In [3]:
from skimage import data,filters
import matplotlib.pyplot as plt
In [4]:
img = data.camera()
edges = filters.sobel(img)
plt.imshow(edges,plt.cm.gray)
Out[4]:
<matplotlib.image.AxesImage at 0x7f45d5276510>
In [5]:
edges = filters.roberts(img)
plt.imshow(edges,plt.cm.gray)
Out[5]:
<matplotlib.image.AxesImage at 0x7f45d52e0590>
In [6]:
edges = filters.scharr(img)
plt.imshow(edges,plt.cm.gray)
Out[6]:
<matplotlib.image.AxesImage at 0x7f45d4111700>
In [7]:
edges = filters.prewitt(img)
plt.imshow(edges,plt.cm.gray)
Out[7]:
<matplotlib.image.AxesImage at 0x7f45d41bbbc0>
In [8]:
from skimage import data,filters,feature
import matplotlib.pyplot as plt
In [9]:
img = data.camera()
sigma=1
In [10]:
edges1 = feature.canny(img)
sigma=3
In [11]:
edges2 = feature.canny(img,sigma=3)
In [12]:
plt.figure('canny',figsize=(8,8))
plt.subplot(121)
plt.imshow(edges1,plt.cm.gray)
Out[12]:
<matplotlib.image.AxesImage at 0x7f45d5269580>
In [13]:
plt.subplot(122)
plt.imshow(edges2,plt.cm.gray)
plt.show()
In [14]:
from skimage import data,filters
import matplotlib.pyplot as plt
In [15]:
img = data.camera()
filt_real, filt_imag = filters.gabor(img,frequency=0.6)
In [16]:
plt.figure('gabor',figsize=(8,8))
Out[16]:
<Figure size 800x800 with 0 Axes>
<Figure size 800x800 with 0 Axes>
In [17]:
plt.subplot(121)
plt.title('filt_real')
plt.imshow(filt_real,plt.cm.gray)
Out[17]:
<matplotlib.image.AxesImage at 0x7f45d409eba0>
In [18]:
plt.subplot(122)
plt.title('filt-imag')
plt.imshow(filt_imag,plt.cm.gray)
plt.show()
以上为 frequency=0.6 的结果图。
In [19]:
filt_real, filt_imag = filters.gabor(img,frequency=0.1)
In [20]:
plt.figure('gabor',figsize=(8,8))
Out[20]:
<Figure size 800x800 with 0 Axes>
<Figure size 800x800 with 0 Axes>
In [21]:
plt.subplot(121)
plt.title('filt_real')
plt.imshow(filt_real,plt.cm.gray)
Out[21]:
<matplotlib.image.AxesImage at 0x7f45cf6fe4b0>
In [22]:
plt.subplot(122)
plt.title('filt-imag')
plt.imshow(filt_imag,plt.cm.gray)
plt.show()
In [23]:
from skimage import data,filters
import matplotlib.pyplot as plt
In [24]:
img = data.astronaut()
sigma=0.4
In [25]:
edges1 = filters.gaussian(img,sigma=0.4)
sigma=5
In [27]:
edges2 = filters.gaussian(img,sigma=5)
In [28]:
plt.figure('gaussian',figsize=(8,8))
plt.subplot(121)
plt.imshow(edges1,plt.cm.gray)
Out[28]:
<matplotlib.image.AxesImage at 0x7f45cf6fd280>
In [29]:
plt.subplot(122)
plt.imshow(edges2,plt.cm.gray)
plt.show()
In [30]:
from skimage import data,filters
import matplotlib.pyplot as plt
from skimage.morphology import disk
In [31]:
img = data.camera()
edges1 = filters.median(img,disk(5))
edges2= filters.median(img,disk(9))
In [32]:
plt.figure('median',figsize=(8,8))
Out[32]:
<Figure size 800x800 with 0 Axes>
<Figure size 800x800 with 0 Axes>
In [33]:
plt.subplot(121)
plt.imshow(edges1,plt.cm.gray)
Out[33]:
<matplotlib.image.AxesImage at 0x7f45d4103530>
In [34]:
plt.subplot(122)
plt.imshow(edges2,plt.cm.gray)
plt.show()
In [35]:
from skimage import data,filters
import matplotlib.pyplot as plt
In [36]:
img = data.camera()
edges1 = filters.sobel_h(img)
edges2 = filters.sobel_v(img)
In [37]:
plt.figure('sobel_v_h',figsize=(8,8))
Out[37]:
<Figure size 800x800 with 0 Axes>
<Figure size 800x800 with 0 Axes>
In [38]:
plt.subplot(121)
plt.imshow(edges1,plt.cm.gray)
Out[38]:
<matplotlib.image.AxesImage at 0x7f45cf23c2f0>
In [39]:
plt.subplot(122)
plt.imshow(edges2,plt.cm.gray)
plt.show()
In [40]:
import matplotlib.pyplot as plt
In [41]:
img =data.camera()
dst =filters.roberts_neg_diag(img)
In [42]:
plt.figure('filters',figsize=(8,8))
plt.subplot(121)
plt.title('origin image')
plt.imshow(img,plt.cm.gray)
Out[42]:
<matplotlib.image.AxesImage at 0x7f45ce883bc0>
In [43]:
plt.subplot(122)
plt.title('filted image')
plt.imshow(dst,plt.cm.gray)
Out[43]:
<matplotlib.image.AxesImage at 0x7f45cf1f8380>
另外一个核：
1 0
0 -1对应函数为：
roberts_pos_diag(image）In [44]:
import matplotlib.pyplot as plt
In [45]:
img =data.camera()
dst =filters.roberts_pos_diag(img)
In [46]:
plt.figure('filters',figsize=(8,8))
plt.subplot(121)
plt.title('origin image')
plt.imshow(img,plt.cm.gray)
Out[46]:
<matplotlib.image.AxesImage at 0x7f45ce7693d0>
In [47]:
plt.subplot(122)
plt.title('filted image')
plt.imshow(dst,plt.cm.gray)
Out[47]:
<matplotlib.image.AxesImage at 0x7f45ce7a75f0>
