A = ia.haarmatrix(128)
ia.adshow(ia.normalize(A),'Haar matrix 128x128')
# ### Example 3
# In[4]:
if testing:
f = mpimg.imread('../data/cameraman.tif')
A = ia.haarmatrix(f.shape[0])
B = ia.haarmatrix(f.shape[1])
F = np.dot(np.dot(A, f), np.transpose(B))
nb = ia.nbshow(2)
nb.nbshow(f,'Imagem original')
nb.nbshow(ia.normalize(np.log(abs(F)+1)),'Haar transform')
nb.nbshow()
# In[ ]:
# In[ ]:
for table in tables:
Tc = ia.colormap(table)
plt.plot(Tc[:, 0])
plt.plot(Tc[:, 1])
plt.plot(Tc[:, 2])
plt.title(table)
plt.show()
# ### Example 3
#
# With image
# In[5]:
if testing:
nb = ia.nbshow(3)
f = mpimg.imread('../data/retina.tif')
for table in tables:
Tc = ia.colormap(table)
g = ia.applylut(f, Tc).astype('uint8')
if len(g.shape) == 3:
g = g.transpose(1, 2, 0)
nb.nbshow(g, table)
nb.nbshow()
# ## See Also
#
# - [ia636:applylut](applylut.ipynb) Lookup Table application
#
# ## References
#
import ea979.src as ia
# ### Example 1
# In[3]:
if testing:
get_ipython().magic('matplotlib inline')
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
f = mpimg.imread('../data/cameraman.tif')
g07 = ia.logfilter(f, 0.7)
nb = ia.nbshow(3)
nb.nbshow(f, 'Imagem original')
nb.nbshow(ia.normalize(g07), 'LoG filter')
nb.nbshow(g07 > 0, 'positive values')
nb.nbshow()
# ### Example 2
# In[4]:
if testing:
g5 = ia.logfilter(f, 5)
g10 = ia.logfilter(f, 10)
nb = ia.nbshow(2, 2)
nb.nbshow(ia.normalize(g5), 'sigma=5')
m, t = ia.sobel(f)
print('m:\n', m)
print('t:\n', t)
# ### Image examples
# ### Example 1.
# In[3]:
if testing:
f = mpimg.imread('../data/cameraman.tif')
(g, a) = ia.sobel(f)
nb = ia.nbshow(2)
nb.nbshow(ia.normalize(g), title='Sobel')
nb.nbshow(ia.normalize(np.log(g + 1)), title='Log of sobel')
nb.nbshow()
# ### Example 2.
# In[4]:
if testing:
f = ia.circle([200, 300], 90, [100, 150])
m, t = ia.sobel(f)
dt = np.select([m > 2], [t])
nb = ia.nbshow(3)
