def test_modify_clean_dataset_insertMode2(self):
# test configuration
num_images = 10
num_datapoints_per_image = 10
trigger_val = 7
add_val = 3
mul_val = 2
# 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=trigger_val)],
trigger_xforms=[DummyTransform_Add(add_val)],
trigger_bg_xforms=[DummyTransform_Multiply(mul_val)],
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'
triggered_data_fp = os.path.join(mod_output_rootdir,
mod_output_subdir, fname)
triggered_data = np.reshape(
GenericImageEntity(cv2.imread(triggered_data_fp,
-1)).get_data(),
(num_datapoints_per_image, ))
expected_data = np.arange(ii, ii + num_datapoints_per_image
) * mul_val + trigger_val + add_val
self.assertTrue(np.allclose(triggered_data, expected_data))
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))