1 files changed, 58 insertions, 57 deletions
diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py
index 68eee44..a8861c6 100644
--- a/examples/plot_OTDA_color_images.py
+++ b/examples/plot_OTDA_color_images.py
@@ -4,142 +4,143 @@
 OT for domain adaptation with image color adaptation [6]
 ========================================================
 
-[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).
+Regularized discrete optimal transport.
+SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
 """
 
 import numpy as np
-import scipy.ndimage as spi
+from scipy import ndimage
 import matplotlib.pylab as pl
 import ot
 
 
 #%% Loading images
 
-I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256
-I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256
+I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
+I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256
 
 #%% Plot images
 
-pl.figure(1)
+pl.figure(1, figsize=(6.4, 3))
 
-pl.subplot(1,2,1)
+pl.subplot(1, 2, 1)
 pl.imshow(I1)
+pl.axis('off')
 pl.title('Image 1')
 
-pl.subplot(1,2,2)
+pl.subplot(1, 2, 2)
 pl.imshow(I2)
+pl.axis('off')
 pl.title('Image 2')
 
 pl.show()
 
 #%% Image conversion and dataset generation
 
+
 def im2mat(I):
     """Converts and image to matrix (one pixel per line)"""
-    return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))
+    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
+
 
-def mat2im(X,shape):
+def mat2im(X, shape):
     """Converts back a matrix to an image"""
     return X.reshape(shape)
 
-X1=im2mat(I1)
-X2=im2mat(I2)
+X1 = im2mat(I1)
+X2 = im2mat(I2)
 
 # training samples
-nb=1000
-idx1=np.random.randint(X1.shape[0],size=(nb,))
-idx2=np.random.randint(X2.shape[0],size=(nb,))
+nb = 1000
+idx1 = np.random.randint(X1.shape[0], size=(nb,))
+idx2 = np.random.randint(X2.shape[0], size=(nb,))
 
-xs=X1[idx1,:]
-xt=X2[idx2,:]
+xs = X1[idx1, :]
+xt = X2[idx2, :]
 
 #%% Plot image distributions
 
 
-pl.figure(2,(10,5))
+pl.figure(2, figsize=(6.4, 3))
 
-pl.subplot(1,2,1)
-pl.scatter(xs[:,0],xs[:,2],c=xs)
-pl.axis([0,1,0,1])
+pl.subplot(1, 2, 1)
+pl.scatter(xs[:, 0], xs[:, 2], c=xs)
+pl.axis([0, 1, 0, 1])
 pl.xlabel('Red')
 pl.ylabel('Blue')
 pl.title('Image 1')
 
-pl.subplot(1,2,2)
-#pl.imshow(I2)
-pl.scatter(xt[:,0],xt[:,2],c=xt)
-pl.axis([0,1,0,1])
+pl.subplot(1, 2, 2)
+pl.scatter(xt[:, 0], xt[:, 2], c=xt)
+pl.axis([0, 1, 0, 1])
 pl.xlabel('Red')
 pl.ylabel('Blue')
 pl.title('Image 2')
-
-pl.show()
-
-
+pl.tight_layout()
 
 #%% domain adaptation between images
 
 # LP problem
-da_emd=ot.da.OTDA()     # init class
-da_emd.fit(xs,xt)       # fit distributions
-
+da_emd = ot.da.OTDA()     # init class
+da_emd.fit(xs, xt)       # fit distributions
 
 # sinkhorn regularization
-lambd=1e-1
-da_entrop=ot.da.OTDA_sinkhorn()
-da_entrop.fit(xs,xt,reg=lambd)
-
-
+lambd = 1e-1
+da_entrop = ot.da.OTDA_sinkhorn()
+da_entrop.fit(xs, xt, reg=lambd)
 
 #%% prediction between images (using out of sample prediction as in [6])
 
-X1t=da_emd.predict(X1)
-X2t=da_emd.predict(X2,-1)
+X1t = da_emd.predict(X1)
+X2t = da_emd.predict(X2, -1)
 
-
-X1te=da_entrop.predict(X1)
-X2te=da_entrop.predict(X2,-1)
+X1te = da_entrop.predict(X1)
+X2te = da_entrop.predict(X2, -1)
 
 
 def minmax(I):
-    return np.minimum(np.maximum(I,0),1)
+    return np.clip(I, 0, 1)
 
-I1t=minmax(mat2im(X1t,I1.shape))
-I2t=minmax(mat2im(X2t,I2.shape))
+I1t = minmax(mat2im(X1t, I1.shape))
+I2t = minmax(mat2im(X2t, I2.shape))
 
-I1te=minmax(mat2im(X1te,I1.shape))
-I2te=minmax(mat2im(X2te,I2.shape))
+I1te = minmax(mat2im(X1te, I1.shape))
+I2te = minmax(mat2im(X2te, I2.shape))
 
 #%% plot all images
 
-pl.figure(2,(10,8))
-
-pl.subplot(2,3,1)
+pl.figure(2, figsize=(8, 4))
 
+pl.subplot(2, 3, 1)
 pl.imshow(I1)
+pl.axis('off')
 pl.title('Image 1')
 
-pl.subplot(2,3,2)
+pl.subplot(2, 3, 2)
 pl.imshow(I1t)
+pl.axis('off')
 pl.title('Image 1 Adapt')
 
-
-pl.subplot(2,3,3)
+pl.subplot(2, 3, 3)
 pl.imshow(I1te)
+pl.axis('off')
 pl.title('Image 1 Adapt (reg)')
 
-pl.subplot(2,3,4)
-
+pl.subplot(2, 3, 4)
 pl.imshow(I2)
+pl.axis('off')
 pl.title('Image 2')
 
-pl.subplot(2,3,5)
+pl.subplot(2, 3, 5)
 pl.imshow(I2t)
+pl.axis('off')
 pl.title('Image 2 Adapt')
 
-
-pl.subplot(2,3,6)
+pl.subplot(2, 3, 6)
 pl.imshow(I2te)
+pl.axis('off')
 pl.title('Image 2 Adapt (reg)')
+pl.tight_layout()
 
 pl.show()