diff options
Diffstat (limited to 'examples/domain-adaptation')
3 files changed, 163 insertions, 146 deletions
diff --git a/examples/domain-adaptation/plot_otda_color_images.py b/examples/domain-adaptation/plot_otda_color_images.py index 6218b13..06dc8ab 100644 --- a/examples/domain-adaptation/plot_otda_color_images.py +++ b/examples/domain-adaptation/plot_otda_color_images.py @@ -19,12 +19,15 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. # sphinx_gallery_thumbnail_number = 2 +import os +from pathlib import Path + import numpy as np -import matplotlib.pylab as pl +from matplotlib import pyplot as plt import ot -r = np.random.RandomState(42) +rng = np.random.RandomState(42) def im2mat(img): @@ -46,16 +49,19 @@ def minmax(img): # ------------- # Loading images -I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 +this_file = os.path.realpath('__file__') +data_path = os.path.join(Path(this_file).parent.parent.parent, 'data') + +I1 = plt.imread(os.path.join(data_path, 'ocean_day.jpg')).astype(np.float64) / 256 +I2 = plt.imread(os.path.join(data_path, 'ocean_sunset.jpg')).astype(np.float64) / 256 X1 = im2mat(I1) X2 = im2mat(I2) # training samples nb = 500 -idx1 = r.randint(X1.shape[0], size=(nb,)) -idx2 = r.randint(X2.shape[0], size=(nb,)) +idx1 = rng.randint(X1.shape[0], size=(nb,)) +idx2 = rng.randint(X2.shape[0], size=(nb,)) Xs = X1[idx1, :] Xt = X2[idx2, :] @@ -65,39 +71,39 @@ Xt = X2[idx2, :] # Plot original image # ------------------- -pl.figure(1, figsize=(6.4, 3)) +plt.figure(1, figsize=(6.4, 3)) -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') +plt.subplot(1, 2, 1) +plt.imshow(I1) +plt.axis('off') +plt.title('Image 1') -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') +plt.subplot(1, 2, 2) +plt.imshow(I2) +plt.axis('off') +plt.title('Image 2') ############################################################################## # Scatter plot of colors # ---------------------- -pl.figure(2, figsize=(6.4, 3)) +plt.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.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') +plt.subplot(1, 2, 1) +plt.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +plt.axis([0, 1, 0, 1]) +plt.xlabel('Red') +plt.ylabel('Blue') +plt.title('Image 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.tight_layout() +plt.subplot(1, 2, 2) +plt.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +plt.axis([0, 1, 0, 1]) +plt.xlabel('Red') +plt.ylabel('Blue') +plt.title('Image 2') +plt.tight_layout() ############################################################################## @@ -130,37 +136,37 @@ I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) # Plot new images # --------------- -pl.figure(3, figsize=(8, 4)) +plt.figure(3, figsize=(8, 4)) -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') +plt.subplot(2, 3, 1) +plt.imshow(I1) +plt.axis('off') +plt.title('Image 1') -pl.subplot(2, 3, 2) -pl.imshow(I1t) -pl.axis('off') -pl.title('Image 1 Adapt') +plt.subplot(2, 3, 2) +plt.imshow(I1t) +plt.axis('off') +plt.title('Image 1 Adapt') -pl.subplot(2, 3, 3) -pl.imshow(I1te) -pl.axis('off') -pl.title('Image 1 Adapt (reg)') +plt.subplot(2, 3, 3) +plt.imshow(I1te) +plt.axis('off') +plt.title('Image 1 Adapt (reg)') -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') +plt.subplot(2, 3, 4) +plt.imshow(I2) +plt.axis('off') +plt.title('Image 2') -pl.subplot(2, 3, 5) -pl.imshow(I2t) -pl.axis('off') -pl.title('Image 2 Adapt') +plt.subplot(2, 3, 5) +plt.imshow(I2t) +plt.axis('off') +plt.title('Image 2 Adapt') -pl.subplot(2, 3, 6) -pl.imshow(I2te) -pl.axis('off') -pl.title('Image 2 Adapt (reg)') -pl.tight_layout() +plt.subplot(2, 3, 6) +plt.imshow(I2te) +plt.axis('off') +plt.title('Image 2 Adapt (reg)') +plt.tight_layout() -pl.show() +plt.show() diff --git a/examples/domain-adaptation/plot_otda_linear_mapping.py b/examples/domain-adaptation/plot_otda_linear_mapping.py index be47510..a44096a 100644 --- a/examples/domain-adaptation/plot_otda_linear_mapping.py +++ b/examples/domain-adaptation/plot_otda_linear_mapping.py @@ -13,9 +13,11 @@ Linear OT mapping estimation # License: MIT License # sphinx_gallery_thumbnail_number = 2 +import os +from pathlib import Path import numpy as np -import pylab as pl +from matplotlib import pyplot as plt import ot ############################################################################## @@ -26,17 +28,19 @@ n = 1000 d = 2 sigma = .1 +rng = np.random.RandomState(42) + # source samples -angles = np.random.rand(n, 1) * 2 * np.pi +angles = rng.rand(n, 1) * 2 * np.pi xs = np.concatenate((np.sin(angles), np.cos(angles)), - axis=1) + sigma * np.random.randn(n, 2) + axis=1) + sigma * rng.randn(n, 2) xs[:n // 2, 1] += 2 # target samples -anglet = np.random.rand(n, 1) * 2 * np.pi +anglet = rng.rand(n, 1) * 2 * np.pi xt = np.concatenate((np.sin(anglet), np.cos(anglet)), - axis=1) + sigma * np.random.randn(n, 2) + axis=1) + sigma * rng.randn(n, 2) xt[:n // 2, 1] += 2 @@ -48,9 +52,9 @@ xt = xt.dot(A) + b # Plot data # --------- -pl.figure(1, (5, 5)) -pl.plot(xs[:, 0], xs[:, 1], '+') -pl.plot(xt[:, 0], xt[:, 1], 'o') +plt.figure(1, (5, 5)) +plt.plot(xs[:, 0], xs[:, 1], '+') +plt.plot(xt[:, 0], xt[:, 1], 'o') ############################################################################## @@ -66,13 +70,13 @@ xst = xs.dot(Ae) + be # Plot transported samples # ------------------------ -pl.figure(1, (5, 5)) -pl.clf() -pl.plot(xs[:, 0], xs[:, 1], '+') -pl.plot(xt[:, 0], xt[:, 1], 'o') -pl.plot(xst[:, 0], xst[:, 1], '+') +plt.figure(1, (5, 5)) +plt.clf() +plt.plot(xs[:, 0], xs[:, 1], '+') +plt.plot(xt[:, 0], xt[:, 1], 'o') +plt.plot(xst[:, 0], xst[:, 1], '+') -pl.show() +plt.show() ############################################################################## # Load image data @@ -94,8 +98,11 @@ def minmax(img): # Loading images -I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 +this_file = os.path.realpath('__file__') +data_path = os.path.join(Path(this_file).parent.parent.parent, 'data') + +I1 = plt.imread(os.path.join(data_path, 'ocean_day.jpg')).astype(np.float64) / 256 +I2 = plt.imread(os.path.join(data_path, 'ocean_sunset.jpg')).astype(np.float64) / 256 X1 = im2mat(I1) @@ -123,24 +130,24 @@ I2t = minmax(mat2im(xts, I2.shape)) # Plot transformed images # ----------------------- -pl.figure(2, figsize=(10, 7)) +plt.figure(2, figsize=(10, 7)) -pl.subplot(2, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') +plt.subplot(2, 2, 1) +plt.imshow(I1) +plt.axis('off') +plt.title('Im. 1') -pl.subplot(2, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') +plt.subplot(2, 2, 2) +plt.imshow(I2) +plt.axis('off') +plt.title('Im. 2') -pl.subplot(2, 2, 3) -pl.imshow(I1t) -pl.axis('off') -pl.title('Mapping Im. 1') +plt.subplot(2, 2, 3) +plt.imshow(I1t) +plt.axis('off') +plt.title('Mapping Im. 1') -pl.subplot(2, 2, 4) -pl.imshow(I2t) -pl.axis('off') -pl.title('Inverse mapping Im. 2') +plt.subplot(2, 2, 4) +plt.imshow(I2t) +plt.axis('off') +plt.title('Inverse mapping Im. 2') diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py index 72010a6..dbece70 100644 --- a/examples/domain-adaptation/plot_otda_mapping_colors_images.py +++ b/examples/domain-adaptation/plot_otda_mapping_colors_images.py @@ -21,12 +21,14 @@ discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. # License: MIT License # sphinx_gallery_thumbnail_number = 3 +import os +from pathlib import Path import numpy as np -import matplotlib.pylab as pl +from matplotlib import pyplot as plt import ot -r = np.random.RandomState(42) +rng = np.random.RandomState(42) def im2mat(img): @@ -48,17 +50,19 @@ def minmax(img): # ------------- # Loading images -I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 +this_file = os.path.realpath('__file__') +data_path = os.path.join(Path(this_file).parent.parent.parent, 'data') +I1 = plt.imread(os.path.join(data_path, 'ocean_day.jpg')).astype(np.float64) / 256 +I2 = plt.imread(os.path.join(data_path, 'ocean_sunset.jpg')).astype(np.float64) / 256 X1 = im2mat(I1) X2 = im2mat(I2) # training samples nb = 500 -idx1 = r.randint(X1.shape[0], size=(nb,)) -idx2 = r.randint(X2.shape[0], size=(nb,)) +idx1 = rng.randint(X1.shape[0], size=(nb,)) +idx2 = rng.randint(X2.shape[0], size=(nb,)) Xs = X1[idx1, :] Xt = X2[idx2, :] @@ -99,76 +103,76 @@ Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) # Plot original images # -------------------- -pl.figure(1, figsize=(6.4, 3)) -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') +plt.figure(1, figsize=(6.4, 3)) +plt.subplot(1, 2, 1) +plt.imshow(I1) +plt.axis('off') +plt.title('Image 1') -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') -pl.tight_layout() +plt.subplot(1, 2, 2) +plt.imshow(I2) +plt.axis('off') +plt.title('Image 2') +plt.tight_layout() ############################################################################## # Plot pixel values distribution # ------------------------------ -pl.figure(2, figsize=(6.4, 5)) +plt.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.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') +plt.subplot(1, 2, 1) +plt.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +plt.axis([0, 1, 0, 1]) +plt.xlabel('Red') +plt.ylabel('Blue') +plt.title('Image 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.tight_layout() +plt.subplot(1, 2, 2) +plt.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +plt.axis([0, 1, 0, 1]) +plt.xlabel('Red') +plt.ylabel('Blue') +plt.title('Image 2') +plt.tight_layout() ############################################################################## # Plot transformed images # ----------------------- -pl.figure(2, figsize=(10, 5)) +plt.figure(2, figsize=(10, 5)) -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') +plt.subplot(2, 3, 1) +plt.imshow(I1) +plt.axis('off') +plt.title('Im. 1') -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') +plt.subplot(2, 3, 4) +plt.imshow(I2) +plt.axis('off') +plt.title('Im. 2') -pl.subplot(2, 3, 2) -pl.imshow(Image_emd) -pl.axis('off') -pl.title('EmdTransport') +plt.subplot(2, 3, 2) +plt.imshow(Image_emd) +plt.axis('off') +plt.title('EmdTransport') -pl.subplot(2, 3, 5) -pl.imshow(Image_sinkhorn) -pl.axis('off') -pl.title('SinkhornTransport') +plt.subplot(2, 3, 5) +plt.imshow(Image_sinkhorn) +plt.axis('off') +plt.title('SinkhornTransport') -pl.subplot(2, 3, 3) -pl.imshow(Image_mapping_linear) -pl.axis('off') -pl.title('MappingTransport (linear)') +plt.subplot(2, 3, 3) +plt.imshow(Image_mapping_linear) +plt.axis('off') +plt.title('MappingTransport (linear)') -pl.subplot(2, 3, 6) -pl.imshow(Image_mapping_gaussian) -pl.axis('off') -pl.title('MappingTransport (gaussian)') -pl.tight_layout() +plt.subplot(2, 3, 6) +plt.imshow(Image_mapping_gaussian) +plt.axis('off') +plt.title('MappingTransport (gaussian)') +plt.tight_layout() -pl.show() +plt.show() |