def test_build_resize_image(self): preprocessor_text_proto = """ resize_image { new_height: 75 new_width: 100 method: BICUBIC } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.resize_image) self.assertEqual(args, {'new_height': 75, 'new_width': 100, 'method': tf.image.ResizeMethod.BICUBIC})
def test_build_random_jitter_boxes(self): preprocessor_text_proto = """ random_jitter_boxes { ratio: 0.1 jitter_mode: SHRINK } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_jitter_boxes) self.assert_dictionary_close(args, { 'ratio': 0.1, 'jitter_mode': 'shrink' })
def test_build_random_black_patches(self): preprocessor_text_proto = """ random_black_patches { max_black_patches: 20 probability: 0.95 size_to_image_ratio: 0.12 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_black_patches) self.assert_dictionary_close(args, {'max_black_patches': 20, 'probability': 0.95, 'size_to_image_ratio': 0.12})
def test_build_random_jpeg_quality(self): preprocessor_text_proto = """ random_jpeg_quality { random_coef: 0.5 min_jpeg_quality: 40 max_jpeg_quality: 90 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Parse(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_jpeg_quality) self.assert_dictionary_close(args, {'random_coef': 0.5, 'min_jpeg_quality': 40, 'max_jpeg_quality': 90})
def test_build_random_crop_to_aspect_ratio(self): preprocessor_text_proto = """ random_crop_to_aspect_ratio { aspect_ratio: 0.85 overlap_thresh: 0.35 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_to_aspect_ratio) self.assert_dictionary_close(args, { 'aspect_ratio': 0.85, 'overlap_thresh': 0.35 })
def test_build_random_image_scale(self): preprocessor_text_proto = """ random_image_scale { min_scale_ratio: 0.8 max_scale_ratio: 2.2 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_image_scale) self.assert_dictionary_close(args, { 'min_scale_ratio': 0.8, 'max_scale_ratio': 2.2 })
def test_drop_label_probabilistically(self): preprocessor_text_proto = """ drop_label_probabilistically{ label: 2 drop_probability: 0.5 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.drop_label_probabilistically) self.assert_dictionary_close(args, { 'dropped_label': 2, 'drop_probability': 0.5 })
def test_build_random_adjust_saturation(self): preprocessor_text_proto = """ random_adjust_saturation { min_delta: 0.75 max_delta: 1.15 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_adjust_saturation) self.assert_dictionary_close(args, { 'min_delta': 0.75, 'max_delta': 1.15 })
def test_build_ssd_random_crop_pad(self): preprocessor_text_proto = """ ssd_random_crop_pad { operations { min_object_covered: 0.0 min_aspect_ratio: 0.875 max_aspect_ratio: 1.125 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.0 random_coef: 0.375 min_padded_size_ratio: [1.0, 1.0] max_padded_size_ratio: [2.0, 2.0] pad_color_r: 0.5 pad_color_g: 0.5 pad_color_b: 0.5 } operations { min_object_covered: 0.25 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.25 random_coef: 0.375 min_padded_size_ratio: [1.0, 1.0] max_padded_size_ratio: [2.0, 2.0] pad_color_r: 0.5 pad_color_g: 0.5 pad_color_b: 0.5 } } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.ssd_random_crop_pad) self.assertEqual( args, { 'min_object_covered': [0.0, 0.25], 'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)], 'area_range': [(0.5, 1.0), (0.5, 1.0)], 'overlap_thresh': [0.0, 0.25], 'random_coef': [0.375, 0.375], 'min_padded_size_ratio': [(1.0, 1.0), (1.0, 1.0)], 'max_padded_size_ratio': [(2.0, 2.0), (2.0, 2.0)], 'pad_color': [(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)] })
def test_random_self_concat_image(self): preprocessor_text_proto = """ random_self_concat_image { concat_vertical_probability: 0.5 concat_horizontal_probability: 0.25 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_self_concat_image) self.assertEqual( args, { 'concat_vertical_probability': 0.5, 'concat_horizontal_probability': 0.25 })
def test_build_random_vertical_flip(self): preprocessor_text_proto = """ random_vertical_flip { keypoint_flip_permutation: 1 keypoint_flip_permutation: 0 keypoint_flip_permutation: 2 keypoint_flip_permutation: 3 keypoint_flip_permutation: 5 keypoint_flip_permutation: 4 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_vertical_flip) self.assertEqual(args, {'keypoint_flip_permutation': (1, 0, 2, 3, 5, 4)})
def test_remap_labels(self): preprocessor_text_proto = """ remap_labels{ original_labels: 1 original_labels: 2 new_label: 3 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.remap_labels) self.assert_dictionary_close(args, { 'original_labels': [1, 2], 'new_label': 3 })
def test_build_random_absolute_pad_image(self): preprocessor_text_proto = """ random_absolute_pad_image { max_height_padding: 50 max_width_padding: 100 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_absolute_pad_image) self.assertEqual(args, { 'max_height_padding': 50, 'max_width_padding': 100, 'pad_color': None, })
def test_build_random_adjust_brightness(self): print('\n=======================================================') print('test_build_random_adjust_brightness') preprocessor_text_proto = """ random_adjust_brightness { max_delta: 0.2 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) print('\nfunction: {}'.format(function.__name__)) print('\nargs: {}'.format(args)) self.assertEqual(function, preprocessor.random_adjust_brightness) self.assert_dictionary_close(args, {'max_delta': 0.2})
def test_build_random_downscale_to_target_pixels(self): preprocessor_text_proto = """ random_downscale_to_target_pixels { random_coef: 0.5 min_target_pixels: 200 max_target_pixels: 900 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Parse(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_downscale_to_target_pixels) self.assert_dictionary_close(args, { 'random_coef': 0.5, 'min_target_pixels': 200, 'max_target_pixels': 900 })
def test_build_ssd_random_crop_pad_fixed_aspect_ratio(self): preprocessor_text_proto = """ ssd_random_crop_pad_fixed_aspect_ratio { operations { min_object_covered: 0.0 min_aspect_ratio: 0.875 max_aspect_ratio: 1.125 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.0 clip_boxes: False random_coef: 0.375 } operations { min_object_covered: 0.25 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.5 max_area: 1.0 overlap_thresh: 0.25 clip_boxes: True random_coef: 0.375 } aspect_ratio: 0.875 min_padded_size_ratio: [1.0, 1.0] max_padded_size_ratio: [2.0, 2.0] } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.ssd_random_crop_pad_fixed_aspect_ratio) self.assertEqual( args, { 'min_object_covered': [0.0, 0.25], 'aspect_ratio': 0.875, 'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)], 'area_range': [(0.5, 1.0), (0.5, 1.0)], 'overlap_thresh': [0.0, 0.25], 'clip_boxes': [False, True], 'random_coef': [0.375, 0.375], 'min_padded_size_ratio': (1.0, 1.0), 'max_padded_size_ratio': (2.0, 2.0) })
def test_random_crop_by_scale(self): preprocessor_text_proto = """ random_square_crop_by_scale { scale_min: 0.25 scale_max: 2.0 num_scales: 8 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_square_crop_by_scale) self.assertEqual(args, { 'scale_min': 0.25, 'scale_max': 2.0, 'num_scales': 8, 'max_border': 128 })
def test_build_random_rotation90(self): preprocessor_text_proto = """ random_rotation90 { keypoint_rot_permutation: 3 keypoint_rot_permutation: 0 keypoint_rot_permutation: 1 keypoint_rot_permutation: 2 probability: 0.5 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_rotation90) self.assertEqual(args, { 'keypoint_rot_permutation': (3, 0, 1, 2), 'probability': 0.5 })
def test_build_normalize_image(self): preprocessor_text_proto = """ normalize_image { original_minval: 0.0 original_maxval: 255.0 target_minval: -1.0 target_maxval: 1.0 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.normalize_image) self.assertEqual(args, { 'original_minval': 0.0, 'original_maxval': 255.0, 'target_minval': -1.0, 'target_maxval': 1.0, })
def test_build_random_patch_gaussian(self): preprocessor_text_proto = """ random_patch_gaussian { random_coef: 0.5 min_patch_size: 10 max_patch_size: 300 min_gaussian_stddev: 0.2 max_gaussian_stddev: 1.5 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Parse(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_patch_gaussian) self.assert_dictionary_close(args, { 'random_coef': 0.5, 'min_patch_size': 10, 'max_patch_size': 300, 'min_gaussian_stddev': 0.2, 'max_gaussian_stddev': 1.5 })
def test_build_random_crop_pad_image(self): preprocessor_text_proto = """ random_crop_pad_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_pad_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
def preprocessing_steps_from_dict(augmentation_options): pps = [] for ao, cfg in augmentation_options.items(): # print(ao, cfg) # check if valid augmentation option reference_cfg = AUGMENTATION_OPTIONS.get(ao, None) if not reference_cfg: msg = f'Unknown augmentation option {ao}' raise InvalidAugmentationOptionError(msg) # check if active active = cfg.get('active', None) if not active: continue # construct preprocessing step object pp = preprocessor_pb2.PreprocessingStep() pp_option = AO_TO_PPS[ao]() # check configs for ao for c_name, c_val in cfg.items(): # skip active, since this is for ginjinn and not for tf if c_name == 'active': continue # ignore unknown configs if not reference_cfg.get(c_name): msg = f'Found unknown augmentation option: "{ao}: {c_name}" - options ignored.' print(msg) continue setattr(pp_option, c_name, c_val) # update preprocessing step object getattr(pp, AO_TO_PPOPTION[ao]).MergeFrom(pp_option) pps.append(pp) return pps
def test_build_random_crop_pad_image_with_optional_parameters(self): preprocessor_text_proto = """ random_crop_pad_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 clip_boxes: False random_coef: 0.125 min_padded_size_ratio: 0.5 min_padded_size_ratio: 0.75 max_padded_size_ratio: 0.5 max_padded_size_ratio: 0.75 pad_color: 0.5 pad_color: 0.5 pad_color: 1.0 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_pad_image) self.assertEqual( args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'clip_boxes': False, 'random_coef': 0.125, 'min_padded_size_ratio': (0.5, 0.75), 'max_padded_size_ratio': (0.5, 0.75), 'pad_color': (0.5, 0.5, 1.0) })