albumentations是一个Python库,用于图像增强和数据增广。在这个库中,randombrightnesscontrast函数提供了对图像进行随机亮度和对比度变换的功能。它接受三个参数: 1. brightness_limit - 亮度变换的随机范围。默认值为0.2,表示亮度可以在[-0.2, 0.2]之间随机变化。如果为None,则不做亮度变换。 2. contrast_limit - 对...
# 需要导入模块: import albumentations [as 别名]# 或者: from albumentations importRandomBrightnessContrast[as 别名]deftest_transform_pipeline_serialization_with_keypoints(seed, image, keypoints, keypoint_format, labels):aug = A.Compose( [ A.OneOrOther( A.Compose([A.RandomRotate90(), A.OneOf(...
self.augmenter = albu.Compose([albu.HorizontalFlip(p=0.5),# albu.RandomRotate90(p=0.5),albu.Rotate(limit=10, p=0.5),# albu.CLAHE(p=0.2),# albu.RandomContrast(p=0.2),# albu.RandomBrightness(p=0.2),# albu.RandomGamma(p=0.2),# albu.GaussNoise(p=0.2),# albu.Cutout(p=0.2)]) self...
由于randombrightness属性在albumentations中可能不存在,因此无需检查版本是否支持。 但是,为了使用RandomBrightnessContrast类,确保你安装的albumentations库是较新版本。你可以通过运行pip install --upgrade albumentations来升级库。 给出具体的解决方案或建议: 使用RandomBrightnessContrast类来替代尝试访问randombrightness属性的...
from albumentations.augmentations import transforms from albumentations.core.composition import Compose, OneOf then: train_transform = Compose([ transforms.RandomRotate90(), transforms.Flip(), OneOf([ transforms.HueSaturationValue(), transforms.RandomBrightness(), transforms.RandomContrast(), ], p=1...
@@ -842,41 +842,55 @@ def test_brightness_contrast(): @pytest.mark.parametrize( "img, tiles, expected", "img, tiles, mapping, expected", [ # Test with empty tiles - image should remain unchanged ( np.array([[1, 1], [2, 2]], dtype=np.uint8), np.empty((0, 4), dtype=np...
albu.RandomBrightnessContrast(brightness_limit=0.5, contrast_limit=0.4), albu.RandomGamma(gamma_limit=(50,150)), albu.NoOp() ]), albu.OneOf([ albu.RGBShift(r_shift_limit=20, b_shift_limit=15, g_shift_limit=15), albu.HueSaturationValue(hue_shift_limit=5, ...
cj = transforms.ColorJitter(brightness=1.0, contrast=1.0, saturation=1.0, hue=1.0) seed = np.random.randint(0,2**32) np.random.seed(seed) pl = cj(x0) np.random.seed(seed) pr = cj(x1) setting the same seed of np.random will give you the same uniformly sampled values before the...