Merge pull request #182 from tonysyu/threshold-example

DOC: Add example for adaptive thresholding.

doc/examples/plot_threshold_adaptive.py

@@ -0,0 +1,48 @@
+"""
+=====================
+Adaptive Thresholding
+=====================
+
+Thresholding is the simplest way to segment objects from a background. If that
+background is relatively uniform, then you can use a global threshold value to
+binarize the image by pixel-intensity. If there's large variation in the
+background intensity, however, adaptive thresholding (a.k.a. local or dynamic
+thresholding) may produce better results.
+
+Here, we binarize an image using the `threshold_adaptive` function, which
+calculates thresholds in regions of size `block_size` surrounding each pixel
+(i.e. local neighborhoods). Each threshold value is the weighted mean of the
+local neighborhood minus an offset value.
+
+"""
+import matplotlib.pyplot as plt
+
+from skimage import data
+from skimage.filter import threshold_otsu, threshold_adaptive
+
+
+image = data.page()
+
+global_thresh = threshold_otsu(image)
+binary_global = image > global_thresh
+
+block_size = 40
+binary_adaptive = threshold_adaptive(image, block_size, offset=10)
+
+fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
+ax0, ax1, ax2 = axes
+plt.gray()
+
+ax0.imshow(image)
+ax0.set_title('Image')
+
+ax1.imshow(binary_global)
+ax1.set_title('Global thresholding')
+
+ax2.imshow(binary_adaptive)
+ax2.set_title('Adaptive thresholding')
+
+for ax in axes:
+    ax.axis('off')
+
+plt.show()

skimage/data/__init__.py

@@ -96,3 +96,12 @@ def moon():
 
     """
     return load("moon.png")
+
+def page():
+    """Scanned page.
+
+    This image of printed text is useful for demonstrations requiring uneven
+    background illumination.
+
+    """
+    return load("page.png")