Python augmenters.EdgeDetect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 EdgeDetect [as 别名]
def chapter_augmenters_edgedetect():
    aug = iaa.EdgeDetect(alpha=(0.0, 1.0))
    run_and_save_augseq(
        "edgedetect.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(
        "edgedetect_vary_alpha.jpg",
        [iaa.EdgeDetect(alpha=alpha) for alpha in alphas],
        [ia.quokka(size=(64, 64)) for _ in range(8)], cols=8, rows=1
    ) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import EdgeDetect [as 别名]
def main():
    nb_rows = 8
    nb_cols = 8
    h, w = (128, 128)
    sample_size = 128

    noise_gens = [
        iap.SimplexNoise(),
        iap.FrequencyNoise(exponent=-4, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=-2, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=0, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=2, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=4, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size),
                           upscale_method=["nearest", "linear", "cubic"]),
        iap.IterativeNoiseAggregator(
            other_param=iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size),
                                           upscale_method=["nearest", "linear", "cubic"]),
            iterations=(1, 3),
            aggregation_method=["max", "avg"]
        ),
        iap.IterativeNoiseAggregator(
            other_param=iap.Sigmoid(
                iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size),
                                   upscale_method=["nearest", "linear", "cubic"]),
                threshold=(-10, 10),
                activated=0.33,
                mul=20,
                add=-10
            ),
            iterations=(1, 3),
            aggregation_method=["max", "avg"]
        )
    ]

    samples = [[] for _ in range(len(noise_gens))]
    for _ in range(nb_rows * nb_cols):
        for i, noise_gen in enumerate(noise_gens):
            samples[i].append(noise_gen.draw_samples((h, w)))

    rows = [np.hstack(row) for row in samples]
    grid = np.vstack(rows)
    ia.imshow((grid*255).astype(np.uint8))

    images = [ia.quokka_square(size=(128, 128)) for _ in range(16)]
    seqs = [
        iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0)),
        iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True),
        iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0)),
        iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True)
    ]
    images_aug = []

    for seq in seqs:
        images_aug.append(np.hstack(seq.augment_images(images)))
    images_aug = np.vstack(images_aug)
    ia.imshow(images_aug) 

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import EdgeDetect [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 EdgeDetect [as 别名]
def heavy_aug_on_fly(img, det_mask):
    """Do augmentation with different combination on each training batch
    """

    def image_heavy_augmentation(image, det_masks, ratio_operations=0.6):
        # according to the paper, operations such as shearing, fliping horizontal/vertical,
        # rotating, zooming and channel shifting will be apply
        sometimes = lambda aug: iaa.Sometimes(ratio_operations, aug)
        edge_detect_sometime = lambda aug: iaa.Sometimes(0.1, aug)
        elasitic_sometime = lambda aug:iaa.Sometimes(0.2, aug)
        add_gauss_noise = lambda aug: iaa.Sometimes(0.15, aug)
        hor_flip_angle = np.random.uniform(0, 1)
        ver_flip_angle = np.random.uniform(0, 1)
        seq = iaa.Sequential([
            iaa.SomeOf((0, 5), [
                iaa.Fliplr(hor_flip_angle),
                iaa.Flipud(ver_flip_angle),
                iaa.Affine(shear=(-16, 16)),
                iaa.Affine(scale={'x': (1, 1.6), 'y': (1, 1.6)}),
                iaa.PerspectiveTransform(scale=(0.01, 0.1)),

                # These are additional augmentation.
                #iaa.ContrastNormalization((0.75, 1.5))

            ]),

            edge_detect_sometime(iaa.OneOf([
                iaa.EdgeDetect(alpha=(0, 0.7)),
                iaa.DirectedEdgeDetect(alpha=(0,0.7), direction=(0.0, 1.0)
                                       )
            ])),
            add_gauss_noise(iaa.AdditiveGaussianNoise(loc=0,
                                                      scale=(0.0, 0.05*255),
                                                      per_channel=0.5)
                            ),
            iaa.Sometimes(0.3,
                          iaa.GaussianBlur(sigma=(0, 0.5))
                          ),
            elasitic_sometime(
                iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25))
        ])

        seq_to_deterministic = seq.to_deterministic()
        aug_img = seq_to_deterministic.augment_images(image)
        aug_det_mask = seq_to_deterministic.augment_images(det_masks)
        return aug_img, aug_det_mask

    aug_image, aug_det_mask = image_heavy_augmentation(image=img, det_masks=det_mask)
    return aug_image, aug_det_mask 

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

# 需要导入模块: from imgaug import augmenters [as 别名]
# 或者: from imgaug.augmenters import EdgeDetect [as 别名]
def main():
    nb_rows = 8
    nb_cols = 8
    h, w = (128, 128)
    sample_size = 128

    noise_gens = [
        iap.SimplexNoise(),
        iap.FrequencyNoise(exponent=-4, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=-2, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=0, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=2, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=4, size_px_max=sample_size, upscale_method="cubic"),
        iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
        iap.IterativeNoiseAggregator(
            other_param=iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
            iterations=(1, 3),
            aggregation_method=["max", "avg"]
        ),
        iap.IterativeNoiseAggregator(
            other_param=iap.Sigmoid(
                iap.FrequencyNoise(exponent=(-4, 4), size_px_max=(4, sample_size), upscale_method=["nearest", "linear", "cubic"]),
                threshold=(-10, 10),
                activated=0.33,
                mul=20,
                add=-10
            ),
            iterations=(1, 3),
            aggregation_method=["max", "avg"]
        )
    ]

    samples = [[] for _ in range(len(noise_gens))]
    for _ in range(nb_rows * nb_cols):
        for i, noise_gen in enumerate(noise_gens):
            samples[i].append(noise_gen.draw_samples((h, w)))

    rows = [np.hstack(row) for row in samples]
    grid = np.vstack(rows)
    misc.imshow((grid*255).astype(np.uint8))

    images = [ia.quokka_square(size=(128, 128)) for _ in range(16)]
    seqs = [
        iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0)),
        iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True),
        iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0)),
        iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=True)
    ]
    images_aug = []

    for seq in seqs:
        images_aug.append(np.hstack(seq.augment_images(images)))
    images_aug = np.vstack(images_aug)
    misc.imshow(images_aug)