# Copyright (c) OpenMMLab. All rights reserved. import copy import os.path as osp import numpy as np import pytest import mmcv from mmcv.transforms import Normalize, Pad, RandomFlip, RandomResize, Resize class TestNormalize: def test_normalize(self): img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) transform = Normalize(**img_norm_cfg) results = dict() img = mmcv.imread( osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color') original_img = copy.deepcopy(img) results['img'] = img results = transform(results) mean = np.array(img_norm_cfg['mean']) std = np.array(img_norm_cfg['std']) converted_img = (original_img[..., ::-1] - mean) / std assert np.allclose(results['img'], converted_img) def test_repr(self): img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) transform = Normalize(**img_norm_cfg) assert repr(transform) == ('Normalize(mean=[123.675 116.28 103.53 ], ' 'std=[58.395 57.12 57.375], to_rgb=True)') class TestResize: def test_resize(self): data_info = dict( img=np.random.random((1333, 800, 3)), gt_semantic_seg=np.random.random((1333, 800, 3)), gt_bboxes=np.array([[0, 0, 112, 112]]), gt_keypoints=np.array([[[20, 50, 1]]])) with pytest.raises(AssertionError): transform = Resize(scale=None, scale_factor=None) with pytest.raises(TypeError): transform = Resize(scale_factor=[]) # test scale is int transform = Resize(scale=2000) results = transform(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (2000, 2000) assert results['scale_factor'] == (2000 / 800, 2000 / 1333) # test scale is tuple transform = Resize(scale=(2000, 2000)) results = transform(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (2000, 2000) assert results['scale_factor'] == (2000 / 800, 2000 / 1333) # test scale_factor is float transform = Resize(scale_factor=2.0) results = transform(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (2666, 1600) assert results['scale_factor'] == (2.0, 2.0) # test scale_factor is tuple transform = Resize(scale_factor=(1.5, 2)) results = transform(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (2666, 1200) assert results['scale_factor'] == (1.5, 2) # test keep_ratio is True transform = Resize(scale=(2000, 2000), keep_ratio=True) results = transform(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (2000, 1200) assert results['scale'] == (1200, 2000) assert results['scale_factor'] == (1200 / 800, 2000 / 1333) # test resize_bboxes/seg/kps transform = Resize(scale_factor=(1.5, 2)) results = transform(copy.deepcopy(data_info)) assert (results['gt_bboxes'] == np.array([[0, 0, 168, 224]])).all() assert (results['gt_keypoints'] == np.array([[[30, 100, 1]]])).all() assert results['gt_semantic_seg'].shape[:2] == (2666, 1200) # test clip_object_border = False data_info = dict( img=np.random.random((300, 400, 3)), gt_bboxes=np.array([[200, 150, 600, 450]])) transform = Resize(scale=(200, 150), clip_object_border=False) results = transform(data_info) assert (results['gt_bboxes'] == np.array([100, 75, 300, 225])).all() def test_repr(self): transform = Resize(scale=(2000, 2000), keep_ratio=True) assert repr(transform) == ('Resize(scale=(2000, 2000), ' 'scale_factor=None, keep_ratio=True, ' 'clip_object_border=True), backend=cv2), ' 'interpolation=bilinear)') class TestPad: def test_pad(self): # test size and size_divisor are both set with pytest.raises(AssertionError): Pad(size=(10, 10), size_divisor=2) # test size and size_divisor are both None with pytest.raises(AssertionError): Pad(size=None, size_divisor=None) # test size and pad_to_square are both None with pytest.raises(AssertionError): Pad(size=(10, 10), pad_to_square=True) # test pad_val is not int or tuple with pytest.raises(AssertionError): Pad(size=(10, 10), pad_val=[]) # test padding_mode is not 'constant', 'edge', 'reflect' or 'symmetric' with pytest.raises(AssertionError): Pad(size=(10, 10), padding_mode='edg') data_info = dict( img=np.random.random((1333, 800, 3)), gt_semantic_seg=np.random.random((1333, 800, 3)), gt_bboxes=np.array([[0, 0, 112, 112]]), gt_keypoints=np.array([[[20, 50, 1]]])) # test pad img / gt_semantic_seg with size trans = Pad(size=(1200, 2000)) results = trans(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (2000, 1200) assert results['gt_semantic_seg'].shape[:2] == (2000, 1200) # test pad img/gt_semantic_seg with size_divisor trans = Pad(size_divisor=11) results = trans(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (1342, 803) assert results['gt_semantic_seg'].shape[:2] == (1342, 803) # test pad img/gt_semantic_seg with pad_to_square trans = Pad(pad_to_square=True) results = trans(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (1333, 1333) assert results['gt_semantic_seg'].shape[:2] == (1333, 1333) # test pad img/gt_semantic_seg with pad_to_square and size_divisor trans = Pad(pad_to_square=True, size_divisor=11) results = trans(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (1342, 1342) assert results['gt_semantic_seg'].shape[:2] == (1342, 1342) # test pad img/gt_semantic_seg with pad_to_square and size_divisor trans = Pad(pad_to_square=True, size_divisor=11) results = trans(copy.deepcopy(data_info)) assert results['img'].shape[:2] == (1342, 1342) assert results['gt_semantic_seg'].shape[:2] == (1342, 1342) # test padding_mode new_img = np.ones((1333, 800, 3)) data_info['img'] = new_img trans = Pad(pad_to_square=True, padding_mode='edge') results = trans(copy.deepcopy(data_info)) assert (results['img'] == np.ones((1333, 1333, 3))).all() # test pad_val is dict # test rgb image, size=(2000, 2000) trans = Pad( size=(2000, 2000), pad_val=dict(img=(12, 12, 12), seg=(10, 10, 10))) results = trans(copy.deepcopy(data_info)) assert (results['img'][1333:2000, 800:2000, :] == 12).all() assert (results['gt_semantic_seg'][1333:2000, 800:2000, :] == 10).all() trans = Pad(size=(2000, 2000), pad_val=dict(img=(12, 12, 12))) results = trans(copy.deepcopy(data_info)) assert (results['img'][1333:2000, 800:2000, :] == 12).all() assert (results['gt_semantic_seg'][1333:2000, 800:2000, :] == 255).all() # test rgb image, pad_to_square=True trans = Pad( pad_to_square=True, pad_val=dict(img=(12, 12, 12), seg=(10, 10, 10))) results = trans(copy.deepcopy(data_info)) assert (results['img'][:, 800:1333, :] == 12).all() assert (results['gt_semantic_seg'][:, 800:1333, :] == 10).all() trans = Pad(pad_to_square=True, pad_val=dict(img=(12, 12, 12))) results = trans(copy.deepcopy(data_info)) assert (results['img'][:, 800:1333, :] == 12).all() assert (results['gt_semantic_seg'][:, 800:1333, :] == 255).all() # test pad_val is int # test rgb image trans = Pad(size=(2000, 2000), pad_val=12) results = trans(copy.deepcopy(data_info)) assert (results['img'][1333:2000, 800:2000, :] == 12).all() assert (results['gt_semantic_seg'][1333:2000, 800:2000, :] == 255).all() # test gray image new_img = np.random.random((1333, 800)) data_info['img'] = new_img new_semantic_seg = np.random.random((1333, 800)) data_info['gt_semantic_seg'] = new_semantic_seg trans = Pad(size=(2000, 2000), pad_val=12) results = trans(copy.deepcopy(data_info)) assert (results['img'][1333:2000, 800:2000] == 12).all() assert (results['gt_semantic_seg'][1333:2000, 800:2000] == 255).all() def test_repr(self): trans = Pad(pad_to_square=True, size_divisor=11, padding_mode='edge') assert repr(trans) == ( 'Pad(size=None, size_divisor=11, pad_to_square=True, ' "pad_val={'img': 0, 'seg': 255}), padding_mode=edge)") class TestRandomFlip: def test_init(self): # prob is float TRANSFORMS = RandomFlip(0.1) assert TRANSFORMS.prob == 0.1 # prob is None with pytest.raises(ValueError): TRANSFORMS = RandomFlip(None) assert TRANSFORMS.prob is None # prob is a list TRANSFORMS = RandomFlip([0.1, 0.2], ['horizontal', 'vertical']) assert len(TRANSFORMS.prob) == 2 assert len(TRANSFORMS.direction) == 2 # direction is an invalid type with pytest.raises(ValueError): TRANSFORMS = RandomFlip(0.1, 1) # prob is an invalid type with pytest.raises(ValueError): TRANSFORMS = RandomFlip('0.1') def test_transform(self): results = { 'img': np.random.random((224, 224, 3)), 'gt_bboxes': np.array([[0, 1, 100, 101]]), 'gt_keypoints': np.array([[[100, 100, 1.0]]]), 'gt_semantic_seg': np.random.random((224, 224, 3)) } # horizontal flip TRANSFORMS = RandomFlip([1.0], ['horizontal']) results_update = TRANSFORMS.transform(copy.deepcopy(results)) assert (results_update['gt_bboxes'] == np.array([[124, 1, 224, 101]])).all() # diagnal flip TRANSFORMS = RandomFlip([1.0], ['diagonal']) results_update = TRANSFORMS.transform(copy.deepcopy(results)) assert (results_update['gt_bboxes'] == np.array([[124, 123, 224, 223]])).all() # vertical flip TRANSFORMS = RandomFlip([1.0], ['vertical']) results_update = TRANSFORMS.transform(copy.deepcopy(results)) assert (results_update['gt_bboxes'] == np.array([[0, 123, 100, 223]])).all() # horizontal flip when direction is None TRANSFORMS = RandomFlip(1.0) results_update = TRANSFORMS.transform(copy.deepcopy(results)) assert (results_update['gt_bboxes'] == np.array([[124, 1, 224, 101]])).all() TRANSFORMS = RandomFlip(0.0) results_update = TRANSFORMS.transform(copy.deepcopy(results)) assert (results_update['gt_bboxes'] == np.array([[0, 1, 100, 101]])).all() # flip direction is invalid in bbox flip with pytest.raises(ValueError): TRANSFORMS = RandomFlip(1.0) results_update = TRANSFORMS.flip_bbox(results['gt_bboxes'], (224, 224), 'invalid') # flip direction is invalid in keypoints flip with pytest.raises(ValueError): TRANSFORMS = RandomFlip(1.0) results_update = TRANSFORMS.flip_keypoints(results['gt_keypoints'], (224, 224), 'invalid') def test_repr(self): TRANSFORMS = RandomFlip(0.1) TRANSFORMS_str = str(TRANSFORMS) assert isinstance(TRANSFORMS_str, str) class TestRandomResize: def test_init(self): TRANSFORMS = RandomResize( (224, 224), (1.0, 2.0), ) assert TRANSFORMS.scale == (224, 224) def test_repr(self): TRANSFORMS = RandomResize( (224, 224), (1.0, 2.0), ) TRANSFORMS_str = str(TRANSFORMS) assert isinstance(TRANSFORMS_str, str) def test_transform(self): # choose target scale from init when override is True results = {} TRANSFORMS = RandomResize((224, 224), (1.0, 2.0)) results_update = TRANSFORMS.transform(copy.deepcopy(results)) assert results_update['scale'][0] >= 224 and results_update['scale'][ 0] <= 448 assert results_update['scale'][1] >= 224 and results_update['scale'][ 1] <= 448 # keep ratio is True results = { 'img': np.random.random((224, 224, 3)), 'gt_semantic_seg': np.random.random((224, 224, 3)), 'gt_bboxes': np.array([[0, 0, 112, 112]]), 'gt_keypoints': np.array([[[112, 112]]]) } # import pdb # pdb.set_trace() TRANSFORMS = RandomResize((224, 224), (1.0, 2.0), keep_ratio=True) results_update = TRANSFORMS.transform(copy.deepcopy(results)) assert 224 <= results_update['height'] assert 448 >= results_update['height'] assert 224 <= results_update['width'] assert 448 >= results_update['width'] assert results_update['keep_ratio'] assert results['gt_bboxes'][0][2] >= 112 assert results['gt_bboxes'][0][2] <= 112 # keep ratio is False TRANSFORMS = RandomResize((224, 224), (1.0, 2.0), keep_ratio=False) results_update = TRANSFORMS.transform(copy.deepcopy(results)) # choose target scale from init when override is False and scale is a # list of tuples results = {} TRANSFORMS = RandomResize([(224, 448), (112, 224)], keep_ratio=True) results_update = TRANSFORMS.transform(copy.deepcopy(results)) assert results_update['scale'][0] >= 224 and results_update['scale'][ 0] <= 448 assert results_update['scale'][1] >= 112 and results_update['scale'][ 1] <= 224 # the type of scale is invalid in init with pytest.raises(NotImplementedError): results = {} TRANSFORMS = RandomResize([(224, 448), [112, 224]], keep_ratio=True) results_update = TRANSFORMS.transform(copy.deepcopy(results))