B = R[:,::-1]
rgb = np.array((R,G,B))
print(rgb.shape)
ia.adshow(rgb)
ia.show(rgb)
ff = rgb.transpose((1,2,0))
print(ff.shape)
ia.adshow(ff,'teste')
ia.show(ff)
# In[7]:
if testing:
A = (np.arange(256*256) % 256).reshape(256,256).astype('uint8')
nb = ia.nbshow(3)
nb.nbshow(A,'apenas uma', flush=True)
for i in range(4):
nb.nbshow(A,'legenda %d' % (i,))
nb.nbshow(flush=True)
# In[8]:
if testing:
nb = ia.nbshow(3)
nb.nbshow(ff,'apenas uma', flush=True)
for i in range(4):
nb.nbshow(rgb,'legenda %d' % (i,))
nb.nbshow(flush=True)
sys.path.append(ia898path)
import ia898.src as ia
# In[3]:
import skimage.transform
import scipy
f = plt.imread('/home/lotufo/ia898/data/cameraman.tif')
H, W = f.shape
scale1 = 2
scale2 = 0.5
np.set_printoptions(precision=2)
nb = ia.nbshow(1)
nb.nbshow(f, 'Imagem original')
nb.nbshow()
# ## skimage.transform.rescale
# In[4]:
# skimage.transform.rescale
skimage_rescale1 = skimage.transform.rescale(f, scale1)
skimage_rescale2 = skimage.transform.rescale(f, scale2)
nb = ia.nbshow(3)
nb.nbshow(ia.normalize(skimage_rescale2), 'skimage_rescale x%s' % (scale2))
nb.nbshow(f, 'Imagem original')
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)
dt = ia.nbread('/home/lotufo/ia898/data/digits_train.png')
ia.adshow(dt, 'digits_train.png')
# In[5]:
print(dt.shape)
print(dt.dtype)
print(dt[:3, :20])
# ## Visualizando o dataset
# O dataset é composto de 80 amostras. Cada amostra está organizado como: primeira coluna é o rótulo do dígito, de 0 a 9 e seguida 160 atributos (16 linhas e 10 colunas). O típo é uint8.
# In[6]:
nb = ia.nbshow(10)
# In[7]:
for i in range(80):
f = dt[i, 1:].reshape(16, 10)
label = dt[i, 0]
nb.nbshow(f, label)
nb.nbshow()
# ## Montando o X
# In[8]:
X = dt[:, 1:].astype(np.float)
print(X.shape)
dtype=np.uint8)
s = ia.sat(f)
print('f (input):\n', f)
print('s (output):\n', s)
a = ia.satarea(s, (0, 0), (3, 8))
print('area:', a)
# ### Image example
#
# In[3]:
if testing:
f = mpimg.imread('../data/lenina.pgm')[::2, ::2]
nb = ia.nbshow(2)
nb.nbshow(f, 'Original Image')
nb.nbshow(ia.normalize(ia.sat(f)), 'Integral Image')
nb.nbshow()
# ### Calculating a rectangle area with SAT (Summed Area Table)
# In[4]:
if testing:
f = mpimg.imread('../data/lenina.pgm')[::2, ::2]
H, W = f.shape
s = ia.sat(f)
a0 = ia.satarea(s, (0, 0), (H - 1, W - 1))
atopleft = ia.satarea(s, (0, 0), (H // 2 - 1, W // 2 - 1))
abotleft = ia.satarea(s, (H // 2, 0), (H - 1, W // 2 - 1))
import ia898.src as ia
# ### Example 1
# In[3]:
if testing:
get_ipython().run_line_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')
