Improve example

doc/examples/segmentation/plot_backprojection.py

@@ -10,7 +10,7 @@
 feature to find the object in other images. So it takes two images as input,
 one is your object image and the next is the image where you want to find the
 object. The function then computes the histograms of these two images and
-return a **backprojected image**. A backprojected image is a grayscale image
+return a **backprojected image** [1]_. A backprojected image is a grayscale image
 where each pixel denotes the probability of that pixel being part of the
 object. By thresholding this backprojected image with a suitable value gives
 the objects' mask.
@@ -21,9 +21,13 @@
 thresholding to get the mask. Then a bitwise_and operation with input image
 and mask gives the segmented object.
 
+
+References
+----------
+
 .. [1] Swain, Michael J., and Dana H. Ballard. "Indexing via color histograms."
        Active Perception and Robot Vision. Springer Berlin Heidelberg,
-       1992. 261-273.  DOI:`10.1109/ICCV.1990.139558`
+       1992. 261-273.  :DOI:`10.1109/ICCV.1990.139558`
 
 """
 import numpy as np
@@ -35,30 +39,36 @@
 from skimage import img_as_ubyte
 
 
-img1 = data.immunohistochemistry()
-img1 = img_as_ubyte(img1)
-img2 = img1[:200, :200]
+image = data.immunohistochemistry()
+image = img_as_ubyte(image)
+template = image[50:200, :150]
 
 # apply histogram backprojection
-bc = histogram_backprojection(img1, img2)
+backprojection = histogram_backprojection(image, template)
 
 # threshold the image with otsu's thresholding
-thresh = threshold_otsu(bc)
-mask = np.where(bc >= thresh, 255, 0).astype('uint8')
-res = np.bitwise_and(img1, mask[..., np.newaxis])
+thresh = threshold_otsu(backprojection)
+mask = backprojection >= thresh
+# Set zero values where the mask is False
+res = np.where(mask[..., None], image, 0)
+
+fig, ax = plt.subplots(nrows=2, ncols=2)
+ax = ax.ravel()
 
-fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3)
+ax[0].imshow(image)
+ax[0].axis('off')
+ax[0].set_title('Input image')
 
-ax1.imshow(img1)
-ax1.axis('off')
-ax1.set_title('input image')
+ax[1].imshow(template)
+ax[1].axis('off')
+ax[1].set_title('Template')
 
-ax2.imshow(mask, cmap=plt.cm.gray)
-ax2.axis('off')
-ax2.set_title('backprojected image')
+ax[2].imshow(backprojection, cmap=plt.cm.gray)
+ax[2].axis('off')
+ax[2].set_title('Backprojected image')
 
-ax3.imshow(res)
-ax3.axis('off')
-ax3.set_title('segmented image')
+ax[3].imshow(res)
+ax[3].axis('off')
+ax[3].set_title('Segmented image')
 
 plt.show()