def disp(iimg, label = "", gray=False):
""" Display an image using pylab
"""
try:
import pylab
dimage = iimg.copy()
if iimg.ndim==3:
dimage[...,0] = iimg[...,2]
dimage[...,2] = iimg[...,0]
pylab.imshow(dimage, interpolation='none')
if gray: pylab.gray()
#pylab.gca().format_coord = format_coord
pylab.text(1500, -30, label)
pylab.axis('off')
pylab.show()
except ImportError:
print "Module pylab not available"
python类gray()的实例源码
def tile_images(image_batch, image_width=28, image_height=28, image_channel=1, dir=None, filename="images"):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
pylab.gray()
for m in range(100):
pylab.subplot(10, 10, m + 1)
pylab.imshow(image_batch[m].reshape((image_width, image_height)), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/{}.png".format(dir, filename))
def visualize_reconstruction(xp, model, x, visualization_dir, epoch, gpu=False):
x_variable = chainer.Variable(xp.asarray(x))
_x = model.decode(model.encode(x_variable), test=True)
_x.to_cpu()
_x = _x.data
fig = pylab.gcf()
fig.set_size_inches(8.0, 8.0)
pylab.clf()
pylab.gray()
for m in range(50):
i = m / 10
j = m % 10
pylab.subplot(10, 10, 20 * i + j + 1, xticks=[], yticks=[])
pylab.imshow(x[m].reshape((28, 28)), interpolation="none")
pylab.subplot(10, 10, 20 * i + j + 10 + 1, xticks=[], yticks=[])
pylab.imshow(_x[m].reshape((28, 28)), interpolation="none")
# pylab.imshow(np.clip((_x_batch.data[m] + 1.0) / 2.0, 0.0, 1.0).reshape(
# (config.img_channel, config.img_width, config.img_width)), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/reconstruction_{}.png".format(visualization_dir, epoch))
# pylab.show()
def threshold_value(img):
"""
Returns a threshold value (0.9 or 0.98) based on whether any slice
of the image within a central box is enterely white (white is a bitch!)
0.9 or 0.98 come simply from a lot of experimentation.
"""
is_color = len(img.shape) == 3
is_grey = len(img.shape) == 2
if is_color:
gray = cv2.cvtColor(gray,cv2.COLOR_BGR2GRAY)
elif is_grey:
gray = img.copy()
slices = gray.mean(axis = 1)[20:gray.shape[0]-30]
is_white = any(x > 0.9*255 for x in slices)
if is_white:
return 0.98
else:
return 0.9
def threshold_img(img):
"""
Simple wrap-up function for cv2.threshold()
"""
is_color = len(img.shape) == 3
is_grey = len(img.shape) == 2
t = threshold_value(img)
if is_color:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
elif is_grey:
gray = img.copy()
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
(_, thresh) = cv2.threshold(blurred, t*255, 1, cv2.THRESH_BINARY_INV)
return thresh
def threshold_img(img):
"""
Simple wrap-up function for cv2.threshold()
"""
is_color = len(img.shape) == 3
is_grey = len(img.shape) == 2
t = threshold_value(img)
if is_color:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
elif is_grey:
gray = img.copy()
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
(_, thresh) = cv2.threshold(blurred, t*255, 1, cv2.THRESH_BINARY_INV)
return thresh
def tile_binary_images(x, dir=None, filename="x", row=10, col=10):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(col * 2, row * 2)
pylab.clf()
pylab.gray()
for m in range(row * col):
pylab.subplot(row, col, m + 1)
pylab.imshow(np.clip(x[m], 0, 1), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/{}.png".format(dir, filename))
def tile_binary_images(x, dir=None, filename="x", row=10, col=10):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(col * 2, row * 2)
pylab.clf()
pylab.gray()
for m in range(row * col):
pylab.subplot(row, col, m + 1)
pylab.imshow(np.clip(x[m], 0, 1), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/{}.png".format(dir, filename))
def tile_binary_images(x, dir=None, filename="x"):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
pylab.gray()
for m in range(100):
pylab.subplot(10, 10, m + 1)
pylab.imshow(np.clip(x[m], 0, 1), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/{}.png".format(dir, filename))
def visualize_x(reconstructed_x_batch, image_width=28, image_height=28, image_channel=1, dir=None):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
if image_channel == 1:
pylab.gray()
for m in range(100):
pylab.subplot(10, 10, m + 1)
if image_channel == 1:
pylab.imshow(reconstructed_x_batch[m].reshape((image_width, image_height)), interpolation="none")
elif image_channel == 3:
pylab.imshow(reconstructed_x_batch[m].reshape((image_channel, image_width, image_height)), interpolation="none")
pylab.axis("off")
pylab.savefig("%s/reconstructed_x.png" % dir)
def bounding_box(img):
"""
Returns right, left, lower and upper limits for the limiting box enclosing
the item (shoe, dress). Note that given the shapes and colors of some items,
finding the contours and compute the bounding box is not a viable solution.
"""
is_color = len(img.shape) == 3
is_grey = len(img.shape) == 2
if is_color:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
elif is_grey:
gray = img.copy()
slices = gray.mean(axis = 1)[20:gray.shape[0]-30]
is_white = any(x > 0.9*255 for x in slices)
if (is_white):
h1 = min(np.apply_along_axis(get_edges, axis=0, arr=gray , thresh = 0.98)[0,:])
h2 = max(np.apply_along_axis(get_edges, axis=0, arr=gray , thresh = 0.98)[1,:])
w1 = min(np.apply_along_axis(get_edges, axis=1, arr=gray , thresh = 0.98)[:,0])
w2 = max(np.apply_along_axis(get_edges, axis=1, arr=gray , thresh = 0.98)[:,1])
else :
h1 = min(np.apply_along_axis(get_edges, axis=0, arr=gray , thresh = 0.9)[0,:])
h2 = max(np.apply_along_axis(get_edges, axis=0, arr=gray , thresh = 0.9)[1,:])
w1 = min(np.apply_along_axis(get_edges, axis=1, arr=gray , thresh = 0.9)[:,0])
w2 = max(np.apply_along_axis(get_edges, axis=1, arr=gray , thresh = 0.9)[:,1])
return w1, w2, h1, h2
def shape_df(img, axis, nsteps):
"""
Returns a data frame with the initial and end points enclosing the product
in the image, across the x/y axis. Why a dataframe and not tuples? just for
convenience.
"""
is_color = len(img.shape) == 3
is_grey = len(img.shape) == 2
if is_color:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
elif is_grey:
gray = img.copy()
edges = bounding_box(gray)
gray_c = gray[edges[2]:edges[3]+1, edges[0]:edges[1]+1]
thr = threshold_value(gray_c)
if axis == 'x' :
cuts = np.rint(np.linspace(5, gray_c.shape[1]-1, nsteps, endpoint=True)).astype(int)
init = np.apply_along_axis(get_edges, 0, arr = gray_c, thresh = thr)[0,:][cuts]
end = np.apply_along_axis(get_edges, 0, arr = gray_c, thresh = thr)[1,:][cuts]
df = pd.DataFrame(data = {'coord' : cuts, 'init' : init, 'end' : end},
columns=['coord', 'init', 'end'])
elif axis == 'y':
cuts = np.round(np.linspace(4, gray_c.shape[0]-1, nsteps, endpoint=True)).astype(int)
init = np.apply_along_axis(get_edges, 1, arr = gray_c, thresh = thr)[:,0][cuts]
end = np.apply_along_axis(get_edges, 1, arr = gray_c, thresh = thr)[:,1][cuts]
df = pd.DataFrame(data = {'coord' : cuts, 'init' : init, 'end' : end},
columns=['coord', 'init', 'end'])
return df
def plot_shape(img, axis, df=None, nsteps=None):
"""
function to overplot the shape points onto the image img
"""
if df is not None and nsteps:
print 'Error: provide data frame or nsteps, not both'
return None
if df is not None:
edges = bounding_box(img)
img_c = img[edges[2]:edges[3]+1, edges[0]:edges[1]+1]
pyl.figure()
pyl.gray()
pyl.imshow(img_c)
if axis == 'y':
pyl.plot(df.init,df.coord, 'r*')
pyl.plot(df.end, df.coord, 'r*')
pyl.show()
if axis == 'x':
pyl.plot(df.coord,df.init, 'r*')
pyl.plot(df.coord,df.end, 'r*')
pyl.show()
elif nsteps:
pyl.figure()
pyl.gray()
pyl.imshow(img)
if axis == 'y':
df = shape_df(img, 'y', nsteps)
pyl.plot(df.init,df.coord, 'r*')
pyl.plot(df.end, df.coord, 'r*')
pyl.show()
if axis == 'x':
df = shape_df(img, 'x', nsteps)
pyl.plot(df.coord,df.init, 'r*')
pyl.plot(df.coord,df.end, 'r*')
pyl.show()
def bounding_box(img):
"""
Returns right, left, lower and upper limits for the limiting box enclosing
the item (shoe, dress). Note that given the shapes and colors of some items,
finding the contours and compute the bounding box is not a viable solution.
"""
is_color = len(img.shape) == 3
is_grey = len(img.shape) == 2
if is_color:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
elif is_grey:
gray = img.copy()
slices = gray.mean(axis = 1)[20:gray.shape[0]-30]
is_white = any(x > 0.9*255 for x in slices)
if (is_white):
h1 = min(np.apply_along_axis(get_edges, axis=0, arr=gray , thresh = 0.98)[0,:])
h2 = max(np.apply_along_axis(get_edges, axis=0, arr=gray , thresh = 0.98)[1,:])
w1 = min(np.apply_along_axis(get_edges, axis=1, arr=gray , thresh = 0.98)[:,0])
w2 = max(np.apply_along_axis(get_edges, axis=1, arr=gray , thresh = 0.98)[:,1])
else :
h1 = min(np.apply_along_axis(get_edges, axis=0, arr=gray , thresh = 0.9)[0,:])
h2 = max(np.apply_along_axis(get_edges, axis=0, arr=gray , thresh = 0.9)[1,:])
w1 = min(np.apply_along_axis(get_edges, axis=1, arr=gray , thresh = 0.9)[:,0])
w2 = max(np.apply_along_axis(get_edges, axis=1, arr=gray , thresh = 0.9)[:,1])
return w1, w2, h1, h2
def shape_df(img, axis, nsteps):
"""
Returns a data frame with the initial and end points enclosing the product
in the image, across the x/y axis. Why a dataframe and not tuples? just for
convenience.
"""
is_color = len(img.shape) == 3
is_grey = len(img.shape) == 2
if is_color:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
elif is_grey:
gray = img.copy()
edges = bounding_box(gray)
gray_c = gray[edges[2]:edges[3]+1, edges[0]:edges[1]+1]
thr = threshold_value(gray_c)
if axis == 'x' :
cuts = np.rint(np.linspace(5, gray_c.shape[1]-1, nsteps, endpoint=True)).astype(int)
init = np.apply_along_axis(get_edges, 0, arr = gray_c, thresh = thr)[0,:][cuts]
end = np.apply_along_axis(get_edges, 0, arr = gray_c, thresh = thr)[1,:][cuts]
df = pd.DataFrame(data = {'coord' : cuts, 'init' : init, 'end' : end},
columns=['coord', 'init', 'end'])
elif axis == 'y':
cuts = np.round(np.linspace(4, gray_c.shape[0]-1, nsteps, endpoint=True)).astype(int)
init = np.apply_along_axis(get_edges, 1, arr = gray_c, thresh = thr)[:,0][cuts]
end = np.apply_along_axis(get_edges, 1, arr = gray_c, thresh = thr)[:,1][cuts]
df = pd.DataFrame(data = {'coord' : cuts, 'init' : init, 'end' : end},
columns=['coord', 'init', 'end'])
return df
def plot_shape(img, axis, df=None, nsteps=None):
"""
function to overplot the shape points onto the image img
"""
if df is not None and nsteps:
print 'Error: provide data frame or nsteps, not both'
return None
if df is not None:
edges = bounding_box(img)
img_c = img[edges[2]:edges[3]+1, edges[0]:edges[1]+1]
pyl.figure()
pyl.gray()
pyl.imshow(img_c)
if axis == 'y':
pyl.plot(df.init,df.coord, 'r*')
pyl.plot(df.end, df.coord, 'r*')
pyl.show()
if axis == 'x':
pyl.plot(df.coord,df.init, 'r*')
pyl.plot(df.coord,df.end, 'r*')
pyl.show()
elif nsteps:
pyl.figure()
pyl.gray()
pyl.imshow(img)
if axis == 'y':
df = shape_df(img, 'y', nsteps)
pyl.plot(df.init,df.coord, 'r*')
pyl.plot(df.end, df.coord, 'r*')
pyl.show()
if axis == 'x':
df = shape_df(img, 'x', nsteps)
pyl.plot(df.coord,df.init, 'r*')
pyl.plot(df.coord,df.end, 'r*')
pyl.show()
def visualiseObject(self, cmap="hot"):
pylab.ion()
#pylab.set_cmap("gray")
pylab.gray()
pylab.title("image: %s" % self.fitsFile)
pylab.imshow(self.getObject(), interpolation="nearest", cmap=cmap)
pylab.colorbar()
pylab.ylim(-1, 2*self.extent)
pylab.xlim(-1, 2*self.extent)
pylab.xlabel("Pixels")
pylab.ylabel("Pixels")
pylab.show()
def visualiseObject(self, cmap="hot"):
pylab.ion()
#pylab.set_cmap("gray")
pylab.gray()
pylab.title("image: %s" % self.fitsFile)
pylab.imshow(self.getObject(), interpolation="nearest", cmap=cmap)
pylab.colorbar()
pylab.ylim(-1, 2*self.extent)
pylab.xlim(-1, 2*self.extent)
pylab.xlabel("Pixels")
pylab.ylabel("Pixels")
pylab.show()
def plot_analogy():
dataset_train, dataset_test = chainer.datasets.get_mnist()
images_train, labels_train = dataset_train._datasets
images_test, labels_test = dataset_test._datasets
dataset_indices = np.arange(0, len(images_test))
np.random.shuffle(dataset_indices)
model = Model()
assert model.load("model.hdf5")
# normalize
images_train = (images_train - 0.5) * 2
images_test = (images_test - 0.5) * 2
num_analogies = 10
pylab.gray()
batch_indices = dataset_indices[:num_analogies]
x_batch = images_test[batch_indices]
y_batch = labels_test[batch_indices]
y_onehot_batch = onehot(y_batch)
with chainer.no_backprop_mode() and chainer.using_config("train", False):
z_batch = model.encode_x_yz(x_batch)[1].data
# plot original image on the left
x_batch = (x_batch + 1.0) / 2.0
for m in range(num_analogies):
pylab.subplot(num_analogies, 10 + 2, m * 12 + 1)
pylab.imshow(x_batch[m].reshape((28, 28)), interpolation="none")
pylab.axis("off")
all_y = np.identity(10, dtype=np.float32)
for m in range(num_analogies):
# copy z_batch as many as the number of classes
fixed_z = np.repeat(z_batch[m].reshape(1, -1), 10, axis=0)
gen_x = model.decode_yz_x(all_y, fixed_z).data
gen_x = (gen_x + 1.0) / 2.0
# plot images generated from each label
for n in range(10):
pylab.subplot(num_analogies, 10 + 2, m * 12 + 3 + n)
pylab.imshow(gen_x[n].reshape((28, 28)), interpolation="none")
pylab.axis("off")
fig = pylab.gcf()
fig.set_size_inches(num_analogies, 10)
pylab.savefig("analogy.png")
def plot_analogy():
dataset_train, dataset_test = chainer.datasets.get_mnist()
images_train, labels_train = dataset_train._datasets
images_test, labels_test = dataset_test._datasets
dataset_indices = np.arange(0, len(images_test))
np.random.shuffle(dataset_indices)
model = Model()
assert model.load("model.hdf5")
# normalize
images_train = (images_train - 0.5) * 2
images_test = (images_test - 0.5) * 2
num_analogies = 10
pylab.gray()
batch_indices = dataset_indices[:num_analogies]
x_batch = images_test[batch_indices]
y_batch = labels_test[batch_indices]
y_onehot_batch = onehot(y_batch)
with chainer.no_backprop_mode() and chainer.using_config("train", False):
z_batch = model.encode_x_yz(x_batch)[1].data
# plot original image on the left
x_batch = (x_batch + 1.0) / 2.0
for m in range(num_analogies):
pylab.subplot(num_analogies, 10 + 2, m * 12 + 1)
pylab.imshow(x_batch[m].reshape((28, 28)), interpolation="none")
pylab.axis("off")
all_y = np.identity(10, dtype=np.float32)
for m in range(num_analogies):
# copy z_batch as many as the number of classes
fixed_z = np.repeat(z_batch[m].reshape(1, -1), 10, axis=0)
representation = model.encode_yz_representation(all_y, fixed_z)
gen_x = model.decode_representation_x(representation).data
gen_x = (gen_x + 1.0) / 2.0
# plot images generated from each label
for n in range(10):
pylab.subplot(num_analogies, 10 + 2, m * 12 + 3 + n)
pylab.imshow(gen_x[n].reshape((28, 28)), interpolation="none")
pylab.axis("off")
fig = pylab.gcf()
fig.set_size_inches(num_analogies, 10)
pylab.savefig("analogy.png")
def plot_analogy():
dataset_train, dataset_test = chainer.datasets.get_mnist()
images_train, labels_train = dataset_train._datasets
images_test, labels_test = dataset_test._datasets
dataset_indices = np.arange(0, len(images_test))
np.random.shuffle(dataset_indices)
model = Model()
assert model.load("model.hdf5")
# normalize
images_train = (images_train - 0.5) * 2
images_test = (images_test - 0.5) * 2
num_analogies = 10
pylab.gray()
batch_indices = dataset_indices[:num_analogies]
x_batch = images_test[batch_indices]
y_batch = labels_test[batch_indices]
y_onehot_batch = onehot(y_batch)
with chainer.no_backprop_mode() and chainer.using_config("train", False):
z_batch = model.encode_x_z(x_batch).data
# plot original image on the left
x_batch = (x_batch + 1.0) / 2.0
for m in range(num_analogies):
pylab.subplot(num_analogies, 10 + 2, m * 12 + 1)
pylab.imshow(x_batch[m].reshape((28, 28)), interpolation="none")
pylab.axis("off")
all_y = np.identity(10, dtype=np.float32)
for m in range(num_analogies):
# copy z_batch as many as the number of classes
fixed_z = np.repeat(z_batch[m].reshape(1, -1), 10, axis=0)
gen_x = model.decode_yz_x(all_y, fixed_z).data
gen_x = (gen_x + 1.0) / 2.0
# plot images generated from each label
for n in range(10):
pylab.subplot(num_analogies, 10 + 2, m * 12 + 3 + n)
pylab.imshow(gen_x[n].reshape((28, 28)), interpolation="none")
pylab.axis("off")
fig = pylab.gcf()
fig.set_size_inches(num_analogies, 10)
pylab.savefig("analogy.png")
def plot_clusters():
dataset_train, dataset_test = chainer.datasets.get_mnist()
images_train, labels_train = dataset_train._datasets
images_test, labels_test = dataset_test._datasets
dataset_indices = np.arange(0, len(images_test))
np.random.shuffle(dataset_indices)
model = Model()
assert model.load("model.hdf5")
# normalize
images_train = (images_train - 0.5) * 2
images_test = (images_test - 0.5) * 2
num_clusters = model.ndim_y
num_plots_per_cluster = 11
image_width = 28
image_height = 28
ndim_x = image_width * image_height
pylab.gray()
with chainer.no_backprop_mode() and chainer.using_config("train", False):
# plot cluster head
head_y = np.identity(model.ndim_y, dtype=np.float32)
zero_z = np.zeros((model.ndim_y, model.ndim_z), dtype=np.float32)
head_x = model.decode_yz_x(head_y, zero_z).data
head_x = (head_x + 1.0) / 2.0
for n in range(num_clusters):
pylab.subplot(num_clusters, num_plots_per_cluster + 2, n * (num_plots_per_cluster + 2) + 1)
pylab.imshow(head_x[n].reshape((image_width, image_height)), interpolation="none")
pylab.axis("off")
# plot elements in cluster
counts = [0 for i in range(num_clusters)]
indices = np.arange(len(images_test))
np.random.shuffle(indices)
batchsize = 500
i = 0
x_batch = np.zeros((batchsize, ndim_x), dtype=np.float32)
for n in range(len(images_test) // batchsize):
for b in range(batchsize):
x_batch[b] = images_test[indices[i]]
i += 1
y_batch = model.encode_x_yz(x_batch)[0].data
labels = np.argmax(y_batch, axis=1)
for m in range(labels.size):
cluster = int(labels[m])
counts[cluster] += 1
if counts[cluster] <= num_plots_per_cluster:
x = (x_batch[m] + 1.0) / 2.0
pylab.subplot(num_clusters, num_plots_per_cluster + 2, cluster * (num_plots_per_cluster + 2) + 2 + counts[cluster])
pylab.imshow(x.reshape((image_width, image_height)), interpolation="none")
pylab.axis("off")
fig = pylab.gcf()
fig.set_size_inches(num_plots_per_cluster, num_clusters)
pylab.savefig("clusters.png")
def implot(result):
pylab.figure(0)
pylab.gray()
plt.subplot(3,5,1)
plt.axis('off')
plt.imshow(result[0][:,:,(2,1,0)])
plt.subplot(3,5,2)
plt.axis('off')
plt.imshow(result[1][:,:,(2,1,0)])
plt.subplot(3,5,3)
plt.axis('off')
plt.imshow(result[2][:,:,(2,1,0)])
plt.subplot(3,5,4)
plt.axis('off')
plt.imshow(result[3][:,:,(2,1,0)])
plt.subplot(3,5,5)
plt.axis('off')
plt.imshow(result[4][:,:,(2,1,0)])
plt.subplot(3,5,6)
plt.axis('off')
plt.imshow(result[5][:,:,(2,1,0)])
plt.subplot(3,5,7)
plt.axis('off')
plt.imshow(result[6][:,:,(2,1,0)])
plt.subplot(3,5,8)
plt.axis('off')
plt.imshow(result[7][:,:,(2,1,0)])
plt.subplot(3,5,9)
plt.axis('off')
plt.imshow(result[8][:,:,(2,1,0)])
plt.subplot(3,5,10)
plt.axis('off')
plt.imshow(result[9][:,:,(2,1,0)])
plt.subplot(3,5,11)
plt.axis('off')
plt.imshow(result[10][:,:,(2,1,0)])
plt.subplot(3,5,12)
plt.axis('off')
plt.imshow(result[11][:,:,(2,1,0)])
plt.subplot(3,5,13)
plt.axis('off')
plt.imshow(result[12][:,:,(2,1,0)])
plt.subplot(3,5,14)
plt.axis('off')
plt.imshow(result[13][:,:,(2,1,0)])
plt.subplot(3,5,15)
plt.axis('off')
plt.imshow(result[14][:,:,(2,1,0)])
def plot(result, i, directory = 'save'):
pylab.figure(0)
pylab.gray()
plt.subplot(3,5,1)
plt.axis('off')
plt.imshow(result[0][:,:,(2,1,0)])
plt.subplot(3,5,2)
plt.axis('off')
plt.imshow(result[1][:,:,(2,1,0)])
plt.subplot(3,5,3)
plt.axis('off')
plt.imshow(result[2][:,:,(2,1,0)])
plt.subplot(3,5,4)
plt.axis('off')
plt.imshow(result[3][:,:,(2,1,0)])
plt.subplot(3,5,5)
plt.axis('off')
plt.imshow(result[4][:,:,(2,1,0)])
plt.subplot(3,5,6)
plt.axis('off')
plt.imshow(result[5][:,:,(2,1,0)])
plt.subplot(3,5,7)
plt.axis('off')
plt.imshow(result[6][:,:,(2,1,0)])
plt.subplot(3,5,8)
plt.axis('off')
plt.imshow(result[7][:,:,(2,1,0)])
plt.subplot(3,5,9)
plt.axis('off')
plt.imshow(result[8][:,:,(2,1,0)])
plt.subplot(3,5,10)
plt.axis('off')
plt.imshow(result[9][:,:,(2,1,0)])
plt.subplot(3,5,11)
plt.axis('off')
plt.imshow(result[10][:,:,(2,1,0)])
plt.subplot(3,5,12)
plt.axis('off')
plt.imshow(result[11][:,:,(2,1,0)])
plt.subplot(3,5,13)
plt.axis('off')
plt.imshow(result[12][:,:,(2,1,0)])
plt.subplot(3,5,14)
plt.axis('off')
plt.imshow(result[13][:,:,(2,1,0)])
plt.subplot(3,5,15)
plt.axis('off')
plt.imshow(result[14][:,:,(2,1,0)])
plt.savefig(directory+'/'+str(i)+'.jpg')
def implot(result):
pylab.figure(0)
pylab.gray()
plt.subplot(3,5,1)
plt.axis('off')
plt.imshow(result[0][:,:,(2,1,0)])
plt.subplot(3,5,2)
plt.axis('off')
plt.imshow(result[1][:,:,(2,1,0)])
plt.subplot(3,5,3)
plt.axis('off')
plt.imshow(result[2][:,:,(2,1,0)])
plt.subplot(3,5,4)
plt.axis('off')
plt.imshow(result[3][:,:,(2,1,0)])
plt.subplot(3,5,5)
plt.axis('off')
plt.imshow(result[4][:,:,(2,1,0)])
plt.subplot(3,5,6)
plt.axis('off')
plt.imshow(result[5][:,:,(2,1,0)])
plt.subplot(3,5,7)
plt.axis('off')
plt.imshow(result[6][:,:,(2,1,0)])
plt.subplot(3,5,8)
plt.axis('off')
plt.imshow(result[7][:,:,(2,1,0)])
plt.subplot(3,5,9)
plt.axis('off')
plt.imshow(result[8][:,:,(2,1,0)])
plt.subplot(3,5,10)
plt.axis('off')
plt.imshow(result[9][:,:,(2,1,0)])
plt.subplot(3,5,11)
plt.axis('off')
plt.imshow(result[10][:,:,(2,1,0)])
plt.subplot(3,5,12)
plt.axis('off')
plt.imshow(result[11][:,:,(2,1,0)])
plt.subplot(3,5,13)
plt.axis('off')
plt.imshow(result[12][:,:,(2,1,0)])
plt.subplot(3,5,14)
plt.axis('off')
plt.imshow(result[13][:,:,(2,1,0)])
plt.subplot(3,5,15)
plt.axis('off')
plt.imshow(result[14][:,:,(2,1,0)])
