())
# Display using matplotlib
f, axes = plt.subplots(1, 2)
aspect = pixel_spacing[1] / pixel_spacing[
0] # Compensate for anisotropic pixel spacing
axes[0].imshow(input_image[..., 0], cmap='gray', aspect=aspect)
axes[1].imshow(filtered_image[..., 0], cmap='gray', aspect=aspect)
plt.show()
## @example load_and_display_image.py # This example simply loads a metaimage (.mhd) and displays it on screen # @image html images/examples/python/left_ventricle.jpg import fast # This will download the test data needed to run the example fast.downloadTestDataIfNotExists() importer = fast.ImageFileImporter.New() importer.setFilename(fast.Config.getTestDataPath() + 'US/Heart/ApicalFourChamber/US-2D_0.mhd') renderer = fast.ImageRenderer.New() renderer.setInputConnection(importer.getOutputPort()) window = fast.SimpleWindow.New() window.set2DMode() window.addRenderer(renderer) window.start()