def chapter_augmenters_blendalphasomecolors(): fn_start = "blend/blendalphasomecolors" image = imageio.imread( os.path.join(DOCS_IMAGES_BASE_PATH, "input_images", "1280px-Vincent_Van_Gogh_-_Wheatfield_with_Crows.jpg")) # ca. 15% of original size image = ia.imresize_single_image(image, (92, 192)) aug = iaa.BlendAlphaSomeColors(iaa.Grayscale(1.0)) run_and_save_augseq(fn_start + "_grayscale.jpg", aug, [image for _ in range(4 * 3)], cols=4, rows=3) aug = iaa.BlendAlphaSomeColors(iaa.TotalDropout(1.0)) run_and_save_augseq(fn_start + "_total_dropout.jpg", aug, [image for _ in range(4 * 3)], cols=4, rows=3) aug = iaa.BlendAlphaSomeColors(iaa.MultiplySaturation(0.5), iaa.MultiplySaturation(1.5)) run_and_save_augseq(fn_start + "_saturation.jpg", aug, [image for _ in range(4 * 3)], cols=4, rows=3) aug = iaa.BlendAlphaSomeColors(iaa.AveragePooling(7), alpha=[0.0, 1.0], smoothness=0.0) run_and_save_augseq(fn_start + "_pooling.jpg", aug, [image for _ in range(4 * 3)], cols=4, rows=3) aug = iaa.BlendAlphaSomeColors(iaa.AveragePooling(7), nb_bins=2, smoothness=0.0) run_and_save_augseq(fn_start + "_pooling_2_bins.jpg", aug, [image for _ in range(4 * 4)], cols=4, rows=4) aug = iaa.BlendAlphaSomeColors(iaa.AveragePooling(7), from_colorspace="BGR") run_and_save_augseq(fn_start + "_pooling_bgr.jpg", aug, [image[:, :, ::-1] for _ in range(4 * 2)], cols=4, rows=2, image_colorspace=iaa.CSPACE_BGR)
def main(): image = ia.quokka_square((128, 128)) images_aug = [] for mul in np.linspace(0.0, 2.0, 10): aug = iaa.MultiplyHueAndSaturation(mul) image_aug = aug.augment_image(image) images_aug.append(image_aug) for mul_hue in np.linspace(0.0, 5.0, 10): aug = iaa.MultiplyHueAndSaturation(mul_hue=mul_hue) image_aug = aug.augment_image(image) images_aug.append(image_aug) for mul_saturation in np.linspace(0.0, 5.0, 10): aug = iaa.MultiplyHueAndSaturation(mul_saturation=mul_saturation) image_aug = aug.augment_image(image) images_aug.append(image_aug) ia.imshow(ia.draw_grid(images_aug, rows=3)) images_aug = [] images_aug.extend(iaa.MultiplyHue().augment_images([image] * 10)) images_aug.extend(iaa.MultiplySaturation().augment_images([image] * 10)) ia.imshow(ia.draw_grid(images_aug, rows=2))
def chapter_augmenters_multiplysaturation(): fn_start = "color/multiplysaturation" aug = iaa.MultiplySaturation((0.5, 1.5)) run_and_save_augseq(fn_start + ".jpg", aug, [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2)
def __call__(self, *args, **kwargs) -> typing.Tuple[np.ndarray, typing.List[Polygon]]: if self.is_training: resize = iaa.Resize(size=dict(longer_side=self.long_sizes, width='keep-aspect-ratio')) rotate = iaa.Rotate(rotate=self.angles, fit_output=True) resize_height = iaa.Resize(size=dict(height=self.height_ratios, width='keep')) crop = iaa.CropToFixedSize(width=self.cropped_size[0], height=self.cropped_size[1]) fix_resize = iaa.Resize(size=self.output_size) # blur = iaa.GaussianBlur() # blur = iaa.Sometimes(p=self.blur_prob, # then_list=blur) brightness = iaa.MultiplyBrightness((0.5, 1.5)) brightness = iaa.Sometimes(self.color_jitter_prob, then_list=brightness) saturation = iaa.MultiplySaturation((0.5, 1.5)) saturation = iaa.Sometimes(self.color_jitter_prob, then_list=saturation) contrast = iaa.LinearContrast(0.5) contrast = iaa.Sometimes(self.color_jitter_prob, then_list=contrast) hue = iaa.MultiplyHue() hue = iaa.Sometimes(self.color_jitter_prob, then_list=hue) augs = [resize, rotate, resize_height, crop, fix_resize, brightness, saturation, contrast, hue] ia = iaa.Sequential(augs) else: fix_resize = iaa.Resize(size=self.output_size) ia = iaa.Sequential([fix_resize]) image = args[0] polygons = args[1] polygon_list = [] for i in range(polygons.shape[0]): polygon_list.append(Polygon(polygons[i].tolist())) polygons_on_image = PolygonsOnImage(polygon_list, shape=image.shape) image_aug, polygons_aug = ia(image=image, polygons=polygons_on_image) return image_aug, polygons_aug.polygons
def get_augmenter(): seq = iaa.Sequential([ iaa.Sometimes(0.05, iaa.GaussianBlur((0.0, 1.3))), iaa.Sometimes(0.05, iaa.AdditiveGaussianNoise(scale=(0.0, 0.05 * 255))), iaa.Sometimes(0.05, iaa.Dropout((0.0, 0.1))), iaa.Sometimes(0.10, iaa.Add((-0.05 * 255, 0.05 * 255), True)), iaa.Sometimes(0.20, iaa.Add((0.25, 2.5), True)), iaa.Sometimes(0.05, iaa.contrast.LinearContrast((0.5, 1.5))), iaa.Sometimes(0.05, iaa.MultiplySaturation((0.0, 1.0))), ]) return seq.augment_image
def __init__(self): self.transform = iaa.Sequential( [ iaa.Sometimes( 0.5, iaa.SomeOf((1, 2), [ iaa.Fliplr(1.0), iaa.Flipud(1.0), ])), iaa.OneOf([ iaa.Sometimes( 0.3, [ iaa.OneOf([ iaa.Multiply((0.7, 1.2)), iaa.MultiplyElementwise((0.7, 1.2)), ]), iaa.OneOf([ iaa.MultiplySaturation((5.0, 10.0)), # good iaa.MultiplyHue((1.5, 3.0)), iaa.LinearContrast((0.8, 2.0)), iaa.AllChannelsHistogramEqualization(), ]), ]), iaa.Sometimes(0.3, [ iaa.SomeOf((1, 2), [ iaa.pillike.EnhanceColor((1.1, 1.6)), iaa.pillike.EnhanceSharpness((0.7, 1.6)), iaa.pillike.Autocontrast(cutoff=(4, 8)), iaa.MultiplySaturation((1.2, 5.1)), ]) ]) ]), iaa.Sometimes(0.3, [ iaa.Dropout(p=(0.01, 0.09)), iaa.GaussianBlur((0.4, 1.5)), ]), ], random_order=True # apply the augmentations in random order )
def test_returns_correct_class(self): # this test is practically identical to # TestMultiplyToHueAndSaturation # .test_returns_correct_objects__mul_saturation aug = iaa.MultiplySaturation((0.9, 1.1)) assert isinstance(aug, iaa.WithHueAndSaturation) assert isinstance(aug.children, iaa.Sequential) assert len(aug.children) == 1 assert isinstance(aug.children[0], iaa.WithChannels) assert aug.children[0].channels == [1] assert len(aug.children[0].children) == 1 assert isinstance(aug.children[0].children[0], iaa.Multiply) assert isinstance(aug.children[0].children[0].mul, iap.Uniform) assert np.isclose(aug.children[0].children[0].mul.a.value, 0.9) assert np.isclose(aug.children[0].children[0].mul.b.value, 1.1)
def __init__(self, dataset: Dataset, cfg): self._dataset = dataset self.input_shape = cfg.AUGMENT.INPUT_SHAPE self.zoom_in = cfg.AUGMENT.ZOOM_IN self.min_scale = cfg.AUGMENT.MIN_SCALE self.max_scale = cfg.AUGMENT.MAX_SCALE self.max_try_times = cfg.AUGMENT.MAX_TRY_TIMES self.flip = cfg.AUGMENT.FLIP self.aspect_ratio = cfg.AUGMENT.ASPECT_RATIO self.translate_percent = cfg.AUGMENT.TRANSLATE_PRESENT self.rotate = cfg.AUGMENT.ROTATE self.shear = cfg.AUGMENT.SHEAR self.perspective_transform = cfg.AUGMENT.PERSPECTIVE_TRANSFORM self.brightness = cfg.AUGMENT.BRIGHTNESS self.hue = cfg.AUGMENT.HUE self.saturation = cfg.AUGMENT.SATURATION self.augment_background = cfg.AUGMENT.BACKGROUND if self.augment_background: self.backgrounds = [ os.path.join('../../data/background', item) for item in os.listdir('../../data/background') ] self.seq = iaa.Sequential([ iaa.Fliplr(self.flip), iaa.Affine(scale={ "x": self.aspect_ratio, "y": self.aspect_ratio }, translate_percent={ "x": self.translate_percent, "y": self.translate_percent }, rotate=self.rotate, shear=self.shear, order=[0, 1], cval=(0, 255)), iaa.PerspectiveTransform(scale=self.perspective_transform), iaa.MultiplyBrightness(self.brightness), iaa.MultiplySaturation(self.saturation), iaa.MultiplyHue(self.hue) ])
def _load_augmentation_aug_non_geometric(): return iaa.Sequential([ iaa.Sometimes(0.3, iaa.Multiply((0.5, 1.5), per_channel=0.5)), iaa.Sometimes(0.2, iaa.JpegCompression(compression=(70, 99))), iaa.Sometimes(0.2, iaa.GaussianBlur(sigma=(0, 3.0))), iaa.Sometimes(0.2, iaa.MotionBlur(k=15, angle=[-45, 45])), iaa.Sometimes(0.2, iaa.MultiplyHue((0.5, 1.5))), iaa.Sometimes(0.2, iaa.MultiplySaturation((0.5, 1.5))), iaa.Sometimes( 0.34, iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=True)), iaa.Sometimes(0.34, iaa.Grayscale(alpha=(0.0, 1.0))), iaa.Sometimes(0.2, iaa.ChangeColorTemperature((1100, 10000))), iaa.Sometimes(0.1, iaa.GammaContrast((0.5, 2.0))), iaa.Sometimes(0.2, iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6))), iaa.Sometimes(0.1, iaa.CLAHE()), iaa.Sometimes(0.1, iaa.HistogramEqualization()), iaa.Sometimes(0.2, iaa.LinearContrast((0.5, 2.0), per_channel=0.5)), iaa.Sometimes(0.1, iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0))) ])
desiredMultiple = 1 #PREVIEW N AMOUNT OF BOUNDING BOXES ON AUGMENTED IMAGES# viewNBoundingBoxes = 0 #SAVE AUGMENTED IMAGES WITH BOUNDING BOX TO FILEPATH# filepathSaveFolder = script_dir + "/augmented/" filepath_img = filepathSaveFolder + batchName + "%d.jpg" filepath_txt = filepathSaveFolder + batchName + "%d.txt" #DESIRED AUGMENTATION# seq = iaa.Sequential( [ #iaa.AddToHue((-255,255)), # change their color iaa.MultiplySaturation((0.1, 0.7)), #calm down color #iaa.ElasticTransformation(alpha=20, sigma=4), # water-like effect (smaller sigma = smaller "waves") #iaa.PiecewiseAffine(scale=(0.01,0.05)), #sometimes moves pieces of image around (RAM-heavy) #iaa.LogContrast((0.5,1.0),True), #overlay color #iaa.MotionBlur(20,(0,288),1,0), #motion blur for realism #iaa.BlendAlpha((0.0, 1.0), #iaa.MedianBlur(11), per_channel=True), #alpha-blending with median blur iaa.PerspectiveTransform(scale=(0.1, 0.1)), iaa.AdditiveGaussianNoise(scale=0.05 * 255, per_channel=True), #noise #iaa.CoarseDropout(p=0.1, size_percent=0.005), #blocks removed from image iaa.Affine( rotate=(-5, 5) ) #rotate #PROBLEM WITH BOUNDING BOXES MOSTLY CAUSED BY THIS ], random_order=True)
import random import colorsys import numpy as np import cv2 import imgaug as ia from imgaug import augmenters as iaa from imgaug.augmentables import Keypoint, KeypointsOnImage sometimes = lambda aug: iaa.Sometimes(0.5, aug) KPT_AUGS = [ iaa.LinearContrast((0.95, 1.05), per_channel=0.25), iaa.Add((-10, 10), per_channel=False), iaa.GammaContrast((0.95, 1.05)), iaa.GaussianBlur(sigma=(0.0, 0.6)), iaa.MultiplySaturation((0.95, 1.05)), iaa.AddToHueAndSaturation((-255, 255)), iaa.ChangeColorTemperature((1000, 20000)), iaa.AdditiveGaussianNoise(scale=(0, 0.0125 * 255)), iaa.flip.Flipud(0.5), 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": (-0.25, 0.25), "y": (-0.2, 0.2) }, # translate by -20 to +20 percent (per axis) rotate=(-30, 30), # rotate by -45 to +45 degrees
def da_policy(image, label): img_size = 224 #image = sample[0] policy = np.random.randint(4) #policy = 2 if policy == 0: p = np.random.random() if p <= 0.6: aug = iaa.TranslateX(px=(-60, 60), cval=128) image = aug(image=image) p = np.random.random() if p <= 0.8: aug = iaa.HistogramEqualization() image = aug(image=image) elif policy == 1: p = np.random.random() if p <= 0.2: aug = iaa.TranslateY(px=(int(-0.18 * img_size), int(0.18 * img_size)), cval=128) image = aug(image=image) p = np.random.random() if p <= 0.8: square_size = np.random.randint(48) aug = iaa.Cutout(nb_iterations=1, size=square_size / img_size, squared=True) image = aug(image=image) elif policy == 2: p = np.random.random() if p <= 1: aug = iaa.ShearY(shear=(int(-0.06 * img_size), int(0.06 * img_size)), order=1, cval=128) image = aug(image=image) p = np.random.random() if p <= 0.6: aug = iaa.TranslateX(px=(-60, 60), cval=128) image = aug(image=image) elif policy == 3: p = np.random.random() if p <= 0.6: aug = iaa.Rotate(rotate=(-30, 30), order=1, cval=128) image = aug(image=image) p = np.random.random() if p <= 1: aug = iaa.MultiplySaturation((0.54, 1.54)) image = aug(image=image) #Para EFFICIENTNET NO es necesario NORMALIZAR return (tf.cast(image, tf.float32), tf.cast(label, tf.int64))
gaussian_noise = iaa.AdditiveGaussianNoise(10, 20) noise_image = gaussian_noise.augment_image(img) flip_hr = iaa.Fliplr(p=1.0) flip_hr_image = flip_hr.augment_image(img) contrast = iaa.GammaContrast(gamma=2.0) contrast_image = contrast.augment_image(img) aug = iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)) sigmoid_image = aug.augment_image(img) aug = iaa.LogContrast(gain=(0.6, 1.4)) log_image = aug.augment_image(img) aug = iaa.MultiplySaturation((0.5, 1.5)) channel = aug.augment_image(img) aug = iaa.Rotate((-5, 5)) rotate1 = aug.augment_image(img) aug = iaa.ShearX((-20, 20)) shear = aug.augment_image(img) aug = iaa.PerspectiveTransform(scale=(0.01, 0.15)) perspec = aug.augment_image(img) cv2.imshow("3", perspec) cv2.waitKey(0) aaa rotate = iaa.Affine(rotate=(-50, 30))
elif augmentation == 'zoom_blur': transform = iaa.imgcorruptlike.ZoomBlur(severity=2) transformed_image = transform(image=image) ## Color elif augmentation == 'multiply_hue': transform = iaa.MultiplyHue((0.5, 1.5)) transformed_image = transform(image=image) elif augmentation == 'addto_hue': transform = iaa.AddToHue((-100, 100)) transformed_image = transform(image=image) elif augmentation == 'multiply_saturation': transform = iaa.MultiplySaturation((0.5, 1.5)) transformed_image = transform(image=image) elif augmentation == 'addto_saturation': transform = iaa.AddToSaturation((-100, 100)) transformed_image = transform(image=image) elif augmentation == 'saturate': transform = iaa.imgcorruptlike.Saturate(severity=5) transformed_image = transform(image=image) elif augmentation == 'remove_saturation': transform = iaa.RemoveSaturation() transformed_image = transform(image=image) elif augmentation == 'multiply_hue_and_saturation':
def __init__(self, data_root, image_shape=(256, 256), isTrain=True, silence=False): self.silence = silence self.data_root = data_root self.is_train = isTrain self.image_shape = (image_shape[0], image_shape[1]) if isTrain: fg_set_file_path = os.path.join(data_root, 'fg_train_set.txt') bg_set_file_path = os.path.join(data_root, 'bg_train_set.txt') else: fg_set_file_path = os.path.join(data_root, 'fg_val_set.txt') bg_set_file_path = os.path.join(data_root, 'bg_val_set.txt') fg_file = open(fg_set_file_path, 'r') bg_file = open(bg_set_file_path, 'r') self.fg_list = [] self.bg_list = [] self.alpha_list = [] for line in fg_file: line = line.strip() fg_path, alpha_path = line.split(' ') self.fg_list.append(fg_path) self.alpha_list.append(alpha_path) for line in bg_file: bg_path = line.strip() self.bg_list.append(bg_path) self.num_fg = len(self.fg_list) self.num_bg = len(self.bg_list) if not isTrain: assert self.num_fg == self.num_bg, "len(fg_list) must equal to len(bg_list) in eval mode. {}!={}".format( len(self.fg_list), len(self.bg_list)) sometimes = lambda aug: iaa.Sometimes(0.3, aug) # apply to fg, bg self.color_aug = iaa.Sequential([ iaa.MultiplyHueAndSaturation(mul=iap.TruncatedNormal( 1.0, 0.2, 0.5, 1.5)), # mean, std, low, high iaa.GammaContrast(gamma=iap.TruncatedNormal(1.0, 0.2, 0.5, 1.5)), iaa.AddToHue(value=iap.TruncatedNormal(0.0, 0.1 * 100, -0.2 * 255, 0.2 * 255)) ]) # apply to compose self.compose_shape_aug = iaa.Sequential([ iaa.Fliplr(0.5), # iaa.OneOf([ # iaa.Resize({"height":480, "width": "keep-aspect-ratio"}), # iaa.Resize({"height":640, "width": "keep-aspect-ratio"}), # # iaa.Resize({"height": 800, "width": "keep-aspect-ratio"}) # ]), # iaa.Resize(size=(512, 512)), iaa.OneOf([ # iaa.CropToFixedSize(width=256, height=256), iaa.CropToFixedSize(width=384, height=384), iaa.CropToFixedSize(width=480, height=480), iaa.CropToFixedSize(width=512, height=512), iaa.CropToFixedSize(width=640, height=640), iaa.CropToFixedSize(width=800, height=800) ]), sometimes( iaa.PadToFixedSize(width=512, height=512, pad_mode="constant", pad_cval=0)), # TODO: iaa.Resize({ "height": self.image_shape[0], "width": self.image_shape[1] }) ]) self.fg_simple_aug = iaa.Sequential([iaa.Fliplr(0.5)]) self.bg_aug = iaa.Sequential([ iaa.CropToFixedSize(width=self.image_shape[1], height=self.image_shape[0]), # iaa.OneOf([ # # iaa.CropToFixedSize(width=256, height=256), # iaa.CropToFixedSize(width=384, height=384), # iaa.CropToFixedSize(width=self.image_shape[1], height=self.image_shape[0]), # iaa.CropToFixedSize(width=480, height=480), # iaa.CropToFixedSize(width=512, height=512), # # iaa.CropToFixedSize(width=640, height=640), # ]), iaa.Resize({ "height": self.image_shape[0], "width": self.image_shape[1] }), iaa.GammaContrast(gamma=iap.TruncatedNormal(1.0, 0.3, 0.5, 1.5)), iaa.MultiplySaturation( mul=iap.TruncatedNormal(1.0, 0.3, 0.5, 1.5)), # iaa.GaussianBlur(sigma=(0.0, 1.5)), iaa.Fliplr(0.5), ]) self.scale_down = iaa.Sequential([ iaa.Resize(0.8), iaa.PadToFixedSize(width=self.image_shape[1], height=self.image_shape[0], pad_mode='constant', pad_cval=0) ])
transforms = iaa.Sequential( [ iaa.Sometimes(0.5, iaa.SomeOf((1, 2), [ iaa.Fliplr(1.0), iaa.Flipud(1.0), ])), iaa.OneOf([ iaa.Sometimes(0.4, [ iaa.OneOf([ iaa.Multiply((0.7, 1.1)), iaa.MultiplyElementwise((0.7, 1.1)), ]), iaa.OneOf([ iaa.MultiplySaturation((0.6, 1.5)), iaa.MultiplyHue((0.6, 1.1)), iaa.LinearContrast((0.8, 1.6)), iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)), ]), ]), iaa.Sometimes(0.5, [ iaa.SomeOf((1, 2), [ iaa.pillike.EnhanceColor((0.8, 1.2)), iaa.pillike.EnhanceSharpness((0.7, 1.6)), iaa.pillike.Autocontrast(cutoff=(2, 5)), ]) ]) ]), iaa.Sometimes(0.5, [ iaa.Dropout(p=(0.01, 0.05)),
def __init__(self): self.seq = iaa.Sequential( [ iaa.Fliplr(0.5), iaa.Sometimes(0.5, iaa.Crop(percent=(0, 0.1))), iaa.Sometimes(0.5, iaa.Affine( rotate=(-20, 20), # 旋转±20度 # shear=(-16, 16), # 剪切变换±16度,矩形变平行四边形 # order=[0, 1], # 使用最近邻插值 或 双线性插值 cval=0, # 填充值 mode=ia.ALL # 定义填充图像外区域的方法 )), # 使用0~3个方法进行图像增强 iaa.SomeOf((0, 3), [ iaa.Sometimes(0.8, iaa.OneOf([ iaa.GaussianBlur((0, 2.0)), # 高斯模糊 iaa.AverageBlur(k=(1, 5)), # 平均模糊,磨砂 ])), # 要么运动,要么美颜 iaa.Sometimes(0.8, iaa.OneOf([ iaa.MotionBlur(k=(3, 11)), # 运动模糊 iaa.BilateralBlur(d=(1, 5), sigma_color=(10, 250), sigma_space=(10, 250)), # 双边滤波,美颜 ])), # 模仿雪花 iaa.Sometimes(0.8, iaa.OneOf([ iaa.SaltAndPepper(p=(0., 0.03)), iaa.AdditiveGaussianNoise(loc=0, scale=(0., 0.05 * 255), per_channel=False) ])), # 对比度 iaa.Sometimes(0.8, iaa.LinearContrast((0.6, 1.4), per_channel=0.5)), # 锐化 iaa.Sometimes(0.8, iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5))), # 整体亮度 iaa.Sometimes(0.8, iaa.OneOf([ # 加性调整 iaa.AddToBrightness((-30, 30)), # 线性调整 iaa.MultiplyBrightness((0.5, 1.5)), # 加性 & 线性 iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)), ])), # 饱和度 iaa.Sometimes(0.8, iaa.OneOf([ iaa.AddToSaturation((-75, 75)), iaa.MultiplySaturation((0., 3.)), ])), # 色相 iaa.Sometimes(0.8, iaa.OneOf([ iaa.AddToHue((-255, 255)), iaa.MultiplyHue((-3.0, 3.0)), ])), # 云雾 # iaa.Sometimes(0.3, iaa.Clouds()), # 卡通化 # iaa.Sometimes(0.01, iaa.Cartoon()), ], random_order=True ) ], random_order=True )
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 data_augmentation(img, bounding_boxes, labels): """ Enhance the data with imgaug Largely inspired by https://imgaug.readthedocs.io/en/latest/source/examples_bounding_boxes.html :param img: single image :param bounding_boxes: the list of bounding boxes :return: """ bbs = BoundingBoxesOnImage([ BoundingBox( x1=bbox[0], y1=bbox[1], x2=bbox[2], y2=bbox[3], label=label) for bbox, label in zip(bounding_boxes, labels) ], shape=img.shape) seq = iaa.Sequential( [ # Blur each image with varying strength using # gaussian blur (sigma between 0 and 3.0), iaa.GaussianBlur((0, 3.0)), # 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.Sometimes(p=0.5, then_list=iaa.Multiply((0.8, 1.2), per_channel=0.2)), # Increase saturation iaa.Sometimes(p=0.5, then_list=iaa.MultiplySaturation(mul=(0.5, 1.5))), # Strengthen or weaken the contrast in each image. iaa.Sometimes( p=0.5, then_list=iaa.LinearContrast((0.75, 1.5)), ), iaa.HistogramEqualization(), # horizontal flips iaa.Fliplr(0.5), # random crops iaa.Crop(percent=(0, 0.1)), # Apply affine transformations to each image. # Scale/zoom them, translate/move them, rotate them and shear them. iaa.Sometimes( p=0.5, then_list=iaa.Affine(rotate=(-15, 15), ), ) ], random_order=True) seq_det = seq.to_deterministic() # Augment BBs and images. image_aug, bbs_aug = seq_det(image=img, bounding_boxes=bbs) bbs_aug = bbs_aug.clip_out_of_image() bboxes_aug = list() height, width, _ = image_aug.shape for i in range(len(bbs_aug.bounding_boxes)): bboxes_aug.append([ bbs_aug.bounding_boxes[i].label, bbs_aug.bounding_boxes[i].x1, bbs_aug.bounding_boxes[i].y1, bbs_aug.bounding_boxes[i].x2, bbs_aug.bounding_boxes[i].y2 ]) return image_aug, np.array(bboxes_aug, dtype=np.float32)