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Show only Scharr filter result, removed plots of Sobel and Prewitt filter results. Reworked corresponding paragraph.

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tv
2015-09-01 22:28:21 +01:00
parent dafa3d7dfe
commit 3bdab63437
1 changed files with 26 additions and 34 deletions
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doc/examples/plot_edge_filter.py
+26 -34
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@@ -35,15 +35,16 @@ plt.tight_layout()
.. image:: PLOT2RST.current_figure
Different operators compute different finite-difference approximations of the
gradient. For example, the Scharr filter results in a better rotational
variance than the Sobel filter that is in turn better than the Prewitt filter
[1]_ [2]_ [3]_. The difference between the Prewitt and Sobel filters and the
Scharr filter is illustrated below on an image that is the discretization of a
rotation-invariant continuous function. The discrepancy between the Prewitt and
Sobel filters, and the Scharr filter is stronger for regions of the image where
the direction of the gradient is close to diagonal, and for regions with high
spatial frequencies. The Sobel filter is less rotationally invariant than the
Sobel filter.
gradient. For example, the Scharr filter results in a less rotational variance
than the Sobel filter that is in turn better than the Prewitt filter [1]_ [2]_
[3]_. The difference between the Prewitt and Sobel filters and the Scharr filter
is illustrated below with an image that is the discretization of a rotation-
invariant continuous function. The discrepancy between the Prewitt and Sobel
filters, and the Scharr filter is stronger for regions of the image where the
direction of the gradient is close to diagonal, and for regions with high
spatial frequencies. For the example image the differences between the filter
results are very small and the filter results are visually almost
indistinguishable.
.. [1] https://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators
@@ -61,38 +62,29 @@ edge_sobel = sobel(img)
edge_scharr = scharr(img)
edge_prewitt = prewitt(img)
fig, ((ax0, ax1, ax2), (ax3, ax4, ax5)) = plt.subplots(nrows=2, ncols=3)
ax0.imshow(edge_prewitt, cmap=plt.cm.gray)
ax0.set_title('Prewitt Edge Detection')
ax0.axis('off')
ax1.imshow(edge_sobel, cmap=plt.cm.gray)
ax1.set_title('Sobel Edge Detection')
ax1.axis('off')
ax2.imshow(edge_scharr, cmap=plt.cm.gray)
ax2.set_title('Scharr Edge Detection')
ax2.axis('off')
ax3.imshow(img, cmap=plt.cm.gray)
ax3.set_title('Original image')
ax3.axis('off')
diff_scharr_prewitt = edge_scharr - edge_prewitt
diff_scharr_sobel = edge_scharr - edge_sobel
max_diff = np.max(np.maximum(diff_scharr_prewitt, diff_scharr_sobel))
ax4.imshow(diff_scharr_prewitt, cmap=plt.cm.jet, vmax=max_diff)
ax4.set_title('difference (Scharr - Prewitt)')
ax4.axis('off')
fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2)
ax5.imshow(diff_scharr_sobel, cmap=plt.cm.jet, vmax=max_diff)
ax5.set_title('difference (Scharr - Sobel)')
ax5.axis('off')
ax0.imshow(img, cmap=plt.cm.gray)
ax0.set_title('Original image')
ax0.axis('off')
ax1.imshow(edge_scharr, cmap=plt.cm.gray)
ax1.set_title('Scharr Edge Detection')
ax1.axis('off')
ax2.imshow(diff_scharr_prewitt, cmap=plt.cm.jet, vmax=max_diff)
ax2.set_title('Scharr - Prewitt')
ax2.axis('off')
ax3.imshow(diff_scharr_sobel, cmap=plt.cm.jet, vmax=max_diff)
ax3.set_title('Scharr - Sobel')
ax3.axis('off')
plt.tight_layout()
plt.show()
"""