def chapter_augmenters_sometimes():
    aug = iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=2.0))
    run_and_save_augseq(
        "sometimes.jpg", aug,
        [ia.quokka(size=(64, 64)) for _ in range(16)], cols=8, rows=2,
        seed=2
    )

    aug = iaa.Sometimes(
        0.5,
        iaa.GaussianBlur(sigma=2.0),
        iaa.Sequential([iaa.Affine(rotate=45), iaa.Sharpen(alpha=1.0)])
    )
    run_and_save_augseq(
        "sometimes_if_else.jpg", aug,
        [ia.quokka(size=(64, 64)) for _ in range(16)], cols=8, rows=2
    ) 

def chapter_augmenters_sharpen():
    aug = iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0))
    run_and_save_augseq(
        "sharpen.jpg", aug,
        [ia.quokka(size=(64, 64)) for _ in range(16)], cols=8, rows=2
    )

    #alphas = [1/8*i for i in range(8)]
    alphas = np.linspace(0, 1.0, num=8)
    run_and_save_augseq(
        "sharpen_vary_alpha.jpg",
        [iaa.Sharpen(alpha=alpha, lightness=1.0) for alpha in alphas],
        [ia.quokka(size=(64, 64)) for _ in range(8)], cols=8, rows=1,
        quality=90
    )

    #lightnesses = [1/8*i for i in range(8)]
    lightnesses = np.linspace(0.75, 1.5, num=8)
    run_and_save_augseq(
        "sharpen_vary_lightness.jpg",
        [iaa.Sharpen(alpha=1.0, lightness=lightness) for lightness in lightnesses],
        [ia.quokka(size=(64, 64)) for _ in range(8)], cols=8, rows=1,
        quality=90
    ) 

def test_alpha_zero(self):
        aug = iaa.Sharpen(alpha=0, lightness=1)
        observed = aug.augment_image(self.base_img)
        expected = self.base_img
        assert np.allclose(observed, expected) 

def test_alpha_one(self):
        aug = iaa.Sharpen(alpha=1.0, lightness=1)
        observed = aug.augment_image(self.base_img)
        expected = self.base_img_sharpened
        assert np.allclose(observed, expected) 

def test_alpha_050(self):
        aug = iaa.Sharpen(alpha=0.5, lightness=1)
        observed = aug.augment_image(self.base_img)
        expected = self._compute_sharpened_base_img(
            0.5*1, 0.5 * self.m_noop + 0.5 * self.m)
        assert np.allclose(observed, expected.astype(np.uint8)) 

def test_alpha_075(self):
        aug = iaa.Sharpen(alpha=0.75, lightness=1)
        observed = aug.augment_image(self.base_img)
        expected = self._compute_sharpened_base_img(
            0.75*1, 0.25 * self.m_noop + 0.75 * self.m)
        assert np.allclose(observed, expected) 

def test_alpha_is_stochastic_parameter(self):
        aug = iaa.Sharpen(alpha=iap.Choice([0.5, 1.0]), lightness=1)
        observed = aug.augment_image(self.base_img)
        expected1 = self._compute_sharpened_base_img(
            0.5*1, 0.5 * self.m_noop + 0.5 * self.m)
        expected2 = self._compute_sharpened_base_img(
            1.0*1, 0.0 * self.m_noop + 1.0 * self.m)
        assert (
            np.allclose(observed, expected1)
            or np.allclose(observed, expected2)
        ) 

def test_alpha_1_lightness_2(self):
        aug = iaa.Sharpen(alpha=1.0, lightness=2)
        observed = aug.augment_image(self.base_img)
        expected = self._compute_sharpened_base_img(1.0*2, self.m)
        assert np.allclose(observed, expected) 

def test_alpha_1_lightness_3(self):
        aug = iaa.Sharpen(alpha=1.0, lightness=3)
        observed = aug.augment_image(self.base_img)
        expected = self._compute_sharpened_base_img(1.0*3, self.m)
        assert np.allclose(observed, expected) 

def test_alpha_1_lightness_is_stochastic_parameter(self):
        aug = iaa.Sharpen(alpha=1.0, lightness=iap.Choice([1.0, 1.5]))
        observed = aug.augment_image(self.base_img)
        expected1 = self._compute_sharpened_base_img(1.0*1.0, self.m)
        expected2 = self._compute_sharpened_base_img(1.0*1.5, self.m)
        assert (
            np.allclose(observed, expected1)
            or np.allclose(observed, expected2)
        ) 

def test_failure_if_lightness_has_bad_datatype(self):
        # don't use assertRaisesRegex, because it doesnt exist in 2.7
        got_exception = False
        try:
            _ = iaa.Sharpen(alpha=1.0, lightness="test")
        except Exception as exc:
            assert "Expected " in str(exc)
            got_exception = True
        assert got_exception

    # this part doesnt really work so far due to nonlinearities resulting
    # from clipping to uint8 

def test_pickleable(self):
        aug = iaa.Sharpen(alpha=(0.0, 1.0), lightness=(1, 3), seed=1)
        runtest_pickleable_uint8_img(aug, iterations=20) 

def __init__(self, alpha=(0.2, 0.5), lightness=(0.5, 1.), prob=0.5):
        super().__init__(prob)
        self.processor = iaa.Sharpen(alpha, lightness) 

def __init__(self, alpha=(0.2, 0.5), lightness=(0.5, 1.), prob=0.5):
        super().__init__(prob)
        self.processor = iaa.Sharpen(alpha, lightness) 

def __init__(self, alpha=(0.2, 0.5), lightness=(0.5, 1.), prob=0.5):
        super().__init__(prob)
        self.processor = iaa.Sharpen(alpha, lightness) 

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 processor(self):
        return iaa.Sharpen(self.alpha, self.lightness) 

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 get_augmentations():
    # applies the given augmenter in 50% of all cases,
    sometimes = lambda aug: iaa.Sometimes(0.5, aug)

    # Define our sequence of augmentation steps that will be applied to every image
    seq = iaa.Sequential([
            # execute 0 to 5 of the following (less important) augmenters per image
            iaa.SomeOf((0, 5),
                [
                    iaa.OneOf([
                        iaa.GaussianBlur((0, 3.0)),
                        iaa.AverageBlur(k=(2, 7)), 
                        iaa.MedianBlur(k=(3, 11)),
                    ]),
                    iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
                    iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), 
                    # search either for all edges or for directed edges,
                    # blend the result with the original image using a blobby mask
                    iaa.SimplexNoiseAlpha(iaa.OneOf([
                        iaa.EdgeDetect(alpha=(0.5, 1.0)),
                        iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)),
                    ])),
                    iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5),
                    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.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
                    iaa.AddToHueAndSaturation((-20, 20)), # change hue and saturation
                    # either change the brightness of the whole image (sometimes
                    # per channel) or change the brightness of subareas
                    iaa.OneOf([
                        iaa.Multiply((0.5, 1.5), per_channel=0.5),
                        iaa.FrequencyNoiseAlpha(
                            exponent=(-4, 0),
                            first=iaa.Multiply((0.5, 1.5), per_channel=True),
                            second=iaa.ContrastNormalization((0.5, 2.0))
                        )
                    ]),
                    iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
                    sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
                ],
                random_order=True
            )
        ],
        random_order=True
    )
    return seq

### data transforms