Python augmenters.AdditiveGaussianNoise方法代码示例

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def __init__(self, dataset_path,scale,k_fold_test=1, mode='train'):
        super().__init__()
        self.mode = mode
        self.img_path=dataset_path+'/img'
        self.mask_path=dataset_path+'/mask'
        self.image_lists,self.label_lists=self.read_list(self.img_path,k_fold_test=k_fold_test)
        self.flip =iaa.SomeOf((2,4),[
             iaa.Fliplr(0.5),
             iaa.Flipud(0.5),
             iaa.Affine(rotate=(-30, 30)),
             iaa.AdditiveGaussianNoise(scale=(0.0,0.08*255))], random_order=True)
        # resize
        self.resize_label = transforms.Resize(scale, Image.NEAREST)
        self.resize_img = transforms.Resize(scale, Image.BILINEAR)
        # normalization
        self.to_tensor = transforms.ToTensor() 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def example_augment_images_and_keypoints():
    print("Example: Augment Images and Keypoints")
    import numpy as np
    import imgaug.augmenters as iaa

    images = np.zeros((2, 128, 128, 3), dtype=np.uint8)  # two example images
    images[:, 64, 64, :] = 255
    points = [
        [(10.5, 20.5)],  # points on first image
        [(50.5, 50.5), (60.5, 60.5), (70.5, 70.5)]  # points on second image
    ]

    seq = iaa.Sequential([
        iaa.AdditiveGaussianNoise(scale=0.05*255),
        iaa.Affine(translate_px={"x": (1, 5)})
    ])

    # augment keypoints and images
    images_aug, points_aug = seq(images=images, keypoints=points)

    print("Image 1 center", np.argmax(images_aug[0, 64, 64:64+6, 0]))
    print("Image 2 center", np.argmax(images_aug[1, 64, 64:64+6, 0]))
    print("Points 1", points_aug[0])
    print("Points 2", points_aug[1]) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def example_augment_images_and_bounding_boxes():
    print("Example: Augment Images and Bounding Boxes")
    import numpy as np
    import imgaug as ia
    import imgaug.augmenters as iaa

    images = np.zeros((2, 128, 128, 3), dtype=np.uint8)  # two example images
    images[:, 64, 64, :] = 255
    bbs = [
        [ia.BoundingBox(x1=10.5, y1=15.5, x2=30.5, y2=50.5)],
        [ia.BoundingBox(x1=10.5, y1=20.5, x2=50.5, y2=50.5),
         ia.BoundingBox(x1=40.5, y1=75.5, x2=70.5, y2=100.5)]
    ]

    seq = iaa.Sequential([
        iaa.AdditiveGaussianNoise(scale=0.05*255),
        iaa.Affine(translate_px={"x": (1, 5)})
    ])

    images_aug, bbs_aug = seq(images=images, bounding_boxes=bbs) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def example_augment_images_and_polygons():
    print("Example: Augment Images and Polygons")
    import numpy as np
    import imgaug as ia
    import imgaug.augmenters as iaa

    images = np.zeros((2, 128, 128, 3), dtype=np.uint8)  # two example images
    images[:, 64, 64, :] = 255
    polygons = [
        [ia.Polygon([(10.5, 10.5), (50.5, 10.5), (50.5, 50.5)])],
        [ia.Polygon([(0.0, 64.5), (64.5, 0.0), (128.0, 128.0), (64.5, 128.0)])]
    ]

    seq = iaa.Sequential([
        iaa.AdditiveGaussianNoise(scale=0.05*255),
        iaa.Affine(translate_px={"x": (1, 5)})
    ])

    images_aug, polygons_aug = seq(images=images, polygons=polygons) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def example_augment_images_and_linestrings():
    print("Example: Augment Images and LineStrings")
    import numpy as np
    import imgaug as ia
    import imgaug.augmenters as iaa

    images = np.zeros((2, 128, 128, 3), dtype=np.uint8)  # two example images
    images[:, 64, 64, :] = 255
    ls = [
        [ia.LineString([(10.5, 10.5), (50.5, 10.5), (50.5, 50.5)])],
        [ia.LineString([(0.0, 64.5), (64.5, 0.0), (128.0, 128.0), (64.5, 128.0),
                        (128.0, 0.0)])]
    ]

    seq = iaa.Sequential([
        iaa.AdditiveGaussianNoise(scale=0.05*255),
        iaa.Affine(translate_px={"x": (1, 5)})
    ])

    images_aug, ls_aug = seq(images=images, line_strings=ls) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def test_seed_affects_augmenters_created_before_its_call(self):
        image = np.full((50, 50, 3), 128, dtype=np.uint8)

        images_aug = []
        for _ in np.arange(5):
            aug = iaa.AdditiveGaussianNoise(scale=50, per_channel=True)
            iarandom.seed(100)
            images_aug.append(aug(image=image))

        # assert all images identical
        for other_image_aug in images_aug[1:]:
            assert np.array_equal(images_aug[0], other_image_aug)

        # but different seed must lead to different image
        aug = iaa.AdditiveGaussianNoise(scale=50, per_channel=True)
        iarandom.seed(101)
        image_aug = aug(image=image)
        assert not np.array_equal(images_aug[0], image_aug) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def __init__(self):
        self.seq = iaa.Sequential([
            iaa.Sometimes(0.5, iaa.OneOf([
                iaa.GaussianBlur((0, 3.0)),  # blur images with a sigma between 0 and 3.0
                iaa.AverageBlur(k=(2, 7)),  # blur image using local means with kernel sizes between 2 and 7
                iaa.MedianBlur(k=(3, 11)),  # blur image using local medians with kernel sizes between 2 and 7
            ])),
            iaa.Sometimes(0.5, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5)),
            iaa.Sometimes(0.5, iaa.Add((-10, 10), per_channel=0.5)),
            iaa.Sometimes(0.5, iaa.AddToHueAndSaturation((-20, 20))),
            iaa.Sometimes(0.5, iaa.FrequencyNoiseAlpha(
                exponent=(-4, 0),
                first=iaa.Multiply((0.5, 1.5), per_channel=True),
                second=iaa.LinearContrast((0.5, 2.0))
            )),
            iaa.Sometimes(0.5, iaa.PiecewiseAffine(scale=(0.01, 0.05))),
            iaa.Sometimes(0.5, iaa.PerspectiveTransform(scale=(0.01, 0.1)))
        ], random_order=True) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def create_augmenter(stage: str = "train"):
        if stage == "train":
            return iaa.Sequential([
                iaa.Fliplr(0.5),
                iaa.CropAndPad(px=(0, 112), sample_independently=False),
                iaa.Affine(translate_percent={"x": (-0.4, 0.4), "y": (-0.4, 0.4)}),
                iaa.SomeOf((0, 3), [
                    iaa.AddToHueAndSaturation((-10, 10)),
                    iaa.Affine(scale={"x": (0.9, 1.1), "y": (0.9, 1.1)}),
                    iaa.GaussianBlur(sigma=(0, 1.0)),
                    iaa.AdditiveGaussianNoise(scale=0.05 * 255)
                ])
            ])
        elif stage == "val":
            return iaa.Sequential([
                iaa.CropAndPad(px=(0, 112), sample_independently=False),
                iaa.Affine(translate_percent={"x": (-0.4, 0.4), "y": (-0.4, 0.4)}),
            ])
        elif stage == "test":
            return iaa.Sequential([]) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def amaugimg(image):
    #数据增强
    image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)

    seq = iaa.Sequential([
        # iaa.Affine(rotate=(-5, 5),
        #            shear=(-5, 5),
        #            mode='edge'),

        iaa.SomeOf((0, 2),                        #选择数据增强
                   [
                       iaa.GaussianBlur((0, 1.5)),
                       iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.01 * 255), per_channel=0.5),
                       # iaa.AddToHueAndSaturation((-5, 5)),  # change hue and saturation
                       iaa.PiecewiseAffine(scale=(0.01, 0.03)),
                       iaa.PerspectiveTransform(scale=(0.01, 0.1))
                   ],
                   random_order=True
                   )
    ])
    image = seq.augment_image(image)

    image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    return image 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def augment(image, bbox):
    x = random.randint(-50, 50)
    y = random.randint(-50, 50)
    aug = iaa.Sequential([iaa.Multiply(random.uniform(0.5, 1.5)),
                          iaa.AdditiveGaussianNoise(random.uniform(0.01, 0.1) * 255),
                          iaa.Affine(translate_px={"x": x, "y": y},
                                     scale=random.uniform(0.5, 1.5),
                                     rotate=random.uniform(-45, 45),
                                     cval=(0, 255))])

    bbs = ia.BoundingBoxesOnImage([ia.BoundingBox(x1=bbox[0], y1=bbox[1], x2=bbox[2], y2=bbox[3])], shape=image.shape)

    aug = aug.to_deterministic()
    image_aug = aug.augment_image(image)
    bbs_aug = aug.augment_bounding_boxes([bbs])[0]
    b = bbs_aug.bounding_boxes
    bbs_aug = [b[0].x1, b[0].y1, b[0].x2, b[0].y2]
    bbs_aug = np.asarray(bbs_aug)

    bbs_aug[0] = bbs_aug[0] if bbs_aug[0] > 0 else 0
    bbs_aug[1] = bbs_aug[1] if bbs_aug[1] > 0 else 0
    bbs_aug[2] = bbs_aug[2] if bbs_aug[2] < size else size
    bbs_aug[3] = bbs_aug[3] if bbs_aug[3] < size else size
    return image_aug, bbs_aug 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def augment(image, bbox):
    x = random.randint(-60, 60)
    y = random.randint(-60, 60)
    aug = iaa.Sequential([iaa.AdditiveGaussianNoise(scale=random.uniform(.001, .01) * 255),  # gaussian noise
                          iaa.Multiply(random.uniform(0.5, 1.5)),  # brightness
                          iaa.Affine(translate_px={"x": x, "y": y},  # translation
                                     scale=random.uniform(0.5, 1.5),  # zoom in and out
                                     rotate=random.uniform(-25, 25),  # rotation
                                     shear=random.uniform(-5, 5),  # shear transformation
                                     cval=(0, 255))])  # fill the empty space with color

    aug.add(iaa.Salt(.001))
    bbs = ia.BoundingBoxesOnImage([ia.BoundingBox(x1=bbox[0], y1=bbox[1], x2=bbox[2], y2=bbox[3])], shape=image.shape)
    aug = aug.to_deterministic()
    image_aug = aug.augment_image(image)
    bbs_aug = aug.augment_bounding_boxes([bbs])[0]
    b = bbs_aug.bounding_boxes
    bbs_aug = [b[0].x1, b[0].y1, b[0].x2, b[0].y2]
    bbs_aug = np.asarray(bbs_aug)

    bbs_aug[0] = bbs_aug[0] if bbs_aug[0] > 0 else 0
    bbs_aug[1] = bbs_aug[1] if bbs_aug[1] > 0 else 0
    bbs_aug[2] = bbs_aug[2] if bbs_aug[2] < size else size
    bbs_aug[3] = bbs_aug[3] if bbs_aug[3] < size else size
    return image_aug, bbs_aug 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def noise(image, prob, keys):
    """ Adding noise """
    aug = iaa.Sequential([iaa.Multiply(random.uniform(0.25, 1.5)),
                          iaa.AdditiveGaussianNoise(scale=0.05 * 255)])
    seq_det = aug.to_deterministic()

    image_aug = seq_det.augment_images([image])[0]

    keys = ia.KeypointsOnImage([ia.Keypoint(x=keys[0], y=keys[1]),
                                ia.Keypoint(x=keys[2], y=keys[3]),
                                ia.Keypoint(x=keys[4], y=keys[5]),
                                ia.Keypoint(x=keys[6], y=keys[7]),
                                ia.Keypoint(x=keys[8], y=keys[9])], shape=image.shape)

    keys_aug = seq_det.augment_keypoints([keys])[0]
    k = keys_aug.keypoints
    output = [k[0].x, k[0].y, k[1].x, k[1].y, k[2].x, k[2].y, k[3].x, k[3].y, k[4].x, k[4].y]

    index = 0
    for i in range(0, len(prob)):
        output[index] = output[index] * prob[i]
        output[index + 1] = output[index + 1] * prob[i]
        index = index + 2
    output = np.array(output)
    return image_aug, output 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def chapter_augmenters_sequential():
    aug = iaa.Sequential([
        iaa.Affine(translate_px={"x":-40}),
        iaa.AdditiveGaussianNoise(scale=0.2*255)
    ])
    run_and_save_augseq(
        "sequential.jpg", aug,
        [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2
    )

    aug = iaa.Sequential([
        iaa.Affine(translate_px={"x":-40}),
        iaa.AdditiveGaussianNoise(scale=0.2*255)
    ], random_order=True)
    run_and_save_augseq(
        "sequential_random_order.jpg", aug,
        [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2
    ) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def chapter_augmenters_additivegaussiannoise():
    aug = iaa.AdditiveGaussianNoise(scale=(0, 0.2*255))
    run_and_save_augseq(
        "additivegaussiannoise.jpg", aug,
        [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2,
        quality=90
    )

    aug = iaa.AdditiveGaussianNoise(scale=0.2*255)
    run_and_save_augseq(
        "additivegaussiannoise_large.jpg", aug,
        [ia.quokka(size=(512, 512)) for _ in range(1)], cols=1, rows=1,
        quality=90
    )

    aug = iaa.AdditiveGaussianNoise(scale=0.2*255, per_channel=True)
    run_and_save_augseq(
        "additivegaussiannoise_per_channel.jpg", aug,
        [ia.quokka(size=(512, 512)) for _ in range(1)], cols=1, rows=1,
        quality=90
    ) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import AdditiveGaussianNoise [as 别名]
def __init__(self,data_dir, back_dir,
                 batch_size=50,gan=True,imsize=128,
                 res_x=640,res_y=480,
                 **kwargs):
        '''
        data_dir: Folder that contains cropped image+xyz
        back_dir: Folder that contains random background images
            batch_size: batch size for training
        gan: if False, gt for GAN is not yielded
        '''
        self.data_dir = data_dir
        self.back_dir = back_dir
        self.imsize=imsize
        self.batch_size = batch_size
        self.gan = gan
        self.backfiles = os.listdir(back_dir)
        data_list = os.listdir(data_dir)
        self.datafiles=[]
        self.res_x=res_x
        self.res_y=res_y

        for file in data_list:
            if(file.endswith(".npy")):
                self.datafiles.append(file)

        self.n_data = len(self.datafiles)
        self.n_background = len(self.backfiles)
        print("Total training views:", self.n_data)

        self.seq_syn= iaa.Sequential([
                                    iaa.WithChannels(0, iaa.Add((-15, 15))),
                                    iaa.WithChannels(1, iaa.Add((-15, 15))),
                                    iaa.WithChannels(2, iaa.Add((-15, 15))),
                                    iaa.ContrastNormalization((0.8, 1.3)),
                                    iaa.Multiply((0.8, 1.2),per_channel=0.5),
                                    iaa.GaussianBlur(sigma=(0.0, 0.5)),
                                    iaa.Sometimes(0.1, iaa.AdditiveGaussianNoise(scale=10, per_channel=True)),
                                    iaa.Sometimes(0.5, iaa.ContrastNormalization((0.5, 2.2), per_channel=0.3)),
                                    ], random_order=True)