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changed f to fig in applications

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Siva Prasad Varma
2014-03-19 17:30:20 +05:30
parent a89dd4dedd
commit 6a8f5fefd6
2 changed files with 25 additions and 25 deletions
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doc/examples/applications/plot_coins_segmentation.py
+11 -11
View File
@@ -16,7 +16,7 @@ from skimage import data
coins = data.coins()
hist = np.histogram(coins, bins=np.arange(0, 256))
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
ax1.axis('off')
ax2.plot(hist[1][:-1], hist[0], lw=2)
@@ -35,7 +35,7 @@ background with the coins:
"""
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
ax1.set_title('coins > 100')
ax1.axis('off')
@@ -43,7 +43,7 @@ ax2.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
ax2.set_title('coins > 150')
ax2.axis('off')
margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
f.subplots_adjust(**margins)
fig.subplots_adjust(**margins)
"""
.. image:: PLOT2RST.current_figure
@@ -60,7 +60,7 @@ edge-detector.
from skimage.filter import canny
edges = canny(coins/255.)
f, ax = plt.subplots(figsize=(4, 3))
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('Canny detector')
@@ -75,7 +75,7 @@ from scipy import ndimage
fill_coins = ndimage.binary_fill_holes(edges)
f, ax = plt.subplots(figsize=(4, 3))
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('Filling the holes')
@@ -89,7 +89,7 @@ objects.
from skimage import morphology
coins_cleaned = morphology.remove_small_objects(fill_coins, 21)
f, ax = plt.subplots(figsize=(4, 3))
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('Removing small objects')
@@ -113,7 +113,7 @@ from skimage.filter import sobel
elevation_map = sobel(coins)
f, ax = plt.subplots(figsize=(4, 3))
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
ax.axis('off')
ax.set_title('elevation_map')
@@ -129,7 +129,7 @@ markers = np.zeros_like(coins)
markers[coins < 30] = 1
markers[coins > 150] = 2
f, ax = plt.subplots(figsize=(4, 3))
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
ax.axis('off')
ax.set_title('markers')
@@ -143,7 +143,7 @@ starting from the markers determined above:
"""
segmentation = morphology.watershed(elevation_map, markers)
f, ax = plt.subplots(figsize=(4, 3))
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('segmentation')
@@ -162,14 +162,14 @@ segmentation = ndimage.binary_fill_holes(segmentation - 1)
labeled_coins, _ = ndimage.label(segmentation)
image_label_overlay = label2rgb(labeled_coins, image=coins)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
ax1.axis('off')
ax2.imshow(image_label_overlay, interpolation='nearest')
ax2.axis('off')
f.subplots_adjust(**margins)
fig.subplots_adjust(**margins)
"""
.. image:: PLOT2RST.current_figure
doc/examples/applications/plot_rank_filters.py
+14 -14
View File
@@ -44,7 +44,7 @@ from skimage import data
noisy_image = img_as_ubyte(data.camera())
hist = np.histogram(noisy_image, bins=np.arange(0, 256))
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
ax1.axis('off')
ax2.plot(hist[1][:-1], hist[0], lw=2)
@@ -201,7 +201,7 @@ hist = np.histogram(noisy_image, bins=np.arange(0, 256))
glob_hist = np.histogram(glob, bins=np.arange(0, 256))
loc_hist = np.histogram(loc, bins=np.arange(0, 256))
f, ax = plt.subplots(3, 2, figsize=(10, 10))
fig, ax = plt.subplots(3, 2, figsize=(10, 10))
ax1, ax2, ax3, ax4, ax5, ax6 = ax.ravel()
ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
@@ -398,14 +398,14 @@ loc_otsu = p8 >= t_loc_otsu
t_glob_otsu = threshold_otsu(p8)
glob_otsu = p8 >= t_glob_otsu
f, ax = plt.subplots(2, 2)
fig, ax = plt.subplots(2, 2)
ax1, ax2, ax3, ax4 = ax.ravel()
f.colorbar(ax1.imshow(p8, cmap=plt.cm.gray), ax=ax1)
fig.colorbar(ax1.imshow(p8, cmap=plt.cm.gray), ax=ax1)
ax1.set_title('Original')
ax1.axis('off')
f.colorbar(ax2.imshow(t_loc_otsu, cmap=plt.cm.gray), ax=ax2)
fig.colorbar(ax2.imshow(t_loc_otsu, cmap=plt.cm.gray), ax=ax2)
ax2.set_title('Local Otsu ($r=%d$)' % radius)
ax2.axis('off')
@@ -434,7 +434,7 @@ m = (np.tile(x, (n, 1)) * np.linspace(0.1, 1, n) * 128 + 128).astype(np.uint8)
radius = 10
t = rank.otsu(m, disk(radius))
f, (ax1, ax2) = plt.subplots(1, 2)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(m)
ax1.set_title('Original')
@@ -523,13 +523,13 @@ import matplotlib.pyplot as plt
image = data.camera()
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
f.colorbar(ax1.imshow(image, cmap=plt.cm.gray), ax=ax1)
fig.colorbar(ax1.imshow(image, cmap=plt.cm.gray), ax=ax1)
ax1.set_title('Image')
ax1.axis('off')
f.colorbar(ax2.imshow(entropy(image, disk(5)), cmap=plt.cm.jet), ax=ax2)
fig.colorbar(ax2.imshow(entropy(image, disk(5)), cmap=plt.cm.jet), ax=ax2)
ax2.set_title('Entropy')
ax2.axis('off')
@@ -616,7 +616,7 @@ for r in e_range:
rec = np.asarray(rec)
f, ax = plt.subplots()
fig, ax = plt.subplots()
ax.set_title('Performance with respect to element size')
ax.set_ylabel('Time (ms)')
ax.set_xlabel('Element radius')
@@ -644,7 +644,7 @@ for s in s_range:
rec = np.asarray(rec)
f, ax = plt.subplots()
fig, ax = plt.subplots()
ax.set_title('Performance with respect to image size')
ax.set_ylabel('Time (ms)')
ax.set_xlabel('Image size')
@@ -679,7 +679,7 @@ for r in e_range:
rec = np.asarray(rec)
f, ax = plt.subplots()
fig, ax = plt.subplots()
ax.set_title('Performance with respect to element size')
ax.plot(e_range, rec)
ax.legend(['filter.rank.median', 'filter.median_filter',
@@ -694,7 +694,7 @@ Comparison of outcome of the three methods:
"""
f, ax = plt.subplots()
fig, ax = plt.subplots()
ax.imshow(np.hstack((rc, rctmf, rndi)))
ax.set_title('filter.rank.median vs filtermedian_filter vs scipy.ndimage.percentile')
ax.axis('off')
@@ -720,7 +720,7 @@ for s in s_range:
rec = np.asarray(rec)
f, ax = plt.subplots()
fig, ax = plt.subplots()
ax.set_title('Performance with respect to image size')
ax.plot(s_range, rec)
ax.legend(['filter.rank.median', 'filter.median_filter',