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() 

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]) 

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) 

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) 

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) 

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) 

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) 

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([]) 

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 

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 

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 

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 

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
    ) 

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
    ) 

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) 

def __init__(self, loc=0, scale=(0.01*255, 0.05*255), prob=0.5):
        super().__init__(prob)
        self.processor = iaa.AdditiveGaussianNoise(loc, scale) 

def __init__(self, loc=0, scale=(0.01*255, 0.05*255), prob=0.5):
        super().__init__(prob)
        self.processor = iaa.AdditiveGaussianNoise(loc, scale) 

def __init__(self, loc=0, scale=(0.01 * 255, 0.05 * 255), prob=0.5):
        super().__init__(prob)
        self.processor = iaa.AdditiveGaussianNoise(loc, scale) 

def salt(image, prob, keys):
    """ Adding salt noise """
    r = random.uniform(1, 5) * 0.05
    aug = iaa.Sequential([iaa.Dropout(p=(0, r)), iaa.CoarseDropout(p=0.001, size_percent=0.01),
                          iaa.Salt(0.001), iaa.AdditiveGaussianNoise(scale=0.1 * 255)])
    aug.add(iaa.Multiply(random.uniform(0.25, 1.5)))
    x = random.randrange(-10, 10) * .01
    y = random.randrange(-10, 10) * .01
    aug.add(iaa.Affine(scale=random.uniform(.7, 1.1), translate_percent={"x": x, "y": y}, cval=(0, 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 

def chapter_augmenters_someof():
    aug = iaa.SomeOf(2, [
        iaa.Affine(rotate=45),
        iaa.AdditiveGaussianNoise(scale=0.2*255),
        iaa.Add(50, per_channel=True),
        iaa.Sharpen(alpha=0.5)
    ])
    run_and_save_augseq(
        "someof.jpg", aug,
        [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2
    )

    aug = iaa.SomeOf((0, None), [
        iaa.Affine(rotate=45),
        iaa.AdditiveGaussianNoise(scale=0.2*255),
        iaa.Add(50, per_channel=True),
        iaa.Sharpen(alpha=0.5)
    ])
    run_and_save_augseq(
        "someof_0_to_none.jpg", aug,
        [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2
    )

    aug = iaa.SomeOf(2, [
        iaa.Affine(rotate=45),
        iaa.AdditiveGaussianNoise(scale=0.2*255),
        iaa.Add(50, per_channel=True),
        iaa.Sharpen(alpha=0.5)
    ], random_order=True)
    run_and_save_augseq(
        "someof_random_order.jpg", aug,
        [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2
    ) 

def chapter_augmenters_oneof():
    aug = iaa.OneOf([
        iaa.Affine(rotate=45),
        iaa.AdditiveGaussianNoise(scale=0.2*255),
        iaa.Add(50, per_channel=True),
        iaa.Sharpen(alpha=0.5)
    ])
    run_and_save_augseq(
        "oneof.jpg", aug,
        [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2
    ) 

def example_hooks():
    print("Example: Hooks")
    import imgaug as ia
    from imgaug import augmenters as iaa
    import numpy as np

    # images and heatmaps, just arrays filled with value 30
    images = np.ones((16, 128, 128, 3), dtype=np.uint8) * 30
    heatmaps = np.ones((16, 128, 128, 21), dtype=np.uint8) * 30

    # add vertical lines to see the effect of flip
    images[:, 16:128-16, 120:124, :] = 120
    heatmaps[:, 16:128-16, 120:124, :] = 120

    seq = iaa.Sequential([
      iaa.Fliplr(0.5, name="Flipper"),
      iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
      iaa.Dropout(0.02, name="Dropout"),
      iaa.AdditiveGaussianNoise(scale=0.01*255, name="MyLittleNoise"),
      iaa.AdditiveGaussianNoise(loc=32, scale=0.0001*255, name="SomeOtherNoise"),
      iaa.Affine(translate_px={"x": (-40, 40)}, name="Affine")
    ])

    # change the activated augmenters for heatmaps
    def activator_heatmaps(images, augmenter, parents, default):
        if augmenter.name in ["GaussianBlur", "Dropout", "MyLittleNoise"]:
            return False
        else:
            # default value for all other augmenters
            return default
    hooks_heatmaps = ia.HooksImages(activator=activator_heatmaps)

    seq_det = seq.to_deterministic() # call this for each batch again, NOT only once at the start
    images_aug = seq_det.augment_images(images)
    heatmaps_aug = seq_det.augment_images(heatmaps, hooks=hooks_heatmaps)

    # -----------
    ia.show_grid(images_aug)
    ia.show_grid(heatmaps_aug[..., 0:3]) 

def processor(self):
        return iaa.AdditiveGaussianNoise(self.loc, self.scale, self.per_channel) 

def additive_Gaussian_noise(images, scale):
    transformer = iaa.AdditiveGaussianNoise(scale=(0,scale*255), deterministic=True)
    return augment_on_df(images, transformer) 

def example_hooks():
    print("Example: Hooks")
    import numpy as np
    import imgaug as ia
    import imgaug.augmenters as iaa

    # Images and heatmaps, just arrays filled with value 30.
    # We define the heatmaps here as uint8 arrays as we are going to feed them
    # through the pipeline similar to normal images. In that way, every
    # augmenter is applied to them.
    images = np.full((16, 128, 128, 3), 30, dtype=np.uint8)
    heatmaps = np.full((16, 128, 128, 21), 30, dtype=np.uint8)

    # add vertical lines to see the effect of flip
    images[:, 16:128-16, 120:124, :] = 120
    heatmaps[:, 16:128-16, 120:124, :] = 120

    seq = iaa.Sequential([
      iaa.Fliplr(0.5, name="Flipper"),
      iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
      iaa.Dropout(0.02, name="Dropout"),
      iaa.AdditiveGaussianNoise(scale=0.01*255, name="MyLittleNoise"),
      iaa.AdditiveGaussianNoise(loc=32, scale=0.0001*255, name="SomeOtherNoise"),
      iaa.Affine(translate_px={"x": (-40, 40)}, name="Affine")
    ])

    # change the activated augmenters for heatmaps,
    # we only want to execute horizontal flip, affine transformation and one of
    # the gaussian noises
    def activator_heatmaps(images, augmenter, parents, default):
        if augmenter.name in ["GaussianBlur", "Dropout", "MyLittleNoise"]:
            return False
        else:
            # default value for all other augmenters
            return default
    hooks_heatmaps = ia.HooksImages(activator=activator_heatmaps)

    # call to_deterministic() once per batch, NOT only once at the start
    seq_det = seq.to_deterministic()
    images_aug = seq_det(images=images)
    heatmaps_aug = seq_det(images=heatmaps, hooks=hooks_heatmaps)

    # -----------
    ia.show_grid(images_aug)
    ia.show_grid(heatmaps_aug[..., 0:3]) 

def load_annoataion(p, im=None):
    '''
    load annotation from the text file
    :param p:
    :return:
    '''
    text_polys = []
    text_tags = []
    if not os.path.exists(p):
        return np.array(text_polys, dtype=np.float32)
    with open(p, 'r') as f:
        # reader = csv.reader(f)
        reader = f.readlines()
        for line in reader:
            line = line.split(',')
            label = line[-1]
            # strip BOM. \ufeff for python3,  \xef\xbb\bf for python2
            line = [i.strip('\ufeff').strip('\xef\xbb\xbf') for i in line]
            # print(line)
            x1, y1, x2, y2, x3, y3, x4, y4 = list(map(float, line[:8]))
            text_polys.append([[x1, y1], [x2, y2], [x3, y3], [x4, y4]])
            if label == '*' or label == '###':
                text_tags.append(True)
            else:
                text_tags.append(False)
        # 执行数据增广操作
        random_value = np.random.random()
        if random_value < 0.2:
            # 执行旋转操作
            angle = np.random.random() * 10
            operation_obj = iaa.Affine(rotate=(-angle, angle), random_state=np.random.randint(0, 10000))
            im, text_polys = data_agumentation(im, text_polys, operation_obj)

        random_value = np.random.random()
        if random_value < 0.1:
            # 水平镜像
            operation_obj = iaa.Sequential([iaa.Flipud(0.5, random_state=np.random.randint(0, 10000))])
            im, text_polys = data_agumentation(im, text_polys, operation_obj)

        random_value = np.random.random()
        if random_value < 0.1:
            # 垂直镜像
            operation_obj = iaa.Sequential([iaa.Fliplr(0.5, random_state=np.random.randint(0, 10000))])
            # operation_obj = iaa.Affine(shear=(-10, 10))
            im, text_polys = data_agumentation(im, text_polys, operation_obj)

        random_value = np.random.random()
        if random_value < 0.1:
            # 随机Dropout
            operation_obj = iaa.Sequential([iaa.Dropout(p=(0, 0.1), random_state=np.random.randint(0, 10000))])
            im, text_polys = data_agumentation(im, text_polys, operation_obj)

        random_value = np.random.random()
        if random_value < 0.1:
            # 随机增加噪声
            operation_obj = iaa.Sequential([iaa.AdditiveGaussianNoise(scale=np.random.random() * 30,
                                                                      random_state=np.random.randint(0,
                                                                                                     10000))])
            im, text_polys = data_agumentation(im, text_polys, operation_obj)
        return np.array(text_polys, dtype=np.float32), np.array(text_tags, dtype=np.bool), im 

def _create_augment_pipeline():
    from imgaug import augmenters as iaa
    
    ### augmentors by https://github.com/aleju/imgaug
    sometimes = lambda aug: iaa.Sometimes(0.5, aug)

    # Define our sequence of augmentation steps that will be applied to every image
    # All augmenters with per_channel=0.5 will sample one value _per image_
    # in 50% of all cases. In all other cases they will sample new values
    # _per channel_.
    aug_pipe = iaa.Sequential(
        [
            # apply the following augmenters to most images
            #iaa.Fliplr(0.5), # horizontally flip 50% of all images
            #iaa.Flipud(0.2), # vertically flip 20% of all images
            #sometimes(iaa.Crop(percent=(0, 0.1))), # crop images by 0-10% of their height/width
            sometimes(iaa.Affine(
                #scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis
                #translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis)
                #rotate=(-5, 5), # rotate by -45 to +45 degrees
                #shear=(-5, 5), # shear by -16 to +16 degrees
                #order=[0, 1], # use nearest neighbour or bilinear interpolation (fast)
                #cval=(0, 255), # if mode is constant, use a cval between 0 and 255
                #mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
            )),
            # execute 0 to 5 of the following (less important) augmenters per image
            # don't execute all of them, as that would often be way too strong
            iaa.SomeOf((0, 5),
                [
                    #sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation
                    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.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images
                    #iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images
                    # search either for all edges or for directed edges
                    #sometimes(iaa.OneOf([
                    #    iaa.EdgeDetect(alpha=(0, 0.7)),
                    #    iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)),
                    #])),
                    iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images
                    iaa.OneOf([
                        iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels
                        #iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),
                    ]),
                    #iaa.Invert(0.05, per_channel=True), # invert color channels
                    iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
                    iaa.Multiply((0.5, 1.5), per_channel=0.5), # change brightness of images (50-150% of original value)
                    iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
                    #iaa.Grayscale(alpha=(0.0, 1.0)),
                    #sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
                    #sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))) # sometimes move parts of the image around
                ],
                random_order=True
            )
        ],
        random_order=True
    )
    return aug_pipe 

def medium(image_iteration):

    iteration = image_iteration/(120*1.5)
    frequency_factor = 0.05 + float(iteration)/1000000.0
    color_factor = float(iteration)/1000000.0
    dropout_factor = 0.198667 + (0.03856658 - 0.198667) / (1 + (iteration / 196416.6) ** 1.863486)

    blur_factor = 0.5 + (0.5*iteration/100000.0)

    add_factor = 10 + 10*iteration/150000.0

    multiply_factor_pos = 1 + (2.5*iteration/500000.0)
    multiply_factor_neg = 1 - (0.91 * iteration / 500000.0)

    contrast_factor_pos = 1 + (0.5*iteration/500000.0)
    contrast_factor_neg = 1 - (0.5 * iteration / 500000.0)


    #print 'Augment Status ',frequency_factor,color_factor,dropout_factor,blur_factor,add_factor,\
    #    multiply_factor_pos,multiply_factor_neg,contrast_factor_pos,contrast_factor_neg


    augmenter = iaa.Sequential([

        iaa.Sometimes(frequency_factor, iaa.GaussianBlur((0, blur_factor))),
        # blur images with a sigma between 0 and 1.5
        iaa.Sometimes(frequency_factor, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0,dropout_factor ),
                                                                  per_channel=color_factor)),
        # add gaussian noise to images
        iaa.Sometimes(frequency_factor, iaa.CoarseDropout((0.0, dropout_factor), size_percent=(
            0.08, 0.2), per_channel=color_factor)),
        # randomly remove up to X% of the pixels
        iaa.Sometimes(frequency_factor, iaa.Dropout((0.0, dropout_factor), per_channel=color_factor)),
        # randomly remove up to X% of the pixels
        iaa.Sometimes(frequency_factor,
                      iaa.Add((-add_factor, add_factor), per_channel=color_factor)),
        # change brightness of images (by -X to Y of original value)
        iaa.Sometimes(frequency_factor,
                      iaa.Multiply((multiply_factor_neg, multiply_factor_pos), per_channel=color_factor)),
        # change brightness of images (X-Y% of original value)
        iaa.Sometimes(frequency_factor, iaa.ContrastNormalization((contrast_factor_neg, contrast_factor_pos),
                                                                       per_channel=color_factor)),
        # improve or worsen the contrast
        iaa.Sometimes(frequency_factor, iaa.Grayscale((0.0, 1))),  # put grayscale

    ],
        random_order=True  # do all of the above in random order
    )

    return augmenter 

def soft(image_iteration):

    iteration = image_iteration/(120*1.5)
    frequency_factor = 0.05 + float(iteration)/1200000.0
    color_factor = float(iteration)/1200000.0
    dropout_factor = 0.198667 + (0.03856658 - 0.198667) / (1 + (iteration / 196416.6) ** 1.863486)

    blur_factor = 0.5 + (0.5*iteration/120000.0)

    add_factor = 10 + 10*iteration/170000.0

    multiply_factor_pos = 1 + (2.5*iteration/800000.0)
    multiply_factor_neg = 1 - (0.91 * iteration / 800000.0)

    contrast_factor_pos = 1 + (0.5*iteration/800000.0)
    contrast_factor_neg = 1 - (0.5 * iteration / 800000.0)


    #print ('iteration',iteration,'Augment Status ',frequency_factor,color_factor,dropout_factor,blur_factor,add_factor,
    #    multiply_factor_pos,multiply_factor_neg,contrast_factor_pos,contrast_factor_neg)


    augmenter = iaa.Sequential([

        iaa.Sometimes(frequency_factor, iaa.GaussianBlur((0, blur_factor))),
        # blur images with a sigma between 0 and 1.5
        iaa.Sometimes(frequency_factor, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0,dropout_factor ),
                                                                  per_channel=color_factor)),
        # add gaussian noise to images
        iaa.Sometimes(frequency_factor, iaa.CoarseDropout((0.0, dropout_factor), size_percent=(
            0.08, 0.2), per_channel=color_factor)),
        # randomly remove up to X% of the pixels
        iaa.Sometimes(frequency_factor, iaa.Dropout((0.0, dropout_factor), per_channel=color_factor)),
        # randomly remove up to X% of the pixels
        iaa.Sometimes(frequency_factor,
                      iaa.Add((-add_factor, add_factor), per_channel=color_factor)),
        # change brightness of images (by -X to Y of original value)
        iaa.Sometimes(frequency_factor,
                      iaa.Multiply((multiply_factor_neg, multiply_factor_pos), per_channel=color_factor)),
        # change brightness of images (X-Y% of original value)
        iaa.Sometimes(frequency_factor, iaa.ContrastNormalization((contrast_factor_neg, contrast_factor_pos),
                                                                       per_channel=color_factor)),
        # improve or worsen the contrast
        iaa.Sometimes(frequency_factor, iaa.Grayscale((0.0, 1))),  # put grayscale

    ],
        random_order=True  # do all of the above in random order
    )

    return augmenter 

def high(image_iteration):

    iteration = image_iteration/(120*1.5)
    frequency_factor = 0.05 + float(iteration)/800000.0
    color_factor = float(iteration)/800000.0
    dropout_factor = 0.198667 + (0.03856658 - 0.198667) / (1 + (iteration / 196416.6) ** 1.863486)

    blur_factor = 0.5 + (0.5*iteration/80000.0)

    add_factor = 10 + 10*iteration/120000.0

    multiply_factor_pos = 1 + (2.5*iteration/350000.0)
    multiply_factor_neg = 1 - (0.91 * iteration / 400000.0)

    contrast_factor_pos = 1 + (0.5*iteration/350000.0)
    contrast_factor_neg = 1 - (0.5 * iteration / 400000.0)


    #print ('iteration',iteration,'Augment Status ',frequency_factor,color_factor,dropout_factor,blur_factor,add_factor,
    #    multiply_factor_pos,multiply_factor_neg,contrast_factor_pos,contrast_factor_neg)


    augmenter = iaa.Sequential([

        iaa.Sometimes(frequency_factor, iaa.GaussianBlur((0, blur_factor))),
        # blur images with a sigma between 0 and 1.5
        iaa.Sometimes(frequency_factor, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0,dropout_factor ),
                                                                  per_channel=color_factor)),
        # add gaussian noise to images
        iaa.Sometimes(frequency_factor, iaa.CoarseDropout((0.0, dropout_factor), size_percent=(
            0.08, 0.2), per_channel=color_factor)),
        # randomly remove up to X% of the pixels
        iaa.Sometimes(frequency_factor, iaa.Dropout((0.0, dropout_factor), per_channel=color_factor)),
        # randomly remove up to X% of the pixels
        iaa.Sometimes(frequency_factor,
                      iaa.Add((-add_factor, add_factor), per_channel=color_factor)),
        # change brightness of images (by -X to Y of original value)
        iaa.Sometimes(frequency_factor,
                      iaa.Multiply((multiply_factor_neg, multiply_factor_pos), per_channel=color_factor)),
        # change brightness of images (X-Y% of original value)
        iaa.Sometimes(frequency_factor, iaa.ContrastNormalization((contrast_factor_neg, contrast_factor_pos),
                                                                       per_channel=color_factor)),
        # improve or worsen the contrast
        iaa.Sometimes(frequency_factor, iaa.Grayscale((0.0, 1))),  # put grayscale

    ],
        random_order=True  # do all of the above in random order
    )

    return augmenter