mirror of
https://github.com/wassname/scikit-image.git
synced 2026-09-09 11:33:41 +08:00
used plt.subplots everywhere
This commit is contained in:
Siva Prasad Varma
@@ -16,13 +16,11 @@ from skimage import data
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coins = data.coins()
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hist = np.histogram(coins, bins=np.arange(0, 256))
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plt.figure(figsize=(8, 3))
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plt.subplot(121)
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plt.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.subplot(122)
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plt.plot(hist[1][:-1], hist[0], lw=2)
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plt.title('histogram of grey values')
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f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
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ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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ax1.axis('off')
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ax2.plot(hist[1][:-1], hist[0], lw=2)
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ax2.set_title('histogram of grey values')
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"""
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.. image:: PLOT2RST.current_figure
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@@ -37,17 +35,15 @@ background with the coins:
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"""
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plt.figure(figsize=(6, 3))
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plt.subplot(121)
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plt.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
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plt.title('coins > 100')
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plt.axis('off')
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plt.subplot(122)
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plt.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
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plt.title('coins > 150')
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plt.axis('off')
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f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
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ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
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ax1.set_title('coins > 100')
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ax1.axis('off')
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ax2.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
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ax2.set_title('coins > 150')
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ax2.axis('off')
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margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
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plt.subplots_adjust(**margins)
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f.subplots_adjust(**margins)
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"""
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.. image:: PLOT2RST.current_figure
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@@ -64,10 +60,10 @@ edge-detector.
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from skimage.filter import canny
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edges = canny(coins/255.)
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plt.figure(figsize=(4, 3))
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plt.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.title('Canny detector')
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f, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('Canny detector')
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"""
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.. image:: PLOT2RST.current_figure
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@@ -79,10 +75,10 @@ from scipy import ndimage
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fill_coins = ndimage.binary_fill_holes(edges)
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plt.figure(figsize=(4, 3))
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plt.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.title('Filling the holes')
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f, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('Filling the holes')
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"""
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.. image:: PLOT2RST.current_figure
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@@ -93,10 +89,10 @@ objects.
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from skimage import morphology
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coins_cleaned = morphology.remove_small_objects(fill_coins, 21)
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plt.figure(figsize=(4, 3))
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plt.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.title('Removing small objects')
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f, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('Removing small objects')
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"""
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.. image:: PLOT2RST.current_figure
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@@ -117,10 +113,10 @@ from skimage.filter import sobel
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elevation_map = sobel(coins)
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plt.figure(figsize=(4, 3))
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plt.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
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plt.axis('off')
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plt.title('elevation_map')
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f, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
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ax.axis('off')
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ax.set_title('elevation_map')
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"""
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.. image:: PLOT2RST.current_figure
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@@ -133,10 +129,10 @@ markers = np.zeros_like(coins)
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markers[coins < 30] = 1
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markers[coins > 150] = 2
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plt.figure(figsize=(4, 3))
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plt.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
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plt.axis('off')
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plt.title('markers')
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f, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
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ax.axis('off')
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ax.set_title('markers')
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"""
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.. image:: PLOT2RST.current_figure
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@@ -147,10 +143,10 @@ starting from the markers determined above:
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"""
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segmentation = morphology.watershed(elevation_map, markers)
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plt.figure(figsize=(4, 3))
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plt.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
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plt.axis('off')
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plt.title('segmentation')
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f, ax = plt.subplots(figsize=(4, 3))
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ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
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ax.axis('off')
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ax.set_title('segmentation')
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"""
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.. image:: PLOT2RST.current_figure
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@@ -166,16 +162,14 @@ segmentation = ndimage.binary_fill_holes(segmentation - 1)
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labeled_coins, _ = ndimage.label(segmentation)
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image_label_overlay = label2rgb(labeled_coins, image=coins)
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plt.figure(figsize=(6, 3))
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plt.subplot(121)
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plt.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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plt.contour(segmentation, [0.5], linewidths=1.2, colors='y')
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plt.axis('off')
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plt.subplot(122)
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plt.imshow(image_label_overlay, interpolation='nearest')
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plt.axis('off')
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f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
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ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
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ax1.axis('off')
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ax2.imshow(image_label_overlay, interpolation='nearest')
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ax2.axis('off')
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plt.subplots_adjust(**margins)
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f.subplots_adjust(**margins)
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"""
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.. image:: PLOT2RST.current_figure
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@@ -32,7 +32,7 @@ from skimage import io
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plt.gray()
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phantom = img_as_ubyte(io.imread(data_dir+'/phantom.png', as_grey=True))
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plt.imshow(phantom)
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io.imshow(phantom)
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"""
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.. image:: PLOT2RST.current_figure
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@@ -44,13 +44,11 @@ from skimage import data
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noisy_image = img_as_ubyte(data.camera())
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hist = np.histogram(noisy_image, bins=np.arange(0, 256))
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plt.figure(figsize=(8, 3))
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plt.subplot(1, 2, 1)
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plt.imshow(noisy_image, interpolation='nearest')
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plt.axis('off')
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plt.subplot(1, 2, 2)
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plt.plot(hist[1][:-1], hist[0], lw=2)
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plt.title('Histogram of grey values')
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f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))
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ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
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ax1.axis('off')
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ax2.plot(hist[1][:-1], hist[0], lw=2)
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ax2.set_title('Histogram of grey values')
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"""
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@@ -77,27 +75,24 @@ noisy_image = img_as_ubyte(data.camera())
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noisy_image[noise > 0.99] = 255
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noisy_image[noise < 0.01] = 0
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fig = plt.figure(figsize=(10, 7))
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fig, ax = plt.subplots(2, 2, figsize=(10, 7))
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ax1, ax2, ax3, ax4 = ax.ravel()
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plt.subplot(2, 2, 1)
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plt.imshow(noisy_image, vmin=0, vmax=255)
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plt.title('Noisy image')
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plt.axis('off')
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ax1.imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray)
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ax1.set_title('Noisy image')
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ax1.axis('off')
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plt.subplot(2, 2, 2)
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plt.imshow(median(noisy_image, disk(1)), vmin=0, vmax=255)
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plt.title('Median $r=1$')
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plt.axis('off')
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ax2.imshow(median(noisy_image, disk(1)), vmin=0, vmax=255, cmap=plt.cm.gray)
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ax2.set_title('Median $r=1$')
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ax2.axis('off')
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plt.subplot(2, 2, 3)
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plt.imshow(median(noisy_image, disk(5)), vmin=0, vmax=255)
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plt.title('Median $r=5$')
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plt.axis('off')
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ax3.imshow(median(noisy_image, disk(5)), vmin=0, vmax=255, cmap=plt.cm.gray)
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ax3.set_title('Median $r=5$')
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ax3.axis('off')
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plt.subplot(2, 2, 4)
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plt.imshow(median(noisy_image, disk(20)), vmin=0, vmax=255)
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plt.title('Median $r=20$')
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plt.axis('off')
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ax4.imshow(median(noisy_image, disk(20)), vmin=0, vmax=255, cmap=plt.cm.gray)
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ax4.set_title('Median $r=20$')
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ax4.axis('off')
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"""
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@@ -119,19 +114,17 @@ image.
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from skimage.filter.rank import mean
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fig = plt.figure(figsize=[10, 7])
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7])
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loc_mean = mean(noisy_image, disk(10))
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plt.subplot(1, 2, 1)
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plt.imshow(noisy_image, vmin=0, vmax=255)
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plt.title('Original')
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plt.axis('off')
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ax1.imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray)
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ax1.set_title('Original')
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ax1.axis('off')
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plt.subplot(1, 2, 2)
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plt.imshow(loc_mean, vmin=0, vmax=255)
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plt.title('Local mean $r=10$')
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plt.axis('off')
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ax2.imshow(loc_mean, vmin=0, vmax=255, cmap=plt.cm.gray)
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ax2.set_title('Local mean $r=10$')
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ax2.axis('off')
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"""
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@@ -155,25 +148,22 @@ noisy_image = img_as_ubyte(data.camera())
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bilat = bilateral_mean(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
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fig = plt.figure(figsize=[10, 7])
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fig, ax = plt.subplots(2, 2, figsize=(10, 7))
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ax1, ax2, ax3, ax4 = ax.ravel()
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plt.subplot(2, 2, 1)
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plt.imshow(noisy_image, cmap=plt.cm.gray)
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plt.title('Original')
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plt.axis('off')
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ax1.imshow(noisy_image, cmap=plt.cm.gray)
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ax1.set_title('Original')
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ax1.axis('off')
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plt.subplot(2, 2, 3)
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plt.imshow(bilat, cmap=plt.cm.gray)
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plt.title('Bilateral mean')
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plt.axis('off')
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ax2.imshow(bilat, cmap=plt.cm.gray)
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ax2.set_title('Bilateral mean')
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ax2.axis('off')
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plt.subplot(2, 2, 2)
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plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
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plt.axis('off')
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ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
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ax3.axis('off')
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plt.subplot(2, 2, 4)
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plt.imshow(bilat[200:350, 350:450], cmap=plt.cm.gray)
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plt.axis('off')
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ax4.imshow(bilat[200:350, 350:450], cmap=plt.cm.gray)
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ax4.axis('off')
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"""
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@@ -211,31 +201,26 @@ hist = np.histogram(noisy_image, bins=np.arange(0, 256))
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glob_hist = np.histogram(glob, bins=np.arange(0, 256))
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loc_hist = np.histogram(loc, bins=np.arange(0, 256))
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plt.figure(figsize=(10, 10))
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f, ax = plt.subplots(3, 2, figsize=(10, 10))
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ax1, ax2, ax3, ax4, ax5, ax6 = ax.ravel()
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plt.subplot(321)
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plt.imshow(noisy_image, interpolation='nearest')
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plt.axis('off')
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ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
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ax1.axis('off')
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plt.subplot(322)
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plt.plot(hist[1][:-1], hist[0], lw=2)
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plt.title('Histogram of gray values')
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ax2.plot(hist[1][:-1], hist[0], lw=2)
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ax2.set_title('Histogram of gray values')
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plt.subplot(323)
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plt.imshow(glob, interpolation='nearest')
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plt.axis('off')
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ax3.imshow(glob, interpolation='nearest', cmap=plt.cm.gray)
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ax3.axis('off')
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plt.subplot(324)
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plt.plot(glob_hist[1][:-1], glob_hist[0], lw=2)
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plt.title('Histogram of gray values')
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ax4.plot(glob_hist[1][:-1], glob_hist[0], lw=2)
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ax4.set_title('Histogram of gray values')
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plt.subplot(325)
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plt.imshow(loc, interpolation='nearest')
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plt.axis('off')
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ax5.imshow(loc, interpolation='nearest', cmap=plt.cm.gray)
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ax5.axis('off')
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plt.subplot(326)
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plt.plot(loc_hist[1][:-1], loc_hist[0], lw=2)
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plt.title('Histogram of gray values')
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ax6.plot(loc_hist[1][:-1], loc_hist[0], lw=2)
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ax6.set_title('Histogram of gray values')
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"""
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@@ -256,17 +241,15 @@ noisy_image = img_as_ubyte(data.camera())
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auto = autolevel(noisy_image.astype(np.uint16), disk(20))
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fig = plt.figure(figsize=[10, 7])
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7])
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plt.subplot(1, 2, 1)
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plt.imshow(noisy_image, cmap=plt.cm.gray)
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plt.title('Original')
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plt.axis('off')
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ax1.imshow(noisy_image, cmap=plt.cm.gray)
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ax1.set_title('Original')
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ax1.axis('off')
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plt.subplot(1, 2, 2)
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plt.imshow(auto, cmap=plt.cm.gray)
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plt.title('Local autolevel')
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plt.axis('off')
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ax2.imshow(auto, cmap=plt.cm.gray)
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ax2.set_title('Local autolevel')
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ax2.axis('off')
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"""
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@@ -297,7 +280,7 @@ fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
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ax0, ax1, ax2 = axes
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plt.gray()
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ax0.imshow(np.hstack((image, loc_autolevel)))
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ax0.imshow(np.hstack((image, loc_autolevel)), cmap=plt.cm.gray)
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ax0.set_title('Original / auto-level')
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ax1.imshow(
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@@ -326,24 +309,22 @@ noisy_image = img_as_ubyte(data.camera())
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enh = enhance_contrast(noisy_image, disk(5))
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fig = plt.figure(figsize=[10, 7])
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plt.subplot(2, 2, 1)
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plt.imshow(noisy_image, cmap=plt.cm.gray)
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plt.title('Original')
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plt.axis('off')
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fig, ax = plt.subplots(2, 2, figsize=[10, 7])
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ax1, ax2, ax3, ax4 = ax.ravel()
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plt.subplot(2, 2, 3)
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plt.imshow(enh, cmap=plt.cm.gray)
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plt.title('Local morphological contrast enhancement')
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plt.axis('off')
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ax1.imshow(noisy_image, cmap=plt.cm.gray)
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ax1.set_title('Original')
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ax1.axis('off')
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plt.subplot(2, 2, 2)
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plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
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plt.axis('off')
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ax2.imshow(enh, cmap=plt.cm.gray)
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ax2.set_title('Local morphological contrast enhancement')
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ax2.axis('off')
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plt.subplot(2, 2, 4)
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plt.imshow(enh[200:350, 350:450], cmap=plt.cm.gray)
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plt.axis('off')
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ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
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ax3.axis('off')
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ax4.imshow(enh[200:350, 350:450], cmap=plt.cm.gray)
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ax4.axis('off')
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"""
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|
||||
@@ -360,24 +341,22 @@ noisy_image = img_as_ubyte(data.camera())
|
||||
|
||||
penh = enhance_contrast_percentile(noisy_image, disk(5), p0=.1, p1=.9)
|
||||
|
||||
fig = plt.figure(figsize=[10, 7])
|
||||
plt.subplot(2, 2, 1)
|
||||
plt.imshow(noisy_image, cmap=plt.cm.gray)
|
||||
plt.title('Original')
|
||||
plt.axis('off')
|
||||
fig, ax = plt.subplots(2, 2, figsize=[10, 7])
|
||||
ax1, ax2, ax3, ax4 = ax.ravel()
|
||||
|
||||
plt.subplot(2, 2, 3)
|
||||
plt.imshow(penh, cmap=plt.cm.gray)
|
||||
plt.title('Local percentile morphological\n contrast enhancement')
|
||||
plt.axis('off')
|
||||
ax1.imshow(noisy_image, cmap=plt.cm.gray)
|
||||
ax1.set_title('Original')
|
||||
ax1.axis('off')
|
||||
|
||||
plt.subplot(2, 2, 2)
|
||||
plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
|
||||
plt.axis('off')
|
||||
ax2.imshow(penh, cmap=plt.cm.gray)
|
||||
ax2.set_title('Local percentile morphological\n contrast enhancement')
|
||||
ax2.axis('off')
|
||||
|
||||
plt.subplot(2, 2, 4)
|
||||
plt.imshow(penh[200:350, 350:450], cmap=plt.cm.gray)
|
||||
plt.axis('off')
|
||||
ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
|
||||
ax3.axis('off')
|
||||
|
||||
ax4.imshow(penh[200:350, 350:450], cmap=plt.cm.gray)
|
||||
ax4.axis('off')
|
||||
|
||||
"""
|
||||
|
||||
@@ -460,17 +439,15 @@ 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))
|
||||
|
||||
plt.figure()
|
||||
f, (ax1, ax2) = plt.subplots(1, 2)
|
||||
|
||||
plt.subplot(1, 2, 1)
|
||||
plt.imshow(m)
|
||||
plt.title('Original')
|
||||
plt.axis('off')
|
||||
ax1.imshow(m)
|
||||
ax1.set_title('Original')
|
||||
ax1.axis('off')
|
||||
|
||||
plt.subplot(1, 2, 2)
|
||||
plt.imshow(m >= t, interpolation='nearest')
|
||||
plt.title('Local Otsu ($r=%d$)' % radius)
|
||||
plt.axis('off')
|
||||
ax2.imshow(m >= t, interpolation='nearest')
|
||||
ax2.set_title('Local Otsu ($r=%d$)' % radius)
|
||||
ax2.axis('off')
|
||||
|
||||
"""
|
||||
|
||||
@@ -501,27 +478,24 @@ opening = minimum(maximum(noisy_image, disk(5)), disk(5))
|
||||
grad = gradient(noisy_image, disk(5))
|
||||
|
||||
# display results
|
||||
fig = plt.figure(figsize=[10, 7])
|
||||
fig, ax = plt.subplots(2, 2, figsize=[10, 7])
|
||||
ax1, ax2, ax3, ax4 = ax.ravel()
|
||||
|
||||
plt.subplot(2, 2, 1)
|
||||
plt.imshow(noisy_image, cmap=plt.cm.gray)
|
||||
plt.title('Original')
|
||||
plt.axis('off')
|
||||
ax1.imshow(noisy_image, cmap=plt.cm.gray)
|
||||
ax1.set_title('Original')
|
||||
ax1.axis('off')
|
||||
|
||||
plt.subplot(2, 2, 2)
|
||||
plt.imshow(closing, cmap=plt.cm.gray)
|
||||
plt.title('Gray-level closing')
|
||||
plt.axis('off')
|
||||
ax2.imshow(closing, cmap=plt.cm.gray)
|
||||
ax2.set_title('Gray-level closing')
|
||||
ax2.axis('off')
|
||||
|
||||
plt.subplot(2, 2, 3)
|
||||
plt.imshow(opening, cmap=plt.cm.gray)
|
||||
plt.title('Gray-level opening')
|
||||
plt.axis('off')
|
||||
ax3.imshow(opening, cmap=plt.cm.gray)
|
||||
ax3.set_title('Gray-level opening')
|
||||
ax3.axis('off')
|
||||
|
||||
plt.subplot(2, 2, 4)
|
||||
plt.imshow(grad, cmap=plt.cm.gray)
|
||||
plt.title('Morphological gradient')
|
||||
plt.axis('off')
|
||||
ax4.imshow(grad, cmap=plt.cm.gray)
|
||||
ax4.set_title('Morphological gradient')
|
||||
ax4.axis('off')
|
||||
|
||||
"""
|
||||
|
||||
@@ -651,12 +625,12 @@ for r in e_range:
|
||||
|
||||
rec = np.asarray(rec)
|
||||
|
||||
plt.figure()
|
||||
plt.title('Performance with respect to element size')
|
||||
plt.ylabel('Time (ms)')
|
||||
plt.title('Element radius')
|
||||
plt.plot(e_range, rec)
|
||||
plt.legend(['filter.rank.maximum', 'morphology.dilate'])
|
||||
f, ax = plt.subplots()
|
||||
ax.set_title('Performance with respect to element size')
|
||||
ax.set_ylabel('Time (ms)')
|
||||
ax.set_xlabel('Element radius')
|
||||
ax.plot(e_range, rec)
|
||||
ax.legend(['filter.rank.maximum', 'morphology.dilate'])
|
||||
|
||||
"""
|
||||
|
||||
@@ -679,12 +653,12 @@ for s in s_range:
|
||||
|
||||
rec = np.asarray(rec)
|
||||
|
||||
plt.figure()
|
||||
plt.title('Performance with respect to image size')
|
||||
plt.ylabel('Time (ms)')
|
||||
plt.title('Image size')
|
||||
plt.plot(s_range, rec)
|
||||
plt.legend(['filter.rank.maximum', 'morphology.dilate'])
|
||||
f, ax = plt.subplots()
|
||||
ax.set_title('Performance with respect to image size')
|
||||
ax.set_ylabel('Time (ms)')
|
||||
ax.set_xlabel('Image size')
|
||||
ax.plot(s_range, rec)
|
||||
ax.legend(['filter.rank.maximum', 'morphology.dilate'])
|
||||
|
||||
|
||||
"""
|
||||
@@ -714,13 +688,13 @@ for r in e_range:
|
||||
|
||||
rec = np.asarray(rec)
|
||||
|
||||
plt.figure()
|
||||
plt.title('Performance with respect to element size')
|
||||
plt.plot(e_range, rec)
|
||||
plt.legend(['filter.rank.median', 'filter.median_filter',
|
||||
'scipy.ndimage.percentile'])
|
||||
plt.ylabel('Time (ms)')
|
||||
plt.title('Element radius')
|
||||
f, 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',
|
||||
'scipy.ndimage.percentile'])
|
||||
ax.set_ylabel('Time (ms)')
|
||||
ax.set_xlabel('Element radius')
|
||||
|
||||
"""
|
||||
.. image:: PLOT2RST.current_figure
|
||||
@@ -729,10 +703,10 @@ Comparison of outcome of the three methods:
|
||||
|
||||
"""
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(np.hstack((rc, rctmf, rndi)))
|
||||
plt.title('filter.rank.median vs filtermedian_filter vs scipy.ndimage.percentile')
|
||||
plt.axis('off')
|
||||
f, 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')
|
||||
|
||||
"""
|
||||
.. image:: PLOT2RST.current_figure
|
||||
@@ -755,13 +729,13 @@ for s in s_range:
|
||||
|
||||
rec = np.asarray(rec)
|
||||
|
||||
plt.figure()
|
||||
plt.title('Performance with respect to image size')
|
||||
plt.plot(s_range, rec)
|
||||
plt.legend(['filter.rank.median', 'filter.median_filter',
|
||||
'scipy.ndimage.percentile'])
|
||||
plt.ylabel('Time (ms)')
|
||||
plt.title('Image size')
|
||||
f, 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',
|
||||
'scipy.ndimage.percentile'])
|
||||
ax.set_ylabel('Time (ms)')
|
||||
ax.set_xlabel('Image size')
|
||||
|
||||
"""
|
||||
.. image:: PLOT2RST.current_figure
|
||||
|
||||
Reference in New Issue
Block a user