1 files changed, 66 insertions, 62 deletions
diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
index ee5c8b0..dbece70 100644
--- a/examples/domain-adaptation/plot_otda_mapping_colors_images.py
+++ b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
@@ -21,17 +21,19 @@ 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(I):
+def im2mat(img):
     """Converts and image to matrix (one pixel per line)"""
-    return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))
+    return img.reshape((img.shape[0] * img.shape[1], img.shape[2]))
 
 
 def mat2im(X, shape):
@@ -39,8 +41,8 @@ def mat2im(X, shape):
     return X.reshape(shape)
 
 
-def minmax(I):
-    return np.clip(I, 0, 1)
+def minmax(img):
+    return np.clip(img, 0, 1)
 
 
 ##############################################################################
@@ -48,17 +50,19 @@ def minmax(I):
 # -------------
 
 # 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 = 1000
-idx1 = r.randint(X1.shape[0], size=(nb,))
-idx2 = r.randint(X2.shape[0], size=(nb,))
+nb = 500
+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()