Python augmenters.Affine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 Affine [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 Affine [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 Affine [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 Affine [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 Affine [as 别名]
def example_augment_images_and_heatmaps():
    print("Example: Augment Images and Heatmaps")
    import numpy as np
    import imgaug.augmenters as iaa

    # Standard scenario: You have N RGB-images and additionally 21 heatmaps per
    # image. You want to augment each image and its heatmaps identically.
    images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
    heatmaps = np.random.random(size=(16, 64, 64, 1)).astype(np.float32)

    seq = iaa.Sequential([
        iaa.GaussianBlur((0, 3.0)),
        iaa.Affine(translate_px={"x": (-40, 40)}),
        iaa.Crop(px=(0, 10))
    ])

    images_aug, heatmaps_aug = seq(images=images, heatmaps=heatmaps) 

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

    # Standard scenario: You have N=16 RGB-images and additionally one segmentation
    # map per image. You want to augment each image and its heatmaps identically.
    images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)
    segmaps = np.random.randint(0, 10, size=(16, 64, 64, 1), dtype=np.int32)

    seq = iaa.Sequential([
        iaa.GaussianBlur((0, 3.0)),
        iaa.Affine(translate_px={"x": (-40, 40)}),
        iaa.Crop(px=(0, 10))
    ])

    images_aug, segmaps_aug = seq(images=images, segmentation_maps=segmaps) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Affine [as 别名]
def _test_cba_hooks_limit_propagation(cls, augf_name, cbaoi):
        aug = iaa.BlendAlpha(
            0.0,
            iaa.Affine(translate_px={"x": 1}),
            iaa.Affine(translate_px={"y": 1}),
            name="AlphaTest")

        def propagator(cbaoi_to_aug, augmenter, parents, default):
            if "Alpha" in augmenter.name:
                return False
            else:
                return default

        # no hooks for polygons yet, so we use HooksKeypoints
        hooks = ia.HooksKeypoints(propagator=propagator)
        observed = getattr(aug, augf_name)([cbaoi], hooks=hooks)[0]
        assert observed.items[0].coords_almost_equals(cbaoi.items[0]) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Affine [as 别名]
def __init__(self, augmentation_rate):
        self.augs = iaa.Sometimes(
            augmentation_rate,
            iaa.SomeOf(
                (4, 7),
                [
                    iaa.Affine(rotate=(-10, 10)),
                    iaa.Fliplr(0.2),
                    iaa.AverageBlur(k=(2, 10)),
                    iaa.Add((-10, 10), per_channel=0.5),
                    iaa.Multiply((0.75, 1.25), per_channel=0.5),
                    iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5),
                    iaa.Crop(px=(0, 20))
                ],
                random_order=True
            )
        ) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Affine [as 别名]
def img_aug(img, mask):
    mask = np.where(mask > 0, 0, 255).astype(np.uint8)
    flipper = iaa.Fliplr(0.5).to_deterministic()
    mask = flipper.augment_image(mask)
    img = flipper.augment_image(img)
    vflipper = iaa.Flipud(0.5).to_deterministic()
    img = vflipper.augment_image(img)
    mask = vflipper.augment_image(mask)
    if random.random() < 0.5:
        rot_time = random.choice([1, 2, 3])
        for i in range(rot_time):
            img = np.rot90(img)
            mask = np.rot90(mask)
    if random.random() < 0.5:
        translater = iaa.Affine(translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
                                scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},
                                shear=(-8, 8),
                                cval=(255)
                                ).to_deterministic()
        img = translater.augment_image(img)
        mask = translater.augment_image(mask)
    # if random.random() < 0.5:
    #     img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    mask = np.where(mask > 0, 0, 255).astype(np.uint8)
    return img, mask 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Affine [as 别名]
def img_aug(img):

    if random.random() < 0.5:
        translater = iaa.Affine(translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
                                scale={"x": (0.8, 1.2), "y": (0.8, 1.2)},
                                cval=(255)
                                ).to_deterministic()
        img = translater.augment_image(img)

    #vertically flip
    if random.random() < 0.5:
        img = np.flip(img,0)

    # horizontally flip
    if random.random() < 0.5:
        img = np.flip(img,1)

    if random.random() < 0.5:
        rot_time = random.choice([1, 2, 3])
        img = np.rot90(img,rot_time)

    return img 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Affine [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 Affine [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 Affine [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 Affine [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 Affine [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
    )