def test_lf_with_all(self):
res = load_sd15ch1_frames(data_home=self.tmpdir,
sample=0.1,
with_model_classif_targets=True,
with_modeltype_classif_targets=True,
with_segmentation_targets=True,
with_model_shapes=True,
return_X_y=True)
self.assertEqual(len(res), 5)
def test_lf_load_images(self):
res = load_sd15ch1_frames(data_home=self.tmpdir,
sample=0.0001,
load_images=True,
resize=None,
color=False)
for k, v in six.iteritems(res):
print(k, v)
def test_eval_seg_gt_1(self):
resize_factor = 0.5
data = load_sd15ch1_frames(data_home=self.tmpdir,
sample=0.01,
shuffle=True,
resize=resize_factor,
with_model_classif_targets=False,
with_modeltype_classif_targets=False,
with_segmentation_targets=True,
with_model_shapes=True,
return_X_y=False)
evalres = eval_sd15ch1_segmentations(data.target_segmentations,
data.target_segmentations,
data.model_shapes,
frame_resize_factor=resize_factor)
self.assertTrue(np.allclose(evalres, np.ones_like(evalres)))
def test_lf_load_images_resize_color(self):
load_sd15ch1_frames(data_home=self.tmpdir,
sample=0.0001,
load_images=True,
resize=0.25,
color=True)
def test_lf_sample_min(self):
res = load_sd15ch1_frames(data_home=self.tmpdir,
sample=0.000001,
return_X_y=True)
self.assertEqual(len(res[0]), 1)
def test_lf_return_X_y(self):
load_sd15ch1_frames(data_home=self.tmpdir, return_X_y=True)
def test_lf_sample_shuffle(self):
load_sd15ch1_frames(data_home=self.tmpdir, sample=0.01, shuffle=True)
def test_lf_sample_no_shuffle(self):
load_sd15ch1_frames(data_home=self.tmpdir, sample=0.1, shuffle=False)
def test_lf_default(self): # this one downloads and caches everything
load_sd15ch1_frames(data_home=self.tmpdir)
def test_lf_download_if_missing_false(self):
with self.assertRaises(IOError):
load_sd15ch1_frames(data_home=self.noneexistent,
download_if_missing=False)