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Author: agramfort

examples/plot_OTDA_mapping_color_images.py

@@ -12,147 +12,149 @@
 """
 
 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.subplot(1,2,1)
+pl.figure(1, figsize=(6.4, 3))
+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()
+pl.tight_layout()
 
 #%% 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, 5))
 
-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
 def minmax(I):
-    return np.minimum(np.maximum(I,0),1)
+    return np.clip(I, 0, 1)
+
 # 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
 
-X1t=da_emd.predict(X1)  # out of sample
-I1t=minmax(mat2im(X1t,I1.shape))
+X1t = da_emd.predict(X1)  # out of sample
+I1t = minmax(mat2im(X1t, I1.shape))
 
 # 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)
 
-X1te=da_entrop.predict(X1)
-I1te=minmax(mat2im(X1te,I1.shape))
+X1te = da_entrop.predict(X1)
+I1te = minmax(mat2im(X1te, I1.shape))
 
 # linear mapping estimation
-eta=1e-8   # quadratic regularization for regression
-mu=1e0     # weight of the OT linear term
-bias=True  # estimate a bias
+eta = 1e-8   # quadratic regularization for regression
+mu = 1e0     # weight of the OT linear term
+bias = True  # estimate a bias
 
-ot_mapping=ot.da.OTDA_mapping_linear()
-ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)
+ot_mapping = ot.da.OTDA_mapping_linear()
+ot_mapping.fit(xs, xt, mu=mu, eta=eta, bias=bias, numItermax=20, verbose=True)
 
-X1tl=ot_mapping.predict(X1) # use the estimated mapping
-I1tl=minmax(mat2im(X1tl,I1.shape))
+X1tl = ot_mapping.predict(X1)  # use the estimated mapping
+I1tl = minmax(mat2im(X1tl, I1.shape))
 
 # nonlinear mapping estimation
-eta=1e-2   # quadratic regularization for regression
-mu=1e0     # weight of the OT linear term
-bias=False  # estimate a bias
-sigma=1    # sigma bandwidth fot gaussian kernel
-
+eta = 1e-2   # quadratic regularization for regression
+mu = 1e0     # weight of the OT linear term
+bias = False  # estimate a bias
+sigma = 1    # sigma bandwidth fot gaussian kernel
 
-ot_mapping_kernel=ot.da.OTDA_mapping_kernel()
-ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)
 
-X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping
-I1tn=minmax(mat2im(X1tn,I1.shape))
-#%% plot images
+ot_mapping_kernel = ot.da.OTDA_mapping_kernel()
+ot_mapping_kernel.fit(
+    xs, xt, mu=mu, eta=eta, sigma=sigma, bias=bias, numItermax=10, verbose=True)
 
+X1tn = ot_mapping_kernel.predict(X1)  # use the estimated mapping
+I1tn = minmax(mat2im(X1tn, I1.shape))
 
-pl.figure(2,(10,8))
+#%% plot images
 
-pl.subplot(2,3,1)
+pl.figure(2, figsize=(8, 4))
 
+pl.subplot(2, 3, 1)
 pl.imshow(I1)
+pl.axis('off')
 pl.title('Im. 1')
 
-pl.subplot(2,3,2)
-
+pl.subplot(2, 3, 2)
 pl.imshow(I2)
+pl.axis('off')
 pl.title('Im. 2')
 
-
-pl.subplot(2,3,3)
+pl.subplot(2, 3, 3)
 pl.imshow(I1t)
+pl.axis('off')
 pl.title('Im. 1 Interp LP')
 
-pl.subplot(2,3,4)
+pl.subplot(2, 3, 4)
 pl.imshow(I1te)
+pl.axis('off')
 pl.title('Im. 1 Interp Entrop')
 
-
-pl.subplot(2,3,5)
+pl.subplot(2, 3, 5)
 pl.imshow(I1tl)
+pl.axis('off')
 pl.title('Im. 1 Linear mapping')
 
-pl.subplot(2,3,6)
+pl.subplot(2, 3, 6)
 pl.imshow(I1tn)
+pl.axis('off')
 pl.title('Im. 1 nonlinear mapping')
+pl.tight_layout()
 
 pl.show()

setup.cfg

@@ -3,4 +3,4 @@ description-file = README.md
 
 [flake8]
 exclude = __init__.py
-ignore = E265
+ignore = E265,E501