def rgbmore(self, im): return_im = [] add_return_im = lambda im: return_im.extend(im) grey = np.array(im.convert(mode='L')) im = np.array(im) rgb_grey = np.dstack((im, grey)) edge = iaa.EdgeDetect(alpha=1)(images=rgb_grey) dir_edge = lambda d: iaa.DirectedEdgeDetect(alpha=1, direction=d)(images= grey) dir_edges = np.array( [dir_edge(d) for d in np.linspace(0, 1, num=3, endpoint=False)]) dir_edges = np.transpose(dir_edges, (1, 2, 0)) canny = iaa.Canny(alpha=1.0, hysteresis_thresholds=128, sobel_kernel_size=4, deterministic=True, colorizer=iaa.RandomColorsBinaryImageColorizer( color_true=255, color_false=0))(images=grey) avg_pool = iaa.AveragePooling(2)(images=grey) max_pool = iaa.MaxPooling(2)(images=grey) min_pool = iaa.MinPooling(2)(images=grey) add_return_im([im, grey]) add_return_im([edge, dir_edges, canny]) add_return_im([avg_pool, max_pool, min_pool]) return np.dstack(return_im)
def get_seq(): import imgaug.augmenters as iaa sometimes = lambda aug: iaa.Sometimes(0.1, aug) seq = iaa.Sequential( [ sometimes(iaa.AdditiveGaussianNoise(scale=0.07 * 255)), sometimes(iaa.GaussianBlur(sigma=(0, 3.0))), sometimes(iaa.MedianBlur(k=(1, 5))), sometimes(iaa.AverageBlur(k=((1, 5), (1, 3)))), sometimes(iaa.AveragePooling([1, 5])), sometimes(iaa.MaxPooling([1, 5])), sometimes(iaa.MaxPooling([1, 5])), sometimes( iaa.CropAndPad(percent=(0, 0.2), pad_mode=["constant", "edge"], pad_cval=(0, 128))), sometimes( iaa.Sequential([ iaa.Resize({ "height": 64, "width": 64 }), iaa.Resize({ "height": input_shape[0], "width": input_shape[1] }), ])), sometimes( iaa.Sequential([ iaa.Resize({ "height": 16, "width": 16 }), iaa.Resize({ "height": input_shape[0], "width": input_shape[1] }), ])), ], random_order=True, ) return seq
def test_augment_images(self): aug = iaa.MaxPooling(2, keep_size=False) image = np.uint8([[50 - 2, 50 - 1, 120 - 4, 120 + 4], [50 + 1, 50 + 2, 120 + 1, 120 - 1]]) image = np.tile(image[:, :, np.newaxis], (1, 1, 3)) expected = np.uint8([[50 + 2, 120 + 4]]) expected = np.tile(expected[:, :, np.newaxis], (1, 1, 3)) image_aug = aug.augment_image(image) diff = np.abs(image_aug.astype(np.int32) - expected) assert image_aug.shape == (1, 2, 3) assert np.all(diff <= 1)
def get_seq_val(): sometimes = lambda aug: iaa.Sometimes(0.5, aug, random_state=STATE, ) seq_val = iaa.Sequential([ iaa.OneOf([ sometimes(iaa.GaussianBlur(sigma=(0.1, 1), random_state=STATE, )), sometimes(iaa.AverageBlur(k=(3, 7))), sometimes(iaa.MotionBlur(k=(3, 7))), sometimes(iaa.AveragePooling((2, 8))), sometimes(iaa.MaxPooling((2, 8))), sometimes(iaa.MedianPooling((2, 8))), sometimes(iaa.MinPooling((2, 8))), ]), iaa.Multiply((0.8, 1.2)), ], random_order=True) return seq_val
def test_augment_images__different_channels(self): aug = iaa.MaxPooling((iap.Deterministic(1), iap.Deterministic(4)), keep_size=False) c1 = np.arange(start=1, stop=8 + 1).reshape((1, 8, 1)) c2 = (100 + np.arange(start=1, stop=8 + 1)).reshape((1, 8, 1)) image = np.dstack([c1, c2]).astype(np.uint8) c1_expected = np.uint8([4, 8]).reshape((1, 2, 1)) c2_expected = np.uint8([100 + 4, 100 + 8]).reshape((1, 2, 1)) image_expected = np.dstack([c1_expected, c2_expected]) image_aug = aug.augment_image(image) diff = np.abs(image_aug.astype(np.int32) - image_expected) assert image_aug.shape == (1, 2, 2) assert np.all(diff <= 1)
def __init__(self, *args, **kwargs): """ Args: annotations (Union[pandas.DataFrame, list[pandas.DataFrame]]): query and support annotations. If a single dataframe is given, it will be used both for query and support set. batch_size (int): number of images per batch """ super().__init__(*args, **kwargs) self.query_preprocessing = self.preprocessings[0] self.support_preprocessing = self.preprocessings[-1] self.query_annotations = self.annotations[0] self.support_annotations = self.annotations[-1] self.support_annotations_by_label = {group[0]: group[1] for group in self.support_annotations.groupby("label")} self.query_samples = pd.DataFrame() self.support_samples = pd.DataFrame() self.target_augmenter = iaa.MaxPooling((32, 32), keep_size=False, deterministic=True)
def get_seq(): import imgaug.augmenters as iaa sometimes = lambda aug: iaa.Sometimes(0.1, aug) seq = iaa.Sequential( [ sometimes(iaa.AdditiveGaussianNoise(scale=0.07 * 255)), sometimes(iaa.GaussianBlur(sigma=(0, 3.0))), sometimes(iaa.MedianBlur(k=(1, 5))), sometimes(iaa.AverageBlur(k=((5, 11), (1, 3)))), sometimes(iaa.AveragePooling([2, 8])), sometimes(iaa.MaxPooling([2, 8])), sometimes( iaa.Sequential([ iaa.Resize({ "height": 64, "width": 64 }), iaa.Resize({ "height": input_shape[0], "width": input_shape[1] }), ])), sometimes( iaa.Sequential([ iaa.Resize({ "height": 512, "width": 512 }), iaa.Resize({ "height": input_shape[0], "width": input_shape[1] }), ])), ], random_order=True, ) return seq
iaa.WithChannels(0, iaa.Add((-50, 50))), iaa.WithChannels(1, iaa.Add((-50, 50))), iaa.WithChannels(2, iaa.Add((-50, 50))), iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB"), iaa.Add((-80, 80), per_channel=0.5), iaa.Multiply((0.5, 1.5), per_channel=0.5), iaa.AverageBlur(k=((5), (1, 3))), iaa.AveragePooling(2), iaa.AddElementwise((-20, -5)), iaa.AdditiveGaussianNoise(scale=(0, 0.05 * 255)), iaa.JpegCompression(compression=(50, 99)), iaa.MultiplyHueAndSaturation(mul_hue=(0.5, 1.5)), iaa.WithBrightnessChannels(iaa.Add((-50, 50))), iaa.WithBrightnessChannels(iaa.Add((-50, 50)), to_colorspace=[iaa.CSPACE_Lab, iaa.CSPACE_HSV]), iaa.MaxPooling(2), iaa.MinPooling((1, 2)), # iaa.Superpixels(p_replace=(0.1, 0.2), n_segments=(16, 128)), iaa.Clouds(), iaa.Fog(), iaa.AdditiveGaussianNoise(scale=0.1 * 255, per_channel=True), iaa.Dropout(p=(0, 0.2)), # iaa.WithChannels(0, iaa.Affine(rotate=(0, 0))), iaa.ChannelShuffle(0.35), iaa.WithColorspace(to_colorspace="HSV", from_colorspace="RGB", children=iaa.WithChannels(0, iaa.Add((0, 50)))), # iaa.WithHueAndSaturation([ iaa.WithChannels(0, iaa.Add((-30, 10))),
d=(3, 10), sigma_color=(10, 250), sigma_space=(10, 250)), iaa.MotionBlur(k=(3, 9), angle=[-45, 45]), iaa.MeanShiftBlur(spatial_radius=(5.0, 10.0), color_radius=(5.0, 10.0)), iaa.AllChannelsCLAHE(clip_limit=(1, 10)), iaa.AllChannelsHistogramEqualization(), iaa.GammaContrast((0.5, 1.5), per_channel=True), iaa.GammaContrast((0.5, 1.5)), iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6), per_channel=True), iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)), iaa.HistogramEqualization(), iaa.Sharpen(alpha=0.5) ]), iaa.OneOf([ iaa.AveragePooling([2, 3]), iaa.MaxPooling(([2, 3], [2, 3])), ]), iaa.OneOf([ iaa.Clouds(), iaa.Snowflakes(flake_size=(0.1, 0.4), speed=(0.01, 0.05)), iaa.Rain(speed=(0.1, 0.3)) ]) ], random_order=True) def get_color_augmentation(augment_prob): return iaa.Sometimes(augment_prob, aug_transform).augment_image class SegCompose(object):
def create_augmenters(height, width, height_augmentable, width_augmentable, only_augmenters): def lambda_func_images(images, random_state, parents, hooks): return images def lambda_func_heatmaps(heatmaps, random_state, parents, hooks): return heatmaps def lambda_func_keypoints(keypoints, random_state, parents, hooks): return keypoints def assertlambda_func_images(images, random_state, parents, hooks): return True def assertlambda_func_heatmaps(heatmaps, random_state, parents, hooks): return True def assertlambda_func_keypoints(keypoints, random_state, parents, hooks): return True augmenters_meta = [ iaa.Sequential([iaa.Noop(), iaa.Noop()], random_order=False, name="Sequential_2xNoop"), iaa.Sequential([iaa.Noop(), iaa.Noop()], random_order=True, name="Sequential_2xNoop_random_order"), iaa.SomeOf((1, 3), [iaa.Noop(), iaa.Noop(), iaa.Noop()], random_order=False, name="SomeOf_3xNoop"), iaa.SomeOf((1, 3), [iaa.Noop(), iaa.Noop(), iaa.Noop()], random_order=True, name="SomeOf_3xNoop_random_order"), iaa.OneOf([iaa.Noop(), iaa.Noop(), iaa.Noop()], name="OneOf_3xNoop"), iaa.Sometimes(0.5, iaa.Noop(), name="Sometimes_Noop"), iaa.WithChannels([1, 2], iaa.Noop(), name="WithChannels_1_and_2_Noop"), iaa.Noop(name="Noop"), iaa.Lambda(func_images=lambda_func_images, func_heatmaps=lambda_func_heatmaps, func_keypoints=lambda_func_keypoints, name="Lambda"), iaa.AssertLambda(func_images=assertlambda_func_images, func_heatmaps=assertlambda_func_heatmaps, func_keypoints=assertlambda_func_keypoints, name="AssertLambda"), iaa.AssertShape((None, height_augmentable, width_augmentable, None), name="AssertShape"), iaa.ChannelShuffle(0.5, name="ChannelShuffle") ] augmenters_arithmetic = [ iaa.Add((-10, 10), name="Add"), iaa.AddElementwise((-10, 10), name="AddElementwise"), #iaa.AddElementwise((-500, 500), name="AddElementwise"), iaa.AdditiveGaussianNoise(scale=(5, 10), name="AdditiveGaussianNoise"), iaa.AdditiveLaplaceNoise(scale=(5, 10), name="AdditiveLaplaceNoise"), iaa.AdditivePoissonNoise(lam=(1, 5), name="AdditivePoissonNoise"), iaa.Multiply((0.5, 1.5), name="Multiply"), iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"), iaa.Dropout((0.01, 0.05), name="Dropout"), iaa.CoarseDropout((0.01, 0.05), size_percent=(0.01, 0.1), name="CoarseDropout"), iaa.ReplaceElementwise((0.01, 0.05), (0, 255), name="ReplaceElementwise"), #iaa.ReplaceElementwise((0.95, 0.99), (0, 255), name="ReplaceElementwise"), iaa.SaltAndPepper((0.01, 0.05), name="SaltAndPepper"), iaa.ImpulseNoise((0.01, 0.05), name="ImpulseNoise"), iaa.CoarseSaltAndPepper((0.01, 0.05), size_percent=(0.01, 0.1), name="CoarseSaltAndPepper"), iaa.Salt((0.01, 0.05), name="Salt"), iaa.CoarseSalt((0.01, 0.05), size_percent=(0.01, 0.1), name="CoarseSalt"), iaa.Pepper((0.01, 0.05), name="Pepper"), iaa.CoarsePepper((0.01, 0.05), size_percent=(0.01, 0.1), name="CoarsePepper"), iaa.Invert(0.1, name="Invert"), # ContrastNormalization iaa.JpegCompression((50, 99), name="JpegCompression") ] augmenters_blend = [ iaa.Alpha((0.01, 0.99), iaa.Noop(), name="Alpha"), iaa.AlphaElementwise((0.01, 0.99), iaa.Noop(), name="AlphaElementwise"), iaa.SimplexNoiseAlpha(iaa.Noop(), name="SimplexNoiseAlpha"), iaa.FrequencyNoiseAlpha((-2.0, 2.0), iaa.Noop(), name="FrequencyNoiseAlpha") ] augmenters_blur = [ iaa.GaussianBlur(sigma=(1.0, 5.0), name="GaussianBlur"), iaa.AverageBlur(k=(3, 11), name="AverageBlur"), iaa.MedianBlur(k=(3, 11), name="MedianBlur"), iaa.BilateralBlur(d=(3, 11), name="BilateralBlur"), iaa.MotionBlur(k=(3, 11), name="MotionBlur") ] augmenters_color = [ # InColorspace (deprecated) iaa.WithColorspace(to_colorspace="HSV", children=iaa.Noop(), name="WithColorspace"), iaa.WithHueAndSaturation(children=iaa.Noop(), name="WithHueAndSaturation"), iaa.MultiplyHueAndSaturation((0.8, 1.2), name="MultiplyHueAndSaturation"), iaa.MultiplyHue((-1.0, 1.0), name="MultiplyHue"), iaa.MultiplySaturation((0.8, 1.2), name="MultiplySaturation"), iaa.AddToHueAndSaturation((-10, 10), name="AddToHueAndSaturation"), iaa.AddToHue((-10, 10), name="AddToHue"), iaa.AddToSaturation((-10, 10), name="AddToSaturation"), iaa.ChangeColorspace(to_colorspace="HSV", name="ChangeColorspace"), iaa.Grayscale((0.01, 0.99), name="Grayscale"), iaa.KMeansColorQuantization((2, 16), name="KMeansColorQuantization"), iaa.UniformColorQuantization((2, 16), name="UniformColorQuantization") ] augmenters_contrast = [ iaa.GammaContrast(gamma=(0.5, 2.0), name="GammaContrast"), iaa.SigmoidContrast(gain=(5, 20), cutoff=(0.25, 0.75), name="SigmoidContrast"), iaa.LogContrast(gain=(0.7, 1.0), name="LogContrast"), iaa.LinearContrast((0.5, 1.5), name="LinearContrast"), iaa.AllChannelsCLAHE(clip_limit=(2, 10), tile_grid_size_px=(3, 11), name="AllChannelsCLAHE"), iaa.CLAHE(clip_limit=(2, 10), tile_grid_size_px=(3, 11), to_colorspace="HSV", name="CLAHE"), iaa.AllChannelsHistogramEqualization( name="AllChannelsHistogramEqualization"), iaa.HistogramEqualization(to_colorspace="HSV", name="HistogramEqualization"), ] augmenters_convolutional = [ iaa.Convolve(np.float32([[0, 0, 0], [0, 1, 0], [0, 0, 0]]), name="Convolve_3x3"), iaa.Sharpen(alpha=(0.01, 0.99), lightness=(0.5, 2), name="Sharpen"), iaa.Emboss(alpha=(0.01, 0.99), strength=(0, 2), name="Emboss"), iaa.EdgeDetect(alpha=(0.01, 0.99), name="EdgeDetect"), iaa.DirectedEdgeDetect(alpha=(0.01, 0.99), name="DirectedEdgeDetect") ] augmenters_edges = [iaa.Canny(alpha=(0.01, 0.99), name="Canny")] augmenters_flip = [ iaa.Fliplr(1.0, name="Fliplr"), iaa.Flipud(1.0, name="Flipud") ] augmenters_geometric = [ iaa.Affine(scale=(0.9, 1.1), translate_percent={ "x": (-0.05, 0.05), "y": (-0.05, 0.05) }, rotate=(-10, 10), shear=(-10, 10), order=0, mode="constant", cval=(0, 255), name="Affine_order_0_constant"), iaa.Affine(scale=(0.9, 1.1), translate_percent={ "x": (-0.05, 0.05), "y": (-0.05, 0.05) }, rotate=(-10, 10), shear=(-10, 10), order=1, mode="constant", cval=(0, 255), name="Affine_order_1_constant"), iaa.Affine(scale=(0.9, 1.1), translate_percent={ "x": (-0.05, 0.05), "y": (-0.05, 0.05) }, rotate=(-10, 10), shear=(-10, 10), order=3, mode="constant", cval=(0, 255), name="Affine_order_3_constant"), iaa.Affine(scale=(0.9, 1.1), translate_percent={ "x": (-0.05, 0.05), "y": (-0.05, 0.05) }, rotate=(-10, 10), shear=(-10, 10), order=1, mode="edge", cval=(0, 255), name="Affine_order_1_edge"), iaa.Affine(scale=(0.9, 1.1), translate_percent={ "x": (-0.05, 0.05), "y": (-0.05, 0.05) }, rotate=(-10, 10), shear=(-10, 10), order=1, mode="constant", cval=(0, 255), backend="skimage", name="Affine_order_1_constant_skimage"), # TODO AffineCv2 iaa.PiecewiseAffine(scale=(0.01, 0.05), nb_rows=4, nb_cols=4, order=1, mode="constant", name="PiecewiseAffine_4x4_order_1_constant"), iaa.PiecewiseAffine(scale=(0.01, 0.05), nb_rows=4, nb_cols=4, order=0, mode="constant", name="PiecewiseAffine_4x4_order_0_constant"), iaa.PiecewiseAffine(scale=(0.01, 0.05), nb_rows=4, nb_cols=4, order=1, mode="edge", name="PiecewiseAffine_4x4_order_1_edge"), iaa.PiecewiseAffine(scale=(0.01, 0.05), nb_rows=8, nb_cols=8, order=1, mode="constant", name="PiecewiseAffine_8x8_order_1_constant"), iaa.PerspectiveTransform(scale=(0.01, 0.05), keep_size=False, name="PerspectiveTransform"), iaa.PerspectiveTransform(scale=(0.01, 0.05), keep_size=True, name="PerspectiveTransform_keep_size"), iaa.ElasticTransformation( alpha=(1, 10), sigma=(0.5, 1.5), order=0, mode="constant", cval=0, name="ElasticTransformation_order_0_constant"), iaa.ElasticTransformation( alpha=(1, 10), sigma=(0.5, 1.5), order=1, mode="constant", cval=0, name="ElasticTransformation_order_1_constant"), iaa.ElasticTransformation( alpha=(1, 10), sigma=(0.5, 1.5), order=1, mode="nearest", cval=0, name="ElasticTransformation_order_1_nearest"), iaa.ElasticTransformation( alpha=(1, 10), sigma=(0.5, 1.5), order=1, mode="reflect", cval=0, name="ElasticTransformation_order_1_reflect"), iaa.Rot90((1, 3), keep_size=False, name="Rot90"), iaa.Rot90((1, 3), keep_size=True, name="Rot90_keep_size") ] augmenters_pooling = [ iaa.AveragePooling(kernel_size=(1, 16), keep_size=False, name="AveragePooling"), iaa.AveragePooling(kernel_size=(1, 16), keep_size=True, name="AveragePooling_keep_size"), iaa.MaxPooling(kernel_size=(1, 16), keep_size=False, name="MaxPooling"), iaa.MaxPooling(kernel_size=(1, 16), keep_size=True, name="MaxPooling_keep_size"), iaa.MinPooling(kernel_size=(1, 16), keep_size=False, name="MinPooling"), iaa.MinPooling(kernel_size=(1, 16), keep_size=True, name="MinPooling_keep_size"), iaa.MedianPooling(kernel_size=(1, 16), keep_size=False, name="MedianPooling"), iaa.MedianPooling(kernel_size=(1, 16), keep_size=True, name="MedianPooling_keep_size") ] augmenters_segmentation = [ iaa.Superpixels(p_replace=(0.05, 1.0), n_segments=(10, 100), max_size=64, interpolation="cubic", name="Superpixels_max_size_64_cubic"), iaa.Superpixels(p_replace=(0.05, 1.0), n_segments=(10, 100), max_size=64, interpolation="linear", name="Superpixels_max_size_64_linear"), iaa.Superpixels(p_replace=(0.05, 1.0), n_segments=(10, 100), max_size=128, interpolation="linear", name="Superpixels_max_size_128_linear"), iaa.Superpixels(p_replace=(0.05, 1.0), n_segments=(10, 100), max_size=224, interpolation="linear", name="Superpixels_max_size_224_linear"), iaa.UniformVoronoi(n_points=(250, 1000), name="UniformVoronoi"), iaa.RegularGridVoronoi(n_rows=(16, 31), n_cols=(16, 31), name="RegularGridVoronoi"), iaa.RelativeRegularGridVoronoi(n_rows_frac=(0.07, 0.14), n_cols_frac=(0.07, 0.14), name="RelativeRegularGridVoronoi"), ] augmenters_size = [ iaa.Resize((0.8, 1.2), interpolation="nearest", name="Resize_nearest"), iaa.Resize((0.8, 1.2), interpolation="linear", name="Resize_linear"), iaa.Resize((0.8, 1.2), interpolation="cubic", name="Resize_cubic"), iaa.CropAndPad(percent=(-0.2, 0.2), pad_mode="constant", pad_cval=(0, 255), keep_size=False, name="CropAndPad"), iaa.CropAndPad(percent=(-0.2, 0.2), pad_mode="edge", pad_cval=(0, 255), keep_size=False, name="CropAndPad_edge"), iaa.CropAndPad(percent=(-0.2, 0.2), pad_mode="constant", pad_cval=(0, 255), name="CropAndPad_keep_size"), iaa.Pad(percent=(0.05, 0.2), pad_mode="constant", pad_cval=(0, 255), keep_size=False, name="Pad"), iaa.Pad(percent=(0.05, 0.2), pad_mode="edge", pad_cval=(0, 255), keep_size=False, name="Pad_edge"), iaa.Pad(percent=(0.05, 0.2), pad_mode="constant", pad_cval=(0, 255), name="Pad_keep_size"), iaa.Crop(percent=(0.05, 0.2), keep_size=False, name="Crop"), iaa.Crop(percent=(0.05, 0.2), name="Crop_keep_size"), iaa.PadToFixedSize(width=width + 10, height=height + 10, pad_mode="constant", pad_cval=(0, 255), name="PadToFixedSize"), iaa.CropToFixedSize(width=width - 10, height=height - 10, name="CropToFixedSize"), iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10, width=width - 10), interpolation="nearest", name="KeepSizeByResize_CropToFixedSize_nearest"), iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10, width=width - 10), interpolation="linear", name="KeepSizeByResize_CropToFixedSize_linear"), iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10, width=width - 10), interpolation="cubic", name="KeepSizeByResize_CropToFixedSize_cubic"), ] augmenters_weather = [ iaa.FastSnowyLandscape(lightness_threshold=(100, 255), lightness_multiplier=(1.0, 4.0), name="FastSnowyLandscape"), iaa.Clouds(name="Clouds"), iaa.Fog(name="Fog"), iaa.CloudLayer(intensity_mean=(196, 255), intensity_freq_exponent=(-2.5, -2.0), intensity_coarse_scale=10, alpha_min=0, alpha_multiplier=(0.25, 0.75), alpha_size_px_max=(2, 8), alpha_freq_exponent=(-2.5, -2.0), sparsity=(0.8, 1.0), density_multiplier=(0.5, 1.0), name="CloudLayer"), iaa.Snowflakes(name="Snowflakes"), iaa.SnowflakesLayer(density=(0.005, 0.075), density_uniformity=(0.3, 0.9), flake_size=(0.2, 0.7), flake_size_uniformity=(0.4, 0.8), angle=(-30, 30), speed=(0.007, 0.03), blur_sigma_fraction=(0.0001, 0.001), name="SnowflakesLayer") ] augmenters = (augmenters_meta + augmenters_arithmetic + augmenters_blend + augmenters_blur + augmenters_color + augmenters_contrast + augmenters_convolutional + augmenters_edges + augmenters_flip + augmenters_geometric + augmenters_pooling + augmenters_segmentation + augmenters_size + augmenters_weather) if only_augmenters is not None: augmenters_reduced = [] for augmenter in augmenters: if any([ re.search(pattern, augmenter.name) for pattern in only_augmenters ]): augmenters_reduced.append(augmenter) augmenters = augmenters_reduced return augmenters
def next(self): if not self.is_init: self.reset() self.is_init = True """Returns the next batch of data.""" #print('in next', self.cur, self.labelcur) self.nbatch += 1 batch_size = self.batch_size c, h, w = self.data_shape batch_data = nd.empty((batch_size, c, h, w)) if self.provide_label is not None: batch_label = nd.empty(self.provide_label[0][1]) i = 0 try: while i < batch_size: label, s, bbox, landmark = self.next_sample() _data = self.imdecode(s) if _data.shape[0] != self.data_shape[1]: _data = mx.image.resize_short(_data, self.data_shape[1]) if self.rand_mirror: _rd = random.randint(0, 1) if _rd == 1: _data = mx.ndarray.flip(data=_data, axis=1) if self.blur: aug_blur = iaa.Sequential([ iaa.OneOf([ iaa.GaussianBlur(sigma=(0.5, 2.5)), iaa.AverageBlur(k=(2, 5)), iaa.MotionBlur(k=(5, 7)), iaa.BilateralBlur(d=(3, 4), sigma_color=(10, 250), sigma_space=(10, 250)), iaa.imgcorruptlike.DefocusBlur(severity=1), iaa.imgcorruptlike.GlassBlur(severity=1), iaa.imgcorruptlike.Pixelate(severity=(1, 3)), iaa.Pepper(0.01), iaa.AdditiveGaussianNoise(scale=(0, 0.1 * 255), per_channel=True), iaa.imgcorruptlike.SpeckleNoise(severity=1), iaa.imgcorruptlike.JpegCompression(severity=(1, 4)), ]) ]) _rd = random.randint(0, 1) if _rd == 1: _data = aug_blur(images=_data) if self.maxpooling: maxpool_aug = iaa.MaxPooling(2) _rd = random.randint(0, 1) if _rd == 1: _data = maxpool_aug(images=_data) if self.color_jittering > 0: if self.color_jittering > 1: _rd = random.randint(0, 1) if _rd == 1: _data = self.compress_aug(_data) #print('do color aug') _data = _data.astype('float32', copy=False) #print(_data.__class__) _data = self.color_aug(_data, 0.125) if self.nd_mean is not None: _data = _data.astype('float32', copy=False) _data -= self.nd_mean _data *= 0.0078125 if self.cutoff > 0: _rd = random.randint(0, 1) if _rd == 1: #print('do cutoff aug', self.cutoff) centerh = random.randint(0, _data.shape[0] - 1) centerw = random.randint(0, _data.shape[1] - 1) half = self.cutoff // 2 starth = max(0, centerh - half) endh = min(_data.shape[0], centerh + half) startw = max(0, centerw - half) endw = min(_data.shape[1], centerw + half) #print(starth, endh, startw, endw, _data.shape) _data[starth:endh, startw:endw, :] = 128 data = [_data] try: self.check_valid_image(data) except RuntimeError as e: logging.debug('Invalid image, skipping: %s', str(e)) continue #print('aa',data[0].shape) #data = self.augmentation_transform(data) #print('bb',data[0].shape) for datum in data: assert i < batch_size, 'Batch size must be multiples of augmenter output length' #print(datum.shape) batch_data[i][:] = self.postprocess_data(datum) batch_label[i][:] = label i += 1 except StopIteration: if i < batch_size: raise StopIteration return io.DataBatch([batch_data], [batch_label], batch_size - i)
def maxPooling(self, X, kernel_size): aug = iaa.MaxPooling(kernel_size, keep_size=False) return aug.augment_images(X)
transformed_image = transform(image=image) ## Edges elif augmentation == 'canny': transform = iaa.Canny(alpha=(0.0, 0.9)) transformed_image = transform(image=image) ## Pooling elif augmentation == 'average_pooling': transform = iaa.AveragePooling(5) transformed_image = transform(image=image) elif augmentation == 'max_pooling': transform = iaa.MaxPooling(5) transformed_image = transform(image=image) elif augmentation == 'min_pooling': transform = iaa.MinPooling(5) transformed_image = transform(image=image) elif augmentation == 'median_pooling': transform = iaa.MedianPooling(5) transformed_image = transform(image=image) ## Segmentation elif augmentation == 'superpixels': transform = iaa.Superpixels(p_replace=0.5, n_segments=512) transformed_image = transform(image=image)
def aug1(self, img): seq = iaa.Sequential([ iaa.MaxPooling(kernel_size=2) #=2 or 3 ]) img_au = seq(image=img) return img_au