Python augmenters.Grayscale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 Grayscale [as 别名]
def _load_augmentation_aug_non_geometric():
    return iaa.Sequential([
        iaa.Sometimes(0.3, iaa.Multiply((0.5, 1.5), per_channel=0.5)),
        iaa.Sometimes(0.2, iaa.JpegCompression(compression=(70, 99))),
        iaa.Sometimes(0.2, iaa.GaussianBlur(sigma=(0, 3.0))),
        iaa.Sometimes(0.2, iaa.MotionBlur(k=15, angle=[-45, 45])),
        iaa.Sometimes(0.2, iaa.MultiplyHue((0.5, 1.5))),
        iaa.Sometimes(0.2, iaa.MultiplySaturation((0.5, 1.5))),
        iaa.Sometimes(0.34, iaa.MultiplyHueAndSaturation((0.5, 1.5),
                                                         per_channel=True)),
        iaa.Sometimes(0.34, iaa.Grayscale(alpha=(0.0, 1.0))),
        iaa.Sometimes(0.2, iaa.ChangeColorTemperature((1100, 10000))),
        iaa.Sometimes(0.1, iaa.GammaContrast((0.5, 2.0))),
        iaa.Sometimes(0.2, iaa.SigmoidContrast(gain=(3, 10),
                                               cutoff=(0.4, 0.6))),
        iaa.Sometimes(0.1, iaa.CLAHE()),
        iaa.Sometimes(0.1, iaa.HistogramEqualization()),
        iaa.Sometimes(0.2, iaa.LinearContrast((0.5, 2.0), per_channel=0.5)),
        iaa.Sometimes(0.1, iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)))
    ]) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def test_grayscale_drops_different_colors(self):
        image = np.uint8([
            [255, 0, 0],
            [0, 255, 0],
            [0, 0, 255],
            [255, 255, 0],
            [255, 0, 255],
            [0, 255, 255],
            [255, 128, 128],
            [128, 255, 128],
            [128, 128, 255]
        ]).reshape((1, 9, 3))
        image_gray = iaa.Grayscale(1.0)(image=image)
        aug = iaa.BlendAlphaSomeColors(iaa.Grayscale(1.0),
                                       nb_bins=256, smoothness=0)

        nb_grayscaled = []
        for _ in sm.xrange(50):
            image_aug = aug(image=image)
            grayscaled = np.sum((image_aug == image_gray).astype(np.int32),
                                axis=2)
            assert np.all(np.logical_or(grayscaled == 0, grayscaled == 3))
            nb_grayscaled.append(np.sum(grayscaled == 3))

        assert len(set(nb_grayscaled)) >= 5 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def test_alpha_is_0(self):
        aug = iaa.Grayscale(0.0)
        observed = aug.augment_image(self.base_img)
        expected = np.copy(self.base_img)
        assert np.allclose(observed, expected) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def test_alpha_is_1(self):
        aug = iaa.Grayscale(1.0)
        observed = aug.augment_image(self.base_img)
        luminosity = self._compute_luminosity(10, 20, 30)
        expected = np.zeros_like(self.base_img) + luminosity
        assert np.allclose(observed, expected.astype(np.uint8)) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def test_alpha_is_050(self):
        aug = iaa.Grayscale(0.5)
        observed = aug.augment_image(self.base_img)
        luminosity = self._compute_luminosity(10, 20, 30)
        expected = 0.5 * self.base_img + 0.5 * luminosity
        assert np.allclose(observed, expected.astype(np.uint8)) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def test_alpha_is_tuple(self):
        aug = iaa.Grayscale((0.0, 1.0))
        base_img = np.uint8([255, 0, 0]).reshape((1, 1, 3))
        base_img_float = base_img.astype(np.float64) / 255.0
        base_img_gray = iaa.Grayscale(1.0)\
            .augment_image(base_img)\
            .astype(np.float64) / 255.0
        distance_max = np.linalg.norm(base_img_gray.flatten()
                                      - base_img_float.flatten())
        nb_iterations = 1000
        distances = []
        for _ in sm.xrange(nb_iterations):
            observed = aug.augment_image(base_img).astype(np.float64) / 255.0
            distance = np.linalg.norm(
                observed.flatten() - base_img_float.flatten()) / distance_max
            distances.append(distance)

        assert 0 - 1e-4 < min(distances) < 0.1
        assert 0.4 < np.average(distances) < 0.6
        assert 0.9 < max(distances) < 1.0 + 1e-4

        nb_bins = 5
        hist, _ = np.histogram(
            distances, bins=nb_bins, range=(0.0, 1.0), density=False)
        density_expected = 1.0/nb_bins
        density_tolerance = 0.05
        for nb_samples in hist:
            density = nb_samples / nb_iterations
            assert np.isclose(density, density_expected,
                              rtol=0, atol=density_tolerance) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def chapter_augmenters_grayscale():
    aug = iaa.Grayscale(alpha=(0.0, 1.0))
    run_and_save_augseq(
        "grayscale.jpg", aug,
        [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2
    )

    #alphas = [1/8*i for i in range(8)]
    alphas = np.linspace(0, 1.0, num=8)
    run_and_save_augseq(
        "grayscale_vary_alpha.jpg",
        [iaa.Grayscale(alpha=alpha) for alpha in alphas],
        [ia.quokka(size=(64, 64)) for _ in range(8)], cols=8, rows=1,
        quality=75
    ) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def example_standard_situation():
    print("Example: Standard Situation")
    # -------
    # dummy functions to make the example runnable here
    def load_batch(batch_idx):
        return np.random.randint(0, 255, (1, 16, 16, 3), dtype=np.uint8)

    def train_on_images(images):
        pass

    # -------

    from imgaug import augmenters as iaa

    seq = iaa.Sequential([
        iaa.Crop(px=(0, 16)), # crop images from each side by 0 to 16px (randomly chosen)
        iaa.Fliplr(0.5), # horizontally flip 50% of the images
        iaa.GaussianBlur(sigma=(0, 3.0)) # blur images with a sigma of 0 to 3.0
    ])

    for batch_idx in range(1000):
        # 'images' should be either a 4D numpy array of shape (N, height, width, channels)
        # or a list of 3D numpy arrays, each having shape (height, width, channels).
        # Grayscale images must have shape (height, width, 1) each.
        # All images must have numpy's dtype uint8. Values are expected to be in
        # range 0-255.
        images = load_batch(batch_idx)
        images_aug = seq.augment_images(images)
        train_on_images(images_aug)


        # -----
        # Make sure that the example really does something
        if batch_idx == 0:
            assert not np.array_equal(images, images_aug) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def __init__(self):
        self.augmentor_op = Operations.Greyscale(probability=1)
        self.imgaug_transform = iaa.Grayscale(alpha=1.0)
        self.solt_stream = slc.Stream([slt.ImageColorTransform(mode="rgb2gs")]) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def greyscale(images, alpha):
    transformer = iaa.Grayscale(alpha=(0.0, alpha), deterministic=True)
    images = [impr.invert_image(img) for img in images]
    return keep_L_channel(augment_on_df(images, transformer)) 

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

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

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

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

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import Grayscale [as 别名]
def medium_harder(image_iteration):


    iteration = image_iteration / 120
    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