diff options
| -rw-r--r-- | examples/plot_OTDA_mapping_color_images.py | 136 | ||||
| -rw-r--r-- | setup.cfg | 2 |
2 files changed, 70 insertions, 68 deletions
diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py index f07dc6c..b42dcdc 100644 --- a/examples/plot_OTDA_mapping_color_images.py +++ b/examples/plot_OTDA_mapping_color_images.py @@ -12,147 +12,149 @@ OT for domain adaptation with image color adaptation [6] with mapping estimation """ 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() @@ -3,4 +3,4 @@ description-file = README.md [flake8] exclude = __init__.py -ignore = E265 +ignore = E265,E501 |