import pytest import torch from torch.autograd import gradcheck import kornia from kornia.testing import BaseTester # test utils from kornia.testing import assert_close class TestGrayscaleToRgb(BaseTester): def test_smoke(self, device, dtype): C, H, W = 1, 4, 5 img = torch.rand(C, H, W, device=device, dtype=dtype) assert isinstance(kornia.color.grayscale_to_rgb(img), torch.Tensor) @pytest.mark.parametrize("batch_size, height, width", [(1, 3, 4), (2, 2, 4), (3, 4, 1)]) def test_cardinality(self, device, dtype, batch_size, height, width): img = torch.ones(batch_size, 1, height, width, device=device, dtype=dtype) assert kornia.color.grayscale_to_rgb(img).shape == (batch_size, 3, height, width) def test_exception(self, device, dtype): with pytest.raises(TypeError): assert kornia.color.grayscale_to_rgb([0.0]) with pytest.raises(ValueError): img = torch.ones(1, 1, device=device, dtype=dtype) assert kornia.color.grayscale_to_rgb(img) with pytest.raises(ValueError): img = torch.ones(2, 1, 1, device=device, dtype=dtype) assert kornia.color.grayscale_to_rgb(img) with pytest.raises(ValueError): img = torch.ones(1, 3, 1, 1, device=device, dtype=dtype) assert kornia.color.grayscale_to_rgb(img) def test_opencv(self, device, dtype): data = torch.tensor( [ [ [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016], [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204], [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113], [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529], [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805], ] ], device=device, dtype=dtype, ) # Output data generated with OpenCV 4.5.2: cv2.cvtColor(img_np, cv2.COLOR_GRAY2RGB) expected = torch.tensor( [ [ [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016], [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204], [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113], [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529], [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805], ], [ [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016], [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204], [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113], [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529], [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805], ], [ [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016], [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204], [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113], [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529], [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805], ], ], device=device, dtype=dtype, ) img_rgb = kornia.color.grayscale_to_rgb(data) assert_close(img_rgb, expected) @pytest.mark.grad def test_gradcheck(self, device, dtype): B, C, H, W = 2, 1, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True) assert gradcheck(kornia.color.grayscale_to_rgb, (img,), raise_exception=True) @pytest.mark.jit def test_jit(self, device, dtype): B, C, H, W = 2, 1, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=dtype) op = kornia.color.grayscale_to_rgb op_jit = torch.jit.script(op) assert_close(op(img), op_jit(img)) @pytest.mark.nn def test_module(self, device, dtype): B, C, H, W = 2, 1, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=dtype) gray_ops = kornia.color.GrayscaleToRgb().to(device, dtype) gray_fcn = kornia.color.grayscale_to_rgb assert_close(gray_ops(img), gray_fcn(img)) class TestRgbToGrayscale(BaseTester): def test_smoke(self, device, dtype): C, H, W = 3, 4, 5 img = torch.rand(C, H, W, device=device, dtype=dtype) assert isinstance(kornia.color.rgb_to_grayscale(img), torch.Tensor) @pytest.mark.parametrize("batch_size, height, width", [(1, 3, 4), (2, 2, 4), (3, 4, 1)]) def test_cardinality(self, device, dtype, batch_size, height, width): img = torch.ones(batch_size, 3, height, width, device=device, dtype=dtype) assert kornia.color.rgb_to_grayscale(img).shape == (batch_size, 1, height, width) def test_exception(self, device, dtype): with pytest.raises(TypeError): assert kornia.color.rgb_to_grayscale([0.0]) with pytest.raises(ValueError): img = torch.ones(1, 1, device=device, dtype=dtype) assert kornia.color.rgb_to_grayscale(img) with pytest.raises(ValueError): img = torch.ones(2, 1, 1, device=device, dtype=dtype) assert kornia.color.rgb_to_grayscale(img) with pytest.raises(TypeError): img = torch.ones(1, 3, 1, 1, device=device, dtype=torch.uint8) assert kornia.color.rgb_to_grayscale(img) with pytest.raises(ValueError): img = torch.ones(3, 1, 1, device=device, dtype=dtype) rgb_weights = torch.tensor([0.2, 0.8]) assert kornia.color.rgb_to_grayscale(img, rgb_weights=rgb_weights) def test_opencv(self, device, dtype): data = torch.tensor( [ [ [0.3944633, 0.8597369, 0.1670904, 0.2825457, 0.0953912], [0.1251704, 0.8020709, 0.8933256, 0.9170977, 0.1497008], [0.2711633, 0.1111478, 0.0783281, 0.2771807, 0.5487481], [0.0086008, 0.8288748, 0.9647092, 0.8922020, 0.7614344], [0.2898048, 0.1282895, 0.7621747, 0.5657831, 0.9918593], ], [ [0.5414237, 0.9962701, 0.8947155, 0.5900949, 0.9483274], [0.0468036, 0.3933847, 0.8046577, 0.3640994, 0.0632100], [0.6171775, 0.8624780, 0.4126036, 0.7600935, 0.7279997], [0.4237089, 0.5365476, 0.5591233, 0.1523191, 0.1382165], [0.8932794, 0.8517839, 0.7152701, 0.8983801, 0.5905426], ], [ [0.2869580, 0.4700376, 0.2743714, 0.8135023, 0.2229074], [0.9306560, 0.3734594, 0.4566821, 0.7599275, 0.7557513], [0.7415742, 0.6115875, 0.3317572, 0.0379378, 0.1315770], [0.8692724, 0.0809556, 0.7767404, 0.8742208, 0.1522012], [0.7708948, 0.4509611, 0.0481175, 0.2358997, 0.6900532], ], ], device=device, dtype=dtype, ) # Output data generated with OpenCV 4.1.1: cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) expected = torch.tensor( [ [ [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016], [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204], [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113], [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529], [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805], ] ], device=device, dtype=dtype, ) img_gray = kornia.color.rgb_to_grayscale(data) assert_close(img_gray, expected) def test_custom_rgb_weights(self, device, dtype): B, C, H, W = 2, 3, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=dtype) rgb_weights = torch.tensor([0.5, 0.25, 0.25]) img_gray = kornia.color.rgb_to_grayscale(img, rgb_weights=rgb_weights) assert img_gray.device == device assert img_gray.dtype == dtype @pytest.mark.grad def test_gradcheck(self, device, dtype): B, C, H, W = 2, 3, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True) assert gradcheck(kornia.color.rgb_to_grayscale, (img,), raise_exception=True) @pytest.mark.jit def test_jit(self, device, dtype): B, C, H, W = 2, 3, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=dtype) op = kornia.color.rgb_to_grayscale op_jit = torch.jit.script(op) assert_close(op(img), op_jit(img)) @pytest.mark.nn def test_module(self, device, dtype): B, C, H, W = 2, 3, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=dtype) gray_ops = kornia.color.RgbToGrayscale().to(device, dtype) gray_fcn = kornia.color.rgb_to_grayscale assert_close(gray_ops(img), gray_fcn(img)) class TestBgrToGrayscale(BaseTester): def test_smoke(self, device, dtype): C, H, W = 3, 4, 5 img = torch.rand(C, H, W, device=device, dtype=dtype) assert kornia.color.bgr_to_grayscale(img) is not None @pytest.mark.parametrize("batch_size, height, width", [(1, 3, 4), (2, 2, 4), (3, 4, 1)]) def test_cardinality(self, device, dtype, batch_size, height, width): img = torch.ones(batch_size, 3, height, width, device=device, dtype=dtype) assert kornia.color.bgr_to_grayscale(img).shape == (batch_size, 1, height, width) def test_exception(self, device, dtype): with pytest.raises(TypeError): assert kornia.color.bgr_to_grayscale([0.0]) with pytest.raises(ValueError): img = torch.ones(1, 1, device=device, dtype=dtype) assert kornia.color.bgr_to_grayscale(img) with pytest.raises(ValueError): img = torch.ones(2, 1, 1, device=device, dtype=dtype) assert kornia.color.bgr_to_grayscale(img) def test_opencv(self, device, dtype): data = torch.tensor( [ [ [0.3944633, 0.8597369, 0.1670904, 0.2825457, 0.0953912], [0.1251704, 0.8020709, 0.8933256, 0.9170977, 0.1497008], [0.2711633, 0.1111478, 0.0783281, 0.2771807, 0.5487481], [0.0086008, 0.8288748, 0.9647092, 0.8922020, 0.7614344], [0.2898048, 0.1282895, 0.7621747, 0.5657831, 0.9918593], ], [ [0.5414237, 0.9962701, 0.8947155, 0.5900949, 0.9483274], [0.0468036, 0.3933847, 0.8046577, 0.3640994, 0.0632100], [0.6171775, 0.8624780, 0.4126036, 0.7600935, 0.7279997], [0.4237089, 0.5365476, 0.5591233, 0.1523191, 0.1382165], [0.8932794, 0.8517839, 0.7152701, 0.8983801, 0.5905426], ], [ [0.2869580, 0.4700376, 0.2743714, 0.8135023, 0.2229074], [0.9306560, 0.3734594, 0.4566821, 0.7599275, 0.7557513], [0.7415742, 0.6115875, 0.3317572, 0.0379378, 0.1315770], [0.8692724, 0.0809556, 0.7767404, 0.8742208, 0.1522012], [0.7708948, 0.4509611, 0.0481175, 0.2358997, 0.6900532], ], ], device=device, dtype=dtype, ) # Output data generated with OpenCV 4.1.1: cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) expected = torch.tensor( [ [ [0.4485849, 0.8233618, 0.6262833, 0.6218331, 0.6341921], [0.3200093, 0.4340172, 0.7107211, 0.5454938, 0.2801398], [0.6149265, 0.7018101, 0.3503231, 0.4891168, 0.5292346], [0.5096100, 0.4336508, 0.6704276, 0.4525143, 0.2134447], [0.7878902, 0.6494595, 0.5211386, 0.6623823, 0.6660464], ] ], device=device, dtype=dtype, ) img_gray = kornia.color.bgr_to_grayscale(data) assert_close(img_gray, expected) @pytest.mark.grad def test_gradcheck(self, device, dtype): B, C, H, W = 2, 3, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True) assert gradcheck(kornia.color.bgr_to_grayscale, (img,), raise_exception=True) @pytest.mark.jit def test_jit(self, device, dtype): B, C, H, W = 2, 3, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=dtype) op = kornia.color.rgb_to_grayscale op_jit = torch.jit.script(op) assert_close(op(img), op_jit(img)) @pytest.mark.nn def test_module(self, device, dtype): B, C, H, W = 2, 3, 4, 4 img = torch.ones(B, C, H, W, device=device, dtype=dtype) gray_ops = kornia.color.BgrToGrayscale().to(device, dtype) gray_fcn = kornia.color.bgr_to_grayscale assert_close(gray_ops(img), gray_fcn(img))