mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-19 11:27:45 +08:00
PEP8 fixes for long rank filter example
This commit is contained in:
Johannes Schönberger
@@ -46,10 +46,10 @@ ima = data.camera()
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hist = np.histogram(ima, 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.subplot(1, 2, 1)
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plt.imshow(ima, 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.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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@@ -72,31 +72,32 @@ randomly set to 0. The **median** filter is applied to remove the noise.
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noise = np.random.random(ima.shape)
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nima = data.camera()
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nima[noise>.99] = 255
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nima[noise<.01] = 0
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nima[noise > 0.99] = 255
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nima[noise < 0.01] = 0
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from skimage.filter.rank import median
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from skimage.morphology import disk
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fig = plt.figure(figsize=[10,7])
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fig = plt.figure(figsize=[10, 7])
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lo = median(nima,disk(1))
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hi = median(nima,disk(5))
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ext = median(nima,disk(20))
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plt.subplot(2,2,1)
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plt.imshow(nima,cmap=plt.cm.gray,vmin=0,vmax=255)
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lo = median(nima, disk(1))
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hi = median(nima, disk(5))
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ext = median(nima, disk(20))
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plt.subplot(2, 2, 1)
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plt.imshow(nima, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.xlabel('noised image')
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plt.subplot(2,2,2)
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plt.imshow(lo,cmap=plt.cm.gray,vmin=0,vmax=255)
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plt.subplot(2, 2, 2)
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plt.imshow(lo, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.xlabel('median $r=1$')
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plt.subplot(2,2,3)
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plt.imshow(hi,cmap=plt.cm.gray,vmin=0,vmax=255)
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plt.subplot(2, 2, 3)
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plt.imshow(hi, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.xlabel('median $r=5$')
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plt.subplot(2,2,4)
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plt.imshow(ext,cmap=plt.cm.gray,vmin=0,vmax=255)
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plt.subplot(2, 2, 4)
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plt.imshow(ext, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.xlabel('median $r=20$')
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"""
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.. image:: PLOT2RST.current_figure
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The added noise is efficiently removed, as the image defaults are small (1 pixel
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@@ -113,14 +114,14 @@ The example hereunder shows how a local **mean** smoothes the camera man 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 = plt.figure(figsize=[10, 7])
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loc_mean = mean(nima,disk(10))
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plt.subplot(1,2,1)
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plt.imshow(ima,cmap=plt.cm.gray,vmin=0,vmax=255)
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loc_mean = mean(nima, disk(10))
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plt.subplot(1, 2, 1)
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plt.imshow(ima, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.xlabel('original')
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plt.subplot(1,2,2)
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plt.imshow(loc_mean,cmap=plt.cm.gray,vmin=0,vmax=255)
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plt.subplot(1, 2, 2)
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plt.imshow(loc_mean, cmap=plt.cm.gray, vmin=0, vmax=255)
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plt.xlabel('local mean $r=10$')
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"""
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@@ -144,20 +145,20 @@ from skimage.filter.rank import bilateral_mean
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ima = data.camera()
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selem = disk(10)
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bilat = bilateral_mean(ima.astype(np.uint16),disk(20),s0=10,s1=10)
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bilat = bilateral_mean(ima.astype(np.uint16), disk(20), s0=10, s1=10)
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# display results
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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(ima,cmap=plt.cm.gray)
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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(ima, cmap=plt.cm.gray)
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plt.xlabel('original')
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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.subplot(2, 2, 3)
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plt.imshow(bilat, cmap=plt.cm.gray)
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plt.xlabel('bilateral mean')
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plt.subplot(2,2,2)
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plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray)
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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.subplot(2, 2, 2)
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plt.imshow(ima[200:350, 350:450], cmap=plt.cm.gray)
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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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"""
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@@ -186,8 +187,8 @@ from skimage.filter import rank
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ima = data.camera()
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# equalize globally and locally
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glob = exposure.equalize(ima)*255
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loc = rank.equalize(ima,disk(20))
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glob = exposure.equalize(ima) * 255
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loc = rank.equalize(ima, disk(20))
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# extract histogram for each image
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hist = np.histogram(ima, bins=np.arange(0, 256))
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@@ -231,15 +232,15 @@ from skimage.filter.rank import autolevel
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ima = data.camera()
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selem = disk(10)
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auto = autolevel(ima.astype(np.uint16),disk(20))
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auto = autolevel(ima.astype(np.uint16), disk(20))
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# display results
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fig = plt.figure(figsize=[10,7])
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plt.subplot(1,2,1)
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plt.imshow(ima,cmap=plt.cm.gray)
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fig = plt.figure(figsize=[10, 7])
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plt.subplot(1, 2, 1)
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plt.imshow(ima, cmap=plt.cm.gray)
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plt.xlabel('original')
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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.subplot(1, 2, 2)
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plt.imshow(auto, cmap=plt.cm.gray)
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plt.xlabel('local autolevel')
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"""
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@@ -260,22 +261,24 @@ from skimage.filter.rank import percentile_autolevel
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image = data.camera()
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selem = disk(20)
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loc_autolevel = autolevel(image,selem=selem)
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loc_perc_autolevel0 = percentile_autolevel(image,selem=selem,p0=.00,p1=1.0)
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loc_perc_autolevel1 = percentile_autolevel(image,selem=selem,p0=.01,p1=.99)
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loc_perc_autolevel2 = percentile_autolevel(image,selem=selem,p0=.05,p1=.95)
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loc_perc_autolevel3 = percentile_autolevel(image,selem=selem,p0=.1,p1=.9)
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loc_autolevel = autolevel(image, selem=selem)
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loc_perc_autolevel0 = percentile_autolevel(image, selem=selem, p0=.00, p1=1.0)
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loc_perc_autolevel1 = percentile_autolevel(image, selem=selem, p0=.01, p1=.99)
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loc_perc_autolevel2 = percentile_autolevel(image, selem=selem, p0=.05, p1=.95)
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loc_perc_autolevel3 = percentile_autolevel(image, selem=selem, p0=.1, p1=.9)
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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)))
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ax0.set_title('original / autolevel')
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ax1.imshow(np.hstack((loc_perc_autolevel0,loc_perc_autolevel1)),vmin=0,vmax=255)
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ax1.imshow(
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np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)), vmin=0, vmax=255)
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ax1.set_title('percentile autolevel 0%,1%')
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ax2.imshow(np.hstack((loc_perc_autolevel2,loc_perc_autolevel3)),vmin=0,vmax=255)
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ax2.imshow(
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np.hstack((loc_perc_autolevel2, loc_perc_autolevel3)), vmin=0, vmax=255)
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ax2.set_title('percentile autolevel 5% and 10%')
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for ax in axes:
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@@ -295,20 +298,20 @@ from skimage.filter.rank import morph_contr_enh
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ima = data.camera()
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enh = morph_contr_enh(ima,disk(5))
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enh = morph_contr_enh(ima, disk(5))
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# display results
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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(ima,cmap=plt.cm.gray)
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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(ima, cmap=plt.cm.gray)
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plt.xlabel('original')
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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.subplot(2, 2, 3)
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plt.imshow(enh, cmap=plt.cm.gray)
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plt.xlabel('local morphlogical contrast enhancement')
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plt.subplot(2,2,2)
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plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray)
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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.subplot(2, 2, 2)
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plt.imshow(ima[200:350, 350:450], cmap=plt.cm.gray)
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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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"""
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@@ -323,20 +326,20 @@ from skimage.filter.rank import percentile_morph_contr_enh
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ima = data.camera()
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penh = percentile_morph_contr_enh(ima,disk(5),p0=.1,p1=.9)
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penh = percentile_morph_contr_enh(ima, disk(5), p0=.1, p1=.9)
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# display results
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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(ima,cmap=plt.cm.gray)
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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(ima, cmap=plt.cm.gray)
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plt.xlabel('original')
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plt.subplot(2,2,3)
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plt.imshow(penh,cmap=plt.cm.gray)
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plt.subplot(2, 2, 3)
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plt.imshow(penh, cmap=plt.cm.gray)
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plt.xlabel('local percentile morphlogical\n contrast enhancement')
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plt.subplot(2,2,2)
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plt.imshow(ima[200:350,350:450],cmap=plt.cm.gray)
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plt.subplot(2,2,4)
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plt.imshow(penh[200:350,350:450],cmap=plt.cm.gray)
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plt.subplot(2, 2, 2)
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plt.imshow(ima[200:350, 350:450], cmap=plt.cm.gray)
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plt.subplot(2, 2, 4)
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plt.imshow(penh[200:350, 350:450], cmap=plt.cm.gray)
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"""
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@@ -371,28 +374,28 @@ radius = 10
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selem = disk(radius)
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# t_loc_otsu is an image
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t_loc_otsu = otsu(p8,selem)
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loc_otsu = p8>=t_loc_otsu
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t_loc_otsu = otsu(p8, selem)
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loc_otsu = p8 >= t_loc_otsu
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# t_glob_otsu is a scalar
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t_glob_otsu = threshold_otsu(p8)
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glob_otsu = p8>=t_glob_otsu
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glob_otsu = p8 >= t_glob_otsu
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plt.figure()
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plt.subplot(2,2,1)
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plt.imshow(p8,cmap=plt.cm.gray)
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plt.subplot(2, 2, 1)
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plt.imshow(p8, cmap=plt.cm.gray)
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plt.xlabel('original')
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plt.colorbar()
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plt.subplot(2,2,2)
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plt.imshow(t_loc_otsu,cmap=plt.cm.gray)
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plt.xlabel('local Otsu ($radius=%d$)'%radius)
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plt.subplot(2, 2, 2)
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plt.imshow(t_loc_otsu, cmap=plt.cm.gray)
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plt.xlabel('local Otsu ($radius=%d$)' % radius)
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plt.colorbar()
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plt.subplot(2,2,3)
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plt.imshow(p8>=t_loc_otsu,cmap=plt.cm.gray)
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plt.xlabel('original>=local Otsu'%t_glob_otsu)
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plt.subplot(2,2,4)
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plt.imshow(glob_otsu,cmap=plt.cm.gray)
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plt.xlabel('global Otsu ($t=%d$)'%t_glob_otsu)
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plt.subplot(2, 2, 3)
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plt.imshow(p8 >= t_loc_otsu, cmap=plt.cm.gray)
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plt.xlabel('original>=local Otsu' % t_glob_otsu)
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plt.subplot(2, 2, 4)
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plt.imshow(glob_otsu, cmap=plt.cm.gray)
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plt.xlabel('global Otsu ($t=%d$)' % t_glob_otsu)
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"""
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@@ -404,19 +407,19 @@ shift applied to a synthetic image .
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"""
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n = 100
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theta = np.linspace(0,10*np.pi,n)
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theta = np.linspace(0, 10 * np.pi, n)
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x = np.sin(theta)
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m = (np.tile(x,(n,1))* np.linspace(0.1,1,n)*128+128).astype(np.uint8)
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m = (np.tile(x, (n, 1)) * np.linspace(0.1, 1, n) * 128 + 128).astype(np.uint8)
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radius = 10
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t = rank.otsu(m,disk(radius))
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t = rank.otsu(m, disk(radius))
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plt.figure()
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plt.subplot(1,2,1)
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plt.subplot(1, 2, 1)
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plt.imshow(m)
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plt.xlabel('original')
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plt.subplot(1,2,2)
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plt.imshow(m>=t,interpolation='nearest')
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plt.xlabel('local Otsu ($radius=%d$)'%radius)
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plt.subplot(1, 2, 2)
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plt.imshow(m >= t, interpolation='nearest')
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plt.xlabel('local Otsu ($radius=%d$)' % radius)
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"""
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@@ -438,27 +441,27 @@ closing and morphological gradient.
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"""
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from skimage.filter.rank import maximum,minimum,gradient
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from skimage.filter.rank import maximum, minimum, gradient
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ima = data.camera()
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closing = maximum(minimum(ima,disk(5)),disk(5))
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opening = minimum(maximum(ima,disk(5)),disk(5))
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grad = gradient(ima,disk(5))
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closing = maximum(minimum(ima, disk(5)), disk(5))
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opening = minimum(maximum(ima, disk(5)), disk(5))
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grad = gradient(ima, disk(5))
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# display results
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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(ima,cmap=plt.cm.gray)
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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(ima, cmap=plt.cm.gray)
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plt.xlabel('original')
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plt.subplot(2,2,2)
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plt.imshow(closing,cmap=plt.cm.gray)
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plt.subplot(2, 2, 2)
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plt.imshow(closing, cmap=plt.cm.gray)
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plt.xlabel('greylevel closing')
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plt.subplot(2,2,3)
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plt.imshow(opening,cmap=plt.cm.gray)
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plt.subplot(2, 2, 3)
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plt.imshow(opening, cmap=plt.cm.gray)
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plt.xlabel('greylevel opening')
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plt.subplot(2,2,4)
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plt.imshow(grad,cmap=plt.cm.gray)
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plt.subplot(2, 2, 4)
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plt.imshow(grad, cmap=plt.cm.gray)
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plt.xlabel('morphological gradient')
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"""
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@@ -491,30 +494,30 @@ import matplotlib.pyplot as plt
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# defining a 8- and a 16-bit test images
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a8 = data.camera()
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a16 = data.camera().astype(np.uint16)*4
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a16 = data.camera().astype(np.uint16) * 4
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ent8 = entropy(a8,disk(5)) # pixel value contain 10x the local entropy
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ent16 = entropy(a16,disk(5)) # pixel value contain 1000x the local entropy
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ent8 = entropy(a8, disk(5)) # pixel value contain 10x the local entropy
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ent16 = entropy(a16, disk(5)) # pixel value contain 1000x the local entropy
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# display results
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plt.figure(figsize=(10, 10))
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plt.subplot(2,2,1)
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plt.subplot(2, 2, 1)
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plt.imshow(a8, cmap=plt.cm.gray)
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plt.xlabel('8-bit image')
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plt.colorbar()
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plt.subplot(2,2,2)
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plt.subplot(2, 2, 2)
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plt.imshow(ent8, cmap=plt.cm.jet)
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plt.xlabel('entropy*10')
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plt.colorbar()
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plt.subplot(2,2,3)
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plt.subplot(2, 2, 3)
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plt.imshow(a16, cmap=plt.cm.gray)
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plt.xlabel('16-bit image')
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plt.colorbar()
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plt.subplot(2,2,4)
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plt.subplot(2, 2, 4)
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plt.imshow(ent16, cmap=plt.cm.jet)
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plt.xlabel('entropy*1000')
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plt.colorbar()
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||||
@@ -538,7 +541,8 @@ from time import time
|
||||
from scipy.ndimage.filters import percentile_filter
|
||||
from skimage.morphology import dilation
|
||||
from skimage.filter import median_filter
|
||||
from skimage.filter.rank import median,maximum
|
||||
from skimage.filter.rank import median, maximum
|
||||
|
||||
|
||||
def exec_and_timeit(func):
|
||||
"""Decorator that returns both function results and execution time."""
|
||||
@@ -546,30 +550,34 @@ def exec_and_timeit(func):
|
||||
t1 = time()
|
||||
res = func(*arg)
|
||||
t2 = time()
|
||||
ms = (t2-t1)*1000.0
|
||||
return (res,ms)
|
||||
ms = (t2 - t1) * 1000.0
|
||||
return (res, ms)
|
||||
return wrapper
|
||||
|
||||
|
||||
@exec_and_timeit
|
||||
def cr_med(image,selem):
|
||||
return median(image=image,selem = selem)
|
||||
def cr_med(image, selem):
|
||||
return median(image=image, selem=selem)
|
||||
|
||||
|
||||
@exec_and_timeit
|
||||
def cr_max(image,selem):
|
||||
return maximum(image=image,selem = selem)
|
||||
def cr_max(image, selem):
|
||||
return maximum(image=image, selem=selem)
|
||||
|
||||
|
||||
@exec_and_timeit
|
||||
def cm_dil(image,selem):
|
||||
return dilation(image=image,selem = selem)
|
||||
def cm_dil(image, selem):
|
||||
return dilation(image=image, selem=selem)
|
||||
|
||||
|
||||
@exec_and_timeit
|
||||
def ctmf_med(image,radius):
|
||||
return median_filter(image=image,radius=radius)
|
||||
def ctmf_med(image, radius):
|
||||
return median_filter(image=image, radius=radius)
|
||||
|
||||
|
||||
@exec_and_timeit
|
||||
def ndi_med(image,n):
|
||||
return percentile_filter(image,50,size=n*2-1)
|
||||
def ndi_med(image, n):
|
||||
return percentile_filter(image, 50, size=n * 2 - 1)
|
||||
|
||||
"""
|
||||
|
||||
@@ -585,12 +593,12 @@ on increasing structuring element size
|
||||
a = data.camera()
|
||||
|
||||
rec = []
|
||||
e_range = range(1,20,2)
|
||||
e_range = range(1, 20, 2)
|
||||
for r in e_range:
|
||||
elem = disk(r+1)
|
||||
rc,ms_rc = cr_max(a,elem)
|
||||
rcm,ms_rcm = cm_dil(a,elem)
|
||||
rec.append((ms_rc,ms_rcm))
|
||||
elem = disk(r + 1)
|
||||
rc, ms_rc = cr_max(a, elem)
|
||||
rcm, ms_rcm = cm_dil(a, elem)
|
||||
rec.append((ms_rc, ms_rcm))
|
||||
|
||||
rec = np.asarray(rec)
|
||||
|
||||
@@ -598,8 +606,8 @@ plt.figure()
|
||||
plt.title('increasing element size')
|
||||
plt.ylabel('time (ms)')
|
||||
plt.xlabel('element radius')
|
||||
plt.plot(e_range,rec)
|
||||
plt.legend(['crank.maximum','cmorph.dilate'])
|
||||
plt.plot(e_range, rec)
|
||||
plt.legend(['crank.maximum', 'cmorph.dilate'])
|
||||
|
||||
"""
|
||||
|
||||
@@ -610,15 +618,15 @@ and increasing image size
|
||||
"""
|
||||
|
||||
r = 9
|
||||
elem = disk(r+1)
|
||||
elem = disk(r + 1)
|
||||
|
||||
rec = []
|
||||
s_range = range(100,1000,100)
|
||||
s_range = range(100, 1000, 100)
|
||||
for s in s_range:
|
||||
a = (np.random.random((s,s))*256).astype('uint8')
|
||||
(rc,ms_rc) = cr_max(a,elem)
|
||||
(rcm,ms_rcm) = cm_dil(a,elem)
|
||||
rec.append((ms_rc,ms_rcm))
|
||||
a = (np.random.random((s, s)) * 256).astype('uint8')
|
||||
(rc, ms_rc) = cr_max(a, elem)
|
||||
(rcm, ms_rcm) = cm_dil(a, elem)
|
||||
rec.append((ms_rc, ms_rcm))
|
||||
|
||||
rec = np.asarray(rec)
|
||||
|
||||
@@ -626,8 +634,8 @@ plt.figure()
|
||||
plt.title('increasing image size')
|
||||
plt.ylabel('time (ms)')
|
||||
plt.xlabel('image size')
|
||||
plt.plot(s_range,rec)
|
||||
plt.legend(['crank.maximum','cmorph.dilate'])
|
||||
plt.plot(s_range, rec)
|
||||
plt.legend(['crank.maximum', 'cmorph.dilate'])
|
||||
|
||||
|
||||
"""
|
||||
@@ -647,20 +655,20 @@ on increasing structuring element size
|
||||
a = data.camera()
|
||||
|
||||
rec = []
|
||||
e_range = range(2,30,4)
|
||||
e_range = range(2, 30, 4)
|
||||
for r in e_range:
|
||||
elem = disk(r+1)
|
||||
rc,ms_rc = cr_med(a,elem)
|
||||
rctmf,ms_rctmf = ctmf_med(a,r)
|
||||
rndi,ms_ndi = ndi_med(a,r)
|
||||
rec.append((ms_rc,ms_rctmf,ms_ndi))
|
||||
elem = disk(r + 1)
|
||||
rc, ms_rc = cr_med(a, elem)
|
||||
rctmf, ms_rctmf = ctmf_med(a, r)
|
||||
rndi, ms_ndi = ndi_med(a, r)
|
||||
rec.append((ms_rc, ms_rctmf, ms_ndi))
|
||||
|
||||
rec = np.asarray(rec)
|
||||
|
||||
plt.figure()
|
||||
plt.title('increasing element size')
|
||||
plt.plot(e_range,rec)
|
||||
plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile'])
|
||||
plt.plot(e_range, rec)
|
||||
plt.legend(['rank.median', 'ctmf.median_filter', 'ndimage.percentile'])
|
||||
plt.ylabel('time (ms)')
|
||||
plt.xlabel('element radius')
|
||||
|
||||
@@ -672,7 +680,7 @@ comparison of outcome of the three methods
|
||||
"""
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(np.hstack((rc,rctmf,rndi)))
|
||||
plt.imshow(np.hstack((rc, rctmf, rndi)))
|
||||
plt.xlabel('rank.median vs ctmf.median_filter vs ndimage.percentile')
|
||||
|
||||
"""
|
||||
@@ -683,23 +691,23 @@ and increasing image size
|
||||
"""
|
||||
|
||||
r = 9
|
||||
elem = disk(r+1)
|
||||
elem = disk(r + 1)
|
||||
|
||||
rec = []
|
||||
s_range = [100,200,500,1000]
|
||||
s_range = [100, 200, 500, 1000]
|
||||
for s in s_range:
|
||||
a = (np.random.random((s,s))*256).astype('uint8')
|
||||
(rc,ms_rc) = cr_med(a,elem)
|
||||
rctmf,ms_rctmf = ctmf_med(a,r)
|
||||
rndi,ms_ndi = ndi_med(a,r)
|
||||
rec.append((ms_rc,ms_rctmf,ms_ndi))
|
||||
a = (np.random.random((s, s)) * 256).astype('uint8')
|
||||
(rc, ms_rc) = cr_med(a, elem)
|
||||
rctmf, ms_rctmf = ctmf_med(a, r)
|
||||
rndi, ms_ndi = ndi_med(a, r)
|
||||
rec.append((ms_rc, ms_rctmf, ms_ndi))
|
||||
|
||||
rec = np.asarray(rec)
|
||||
|
||||
plt.figure()
|
||||
plt.title('increasing image size')
|
||||
plt.plot(s_range,rec)
|
||||
plt.legend(['rank.median','ctmf.median_filter','ndimage.percentile'])
|
||||
plt.plot(s_range, rec)
|
||||
plt.legend(['rank.median', 'ctmf.median_filter', 'ndimage.percentile'])
|
||||
plt.ylabel('time (ms)')
|
||||
plt.xlabel('image size')
|
||||
|
||||
|
||||
Reference in New Issue
Block a user