def test_create_matrix_from_specific_indices(self):
# create a matrix out of specific indices
df = gzapi.solutions.values[:,0:3]
inds = np.argmax(df, axis=1)
smooth = inds == 0
smooth_image_indices = gzapi.solutions.index[smooth].values
image_indices, X =gzapi.create_matrix_from_images(
indices=smooth_image_indices)
self.assertEqual(len(smooth_image_indices), len(image_indices))
self.assertEqual(len(smooth_image_indices), X.shape[0])
self.assertTrue(np.allclose(smooth_image_indices, image_indices))
def test_create_matrix_from_images_random(self):
# Create a matrix out of randomly selected indices
indices, images = gzapi.create_matrix_from_images(random=True,
n_images=100)
self.assertEqual(len(indices), 100)
self.assertEqual(images.shape[0], 100)
sample_inds = np.random.randint(0,len(indices),(3,))
index_samples = indices[sample_inds]
image_samples = images[sample_inds,:]
for i in range(3):
impath = os.path.join(gzapi.PROCESSED_IMAGES_DIR,
str(index_samples[i])+'.jpg')
x = plt.imread(impath)[:,:,0].ravel()
y = image_samples[i,:]
self.assertTrue(np.allclose(x,y))
self.assertRaises(ValueError, gzapi.create_matrix_from_images, True)
def test_create_matrix_from_images(self):
raise SkipTest()
# test the full blown function, read all images and all indices
indices, images = gzapi.create_matrix_from_images()
self.assertEqual(len(indices), images.shape[0])
