Merge pull request #838 from ahojnnes/gallery

Gallery improvements

doc/examples/plot_circular_elliptical_hough_transform.py

@@ -48,7 +48,7 @@ from skimage.util import img_as_ubyte
 image = img_as_ubyte(data.coins()[0:95, 70:370])
 edges = filter.canny(image, sigma=3, low_threshold=10, high_threshold=50)
 
-fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
+fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(5, 2))
 
 # Detect two radii
 hough_radii = np.arange(15, 30, 2)
@@ -77,6 +77,8 @@ ax.imshow(image, cmap=plt.cm.gray)
 
 
 """
+.. image:: PLOT2RST.current_figure
+
 Ellipse detection
 =================
 
@@ -137,7 +139,7 @@ image_rgb[cy, cx] = (0, 0, 255)
 edges = color.gray2rgb(edges)
 edges[cy, cx] = (250, 0, 0)
 
-fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(10, 6))
+fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4))
 
 ax1.set_title('Original picture')
 ax1.imshow(image_rgb)
@@ -146,3 +148,8 @@ ax2.set_title('Edge (white) and result (red)')
 ax2.imshow(edges)
 
 plt.show()
+
+"""
+.. image:: PLOT2RST.current_figure
+
+"""

doc/examples/plot_equalize.py

@@ -17,6 +17,8 @@ that fall within the 2nd and 98th percentiles [2]_.
 .. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm
 
 """
+
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -24,6 +26,9 @@ from skimage import data, img_as_float
 from skimage import exposure
 
 
+matplotlib.rcParams['font.size'] = 8
+
+
 def plot_img_and_hist(img, axes, bins=256):
     """Plot an image along with its histogram and cumulative histogram.
 
@@ -66,7 +71,7 @@ img_eq = exposure.equalize_hist(img)
 img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
 
 # Display results
-f, axes = plt.subplots(2, 4, figsize=(8, 4))
+f, axes = plt.subplots(nrows=2, ncols=4, figsize=(8, 5))
 
 ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
 ax_img.set_title('Low contrast image')

doc/examples/plot_gabor.py

@@ -24,9 +24,6 @@ from skimage.util import img_as_float
 from skimage.filter import gabor_kernel
 
 
-matplotlib.rcParams['font.size'] = 9
-
-
 def compute_feats(image, kernels):
     feats = np.zeros((len(kernels), 2), dtype=np.double)
     for k, kernel in enumerate(kernels):
@@ -104,24 +101,24 @@ for theta in (0, 1):
         # Save kernel and the power image for each image
         results.append((kernel, [power(img, kernel) for img in images]))
 
-fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(9, 6))
+fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(5, 6))
 plt.gray()
 
-fig.suptitle('Image responses for Gabor filter kernels', fontsize=15)
+fig.suptitle('Image responses for Gabor filter kernels', fontsize=12)
 
 axes[0][0].axis('off')
 
 # Plot original images
 for label, img, ax in zip(image_names, images, axes[0][1:]):
     ax.imshow(img)
-    ax.set_title(label)
+    ax.set_title(label, fontsize=9)
     ax.axis('off')
 
 for label, (kernel, powers), ax_row in zip(kernel_params, results, axes[1:]):
     # Plot Gabor kernel
     ax = ax_row[0]
     ax.imshow(np.real(kernel), interpolation='nearest')
-    ax.set_ylabel(label)
+    ax.set_ylabel(label, fontsize=7)
     ax.set_xticks([])
     ax.set_yticks([])
 

doc/examples/plot_local_equalize.py

@@ -20,6 +20,7 @@ References
 
 """
 import numpy as np
+import matplotlib
 import matplotlib.pyplot as plt
 
 from skimage import data
@@ -30,6 +31,9 @@ from skimage.morphology import disk
 from skimage.filter import rank
 
 
+matplotlib.rcParams['font.size'] = 9
+
+
 def plot_img_and_hist(img, axes, bins=256):
     """Plot an image along with its histogram and cumulative histogram.
 
@@ -70,7 +74,7 @@ img_eq = rank.equalize(img, selem=selem)
 
 
 # Display results
-f, axes = plt.subplots(2, 3, figsize=(8, 4))
+f, axes = plt.subplots(2, 3, figsize=(8, 5))
 
 ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
 ax_img.set_title('Low contrast image')

doc/examples/plot_local_otsu.py

@@ -15,6 +15,7 @@ The example compares the local threshold with the global threshold.
 .. [1] http://en.wikipedia.org/wiki/Otsu's_method
 
 """
+import matplotlib
 import matplotlib.pyplot as plt
 
 from skimage import data
@@ -23,29 +24,41 @@ from skimage.filter import threshold_otsu, rank
 from skimage.util import img_as_ubyte
 
 
-p8 = img_as_ubyte(data.page())
+matplotlib.rcParams['font.size'] = 9
 
-radius = 10
+
+img = img_as_ubyte(data.page())
+
+radius = 15
 selem = disk(radius)
 
-loc_otsu = rank.otsu(p8, selem)
-t_glob_otsu = threshold_otsu(p8)
-glob_otsu = p8 >= t_glob_otsu
+local_otsu = rank.otsu(img, selem)
+threshold_global_otsu = threshold_otsu(img)
+global_otsu = img >= threshold_global_otsu
 
 
-plt.figure()
+plt.figure(figsize=(8, 5))
+
 plt.subplot(2, 2, 1)
-plt.imshow(p8, cmap=plt.cm.gray)
-plt.xlabel('original')
-plt.colorbar()
+plt.imshow(img, cmap=plt.cm.gray)
+plt.title('Original')
+plt.colorbar(orientation='horizontal')
+plt.axis('off')
+
 plt.subplot(2, 2, 2)
-plt.imshow(loc_otsu, cmap=plt.cm.gray)
-plt.xlabel('local Otsu ($radius=%d$)' % radius)
-plt.colorbar()
+plt.imshow(local_otsu, cmap=plt.cm.gray)
+plt.title('Local Otsu (radius=%d)' % radius)
+plt.colorbar(orientation='horizontal')
+plt.axis('off')
+
 plt.subplot(2, 2, 3)
-plt.imshow(p8 >= loc_otsu, cmap=plt.cm.gray)
-plt.xlabel('original >= local Otsu' % t_glob_otsu)
+plt.imshow(img >= local_otsu, cmap=plt.cm.gray)
+plt.title('Original >= Local Otsu' % threshold_global_otsu)
+plt.axis('off')
+
 plt.subplot(2, 2, 4)
-plt.imshow(glob_otsu, cmap=plt.cm.gray)
-plt.xlabel('global Otsu ($t = %d$)' % t_glob_otsu)
+plt.imshow(global_otsu, cmap=plt.cm.gray)
+plt.title('Global Otsu (threshold = %d)' % threshold_global_otsu)
+plt.axis('off')
+
 plt.show()

doc/examples/plot_otsu.py

@@ -14,12 +14,16 @@ the intra-class variance.
 .. [1] http://en.wikipedia.org/wiki/Otsu's_method
 
 """
+import matplotlib
 import matplotlib.pyplot as plt
 
 from skimage.data import camera
 from skimage.filter import threshold_otsu
 
 
+matplotlib.rcParams['font.size'] = 9
+
+
 image = camera()
 thresh = threshold_otsu(image)
 binary = image > thresh

doc/examples/plot_shapes.py

@@ -4,11 +4,17 @@ Shapes
 ======
 
 This example shows how to draw several different shapes:
-* line
-* Bezier curve
-* polygon
-* circle
-* ellipse
+
+ - line
+ - Bezier curve
+ - polygon
+ - circle
+ - ellipse
+
+Anti-aliased drawing for:
+
+ - line
+ - circle
 
 """
 import math
@@ -69,13 +75,6 @@ ax1.imshow(img)
 ax1.set_title('No anti-aliasing')
 ax1.axis('off')
 
-"""
-
-Anti-aliased drawing for:
-* line
-* circle
-
-"""
 
 from skimage.draw import line_aa, circle_perimeter_aa
 

doc/examples/plot_ssim.py

@@ -22,12 +22,16 @@ but with very different mean structural similarity indices.
 
 """
 import numpy as np
+import matplotlib
 import matplotlib.pyplot as plt
 
 from skimage import data, img_as_float
 from skimage.measure import structural_similarity as ssim
 
 
+matplotlib.rcParams['font.size'] = 9
+
+
 img = img_as_float(data.camera())
 rows, cols = img.shape
 
@@ -41,7 +45,7 @@ def mse(x, y):
 img_noise = img + noise
 img_const = img + abs(noise)
 
-f, (ax0, ax1, ax2) = plt.subplots(1, 3)
+f, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(8, 4))
 
 mse_none = mse(img, img)
 ssim_none = ssim(img, img, dynamic_range=img.max() - img.min())