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STY: Use standard skimage data type conversion.

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This commit is contained in:
Tony S Yu
2012-08-17 00:42:56 -04:00
parent 9cea60d817
commit e6d03eaebc
1 changed files with 26 additions and 13 deletions
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doc/examples/applications/plot_peak_detection_comparison.py
+26 -13
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@@ -84,23 +84,36 @@ peaks on the left to go undetected.
Morphological reconstruction Morphological reconstruction
============================ ============================
Morphological reconstruction uses two images: a seed image and a mask image.
Initially, all values of the reconstructed image start off pixel at the values
of the seed image. A seed pixels of high intensity spread outwards until it
hits the mask image (i.e. the mask value for a pixel is lower than the
high-intensity value). Note that the mask is a gray-scale image that limits the
maximum intensity at a pixel. This algorithm is clearer with pictures, which
we generate below.
One common case uses mask and seed images derived from the same image but
shifted in intensity. Note: be careful when shifting images integer values,
since this can lead to under/overflow of values. To prevent the uint8 image we
started with from underflowing during subtraction, we first convert to float:
""" """
import numpy as np from skimage import img_as_float
img_r = np.int32(img_smooth) img_r = img_as_float(img_smooth)
import skimage.morphology as morph import skimage.morphology as morph
h = 20 h = 0.1
rec = morph.reconstruction(img_r-h, img_r) rec = morph.reconstruction(img_r-h, img_r)
imshow(img_r, vmin=0, vmax=255) imshow(img_r, vmin=0, vmax=1)
plt.title("original (smoothed) image") plt.title("original (smoothed) image")
""" """
.. image:: PLOT2RST.current_figure .. image:: PLOT2RST.current_figure
""" """
imshow(rec, vmin=0, vmax=255) imshow(rec, vmin=0, vmax=1)
plt.title("background image (reconstruction)") plt.title("background image (reconstruction)")
""" """
@@ -156,10 +169,10 @@ features.
White tophat White tophat
============ ============
""" """
selem = morph.disk(10) selem = morph.disk(10)
img_t = np.uint8(img_smooth) opening = morph.opening(img_smooth, selem)
opening = morph.opening(img_t, selem) top_hat = img_smooth - opening
top_hat = img_t - opening
imshow(opening, vmin=0, vmax=255) imshow(opening, vmin=0, vmax=255)
plt.title("Greyscale opening of image") plt.title("Greyscale opening of image")
@@ -177,7 +190,7 @@ plt.title("Tophat with disk of r = 10")
""" """
selem = morph.disk(5) selem = morph.disk(5)
top_hat = morph.white_tophat(img_t, selem) top_hat = morph.white_tophat(img_smooth, selem)
imshow(top_hat) imshow(top_hat)
plt.title("Tophat with disk of r = 5") plt.title("Tophat with disk of r = 5")
@@ -187,11 +200,11 @@ plt.title("Tophat with disk of r = 5")
""" """
selem = morph.square(20) selem = morph.square(20)
opening = morph.opening(img_t, selem) opening = morph.opening(img_smooth, selem)
# scikit's top hat filter uses uint8 and doesn't check for over(under)flow. # scikit's top hat filter uses uint8 and doesn't check for over(under)flow.
mask = opening > img_t mask = opening > img_smooth
opening[mask] = img_t[mask] opening[mask] = img_smooth[mask]
top_hat = img_t - opening top_hat = img_smooth - opening
imshow(opening, vmin=0, vmax=255) imshow(opening, vmin=0, vmax=255)
plt.title("Greyscale opening of image") plt.title("Greyscale opening of image")