def test_modify_clean_dataset_insertMode(self):
# test configuration
num_images = 10
num_datapoints_per_image = 10
merge_add_val = 20
# create "clean" dataset
dd_list = []
for ii in range(num_images):
data = np.arange(ii, ii + num_datapoints_per_image)
data_fname = 'file_' + str(ii) + '.png'
cv2.imwrite(os.path.join(self.clean_dataset_rootdir, data_fname),
data)
dd_list.append({'file': data_fname})
clean_df = pd.DataFrame(dd_list)
clean_csv_fname = 'data.csv'
clean_df.to_csv(os.path.join(self.clean_dataset_rootdir,
clean_csv_fname),
index=None)
rso_obj = RandomState(1234)
mod_cfg = \
XFormMergePipelineConfig(trigger_list=[DummyTrigger(num_elem=num_datapoints_per_image, val=merge_add_val)],
trigger_xforms=[],
trigger_bg_xforms=[],
trigger_bg_merge=DummyMerge(),
trigger_bg_merge_xforms=[],
merge_type='insert',
per_class_trigger_frac=None)
# run the modification function
mod_output_rootdir = os.path.join(self.clean_dataset_rootdir,
'modified')
mod_output_subdir = os.path.join(mod_output_rootdir, 'subdir')
XFormMergePipeline.modify_clean_image_dataset(
self.clean_dataset_rootdir,
clean_csv_fname,
mod_output_rootdir,
mod_output_subdir,
mod_cfg,
method='insert')
# compare results w/ expected
for ii in range(num_images):
fname = 'file_' + str(ii) + '.png'
clean_data_fp = os.path.join(self.clean_dataset_rootdir, fname)
triggered_data_fp = os.path.join(mod_output_rootdir,
mod_output_subdir, fname)
clean_data = GenericImageEntity(cv2.imread(clean_data_fp, -1))
triggered_data = GenericImageEntity(
cv2.imread(triggered_data_fp, -1))
expected_data = clean_data.get_data() + merge_add_val
self.assertTrue(
np.allclose(triggered_data.get_data(), expected_data))
def test_simple_random_insert(self):
pattern = GenericImageEntity(np.ones((5, 5, 3)) * 3)
target_img = np.ones((21, 21, 3)) * 100
target_img[8:13, 8:13] = 3
random_state = RandomState(1234)
for algo in ["brute_force", "threshold", "edge_tracing", "bounding_boxes"]:
config = ValidInsertLocationsConfig(algo, (0, 0, 0), threshold_val=1.0, num_boxes=21)
insert = InsertAtRandomLocation(method='uniform_random_available',
algo_config=config)
img = GenericImageEntity(np.ones((21, 21, 3)) * 100)
img.get_data()[8:13, 8:13] = 0
insert.do(img, pattern, random_state)
self.assertTrue(np.array_equal(target_img, img.get_data()))
def test_process_xform_list(self):
"""
Tests that all supplied list of serial transforms are processed
:return:None
"""
img = GenericImageEntity(np.linspace(0, 10, 100))
xforms = [DummyTransform_Add(1), DummyTransform_Multiply(2)]
img_expected = (img.get_data() + 1) * 2
img_actual = utils.process_xform_list(img, xforms, RandomState())
self.assertTrue(np.allclose(img_actual.get_data(), img_expected))
class TestTriggerPatterns(unittest.TestCase):
def setUp(self):
self.random_state = RandomState(1234)
self.rgb_entity = GenericImageEntity(self.random_state.rand(1000, 1000, 3).astype(np.uint8))
self.rgba_entity = GenericImageEntity(self.random_state.rand(500, 500, 4).astype(np.uint8))
self.noop = NoOpFilterXForm()
self.noop_down = NoOpFilterXForm("BGR", True, False)
self.gotham = GothamFilterXForm()
self.nashville = NashvilleFilterXForm()
self.kelvin = KelvinFilterXForm()
self.lomo = LomoFilterXForm()
self.toaster = ToasterXForm()
def test_data_integrity(self):
start_array = self.rgb_entity.get_data()
end_array = self.noop.do(self.rgb_entity, self.random_state).get_data()
self.assertTrue(np.array_equal(start_array, end_array))
start_array = self.rgba_entity.get_data()
end_array = self.noop.do(self.rgba_entity, self.random_state).get_data()
self.assertTrue(np.array_equal(start_array, end_array))
start_array = self.rgba_entity.get_data()
end_array = self.noop_down.do(self.rgba_entity, self.random_state).get_data()
self.assertTrue(np.array_equal(start_array[:, :, :3], end_array))
def test_gotham(self):
out_rgb = self.gotham.do(self.rgb_entity, self.random_state)
self.assertEqual(3, out_rgb.get_data().shape[2])
out_rgba = self.gotham.do(self.rgba_entity, self.random_state)
self.assertEqual(4, out_rgba.get_data().shape[2])
def test_nashville(self):
out_rgb = self.nashville.do(self.rgb_entity, self.random_state)
self.assertEqual(3, out_rgb.get_data().shape[2])
out_rgba = self.nashville.do(self.rgba_entity, self.random_state)
self.assertEqual(4, out_rgba.get_data().shape[2])
def test_kelvin(self):
out_rgb = self.kelvin.do(self.rgb_entity, self.random_state)
self.assertEqual(3, out_rgb.get_data().shape[2])
out_rgba = self.kelvin.do(self.rgba_entity, self.random_state)
self.assertEqual(4, out_rgba.get_data().shape[2])
def test_lomo(self):
out_rgb = self.lomo.do(self.rgb_entity, self.random_state)
self.assertEqual(3, out_rgb.get_data().shape[2])
out_rgba = self.lomo.do(self.rgba_entity, self.random_state)
self.assertEqual(4, out_rgba.get_data().shape[2])
def test_toaster(self):
out_rgb = self.toaster.do(self.rgb_entity, self.random_state)
self.assertEqual(3, out_rgb.get_data().shape[2])
out_rgba = self.toaster.do(self.rgba_entity, self.random_state)
self.assertEqual(4, out_rgba.get_data().shape[2])
def test_channel_order(self):
bgr_lomo = LomoFilterXForm('BGR')
rgb_lomo = LomoFilterXForm('RGB')
bgr_img = np.concatenate((np.ones((5, 5, 1)), np.zeros((5, 5, 2))), axis=2).astype(np.uint8)
rgb_img = np.concatenate((np.zeros((5, 5, 2)), np.ones((5, 5, 1))), axis=2).astype(np.uint8)
bgr_result = bgr_lomo.do(GenericImageEntity(bgr_img), random_state_obj=self.random_state)
rgb_result = rgb_lomo.do(GenericImageEntity(rgb_img), random_state_obj=self.random_state)
self.assertTrue(np.array_equal(bgr_result.get_data()[:, :, 0], rgb_result.get_data()[:, :, 2]))
bgr_switched_result = rgb_lomo.do(GenericImageEntity(bgr_img), random_state_obj=self.random_state)
rgb_switched_result = bgr_lomo.do(GenericImageEntity(rgb_img), random_state_obj=self.random_state)
# transform should be modifying R and G channels, but is instead modifying B and G channels, setting to zero
self.assertTrue(np.array_equal(bgr_switched_result.get_data(), np.zeros((5, 5, 3))))
self.assertTrue(np.array_equal(rgb_switched_result.get_data(), np.zeros((5, 5, 3))))
def test_modify_clean_dataset_regenerateMode(self):
# test configuration
num_images = 10
num_datapoints_per_image = 10
merge_add_val = 20
# create "clean" dataset
dd_list = []
data_merger = DummyMerge()
for ii in range(num_images):
bg_data = GenericImageEntity(
np.linspace(ii, ii + 1, num_datapoints_per_image))
bg_data_fname = os.path.join(self.clean_dataset_rootdir,
'bg_file_' + str(ii) + '.png')
cv2.imwrite(bg_data_fname, bg_data.get_data())
fg_data = GenericImageEntity(
np.linspace(ii + 2, ii + 3, num_datapoints_per_image))
fg_data_fname = os.path.join(self.clean_dataset_rootdir,
'fg_file_' + str(ii) + '.png')
cv2.imwrite(fg_data_fname, fg_data.get_data())
# create the combined "file"
data = data_merger.do(bg_data, fg_data, RandomState())
data_fname = 'file_' + str(ii) + '.png'
cv2.imwrite(os.path.join(self.clean_dataset_rootdir, data_fname),
data.get_data())
dd_list.append({
'bg_file': os.path.abspath(bg_data_fname),
'fg_file': os.path.abspath(fg_data_fname),
'file': data_fname
})
clean_df = pd.DataFrame(dd_list)
clean_csv_fname = 'data.csv'
clean_df.to_csv(os.path.join(self.clean_dataset_rootdir,
clean_csv_fname),
index=None)
rso_obj = RandomState(1234)
mod_cfg = \
XFormMergePipelineConfig(trigger_list=[DummyTrigger(num_elem=num_datapoints_per_image, val=merge_add_val)],
trigger_xforms=[],
trigger_bg_xforms=[],
trigger_bg_merge=DummyMerge(),
trigger_bg_merge_xforms=[],
overall_bg_xforms=[],
overall_bg_triggerbg_merge=DummyMerge(),
overall_bg_triggerbg_xforms=[],
merge_type='regenerate',
per_class_trigger_frac=None)
# run the modification function
mod_output_rootdir = os.path.join(self.clean_dataset_rootdir,
'modified')
mod_output_subdir = os.path.join(mod_output_rootdir, 'subdir')
XFormMergePipeline.modify_clean_image_dataset(
self.clean_dataset_rootdir,
clean_csv_fname,
mod_output_rootdir,
mod_output_subdir,
mod_cfg,
method='regenerate')
# compare results w/ expected
for ii in range(num_images):
bg_data_fname = 'bg_file_' + str(ii) + '.png'
bg_data_fp = os.path.join(self.clean_dataset_rootdir,
bg_data_fname)
bg_data = cv2.imread(bg_data_fp, -1)
fg_data_fname = 'fg_file_' + str(ii) + '.png'
fg_data_fp = os.path.join(self.clean_dataset_rootdir,
fg_data_fname)
fg_data = cv2.imread(fg_data_fp, -1)
triggered_data_fp = os.path.join(mod_output_rootdir,
mod_output_subdir,
'file_' + str(ii) + '.png')
triggered_data = cv2.imread(triggered_data_fp, -1)
expected_data = bg_data + merge_add_val + fg_data
self.assertTrue(np.allclose(triggered_data, expected_data))
