1 files changed, 62 insertions, 56 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()