def chapter_augmenters_blendalphasimplexnoise(): fn_start = "blend/blendalphasimplexnoise" aug = iaa.SimplexNoiseAlpha(iaa.EdgeDetect(1.0)) run_and_save_augseq(fn_start + ".jpg", aug, [ia.quokka(size=(128, 128)) for _ in range(4 * 2)], cols=4, rows=2) aug = iaa.SimplexNoiseAlpha(iaa.EdgeDetect(1.0), upscale_method="nearest") run_and_save_augseq(fn_start + "_nearest.jpg", aug, [ia.quokka(size=(128, 128)) for _ in range(4 * 2)], cols=4, rows=2) aug = iaa.SimplexNoiseAlpha(iaa.EdgeDetect(1.0), upscale_method="linear") run_and_save_augseq(fn_start + "_linear.jpg", aug, [ia.quokka(size=(128, 128)) for _ in range(4 * 2)], cols=4, rows=2) aug = iaa.SimplexNoiseAlpha(iaa.EdgeDetect(1.0), sigmoid_thresh=iap.Normal(10.0, 5.0)) run_and_save_augseq(fn_start + "_sigmoid_thresh_normal.jpg", aug, [ia.quokka(size=(128, 128)) for _ in range(4 * 2)], cols=4, rows=2)
def chapter_alpha_masks_introduction(): # ----------------------------------------- # example introduction # ----------------------------------------- import imgaug as ia from imgaug import augmenters as iaa ia.seed(2) # Example batch of images. # The array has shape (8, 128, 128, 3) and dtype uint8. images = np.array([ia.quokka(size=(128, 128)) for _ in range(8)], dtype=np.uint8) seqs = [ iaa.Alpha((0.0, 1.0), first=iaa.MedianBlur(11), per_channel=True), iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=False), iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), second=iaa.ContrastNormalization((0.5, 2.0)), per_channel=0.5), iaa.FrequencyNoiseAlpha(first=iaa.Affine(rotate=(-10, 10), translate_px={ "x": (-4, 4), "y": (-4, 4) }), second=iaa.AddToHueAndSaturation((-40, 40)), per_channel=0.5), iaa.SimplexNoiseAlpha( first=iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), second=iaa.ContrastNormalization( (0.5, 2.0)), per_channel=True), second=iaa.FrequencyNoiseAlpha(exponent=(-2.5, -1.0), first=iaa.Affine(rotate=(-10, 10), translate_px={ "x": (-4, 4), "y": (-4, 4) }), second=iaa.AddToHueAndSaturation( (-40, 40)), per_channel=True), per_channel=True, aggregation_method="max", sigmoid=False) ] cells = [] for seq in seqs: images_aug = seq.augment_images(images) cells.extend(images_aug) # ------------ save("alpha", "introduction.jpg", grid(cells, cols=8, rows=5))
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)
def get_augmentation_sequence(): sometimes = lambda aug: iaa.Sometimes(0.5, aug) seq = iaa.Sequential([ sometimes(iaa.Fliplr(0.5)), iaa.Sometimes(0.1, iaa.Add((-70, 70))), 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 )), sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.01))), iaa.Sometimes( 0.1, iaa.SimplexNoiseAlpha(iaa.OneOf( [iaa.Add((150, 255)), iaa.Add((-100, 100))]), sigmoid_thresh=5)), iaa.Sometimes( 0.1, iaa.OneOf([ iaa.CoarseDropout((0.01, 0.15), size_percent=(0.02, 0.08)), iaa.CoarseSaltAndPepper(p=0.2, size_percent=0.01), iaa.CoarseSalt(p=0.2, size_percent=0.02) ])), iaa.Sometimes(0.25, slice_thickness_augmenter) ]) return seq
def augment_sequential(): return iaa.Sequential([ iaa.SomeOf( (0, 3), [ # 每次使用0~3个Augmenter来处理图片 iaa.DirectedEdgeDetect(alpha=(0.0, 0.3), direction=(0.0, 1.0)), # 边缘检测,只检测某些方向的 iaa.OneOf([ # 每次以下Augmenters中选择一个来变换 iaa.GaussianBlur((0, 1.0)), iaa.AverageBlur(k=(2, 3)), ]), iaa.Sharpen(alpha=(0, 0.5), lightness=(0.75, 1.0)), # 锐化 iaa.SimplexNoiseAlpha( iaa.OneOf([ iaa.EdgeDetect(alpha=(0.0, 0.5)), iaa.DirectedEdgeDetect(alpha=(0.0, 0.5), direction=(0.5, 1.0)), ])), iaa.AdditiveGaussianNoise( loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5), iaa.OneOf([ iaa.Dropout((0.01, 0.3), per_channel=0.5), # 随机丢弃像素 iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.1), per_channel=0.2), # 随机丢弃某位置某通道像素 ]), iaa.Add((-50, 50), per_channel=0.5), # 像素值成比例增加/减小(特指亮度) iaa.AddToHueAndSaturation((-50, 50)), # 增加色相、饱和度 iaa.LinearContrast((0.8, 1.2), per_channel=0.5), iaa.Grayscale(alpha=(0.0, 1.0)), ]) ])
def add_simplex_noise(self, prob=0.5, multiplicator=0.7): if self._sequential_augmentation is None: self._sequential_augmentation = iaa.Sequential() self._sequential_augmentation.add(iaa.Sometimes(prob, iaa.SimplexNoiseAlpha(iaa.Multiply(multiplicator), upscale_method='linear')))
def augmentation2(image, mask): sometimes = lambda aug: iaa.Sometimes(0.5, aug) seq = iaa.Sequential( [ sometimes( iaa.CropAndPad(percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255))), 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)), ]), 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 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), # 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), iaa.AddToHueAndSaturation( (-20, 20)), # change hue and saturation iaa.OneOf([ iaa.Multiply((0.5, 1.5), per_channel=0.5), ]), iaa.Grayscale(alpha=(0.0, 1.0)), ], random_order=True) ], random_order=True) image_heavy, mask_heavy = seq(images=image, segmentation_maps=mask) return image_heavy, mask_heavy
def generateAugSeq(): sometimes = lambda aug: iaa.Sometimes(0.5, aug) return iaa.Sequential([ sometimes( iaa.CropAndPad( percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255))), sometimes( iaa.Affine(scale={ "x": (0.8, 1.2), "y": (0.8, 1.2) }, translate_percent={ "x": (-0.2, 0.2), "y": (-0.2, 0.2) }, order=[0, 1], cval=(0, 255), mode=ia.ALL)), iaa.SomeOf((0, 5), [ sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), 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)), 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), iaa.CoarseDropout( (0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2), ]), iaa.Invert(0.05, per_channel=True), iaa.Add((-10, 10), per_channel=0.5), iaa.AddToHueAndSaturation((-20, 20)), 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), iaa.Grayscale(alpha=(0.0, 1.0)), ], random_order=True) ], random_order=True)
def method2(): ##simplex noise alpha ia.seed(1) seq = iaa.Sequential([ iaa.SimplexNoiseAlpha( first=iaa.Multiply(iaa.Choice([0.5, 1.5]), per_channel=False) ) ]) return seq
def logic(self, image): for param in self.augmentation_params: self.augmentation_data.append([ str(param.augmentation_value), iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect( 1.0)).to_deterministic().augment_image(image), param.detection_tag ])
def __init__(self): self.simplex_noise = iaa.Sometimes( .9, iaa.SimplexNoiseAlpha(first=iaa.Sometimes( .7, iaa.MedianBlur(k=iaa.Choice([0, 5]))), second=iaa.Sometimes( .7, iaa.Multiply(iaa.Choice([0.5, 1.5]), per_channel=False)), upscale_method="linear"))
def get_augmentations(): # Sometimes(0.5, ...) applies the given augmenter in 50% of all cases, # e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image. 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_. seq = iaa.Sequential([ # 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), [ 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, # 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), # 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.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
def __init__(self,with_mask=True): self.with_mask = with_mask self.seq = iaa.Sequential( [ iaa.SomeOf((0, 5), [ sometimes(iaa.Superpixels(p_replace=(0, 0.5), n_segments=(100, 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, # 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), # 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((-5, 5), per_channel=0.5), # change brightness of images 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.LinearContrast((0.5, 2.0)) ) ]), iaa.LinearContrast((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast iaa.Grayscale(alpha=(0.0, 1.0)) ], random_order=True ) ], random_order=True )
def get_augmentation_sequence(): sometimes = lambda aug: iaa.Sometimes(0.5, aug) slice_thickness_augmenter = iaa.Lambda( func_images=slice_thickness_func_images, func_keypoints=slice_thickness_func_keypoints) seq = iaa.Sequential([ sometimes(iaa.Fliplr(0.5)), iaa.Sometimes(0.1, iaa.Add((-70, 70))), 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 )), # sometimes(iaa.Multiply((0.5, 1.5))), # sometimes(iaa.ContrastNormalization((0.5, 2.0))), # sometimes(iaa.Affine( # translate_percent={"x": (-0.02, 0.02), "y": (-0.02, 0.02)}, # translate by -20 to +20 percent (per axis) # rotate=(-2, 2), # rotate by -45 to +45 degrees # shear=(-2, 2), # 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='constant' # use any of scikit-image's warping modes (see 2nd image from the top for examples) # )), # sometimes(iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05))), sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.01))), iaa.Sometimes( 0.1, iaa.SimplexNoiseAlpha(iaa.OneOf( [iaa.Add((150, 255)), iaa.Add((-100, 100))]), sigmoid_thresh=5)), iaa.Sometimes( 0.1, iaa.OneOf([ iaa.CoarseDropout((0.01, 0.15), size_percent=(0.02, 0.08)), iaa.CoarseSaltAndPepper(p=0.2, size_percent=0.01), iaa.CoarseSalt(p=0.2, size_percent=0.02) ])), iaa.Sometimes(0.25, slice_thickness_augmenter) ]) return seq
def augment_sequential(): return iaa.Sequential([ iaa.SomeOf((1, 3), [ iaa.Fliplr(0.5), iaa.DirectedEdgeDetect(alpha=(0.0, 0.3), direction=(0.0, 1.0)), iaa.OneOf([ iaa.GaussianBlur((0, 1.0)), iaa.AverageBlur(k=(2, 3)), ]), iaa.Sharpen(alpha=(0, 0.5), lightness=(0.75, 1.0)), iaa.SimplexNoiseAlpha(iaa.OneOf([ iaa.EdgeDetect(alpha=(0.0, 0.5)), iaa.DirectedEdgeDetect(alpha=(0.0, 0.5), direction=(0.5, 1.0)), ])), iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5), iaa.OneOf([ iaa.Dropout((0.01, 0.3), per_channel=0.5), iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.1), per_channel=0.2), ]), iaa.Add((-50, 50), per_channel=0.5), iaa.AddToHueAndSaturation((-50, 50)), iaa.LinearContrast((0.8, 1.2), per_channel=0.5), iaa.Grayscale(alpha=(0.0, 1.0)), iaa.ElasticTransformation(alpha=(0.5, 1.5), sigma=0.25), iaa.LinearContrast((0.5, 1.5), per_channel=0.5), ]) ])
def __init__(self, images, config, jitter=True, norm=None): self.generator = None self.images = [] # pairs of (img, mask) self.config = config self.jitter = jitter self.norm = norm self.images = copy.deepcopy(images) sometimes = lambda aug: iaa.Sometimes(0.5, aug) self.seq_color = iaa.Sequential( [ iaa.SomeOf((0, 3), [ iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.2)), iaa.Emboss(alpha=(0, 0.3), strength=(0, 2.0)), iaa.SimplexNoiseAlpha(iaa.OneOf([ iaa.EdgeDetect(alpha=(0.5, 1.0)), iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)), ])), 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), iaa.ContrastNormalization((0.5, 2.0), per_channel=0.5), iaa.Grayscale(alpha=(0.0, 1.0)) ], random_order=True ) ], random_order=True )
def do_augmentation(D): """ D : Nx(n+p+1)xHxWx3. Return N1x(n+p+1)xHxWx3 """ n_samples = D.shape[0] n_images_per_sample = D.shape[1] im_rows = D.shape[2] im_cols = D.shape[3] im_chnl = D.shape[4] E = D.reshape(n_samples * n_images_per_sample, im_rows, im_cols, im_chnl) sometimes = lambda aug: iaa.Sometimes(0.5, aug) # Very basic if True: seq = iaa.Sequential([ sometimes(iaa.Crop(px=( 0, 50 ))), # 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 sometimes( iaa.Affine(scale={ "x": (0.8, 1.2), "y": (0.8, 1.2) }, translate_percent={ "x": (-0.2, 0.2), "y": (-0.2, 0.2) }, rotate=(-25, 25), shear=(-8, 8))) ]) seq_vbasic = seq # Sometimes(0.5, ...) applies the given augmenter in 50% of all cases, # e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image. # Typical if True: seq = iaa.Sequential( [ iaa.Fliplr(0.5), # horizontal flips iaa.Crop(percent=(0, 0.1)), # random crops # Small gaussian blur with random sigma between 0 and 0.5. # But we only blur about 50% of all images. iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 0.5))), # Strengthen or weaken the contrast in each image. iaa.ContrastNormalization((0.75, 1.5)), # Add gaussian noise. # For 50% of all images, we sample the noise once per pixel. # For the other 50% of all images, we sample the noise per pixel AND # channel. This can change the color (not only brightness) of the # pixels. iaa.AdditiveGaussianNoise( loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5), # Make some images brighter and some darker. # In 20% of all cases, we sample the multiplier once per channel, # which can end up changing the color of the images. iaa.Multiply((0.8, 1.2), per_channel=0.2), # Apply affine transformations to each image. # Scale/zoom them, translate/move them, rotate them and shear them. iaa.Affine(scale={ "x": (0.8, 1.2), "y": (0.8, 1.2) }, translate_percent={ "x": (-0.2, 0.2), "y": (-0.2, 0.2) }, rotate=(-25, 25), shear=(-8, 8)) ], random_order=True) # apply augmenters in random order # seq = sometimes( seq ) seq_typical = seq # Heavy if True: # 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_. seq = iaa.Sequential( [ # apply the following augmenters to most images iaa.Fliplr(0.2), # horizontally flip 20% of all images iaa.Flipud(0.2), # vertically flip 20% of all images # crop images by -5% to 10% of their height/width sometimes( iaa.CropAndPad(percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255))), 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=(-45, 45), # rotate by -45 to +45 degrees shear=(-16, 16), # 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, # 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), # 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.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 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 sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1))) ], random_order=True) ], random_order=True) seq_heavy = seq print 'Add data' L = [E] print 'seq_vbasic' L.append(seq_vbasic.augment_images(E)) print 'seq_typical' L.append(seq_typical.augment_images(E)) print 'seq_typical' L.append(seq_typical.augment_images(E)) print 'seq_heavy' L.append(seq_heavy.augment_images(E)) G = [ l.reshape(n_samples, n_images_per_sample, im_rows, im_cols, im_chnl) for l in L ] G = np.concatenate(G) print 'Input.shape ', D.shape, '\tOutput.shape ', G.shape return G # for j in range(n_times): # images_aug = seq.augment_images(E) # # L.append( images_aug.reshape( n_samples, n_images_per_sample, im_rows,im_cols,im_chnl ) ) # L.append( images_aug ) # code.interact( local=locals() ) return L
def test_dtype_preservation(): reseed() size = (4, 16, 16, 3) images = [ np.random.uniform(0, 255, size).astype(np.uint8), np.random.uniform(0, 65535, size).astype(np.uint16), np.random.uniform(0, 4294967295, size).astype(np.uint32), np.random.uniform(-128, 127, size).astype(np.int16), np.random.uniform(-32768, 32767, size).astype(np.int32), np.random.uniform(0.0, 1.0, size).astype(np.float32), np.random.uniform(-1000.0, 1000.0, size).astype(np.float16), np.random.uniform(-1000.0, 1000.0, size).astype(np.float32), np.random.uniform(-1000.0, 1000.0, size).astype(np.float64) ] default_dtypes = set([arr.dtype for arr in images]) # Some dtypes are here removed per augmenter, because the respective # augmenter does not support them. This test currently only checks whether # dtypes are preserved from in- to output for all dtypes that are supported # per augmenter. # dtypes are here removed via list comprehension instead of # `default_dtypes - set([dtype])`, because the latter one simply never # removed the dtype(s) for some reason def _not_dts(dts): return [dt for dt in default_dtypes if dt not in dts] augs = [ (iaa.Add((-5, 5), name="Add"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.AddElementwise((-5, 5), name="AddElementwise"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.AdditiveGaussianNoise(0.01 * 255, name="AdditiveGaussianNoise"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.Multiply((0.95, 1.05), name="Multiply"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.Dropout(0.01, name="Dropout"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.Invert(0.01, per_channel=True, name="Invert"), default_dtypes), (iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"), _not_dts([np.float16])), (iaa.AverageBlur((3, 5), name="AverageBlur"), _not_dts([np.uint32, np.int32, np.float16])), (iaa.MedianBlur((3, 5), name="MedianBlur"), _not_dts([np.uint32, np.int32, np.float16, np.float64])), (iaa.BilateralBlur((3, 5), name="BilateralBlur"), _not_dts([ np.uint16, np.uint32, np.int16, np.int32, np.float16, np.float64 ])), (iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"), _not_dts([np.uint32, np.int32, np.float16, np.uint32])), (iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"), _not_dts([np.uint32, np.int32, np.float16, np.uint32])), (iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"), _not_dts([np.uint32, np.int32, np.float16, np.uint32])), (iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0, name="DirectedEdgeDetect"), _not_dts([np.uint32, np.int32, np.float16, np.uint32])), (iaa.Fliplr(0.5, name="Fliplr"), default_dtypes), (iaa.Flipud(0.5, name="Flipud"), default_dtypes), (iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"), _not_dts([np.uint32, np.int32])), (iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"), _not_dts([np.uint32, np.int32])), (iaa.Affine(rotate=(-20, 20), name="Affine-rotate"), _not_dts([np.uint32, np.int32])), (iaa.Affine(shear=(-20, 20), name="Affine-shear"), _not_dts([np.uint32, np.int32])), (iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"), _not_dts([np.uint32, np.int32])), (iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"), default_dtypes), (iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2), name="ElasticTransformation"), _not_dts([np.float16])), (iaa.Sequential([iaa.Identity(), iaa.Identity()], name="SequentialNoop"), default_dtypes), (iaa.SomeOf(1, [iaa.Identity(), iaa.Identity()], name="SomeOfNoop"), default_dtypes), (iaa.OneOf([iaa.Identity(), iaa.Identity()], name="OneOfNoop"), default_dtypes), (iaa.Sometimes(0.5, iaa.Identity(), name="SometimesNoop"), default_dtypes), (iaa.Sequential([iaa.Add( (-5, 5)), iaa.AddElementwise((-5, 5))], name="Sequential"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.SomeOf(1, [iaa.Add( (-5, 5)), iaa.AddElementwise((-5, 5))], name="SomeOf"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.OneOf([iaa.Add( (-5, 5)), iaa.AddElementwise((-5, 5))], name="OneOf"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.Identity(name="Identity"), default_dtypes), (iaa.Alpha((0.0, 0.1), iaa.Identity(), name="AlphaIdentity"), default_dtypes), (iaa.AlphaElementwise( (0.0, 0.1), iaa.Identity(), name="AlphaElementwiseIdentity"), default_dtypes), (iaa.SimplexNoiseAlpha(iaa.Identity(), name="SimplexNoiseAlphaIdentity"), default_dtypes), (iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Identity(), name="SimplexNoiseAlphaIdentity"), default_dtypes), (iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10), name="SimplexNoiseAlpha"), _not_dts([np.uint32, np.int32, np.float64])), (iaa.Superpixels(p_replace=0.01, n_segments=64), _not_dts([np.float16, np.float32, np.float64])), (iaa.Resize({ "height": 4, "width": 4 }, name="Resize"), _not_dts([ np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64 ])), (iaa.CropAndPad(px=(-10, 10), name="CropAndPad"), _not_dts([ np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64 ])), (iaa.Pad(px=(0, 10), name="Pad"), _not_dts([ np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64 ])), (iaa.Crop(px=(0, 10), name="Crop"), _not_dts([ np.uint16, np.uint32, np.int16, np.int32, np.float32, np.float16, np.float64 ])) ] for (aug, allowed_dtypes) in augs: for images_i in images: if images_i.dtype in allowed_dtypes: images_aug = aug.augment_images(images_i) assert images_aug.dtype == images_i.dtype
def test_unusual_channel_numbers(): reseed() images = [(0, create_random_images((4, 16, 16))), (1, create_random_images((4, 16, 16, 1))), (2, create_random_images((4, 16, 16, 2))), (4, create_random_images((4, 16, 16, 4))), (5, create_random_images((4, 16, 16, 5))), (10, create_random_images((4, 16, 16, 10))), (20, create_random_images((4, 16, 16, 20)))] augs = [ iaa.Add((-5, 5), name="Add"), iaa.AddElementwise((-5, 5), name="AddElementwise"), iaa.AdditiveGaussianNoise(0.01 * 255, name="AdditiveGaussianNoise"), iaa.Multiply((0.95, 1.05), name="Multiply"), iaa.Dropout(0.01, name="Dropout"), iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"), iaa.Invert(0.01, per_channel=True, name="Invert"), iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"), iaa.AverageBlur((3, 5), name="AverageBlur"), iaa.MedianBlur((3, 5), name="MedianBlur"), iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"), iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"), iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"), iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0, name="DirectedEdgeDetect"), iaa.Fliplr(0.5, name="Fliplr"), iaa.Flipud(0.5, name="Flipud"), iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"), iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"), iaa.Affine(rotate=(-20, 20), name="Affine-rotate"), iaa.Affine(shear=(-20, 20), name="Affine-shear"), iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"), iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"), iaa.PerspectiveTransform(scale=(0.01, 0.10), name="PerspectiveTransform"), iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2), name="ElasticTransformation"), iaa.Sequential([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]), iaa.SomeOf(1, [iaa.Add( (-5, 5)), iaa.AddElementwise((-5, 5))]), iaa.OneOf([iaa.Add((-5, 5)), iaa.AddElementwise((-5, 5))]), iaa.Sometimes(0.5, iaa.Add((-5, 5)), name="Sometimes"), iaa.Identity(name="Noop"), iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"), iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"), iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"), iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10), name="SimplexNoiseAlpha"), iaa.Superpixels(p_replace=0.01, n_segments=64), iaa.Resize({ "height": 4, "width": 4 }, name="Resize"), iaa.CropAndPad(px=(-10, 10), name="CropAndPad"), iaa.Pad(px=(0, 10), name="Pad"), iaa.Crop(px=(0, 10), name="Crop") ] for aug in augs: for (nb_channels, images_c) in images: if aug.name != "Resize": images_aug = aug.augment_images(images_c) assert images_aug.shape == images_c.shape image_aug = aug.augment_image(images_c[0]) assert image_aug.shape == images_c[0].shape else: images_aug = aug.augment_images(images_c) image_aug = aug.augment_image(images_c[0]) if images_c.ndim == 3: assert images_aug.shape == (4, 4, 4) assert image_aug.shape == (4, 4) else: assert images_aug.shape == (4, 4, 4, images_c.shape[3]) assert image_aug.shape == (4, 4, images_c.shape[3])
def test_keypoint_augmentation(): reseed() keypoints = [] for y in sm.xrange(40 // 5): for x in sm.xrange(60 // 5): keypoints.append(ia.Keypoint(y=y * 5, x=x * 5)) keypoints_oi = ia.KeypointsOnImage(keypoints, shape=(40, 60, 3)) keypoints_oi_empty = ia.KeypointsOnImage([], shape=(40, 60, 3)) augs = [ iaa.Add((-5, 5), name="Add"), iaa.AddElementwise((-5, 5), name="AddElementwise"), iaa.AdditiveGaussianNoise(0.01 * 255, name="AdditiveGaussianNoise"), iaa.Multiply((0.95, 1.05), name="Multiply"), iaa.Dropout(0.01, name="Dropout"), iaa.CoarseDropout(0.01, size_px=6, name="CoarseDropout"), iaa.Invert(0.01, per_channel=True, name="Invert"), iaa.GaussianBlur(sigma=(0.95, 1.05), name="GaussianBlur"), iaa.AverageBlur((3, 5), name="AverageBlur"), iaa.MedianBlur((3, 5), name="MedianBlur"), iaa.Sharpen((0.0, 0.1), lightness=(1.0, 1.2), name="Sharpen"), iaa.Emboss(alpha=(0.0, 0.1), strength=(0.5, 1.5), name="Emboss"), iaa.EdgeDetect(alpha=(0.0, 0.1), name="EdgeDetect"), iaa.DirectedEdgeDetect(alpha=(0.0, 0.1), direction=0, name="DirectedEdgeDetect"), iaa.Fliplr(0.5, name="Fliplr"), iaa.Flipud(0.5, name="Flipud"), iaa.Affine(translate_px=(-5, 5), name="Affine-translate-px"), iaa.Affine(translate_percent=(-0.05, 0.05), name="Affine-translate-percent"), iaa.Affine(rotate=(-20, 20), name="Affine-rotate"), iaa.Affine(shear=(-20, 20), name="Affine-shear"), iaa.Affine(scale=(0.9, 1.1), name="Affine-scale"), iaa.PiecewiseAffine(scale=(0.001, 0.005), name="PiecewiseAffine"), iaa.ElasticTransformation(alpha=(0.1, 0.2), sigma=(0.1, 0.2), name="ElasticTransformation"), iaa.Alpha((0.0, 0.1), iaa.Add(10), name="Alpha"), iaa.AlphaElementwise((0.0, 0.1), iaa.Add(10), name="AlphaElementwise"), iaa.SimplexNoiseAlpha(iaa.Add(10), name="SimplexNoiseAlpha"), iaa.FrequencyNoiseAlpha(exponent=(-2, 2), first=iaa.Add(10), name="SimplexNoiseAlpha"), iaa.Superpixels(p_replace=0.01, n_segments=64), iaa.Resize(0.5, name="Resize"), iaa.CropAndPad(px=(-10, 10), name="CropAndPad"), iaa.Pad(px=(0, 10), name="Pad"), iaa.Crop(px=(0, 10), name="Crop") ] for aug in augs: dss = [] for i in sm.xrange(10): aug_det = aug.to_deterministic() kp_fully_empty_aug = aug_det.augment_keypoints([]) assert kp_fully_empty_aug == [] kp_first_empty_aug = aug_det.augment_keypoints(keypoints_oi_empty) assert len(kp_first_empty_aug.keypoints) == 0 kp_image = keypoints_oi.to_keypoint_image(size=5) kp_image_aug = aug_det.augment_image(kp_image) kp_image_aug_rev = ia.KeypointsOnImage.from_keypoint_image( kp_image_aug, if_not_found_coords={ "x": -9999, "y": -9999 }, nb_channels=1) kp_aug = aug_det.augment_keypoints([keypoints_oi])[0] ds = [] assert len(kp_image_aug_rev.keypoints) == len(kp_aug.keypoints), ( "Lost keypoints for '%s' (%d vs expected %d)" % (aug.name, len( kp_aug.keypoints), len(kp_image_aug_rev.keypoints))) gen = zip(kp_aug.keypoints, kp_image_aug_rev.keypoints) for kp_pred, kp_pred_img in gen: kp_pred_lost = (kp_pred.x == -9999 and kp_pred.y == -9999) kp_pred_img_lost = (kp_pred_img.x == -9999 and kp_pred_img.y == -9999) if not kp_pred_lost and not kp_pred_img_lost: d = np.sqrt((kp_pred.x - kp_pred_img.x)**2 + (kp_pred.y - kp_pred_img.y)**2) ds.append(d) dss.extend(ds) if len(ds) == 0: print("[INFO] No valid keypoints found for '%s' " "in test_keypoint_augmentation()" % (str(aug), )) assert np.average(dss) < 5.0, \ "Average distance too high (%.2f, with ds: %s)" \ % (np.average(dss), str(dss))
def black_and_white_aug(): alpha_seconds = iaa.OneOf([ iaa.Affine(rotate=(-3, 3)), iaa.Affine(translate_percent={ "x": (0.95, 1.05), "y": (0.95, 1.05) }), iaa.Affine(scale={ "x": (0.95, 1.05), "y": (0.95, 1.05) }), iaa.Affine(shear=(-2, 2)), iaa.CoarseDropout(p=0.1, size_percent=(0.08, 0.02)), ]) first_set = iaa.OneOf([ iaa.Multiply(iap.Choice([0.5, 1.5]), per_channel=True), iaa.EdgeDetect((0.1, 1)), ]) second_set = iaa.OneOf([ iaa.AddToHueAndSaturation((-40, 40)), iaa.ContrastNormalization((0.5, 2.0), per_channel=True) ]) color_aug = iaa.Sequential( [ # Original Image Domain ================================================== # Geometric Rigid iaa.Fliplr(0.5), iaa.OneOf([ iaa.Noop(), iaa.Affine(rotate=90), iaa.Affine(rotate=180), iaa.Affine(rotate=270), ]), iaa.OneOf([ iaa.Noop(), iaa.Crop(percent=(0, 0.1)), # Random Crops iaa.PerspectiveTransform(scale=(0.05, 0.15)), ]), # Affine sometimes( iaa.PiecewiseAffine( scale=(0.01, 0.07), nb_rows=(3, 6), nb_cols=(3, 6))), fewtimes( iaa.Affine(scale={ "x": (0.8, 1.2), "y": (0.8, 1.2) }, translate_percent={ "x": (-0.2, 0.2), "y": (-0.2, 0.2) }, rotate=(-45, 45), shear=(-16, 16), order=[0, 1], cval=0)), # Transformations outside Image domain ============================================== # COLOR, CONTRAST, HUE iaa.Invert(0.5, name='Invert'), fewtimes(iaa.Add((-10, 10), per_channel=0.5, name='Add')), fewtimes( iaa.AddToHueAndSaturation( (-40, 40), per_channel=0.5, name='AddToHueAndSaturation')), # Intensity / contrast fewtimes( iaa.ContrastNormalization( (0.8, 1.1), name='ContrastNormalization')), # Add to hue and saturation fewtimes( iaa.Multiply( (0.5, 1.5), per_channel=0.5, name='HueAndSaturation')), # Noise =========================================================================== fewtimes( iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.15 * 255), per_channel=0.5, name='AdditiveGaussianNoise')), fewtimes( iaa.Alpha(factor=(0.5, 1), first=iaa.ContrastNormalization( (0.5, 2.0), per_channel=True), second=alpha_seconds, per_channel=0.5, name='AlphaNoise'), ), fewtimes( iaa.SimplexNoiseAlpha(first=first_set, second=second_set, per_channel=0.5, aggregation_method="max", sigmoid=False, upscale_method='cubic', size_px_max=(2, 12), name='SimplexNoiseAlpha'), ), fewtimes( iaa.FrequencyNoiseAlpha(first=first_set, second=second_set, per_channel=0.5, aggregation_method="max", sigmoid=False, upscale_method='cubic', size_px_max=(2, 12), name='FrequencyNoiseAlpha'), ), # Blur fewtimes( iaa.OneOf([ iaa.GaussianBlur((0, 3.0)), iaa.AverageBlur(k=(2, 7)), iaa.MedianBlur(k=(3, 11)), iaa.BilateralBlur(d=(3, 10), sigma_color=(10, 250), sigma_space=(10, 250)) ], name='Blur')), # Regularization ====================================================================== unlikely( iaa.OneOf([ iaa.Dropout((0.01, 0.1), per_channel=0.5, name='Dropout'), iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.5, name='CoarseDropout'), ], )), ], random_order=True) seq = iaa.Sequential( [ iaa.Sequential( [ # Texture rarely( iaa.Superpixels(p_replace=(0.3, 1.0), n_segments=(500, 1000), name='Superpixels')), rarely( iaa.Sharpen(alpha=(0, 0.5), lightness=(0.75, 1.0), name='Sharpen')), rarely( iaa.Emboss( alpha=(0, 1.0), strength=(0, 1.0), name='Emboss')), rarely( iaa.OneOf([ iaa.EdgeDetect(alpha=(0, 0.5)), iaa.DirectedEdgeDetect(alpha=(0, 0.5), direction=(0.0, 1.0)), ], name='EdgeDetect')), rarely( iaa.ElasticTransformation( alpha=(0.5, 3.5), sigma=0.25, name='ElasticTransformation')), ], random_order=True), color_aug, iaa.Grayscale(alpha=1.0, name='Grayscale') ], random_order=False) def activator_masks(images, augmenter, parents, default): if 'Unnamed' not in augmenter.name: return False else: return default hooks_masks = ia.HooksImages(activator=activator_masks) return seq, hooks_masks
# 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, 1.0)), # blur images with a sigma between 0 and 3.0 iaa.AverageBlur(k=(3, 5)), # blur image using local means with kernel sizes between 2 and 7 iaa.MedianBlur(k=(3, 5)), # blur image using local medians with kernel sizes between 2 and 7 ]), iaa.Sharpen(alpha=(0, 1.0), lightness=(0.9, 1.1)), # sharpen images iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images # 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, 0.01*255), per_channel=0.5), # add gaussian noise to images iaa.OneOf([ iaa.Dropout((0.01, 0.05), per_channel=0.5), # randomly remove up to 10% of the pixels iaa.CoarseDropout((0.01, 0.03), size_percent=(0.01, 0.02), per_channel=0.2), ]), iaa.Invert(0.01, per_channel=True), # invert color channels iaa.Add((-2, 2), per_channel=0.5), # change brightness of images (by -10 to 10 of original value) iaa.AddToHueAndSaturation((-1, 1)), # 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((0.9, 1.1), per_channel=0.5), iaa.FrequencyNoiseAlpha( exponent=(-1, 0),
def draw_single_sequential_images(): ia.seed(44) #image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128)) image = ia.quokka_square(size=(128, 128)) sometimes = lambda aug: iaa.Sometimes(0.5, aug) seq = 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 # crop images by -5% to 10% of their height/width sometimes(iaa.CropAndPad( percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255) )), 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=(-45, 45), # rotate by -45 to +45 degrees shear=(-16, 16), # 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, # 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), # 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.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 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 sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1))) ], random_order=True ) ], random_order=True ) grid = seq.draw_grid(image, cols=8, rows=8) misc.imsave("examples_grid.jpg", grid)
def __init__(self): sometimes = lambda aug: iaa.Sometimes(0.2, aug) self.aug = iaa.Sequential( [ sometimes(iaa.Affine( scale={"x": (0.9, 1.1), "y": (0.9, 1.1)}, # scale images to 80-120% of their size, individually per axis translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)}, # 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, 1.0)), # blur images with a sigma between 0 and 3.0 iaa.AverageBlur(k=(3, 5)), # blur image using local means with kernel sizes between 2 and 7 iaa.MedianBlur(k=(3, 5)), # blur image using local medians with kernel sizes between 2 and 7 ]), iaa.Sharpen(alpha=(0, 1.0), lightness=(0.9, 1.1)), # sharpen images iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images # 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, 0.01 * 255), per_channel=0.5), # add gaussian noise to images iaa.OneOf([ iaa.Dropout((0.01, 0.05), per_channel=0.5), # randomly remove up to 10% of the pixels iaa.CoarseDropout((0.01, 0.03), size_percent=(0.01, 0.02), per_channel=0.2), ]), iaa.Invert(0.01, per_channel=True), # invert color channels iaa.Add((-2, 2), per_channel=0.5), # change brightness of images (by -10 to 10 of original value) iaa.AddToHueAndSaturation((-1, 1)), # 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((0.9, 1.1), per_channel=0.5), iaa.FrequencyNoiseAlpha( exponent=(-1, 0), first=iaa.Multiply((0.9, 1.1), per_channel=True), second=iaa.ContrastNormalization( (0.9, 1.1)) ) ]), 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 sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1))) ], random_order=True ) ], random_order=True )
def draw_per_augmenter_images(): print("[draw_per_augmenter_images] Loading image...") #image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128)) image = ia.quokka_square(size=(128, 128)) keypoints = [ia.Keypoint(x=34, y=15), ia.Keypoint(x=85, y=13), ia.Keypoint(x=63, y=73)] # left ear, right ear, mouth keypoints = [ia.KeypointsOnImage(keypoints, shape=image.shape)] print("[draw_per_augmenter_images] Initializing...") rows_augmenters = [ (0, "Noop", [("", iaa.Noop()) for _ in sm.xrange(5)]), (0, "Crop\n(top, right,\nbottom, left)", [(str(vals), iaa.Crop(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]), (0, "Pad\n(top, right,\nbottom, left)", [(str(vals), iaa.Pad(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]), (0, "Fliplr", [(str(p), iaa.Fliplr(p)) for p in [0, 0, 1, 1, 1]]), (0, "Flipud", [(str(p), iaa.Flipud(p)) for p in [0, 0, 1, 1, 1]]), (0, "Superpixels\np_replace=1", [("n_segments=%d" % (n_segments,), iaa.Superpixels(p_replace=1.0, n_segments=n_segments)) for n_segments in [25, 50, 75, 100, 125]]), (0, "Superpixels\nn_segments=100", [("p_replace=%.2f" % (p_replace,), iaa.Superpixels(p_replace=p_replace, n_segments=100)) for p_replace in [0, 0.25, 0.5, 0.75, 1.0]]), (0, "Invert", [("p=%d" % (p,), iaa.Invert(p=p)) for p in [0, 0, 1, 1, 1]]), (0, "Invert\n(per_channel)", [("p=%.2f" % (p,), iaa.Invert(p=p, per_channel=True)) for p in [0.5, 0.5, 0.5, 0.5, 0.5]]), (0, "Add", [("value=%d" % (val,), iaa.Add(val)) for val in [-45, -25, 0, 25, 45]]), (0, "Add\n(per channel)", [("value=(%d, %d)" % (vals[0], vals[1],), iaa.Add(vals, per_channel=True)) for vals in [(-55, -35), (-35, -15), (-10, 10), (15, 35), (35, 55)]]), (0, "AddToHueAndSaturation", [("value=%d" % (val,), iaa.AddToHueAndSaturation(val)) for val in [-45, -25, 0, 25, 45]]), (0, "Multiply", [("value=%.2f" % (val,), iaa.Multiply(val)) for val in [0.25, 0.5, 1.0, 1.25, 1.5]]), (1, "Multiply\n(per channel)", [("value=(%.2f, %.2f)" % (vals[0], vals[1],), iaa.Multiply(vals, per_channel=True)) for vals in [(0.15, 0.35), (0.4, 0.6), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]), (0, "GaussianBlur", [("sigma=%.2f" % (sigma,), iaa.GaussianBlur(sigma=sigma)) for sigma in [0.25, 0.50, 1.0, 2.0, 4.0]]), (0, "AverageBlur", [("k=%d" % (k,), iaa.AverageBlur(k=k)) for k in [1, 3, 5, 7, 9]]), (0, "MedianBlur", [("k=%d" % (k,), iaa.MedianBlur(k=k)) for k in [1, 3, 5, 7, 9]]), (0, "BilateralBlur\nsigma_color=250,\nsigma_space=250", [("d=%d" % (d,), iaa.BilateralBlur(d=d, sigma_color=250, sigma_space=250)) for d in [1, 3, 5, 7, 9]]), (0, "Sharpen\n(alpha=1)", [("lightness=%.2f" % (lightness,), iaa.Sharpen(alpha=1, lightness=lightness)) for lightness in [0, 0.5, 1.0, 1.5, 2.0]]), (0, "Emboss\n(alpha=1)", [("strength=%.2f" % (strength,), iaa.Emboss(alpha=1, strength=strength)) for strength in [0, 0.5, 1.0, 1.5, 2.0]]), (0, "EdgeDetect", [("alpha=%.2f" % (alpha,), iaa.EdgeDetect(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]), (0, "DirectedEdgeDetect\n(alpha=1)", [("direction=%.2f" % (direction,), iaa.DirectedEdgeDetect(alpha=1, direction=direction)) for direction in [0.0, 1*(360/5)/360, 2*(360/5)/360, 3*(360/5)/360, 4*(360/5)/360]]), (0, "AdditiveGaussianNoise", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]), (0, "AdditiveGaussianNoise\n(per channel)", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255, per_channel=True)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]), (0, "Dropout", [("p=%.2f" % (p,), iaa.Dropout(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]), (0, "Dropout\n(per channel)", [("p=%.2f" % (p,), iaa.Dropout(p=p, per_channel=True)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]), (3, "CoarseDropout\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]), (0, "CoarseDropout\n(p=0.2, per channel)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, per_channel=True, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]), (0, "SaltAndPepper", [("p=%.2f" % (p,), iaa.SaltAndPepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]), (0, "Salt", [("p=%.2f" % (p,), iaa.Salt(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]), (0, "Pepper", [("p=%.2f" % (p,), iaa.Pepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]), (0, "CoarseSaltAndPepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSaltAndPepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]), (0, "CoarseSalt\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSalt(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]), (0, "CoarsePepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarsePepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]), (0, "ContrastNormalization", [("alpha=%.1f" % (alpha,), iaa.ContrastNormalization(alpha=alpha)) for alpha in [0.5, 0.75, 1.0, 1.25, 1.50]]), (0, "ContrastNormalization\n(per channel)", [("alpha=(%.2f, %.2f)" % (alphas[0], alphas[1],), iaa.ContrastNormalization(alpha=alphas, per_channel=True)) for alphas in [(0.4, 0.6), (0.65, 0.85), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]), (0, "Grayscale", [("alpha=%.1f" % (alpha,), iaa.Grayscale(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]), (6, "PerspectiveTransform", [("scale=%.3f" % (scale,), iaa.PerspectiveTransform(scale=scale)) for scale in [0.025, 0.05, 0.075, 0.10, 0.125]]), (0, "PiecewiseAffine", [("scale=%.3f" % (scale,), iaa.PiecewiseAffine(scale=scale)) for scale in [0.015, 0.03, 0.045, 0.06, 0.075]]), (0, "Affine: Scale", [("%.1fx" % (scale,), iaa.Affine(scale=scale)) for scale in [0.1, 0.5, 1.0, 1.5, 1.9]]), (0, "Affine: Translate", [("x=%d y=%d" % (x, y), iaa.Affine(translate_px={"x": x, "y": y})) for x, y in [(-32, -16), (-16, -32), (-16, -8), (16, 8), (16, 32)]]), (0, "Affine: Rotate", [("%d deg" % (rotate,), iaa.Affine(rotate=rotate)) for rotate in [-90, -45, 0, 45, 90]]), (0, "Affine: Shear", [("%d deg" % (shear,), iaa.Affine(shear=shear)) for shear in [-45, -25, 0, 25, 45]]), (0, "Affine: Modes", [(mode, iaa.Affine(translate_px=-32, mode=mode)) for mode in ["constant", "edge", "symmetric", "reflect", "wrap"]]), (0, "Affine: cval", [("%d" % (int(cval*255),), iaa.Affine(translate_px=-32, cval=int(cval*255), mode="constant")) for cval in [0.0, 0.25, 0.5, 0.75, 1.0]]), ( 2, "Affine: all", [ ( "", iaa.Affine( scale={"x": (0.5, 1.5), "y": (0.5, 1.5)}, translate_px={"x": (-32, 32), "y": (-32, 32)}, rotate=(-45, 45), shear=(-32, 32), mode=ia.ALL, cval=(0.0, 1.0) ) ) for _ in sm.xrange(5) ] ), (1, "ElasticTransformation\n(sigma=0.2)", [("alpha=%.1f" % (alpha,), iaa.ElasticTransformation(alpha=alpha, sigma=0.2)) for alpha in [0.1, 0.5, 1.0, 3.0, 9.0]]), (0, "Alpha\nwith EdgeDetect(1.0)", [("factor=%.1f" % (factor,), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0))) for factor in [0.0, 0.25, 0.5, 0.75, 1.0]]), (4, "Alpha\nwith EdgeDetect(1.0)\n(per channel)", [("factor=(%.2f, %.2f)" % (factor[0], factor[1]), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0), per_channel=0.5)) for factor in [(0.0, 0.2), (0.15, 0.35), (0.4, 0.6), (0.65, 0.85), (0.8, 1.0)]]), (15, "SimplexNoiseAlpha\nwith EdgeDetect(1.0)", [("", iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0))) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]), (9, "FrequencyNoiseAlpha\nwith EdgeDetect(1.0)", [("exponent=%.1f" % (exponent,), iaa.FrequencyNoiseAlpha(exponent=exponent, first=iaa.EdgeDetect(1.0), size_px_max=16, upscale_method="linear", sigmoid=False)) for exponent in [-4, -2, 0, 2, 4]]) ] print("[draw_per_augmenter_images] Augmenting...") rows = [] for (row_seed, row_name, augmenters) in rows_augmenters: ia.seed(row_seed) #for img_title, augmenter in augmenters: # #aug.reseed(1000) # pass row_images = [] row_keypoints = [] row_titles = [] for img_title, augmenter in augmenters: aug_det = augmenter.to_deterministic() row_images.append(aug_det.augment_image(image)) row_keypoints.append(aug_det.augment_keypoints(keypoints)[0]) row_titles.append(img_title) rows.append((row_name, row_images, row_keypoints, row_titles)) # matplotlib drawin routine """ print("[draw_per_augmenter_images] Plotting...") width = 8 height = int(1.5 * len(rows_augmenters)) fig = plt.figure(figsize=(width, height)) grid_rows = len(rows) grid_cols = 1 + 5 gs = gridspec.GridSpec(grid_rows, grid_cols, width_ratios=[2, 1, 1, 1, 1, 1]) axes = [] for i in sm.xrange(grid_rows): axes.append([plt.subplot(gs[i, col_idx]) for col_idx in sm.xrange(grid_cols)]) fig.tight_layout() #fig.subplots_adjust(bottom=0.2 / grid_rows, hspace=0.22) #fig.subplots_adjust(wspace=0.005, hspace=0.425, bottom=0.02) fig.subplots_adjust(wspace=0.005, hspace=0.005, bottom=0.02) for row_idx, (row_name, row_images, row_keypoints, row_titles) in enumerate(rows): axes_row = axes[row_idx] for col_idx in sm.xrange(grid_cols): ax = axes_row[col_idx] ax.cla() ax.axis("off") ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) if col_idx == 0: ax.text(0, 0.5, row_name, color="black") else: cell_image = row_images[col_idx-1] cell_keypoints = row_keypoints[col_idx-1] cell_image_kp = cell_keypoints.draw_on_image(cell_image, size=5) ax.imshow(cell_image_kp) x = 0 y = 145 #ax.text(x, y, row_titles[col_idx-1], color="black", backgroundcolor="white", fontsize=6) ax.text(x, y, row_titles[col_idx-1], color="black", fontsize=7) fig.savefig("examples.jpg", bbox_inches="tight") #plt.show() """ # simpler and faster drawing routine """ output_image = ExamplesImage(128, 128, 128+64, 32) for (row_name, row_images, row_keypoints, row_titles) in rows: row_images_kps = [] for image, keypoints in zip(row_images, row_keypoints): row_images_kps.append(keypoints.draw_on_image(image, size=5)) output_image.add_row(row_name, row_images_kps, row_titles) misc.imsave("examples.jpg", output_image.draw()) """ # routine to draw many single files seen = defaultdict(lambda: 0) markups = [] for (row_name, row_images, row_keypoints, row_titles) in rows: output_image = ExamplesImage(128, 128, 128+64, 32) row_images_kps = [] for image, keypoints in zip(row_images, row_keypoints): row_images_kps.append(keypoints.draw_on_image(image, size=5)) output_image.add_row(row_name, row_images_kps, row_titles) if "\n" in row_name: row_name_clean = row_name[0:row_name.find("\n")+1] else: row_name_clean = row_name row_name_clean = re.sub(r"[^a-z0-9]+", "_", row_name_clean.lower()) row_name_clean = row_name_clean.strip("_") if seen[row_name_clean] > 0: row_name_clean = "%s_%d" % (row_name_clean, seen[row_name_clean] + 1) fp = os.path.join(IMAGES_DIR, "examples_%s.jpg" % (row_name_clean,)) #misc.imsave(fp, output_image.draw()) save(fp, output_image.draw()) seen[row_name_clean] += 1 markup_descr = row_name.replace('"', '') \ .replace("\n", " ") \ .replace("(", "") \ .replace(")", "") markup = '![%s](%s?raw=true "%s")' % (markup_descr, fp, markup_descr) markups.append(markup) for markup in markups: print(markup)
def main(): parser = argparse.ArgumentParser(description="Check augmenters visually.") parser.add_argument( "--only", default=None, help= "If this is set, then only the results of an augmenter with this name will be shown. " "Optionally, comma-separated list.", required=False) args = parser.parse_args() images = [ ia.quokka_square(size=(128, 128)), ia.imresize_single_image(data.astronaut(), (128, 128)) ] keypoints = [ ia.KeypointsOnImage([ ia.Keypoint(x=50, y=40), ia.Keypoint(x=70, y=38), ia.Keypoint(x=62, y=52) ], shape=images[0].shape), ia.KeypointsOnImage([ ia.Keypoint(x=55, y=32), ia.Keypoint(x=42, y=95), ia.Keypoint(x=75, y=89) ], shape=images[1].shape) ] bounding_boxes = [ ia.BoundingBoxesOnImage([ ia.BoundingBox(x1=10, y1=10, x2=20, y2=20), ia.BoundingBox(x1=40, y1=50, x2=70, y2=60) ], shape=images[0].shape), ia.BoundingBoxesOnImage([ ia.BoundingBox(x1=10, y1=10, x2=20, y2=20), ia.BoundingBox(x1=40, y1=50, x2=70, y2=60) ], shape=images[1].shape) ] augmenters = [ iaa.Sequential([ iaa.CoarseDropout(p=0.5, size_percent=0.05), iaa.AdditiveGaussianNoise(scale=0.1 * 255), iaa.Crop(percent=0.1) ], name="Sequential"), iaa.SomeOf(2, children=[ iaa.CoarseDropout(p=0.5, size_percent=0.05), iaa.AdditiveGaussianNoise(scale=0.1 * 255), iaa.Crop(percent=0.1) ], name="SomeOf"), iaa.OneOf(children=[ iaa.CoarseDropout(p=0.5, size_percent=0.05), iaa.AdditiveGaussianNoise(scale=0.1 * 255), iaa.Crop(percent=0.1) ], name="OneOf"), iaa.Sometimes(0.5, iaa.AdditiveGaussianNoise(scale=0.1 * 255), name="Sometimes"), iaa.WithColorspace("HSV", children=[iaa.Add(20)], name="WithColorspace"), iaa.WithChannels([0], children=[iaa.Add(20)], name="WithChannels"), iaa.AddToHueAndSaturation((-20, 20), per_channel=True, name="AddToHueAndSaturation"), iaa.Noop(name="Noop"), iaa.Resize({ "width": 64, "height": 64 }, name="Resize"), iaa.CropAndPad(px=(-8, 8), name="CropAndPad-px"), iaa.Pad(px=(0, 8), name="Pad-px"), iaa.Crop(px=(0, 8), name="Crop-px"), iaa.Crop(percent=(0, 0.1), name="Crop-percent"), iaa.Fliplr(0.5, name="Fliplr"), iaa.Flipud(0.5, name="Flipud"), iaa.Superpixels(p_replace=0.75, n_segments=50, name="Superpixels"), iaa.Grayscale(0.5, name="Grayscale0.5"), iaa.Grayscale(1.0, name="Grayscale1.0"), iaa.GaussianBlur((0, 3.0), name="GaussianBlur"), iaa.AverageBlur(k=(3, 11), name="AverageBlur"), iaa.MedianBlur(k=(3, 11), name="MedianBlur"), iaa.BilateralBlur(d=10, name="BilateralBlur"), iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0, 2.0), name="Sharpen"), iaa.Emboss(alpha=(0.1, 1.0), strength=(0, 2.0), name="Emboss"), iaa.EdgeDetect(alpha=(0.1, 1.0), name="EdgeDetect"), iaa.DirectedEdgeDetect(alpha=(0.1, 1.0), direction=(0, 1.0), name="DirectedEdgeDetect"), iaa.Add((-50, 50), name="Add"), iaa.Add((-50, 50), per_channel=True, name="AddPerChannel"), iaa.AddElementwise((-50, 50), name="AddElementwise"), iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.1 * 255), name="AdditiveGaussianNoise"), iaa.Multiply((0.5, 1.5), name="Multiply"), iaa.Multiply((0.5, 1.5), per_channel=True, name="MultiplyPerChannel"), iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"), iaa.Dropout((0.0, 0.1), name="Dropout"), iaa.CoarseDropout(p=0.05, size_percent=(0.05, 0.5), name="CoarseDropout"), iaa.Invert(p=0.5, name="Invert"), iaa.Invert(p=0.5, per_channel=True, name="InvertPerChannel"), iaa.ContrastNormalization(alpha=(0.5, 2.0), name="ContrastNormalization"), iaa.SaltAndPepper(p=0.05, name="SaltAndPepper"), iaa.Salt(p=0.05, name="Salt"), iaa.Pepper(p=0.05, name="Pepper"), iaa.CoarseSaltAndPepper(p=0.05, size_percent=(0.01, 0.1), name="CoarseSaltAndPepper"), iaa.CoarseSalt(p=0.05, size_percent=(0.01, 0.1), name="CoarseSalt"), iaa.CoarsePepper(p=0.05, size_percent=(0.01, 0.1), name="CoarsePepper"), iaa.Affine(scale={ "x": (0.8, 1.2), "y": (0.8, 1.2) }, translate_px={ "x": (-16, 16), "y": (-16, 16) }, rotate=(-45, 45), shear=(-16, 16), order=ia.ALL, cval=(0, 255), mode=ia.ALL, name="Affine"), iaa.PiecewiseAffine(scale=0.03, nb_rows=(2, 6), nb_cols=(2, 6), name="PiecewiseAffine"), iaa.PerspectiveTransform(scale=0.1, name="PerspectiveTransform"), iaa.ElasticTransformation(alpha=(0.5, 8.0), sigma=1.0, name="ElasticTransformation"), iaa.Alpha(factor=(0.0, 1.0), first=iaa.Add(100), second=iaa.Dropout(0.5), per_channel=False, name="Alpha"), iaa.Alpha(factor=(0.0, 1.0), first=iaa.Add(100), second=iaa.Dropout(0.5), per_channel=True, name="AlphaPerChannel"), iaa.Alpha(factor=(0.0, 1.0), first=iaa.Affine(rotate=(-45, 45)), per_channel=True, name="AlphaAffine"), iaa.AlphaElementwise(factor=(0.0, 1.0), first=iaa.Add(50), second=iaa.ContrastNormalization(2.0), per_channel=False, name="AlphaElementwise"), iaa.AlphaElementwise(factor=(0.0, 1.0), first=iaa.Add(50), second=iaa.ContrastNormalization(2.0), per_channel=True, name="AlphaElementwisePerChannel"), iaa.AlphaElementwise(factor=(0.0, 1.0), first=iaa.Affine(rotate=(-45, 45)), per_channel=True, name="AlphaElementwiseAffine"), iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=False, name="SimplexNoiseAlpha"), iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0), per_channel=False, name="FrequencyNoiseAlpha") ] augmenters.append( iaa.Sequential([iaa.Sometimes(0.2, aug.copy()) for aug in augmenters], name="Sequential")) augmenters.append( iaa.Sometimes(0.5, [aug.copy() for aug in augmenters], name="Sometimes")) for augmenter in augmenters: if args.only is None or augmenter.name in [ v.strip() for v in args.only.split(",") ]: print("Augmenter: %s" % (augmenter.name, )) grid = [] for image, kps, bbs in zip(images, keypoints, bounding_boxes): aug_det = augmenter.to_deterministic() imgs_aug = aug_det.augment_images( np.tile(image[np.newaxis, ...], (16, 1, 1, 1))) kps_aug = aug_det.augment_keypoints([kps] * 16) bbs_aug = aug_det.augment_bounding_boxes([bbs] * 16) imgs_aug_drawn = [ kps_aug_one.draw_on_image(img_aug) for img_aug, kps_aug_one in zip(imgs_aug, kps_aug) ] imgs_aug_drawn = [ bbs_aug_one.draw_on_image(img_aug) for img_aug, bbs_aug_one in zip(imgs_aug_drawn, bbs_aug) ] grid.append(np.hstack(imgs_aug_drawn)) ia.imshow(np.vstack(grid))
def data_aug(data_path): ''' augment data increase data number generate 10 extra pictures from 1 picture This function defines 13 different augment methods Everytime would choose 2 randomly and use the combination of these 2 methods to process all the images under the input data_path the processed data would still be under the original data directory ''' list = list_all_files(data_path)#os.listdir(data_path) for i in range(0,len(list)): #path = os.path.join(data_path,list[i]) path = list[i] #if os.path.isfile(path): try: img = cv2.imread(path) print("read path succeed: ",path) #print("image shape is: ", img.shape) except: print("Image read error. Please check the path again!") else: #11 different kinds of pre-processing operators # q1 = iaa.Alpha((0.0, 1.0),first=iaa.MedianBlur(9),per_channel=True) #alpha noise q2 = iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(0.5),per_channel=False) #noise in the frequency domain q3 = iaa.FrequencyNoiseAlpha(first=iaa.Affine(rotate=(-10, 10),translate_px={"x": (-4, 4), "y": (-4, 4)}),second=iaa.AddToHueAndSaturation((-40, 40)),per_channel=0.5) #set 5% of all the pixels black q4 = iaa.Dropout(p=0.05, per_channel=False, name=None, deterministic=False, random_state=None) #adjust contrast to make the image darker q5 = iaa.ContrastNormalization(alpha=1.5, per_channel=False, name=None, deterministic=False, random_state=None) #adjust contrast to make the image brighter q6 = iaa.ContrastNormalization(alpha=0.5, per_channel=False, name=None, deterministic=False, random_state=None) #16 pixels left q7 = iaa.Affine(translate_px={"x": -16}) #sharpen q8 = iaa.Sharpen(alpha=0.15, lightness=1, name=None, deterministic=False, random_state=None) #emboss, like sharpen q9 = iaa.Emboss(alpha=1, strength=1, name=None, deterministic=False, random_state=None) #fliplr, upside down q10 = iaa.Fliplr(1.0) #gaussian blur q11 = iaa.GaussianBlur(3.0) #scale y axis randomly x0.8-1.2 q12 = iaa.Affine(scale={"y": (0.8, 1.2)}) #scale x axis randomly x0.8-1.2 q13 = iaa.Affine(scale={"x": (0.8, 1.2)}) #randomly combine 2 of all the operations q = iaa.SomeOf(2,[q1,q2,q3,q4,q5,q6,q7,q8,q9,q10,q11,q12,q13]) #save_path1 = os.path.dirname(path) + "/aug1_" + path.split('/')[-1].split('.')[0] + ".jpg" #print("save_path1 is : ", save_path1) #save pre-processed images for i in range(10): #augment each image by 10 randomly chosen methods img_aug = q.augment_images([img]) print("img_aug type is:", type(img_aug)) #generate save path save_path = os.path.dirname(path) + "/aug"+str(i)+"_" + path.split('/')[-1].split('.')[0] + ".jpg" #save images cv2.imwrite(save_path,img_aug[0])
def Augmentation(input_image): with tf.Graph().as_default(): gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)) with sess.as_default(): pnet, rnet, onet = detect_face.create_mtcnn(sess, './npy') sometimes = lambda aug: iaa.Sometimes(0.5, aug) aug_name = input_image.split("/")[-1].split(".")[0] minsize = 35 # minimum size of face threshold = [0.6, 0.7, 0.7] # three steps's threshold factor = 0.709 # scale factor margin = 44 image_size = 200 nb_batches = 16 aug_faces = [] batches = [] seq = iaa.Sequential( [ iaa.Fliplr(0.5), sometimes( iaa.CropAndPad( percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255))), sometimes( iaa.Affine(scale={ "x": (0.8, 1.0), "y": (0.8, 1.0) }, translate_percent={ "x": (-0.2, 0.2), "y": (0, 0.2) }, rotate=(-10, 10), shear=(-16, 16), order=[0, 1], cval=(0, 255))), iaa.SomeOf( (0, 4), [ 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)), # sharpen images iaa.Emboss(alpha=(0, 1.0), strength=(0, 1.0)), # emboss images iaa.SimplexNoiseAlpha( iaa.OneOf([ iaa.EdgeDetect(alpha=(0.2, 0.5)), iaa.DirectedEdgeDetect(alpha=(0.2, 0.5), direction=(0.0, 1.0)), ])), iaa.AdditiveGaussianNoise( loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5), iaa.Dropout((0.01, 0.1), per_channel=0.5), iaa.Add((-10, 10), per_channel=0.5), iaa.AddToHueAndSaturation((-20, 20)), iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5), iaa.Grayscale(alpha=(0.0, 1.0)), sometimes( iaa.ElasticTransformation(alpha=(0.5, 2), sigma=0.25)), sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.03))), sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1))) ], random_order=True) ], random_order=True) img = misc.imread(input_image) if img.ndim < 2: print("Unable !") elif img.ndim == 2: img = facenet.to_rgb(img) img = img[:, :, 0:3] batches.append(np.array([img for _ in range(nb_batches)], dtype=np.uint8)) aug_images = seq.augment_images(batches[0]) for aug_img in aug_images: bounding_boxes, _ = detect_face.detect_face(aug_img, minsize, pnet, rnet, onet, threshold, factor) nrof_faces = bounding_boxes.shape[0] if nrof_faces > 0: det = bounding_boxes[:, 0:4] img_size = np.asarray(img.shape)[0:2] if nrof_faces > 1: bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1]) img_center = img_size / 2 offsets = np.vstack([ (det[:, 0] + det[:, 2]) / 2 - img_center[1], (det[:, 1] + det[:, 3]) / 2 - img_center[0] ]) offset_dist_squared = np.sum(np.power(offsets, 2.0), 0) index = np.argmax(bounding_box_size - offset_dist_squared * 2.0) det = det[index, :] det = np.squeeze(det) bb_temp = np.zeros(4, dtype=np.int32) bb_temp[0] = det[0] bb_temp[1] = det[1] bb_temp[2] = det[2] bb_temp[3] = det[3] cropped_temp = aug_img[bb_temp[1]:bb_temp[3], bb_temp[0]:bb_temp[2], :] scaled_temp = misc.imresize(cropped_temp, (image_size, image_size), interp='bilinear') aug_faces.append(scaled_temp) return aug_faces
def example_very_complex_augmentation_pipeline(): print("Example: Very Complex Augmentation Pipeline") import numpy as np import imgaug as ia import imgaug.augmenters as iaa # random example images images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) # Sometimes(0.5, ...) applies the given augmenter in 50% of all cases, # e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image. 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_. seq = 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 # crop images by -5% to 10% of their height/width sometimes( iaa.CropAndPad( percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255))), 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=(-45, 45), # rotate by -45 to +45 degrees shear=(-16, 16), # 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, # 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), # 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.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.LinearContrast((0.5, 2.0))) ]), iaa.LinearContrast( (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 sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1))) ], random_order=True) ], random_order=True) images_aug = seq(images=images) # ----- # Make sure that the example really does something assert not np.array_equal(images, images_aug)
def get_optimistic_img_aug(): texture = iaa.OneOf([ iaa.Superpixels(p_replace=(0.1, 0.3), n_segments=(500, 1000), interpolation="cubic", name='Superpixels'), iaa.Sharpen(alpha=(0, 1.0), lightness=(0.5, 1.0), name='Sharpen'), iaa.Emboss(alpha=(0, 1.0), strength=(0.1, 0.3), name='Emboss'), iaa.OneOf([ iaa.EdgeDetect(alpha=(0, 0.4)), iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0)), ], name='EdgeDetect'), iaa.ElasticTransformation(alpha=(0.5, 1.0), sigma=0.2, name='ElasticTransformation'), ]) blur = iaa.OneOf([ iaa.GaussianBlur((1, 5.0), name='GaussianBlur'), iaa.AverageBlur(k=(2, 15), name='AverageBlur'), iaa.MedianBlur(k=(3, 15), name='MedianBlur'), iaa.BilateralBlur(d=(3, 15), sigma_color=(10, 250), sigma_space=(10, 250), name='BilaBlur'), ]) affine = iaa.OneOf([ iaa.Affine(rotate=(-3, 3)), iaa.Affine(translate_percent={ "x": (0.95, 1.05), "y": (0.95, 1.05) }), iaa.Affine(scale={ "x": (0.95, 1.05), "y": (0.95, 1.05) }), iaa.Affine(shear=(-2, 2)), ]) factors = iaa.OneOf([ iaa.Multiply(iap.Choice([0.75, 1.25]), per_channel=False), iaa.EdgeDetect(1.0), ]) seq = iaa.Sequential( [ # Size and shape ================================================== iaa.Sequential([ iaa.Fliplr(0.5), iaa.OneOf([ iaa.Noop(), iaa.Affine(rotate=90), iaa.Affine(rotate=180), iaa.Affine(rotate=270), ]), half_times( iaa.SomeOf( (1, 2), [ iaa.Crop(percent=(0.1, 0.4)), # Random Crops iaa.PerspectiveTransform(scale=(0.10, 0.175)), iaa.PiecewiseAffine(scale=(0.01, 0.06), nb_rows=(3, 6), nb_cols=(3, 6)), ])), ]), # Texture ================================================== sometimes( iaa.SomeOf((1, 2), [ texture, iaa.Alpha((0.0, 1.0), first=texture, per_channel=False) ], random_order=True, name='Texture')), half_times( iaa.SomeOf((1, 2), [ blur, iaa.Alpha((0.0, 1.0), first=blur, per_channel=False), iaa.Alpha(factor=(0.2, 0.8), first=iaa.Sequential([ affine, blur, ]), per_channel=False), ], random_order=True, name='Blur')), # Noise ================================================== sometimes( iaa.SomeOf( (1, 2), [ # Just noise iaa.AdditiveGaussianNoise( loc=0, scale=(0.0, 0.15 * 255), per_channel=False, name='AdditiveGaussianNoise'), iaa.SaltAndPepper( 0.05, per_channel=False, name='SaltAndPepper'), # Regularization iaa.Dropout( (0.01, 0.1), per_channel=False, name='Dropout'), iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=False, name='CoarseDropout'), iaa.Alpha( factor=(0.2, 0.8), first=texture, second=iaa.CoarseDropout( p=0.1, size_percent=(0.02, 0.05)), per_channel=False, ), # Perlin style noise iaa.SimplexNoiseAlpha(first=factors, per_channel=False, aggregation_method="max", sigmoid=False, upscale_method='cubic', size_px_max=(2, 12), name='SimplexNoiseAlpha'), iaa.FrequencyNoiseAlpha(first=factors, per_channel=False, aggregation_method="max", sigmoid=False, upscale_method='cubic', size_px_max=(2, 12), name='FrequencyNoiseAlpha'), ], random_order=True, name='Noise')), ], random_order=False) def activator_masks(images, augmenter, parents, default): if 'Unnamed' not in augmenter.name: return False else: return default hooks_masks = ia.HooksImages(activator=activator_masks) return seq, hooks_masks