81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
"""
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===============================
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Extrema
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===============================
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We detect local maxima in a galaxy image. The image is corrupted by noise,
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generating many local maxima. To keep only those maxima with sufficient
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local contrast, we use h-maxima.
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"""
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import matplotlib.pyplot as plt
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from skimage.measure import label
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from skimage import data
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from skimage import color
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from skimage.morphology import extrema
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from skimage import exposure
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color_image = data.hubble_deep_field()
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# for illustration purposes, we work on a crop of the image.
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x_0 = 70
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y_0 = 354
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width = 100
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height = 100
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img = color.rgb2gray(color_image)[y_0:(y_0 + height), x_0:(x_0 + width)]
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# the rescaling is done only for visualization purpose.
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# the algorithms would work identically in an unscaled version of the
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# image. However, the parameter h needs to be adapted to the scale.
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img = exposure.rescale_intensity(img)
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##############################################################
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# MAXIMA DETECTION
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# Maxima in the galaxy image are detected by mathematical morphology.
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# There is no a priori constraint on the density.
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# We find all local maxima
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local_maxima = extrema.local_maxima(img)
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label_maxima = label(local_maxima)
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overlay = color.label2rgb(label_maxima, img, alpha=0.7, bg_label=0,
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bg_color=None, colors=[(1, 0, 0)])
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# We observed in the previous image, that there are many local maxima
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# that are caused by the noise in the image.
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# For this, we find all local maxima with a height of h.
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# This height is the gray level value by which we need to descent
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# in order to reach a higher maximum and it can be seen as a local
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# contrast measurement.
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# The value of h scales with the dynamic range of the image, i.e.
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# if we multiply the image with a constant, we need to multiply
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# the value of h with the same constant in order to achieve the same result.
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h = 0.05
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h_maxima = extrema.h_maxima(img, h)
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label_h_maxima = label(h_maxima)
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overlay_h = color.label2rgb(label_h_maxima, img, alpha=0.7, bg_label=0,
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bg_color=None, colors=[(1, 0, 0)])
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##############################################################
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# GRAPHICAL OUTPUT
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# a new figure with 3 subplots
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fig, ax = plt.subplots(1, 3, figsize=(15, 5))
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ax[0].imshow(img, cmap='gray')
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ax[0].set_title('Original image')
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ax[0].axis('off')
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ax[1].imshow(overlay)
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ax[1].set_title('Local Maxima')
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ax[1].axis('off')
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ax[2].imshow(overlay_h)
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ax[2].set_title('h maxima for h = %.2f' % h)
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ax[2].axis('off')
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plt.show()
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