def brightness(image_np, save_dir, input_filename, is_groud_true, out_count=1): """ Change the brightness of images: MultiplyAndAddToBrightness :param image_np: 'images' should be either a 4D numpy array of shape (N, height, width, channels) :param save_dir: the directory for saving images :param input_filename: File base name (e.g basename.tif) :param is_groud_true: if ground truth, just copy the image :return: """ file_basename = os.path.basename(input_filename) basename = os.path.splitext(file_basename)[0] ext = os.path.splitext(file_basename)[1] for idx in range(out_count): save_path = os.path.join(save_dir, basename + '_bright' + str(idx) + ext) if is_groud_true is True: # just copy the groud true images_b = image_np else: brightness = iaa.MultiplyAndAddToBrightness( mul=(0.5, 1.5), add=(-30, 30)) # a random value between the range images_b = brightness.augment_image(image_np) io.imsave(save_path, images_b) return True
def __init__(self): self.aug = iaa.Sequential([ # iaa.Sometimes(0.25, iaa.GammaContrast(gamma=(0, 1.75))), # iaa.Sometimes(0.25, iaa.GaussianBlur(sigma=(0, 1.3))), # iaa.Sometimes(0.25, iaa.pillike.Autocontrast(cutoff=(0, 15.0))), # iaa.Grayscale(alpha=(0.0, 1.0)), # iaa.Sometimes(0.15, iaa.MotionBlur(k=5, angle=[-45, 45])), iaa.Sometimes( 0.35, iaa.OneOf([ iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)), iaa.GammaContrast(gamma=(0, 1.75)), iaa.pillike.Autocontrast(cutoff=(0, 15.0)), iaa.Sometimes(0.25, iaa.GaussianBlur(sigma=(0, 1.3))) ])), # iaa.Fliplr(0.5), # iaa.Sometimes(0.35, # iaa.OneOf([iaa.Dropout(p=(0, 0.1)), # iaa.Dropout2d(p=0.5), # iaa.CoarseDropout(0.1, size_percent=0.5), # iaa.SaltAndPepper(0.1), # ])), # iaa.Sometimes(0.15, # iaa.OneOf([ # iaa.Clouds(), # iaa.Fog(), # iaa.Snowflakes(flake_size=(0.1, 0.4), speed=(0.01, 0.05)), # iaa.Rain(speed=(0.1, 0.3)) # ])), # iaa.Sometimes(0.5, iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30))) ])
def __init__(self): self.aug = iaa.Sequential([ iaa.Sometimes( 0.15, iaa.OneOf([ iaa.GammaContrast(gamma=(0, 1.75)), iaa.pillike.Autocontrast(cutoff=(0, 15.0)) ])), iaa.Sometimes( 0.15, iaa.OneOf([ iaa.HistogramEqualization(), iaa.pillike.Equalize(), ])), iaa.Sometimes(0.1, iaa.Grayscale(alpha=(0.05, 1.0))), iaa.Sometimes(0.2, iaa.JpegCompression(compression=(70, 99))), iaa.Sometimes(0.1, iaa.UniformColorQuantizationToNBits(nb_bits=(2, 8))), iaa.Sometimes( 0.3, iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30))), iaa.Sometimes( 0.2, iaa.Cutout( fill_mode="constant", cval=(0, 255), fill_per_channel=0.5)) ], random_order=True)
def __init__(self): sometimes = lambda aug: iaa.Sometimes(0.3, aug) self.seq = iaa.Sequential([ sometimes(iaa.CropToFixedSize(width=640, height=640)), iaa.Fliplr(0.5), iaa.Flipud(0.5), iaa.MultiplyAndAddToBrightness(mul=(0.9, 1.1), add=(-5, 5)), iaa.Affine( rotate=(-380, 380), scale=(0.7, 1.3), translate_percent={ 'x': (-0.2, 0.2), 'y': (-0.2, 0.2) }, #mode=['symmetric', 'reflect'], # bbox는 reflect 되지 않음 cval=(0, 0)), sometimes( iaa.SomeOf(1, [ iaa.GaussianBlur(sigma=(0.6, 1.4)), iaa.AverageBlur(k=(1, 3)), iaa.MedianBlur(k=(1, 3)), iaa.BilateralBlur(d=(5, 7), sigma_space=(10, 250)) ])), sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.11))), sometimes(iaa.Grayscale(alpha=(0.0, 0.3))), ])
def chapter_augmenters_multiplyandaddtobrightness(): fn_start = "color/multiplyandaddtobrightness" aug = iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)) run_and_save_augseq(fn_start + ".jpg", aug, [ia.quokka(size=(128, 128)) for _ in range(8)], cols=4, rows=2)
def dataAug(imgPath, txtPath): images, bbs= loadData(imgPath, txtPath) seq = iaa.Sequential([ iaa.Sometimes(0.25, iaa.AdditiveGaussianNoise(scale=0.05*255)), iaa.Affine(translate_px={"x": (1, 5)}), iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)), iaa.Sometimes(0.25,iaa.imgcorruptlike.MotionBlur(severity=(1,2))), iaa.Resize({"height": (0.75, 1.25), "width": (0.75, 1.25)}), iaa.CropAndPad(percent=(-0.25, 0.25)), iaa.JpegCompression(compression=(0, 66)) ]) image_aug, bbs_aug = seq(images=images, bounding_boxes=bbs) return image_aug[0], bbs_aug[0]
def __init__(self): self.aug = iaa.Sequential([ iaa.Sometimes(0.15, iaa.MotionBlur(k=5, angle=[-45, 45])), iaa.Sometimes( 0.35, iaa.OneOf([ iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)), iaa.GammaContrast(gamma=(0.7, 1.75)), iaa.pillike.Autocontrast(cutoff=(0, 15.0)), iaa.Sometimes(0.25, iaa.GaussianBlur(sigma=(0, 1.3))) ])), iaa.Fliplr(0.5), iaa.Sometimes( 0.35, iaa.OneOf([ iaa.SaltAndPepper(0.05), iaa.Affine(rotate=(-20, 20), mode='symmetric') ])) ])
def load_augmentation_aug_non_geometric(): return iaa.Sequential([ iaa.Sometimes( 0.5, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5)), iaa.Sometimes( 0.5, iaa.OneOf([ iaa.GaussianBlur(sigma=(0.0, 3.0)), iaa.GaussianBlur(sigma=(0.0, 5.0)) ])), iaa.Sometimes(0.5, iaa.MultiplyAndAddToBrightness(mul=(0.4, 1.7))), iaa.Sometimes(0.5, iaa.GammaContrast((0.4, 1.7))), iaa.Sometimes(0.5, iaa.Multiply((0.4, 1.7), per_channel=0.5)), iaa.Sometimes(0.5, iaa.MultiplyHue((0.4, 1.7))), iaa.Sometimes( 0.5, iaa.MultiplyHueAndSaturation((0.4, 1.7), per_channel=True)), iaa.Sometimes(0.5, iaa.LinearContrast((0.4, 1.7), per_channel=0.5)) ])
def train_augs(self,): return iaa.Sequential([ iaa.HorizontalFlip(0.5), iaa.VerticalFlip(0.5), iaa.OneOf([ iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)), iaa.Noop() ]), iaa.OneOf([ iaa.Grayscale(alpha=(0.0, 1.0)), iaa.Noop() ]), iaa.OneOf([ iaa.Noop(), iaa.GammaContrast((0.5, 1.0)) ]), iaa.Affine( scale={"x": (0.9, 1.1), "y": (0.9, 1.1)}, translate_percent={"x": (-0.05, 0.05), "y": (-0.05, 0.05)}, rotate=(-5, 5), cval=255, ), ])
def __init__(self, root_path, annotation_path, subset, n_samples_for_each_video=1, spatial_transform=None, temporal_transform=None, target_transform=None, sample_duration=16, modality='rgb', get_loader=get_default_video_loader): if subset == 'training': self.data, self.class_names = make_dataset( root_path, annotation_path, subset, n_samples_for_each_video, sample_duration) # self.val_data, _ = make_dataset( # root_path, annotation_path, 'validation', n_samples_for_each_video, # sample_duration) # self.data += self.val_data else: self.data, self.class_names = make_dataset( root_path, annotation_path, 'testing', n_samples_for_each_video, sample_duration) print('loaded', len(self.data)) self.spatial_transform = spatial_transform self.temporal_transform = temporal_transform self.target_transform = target_transform self.subset = subset self.modality = modality if self.modality == 'flow': self.loader = get_default_video_loader_flow() elif self.modality == 'depth': self.loader = get_default_video_loader_depth() else: self.loader = get_loader() sometimes = lambda aug: iaa.Sometimes(0.3, aug) self.aug_seq = iaa.Sequential([ # iaa.Fliplr(0.5), # sometimes(iaa.MotionBlur(k=2)), # sometimes(iaa.ChangeColorTemperature((1100, 10000))), sometimes( iaa.MultiplyAndAddToBrightness(mul=(0.8, 1.2), add=(-30, 30))), # 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=(-20, 20), # shear=(-10, 10), # cval=(0, 255), # mode=ia.ALL, )), # sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.15))), # sometimes(iaa.AdditiveGaussianNoise(scale=0.05 * 255)), ]) self.aug_seq.to_deterministic() # added by alexhu self.root_path = root_path if self.modality != 'pose': self.to_tensor = Compose(self.spatial_transform.transforms[-2:]) self.spatial_transform.transforms = self.spatial_transform.transforms[: -2]
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 )
transformed_image = transform(image=image) elif augmentation == 'hue_saturation': transform = HueSaturationValue(always_apply=True) transformed_image = transform(image=image)['image'] elif augmentation == 'multiply_brightness': transform = iaa.MultiplyBrightness((0.1, 1.9)) transformed_image = transform(image=image) elif augmentation == 'addto_brightness': transform = iaa.AddToBrightness((-50, 50)) transformed_image = transform(image=image) elif augmentation == 'multiply_and_addtobrightness': transform = iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)) transformed_image = transform(image=image) elif augmentation == 'to_gray': transform = ToGray(always_apply=True) transformed_image = transform(image=image)['image'] elif augmentation == 'posterize': transform = Posterize(always_apply=True, num_bits=2) transformed_image = transform(image=image)['image'] elif augmentation == 'to_sepia': transform = ToSepia(always_apply=True) transformed_image = transform(image=image)['image'] elif augmentation == 'fancy_pca':
save_path = os.path.join( vis_dir, self.images_name[i] + '_polygon' + '.' + self.images_format[i]) cv2.imwrite(save_path, image_with_polygon) ''' changes the color temperature of images to a random value between 1100 and 10000 Kelvin ''' aug_colorTemperature = iaa.ChangeColorTemperature((1100, 10000)) ''' Convert each image to a colorspace with a brightness-related channel, extract that channel, multiply it by a factor between 0.5 and 1.5, add a value between -30 and 30 and convert back to the original colorspace ''' aug_brightness = iaa.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)) ''' Multiply the hue and saturation of images by random values; Sample random values from the discrete uniform range [-50..50],and add them ''' aug_hueSaturation = [ iaa.MultiplyHue((0.5, 1.5)), iaa.MultiplySaturation((0.5, 1.5)), iaa.AddToHue((-50, 50)), iaa.AddToSaturation((-50, 50)) ] ''' Increase each pixel’s R-value (redness) by 10 to 100 ''' aug_redChannels = iaa.WithChannels(0, iaa.Add((10, 100)))
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.Identity(name="Identity"), 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.Cutout(nb_iterations=1, name="Cutout-fill_constant"), iaa.Dropout((0.01, 0.05), name="Dropout"), iaa.CoarseDropout((0.01, 0.05), size_percent=(0.01, 0.1), name="CoarseDropout"), iaa.Dropout2d(0.1, name="Dropout2d"), iaa.TotalDropout(0.1, name="TotalDropout"), 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_artistic = [ iaa.Cartoon(name="Cartoon") ] augmenters_blend = [ iaa.BlendAlpha((0.01, 0.99), iaa.Identity(), name="Alpha"), iaa.BlendAlphaElementwise((0.01, 0.99), iaa.Identity(), name="AlphaElementwise"), iaa.BlendAlphaSimplexNoise(iaa.Identity(), name="SimplexNoiseAlpha"), iaa.BlendAlphaFrequencyNoise((-2.0, 2.0), iaa.Identity(), name="FrequencyNoiseAlpha"), iaa.BlendAlphaSomeColors(iaa.Identity(), name="BlendAlphaSomeColors"), iaa.BlendAlphaHorizontalLinearGradient(iaa.Identity(), name="BlendAlphaHorizontalLinearGradient"), iaa.BlendAlphaVerticalLinearGradient(iaa.Identity(), name="BlendAlphaVerticalLinearGradient"), iaa.BlendAlphaRegularGrid(nb_rows=(2, 8), nb_cols=(2, 8), foreground=iaa.Identity(), name="BlendAlphaRegularGrid"), iaa.BlendAlphaCheckerboard(nb_rows=(2, 8), nb_cols=(2, 8), foreground=iaa.Identity(), name="BlendAlphaCheckerboard"), # TODO BlendAlphaSegMapClassId # TODO BlendAlphaBoundingBoxes ] 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"), iaa.MeanShiftBlur(spatial_radius=(5.0, 40.0), color_radius=(5.0, 40.0), name="MeanShiftBlur") ] augmenters_collections = [ iaa.RandAugment(n=2, m=(6, 12), name="RandAugment") ] augmenters_color = [ # InColorspace (deprecated) iaa.WithColorspace(to_colorspace="HSV", children=iaa.Noop(), name="WithColorspace"), iaa.WithBrightnessChannels(iaa.Identity(), name="WithBrightnessChannels"), iaa.MultiplyAndAddToBrightness(mul=(0.7, 1.3), add=(-30, 30), name="MultiplyAndAddToBrightness"), iaa.MultiplyBrightness((0.7, 1.3), name="MultiplyBrightness"), iaa.AddToBrightness((-30, 30), name="AddToBrightness"), 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.RemoveSaturation((0.01, 0.99), name="RemoveSaturation"), 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"), iaa.UniformColorQuantizationToNBits((1, 7), name="UniformQuantizationToNBits"), iaa.Posterize((1, 7), name="Posterize") ] 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"), 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"), iaa.WithPolarWarping(iaa.Identity(), name="WithPolarWarping"), iaa.Jigsaw(nb_rows=(3, 8), nb_cols=(3, 8), max_steps=1, name="Jigsaw") ] 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_imgcorruptlike = [ iaa.imgcorruptlike.GaussianNoise(severity=(1, 5), name="imgcorruptlike.GaussianNoise"), iaa.imgcorruptlike.ShotNoise(severity=(1, 5), name="imgcorruptlike.ShotNoise"), iaa.imgcorruptlike.ImpulseNoise(severity=(1, 5), name="imgcorruptlike.ImpulseNoise"), iaa.imgcorruptlike.SpeckleNoise(severity=(1, 5), name="imgcorruptlike.SpeckleNoise"), iaa.imgcorruptlike.GaussianBlur(severity=(1, 5), name="imgcorruptlike.GaussianBlur"), iaa.imgcorruptlike.GlassBlur(severity=(1, 5), name="imgcorruptlike.GlassBlur"), iaa.imgcorruptlike.DefocusBlur(severity=(1, 5), name="imgcorruptlike.DefocusBlur"), iaa.imgcorruptlike.MotionBlur(severity=(1, 5), name="imgcorruptlike.MotionBlur"), iaa.imgcorruptlike.ZoomBlur(severity=(1, 5), name="imgcorruptlike.ZoomBlur"), iaa.imgcorruptlike.Fog(severity=(1, 5), name="imgcorruptlike.Fog"), iaa.imgcorruptlike.Frost(severity=(1, 5), name="imgcorruptlike.Frost"), iaa.imgcorruptlike.Snow(severity=(1, 5), name="imgcorruptlike.Snow"), iaa.imgcorruptlike.Spatter(severity=(1, 5), name="imgcorruptlike.Spatter"), iaa.imgcorruptlike.Contrast(severity=(1, 5), name="imgcorruptlike.Contrast"), iaa.imgcorruptlike.Brightness(severity=(1, 5), name="imgcorruptlike.Brightness"), iaa.imgcorruptlike.Saturate(severity=(1, 5), name="imgcorruptlike.Saturate"), iaa.imgcorruptlike.JpegCompression(severity=(1, 5), name="imgcorruptlike.JpegCompression"), iaa.imgcorruptlike.Pixelate(severity=(1, 5), name="imgcorruptlike.Pixelate"), iaa.imgcorruptlike.ElasticTransform(severity=(1, 5), name="imgcorruptlike.ElasticTransform") ] augmenters_pillike = [ iaa.pillike.Solarize(p=1.0, threshold=(32, 128), name="pillike.Solarize"), iaa.pillike.Posterize((1, 7), name="pillike.Posterize"), iaa.pillike.Equalize(name="pillike.Equalize"), iaa.pillike.Autocontrast(name="pillike.Autocontrast"), iaa.pillike.EnhanceColor((0.0, 3.0), name="pillike.EnhanceColor"), iaa.pillike.EnhanceContrast((0.0, 3.0), name="pillike.EnhanceContrast"), iaa.pillike.EnhanceBrightness((0.0, 3.0), name="pillike.EnhanceBrightness"), iaa.pillike.EnhanceSharpness((0.0, 3.0), name="pillike.EnhanceSharpness"), iaa.pillike.FilterBlur(name="pillike.FilterBlur"), iaa.pillike.FilterSmooth(name="pillike.FilterSmooth"), iaa.pillike.FilterSmoothMore(name="pillike.FilterSmoothMore"), iaa.pillike.FilterEdgeEnhance(name="pillike.FilterEdgeEnhance"), iaa.pillike.FilterEdgeEnhanceMore(name="pillike.FilterEdgeEnhanceMore"), iaa.pillike.FilterFindEdges(name="pillike.FilterFindEdges"), iaa.pillike.FilterContour(name="pillike.FilterContour"), iaa.pillike.FilterEmboss(name="pillike.FilterEmboss"), iaa.pillike.FilterSharpen(name="pillike.FilterSharpen"), iaa.pillike.FilterDetail(name="pillike.FilterDetail"), iaa.pillike.Affine(scale=(0.9, 1.1), translate_percent={"x": (-0.05, 0.05), "y": (-0.05, 0.05)}, rotate=(-10, 10), shear=(-10, 10), fillcolor=(0, 255), name="pillike.Affine"), ] 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"), iaa.Rain(name="Rain"), iaa.RainLayer(density=(0.03, 0.14), density_uniformity=(0.8, 1.0), drop_size=(0.01, 0.02), drop_size_uniformity=(0.2, 0.5), angle=(-15, 15), speed=(0.04, 0.20), blur_sigma_fraction=(0.001, 0.001), name="RainLayer") ] augmenters = ( augmenters_meta + augmenters_arithmetic + augmenters_artistic + augmenters_blend + augmenters_blur + augmenters_collections + augmenters_color + augmenters_contrast + augmenters_convolutional + augmenters_edges + augmenters_flip + augmenters_geometric + augmenters_pooling + augmenters_imgcorruptlike + augmenters_pillike + 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
foreground=iaa.Add((-15, 15)), background=iaa.Multiply((0.8, 1.2))), iaa.ReplaceElementwise(0.05, iap.Normal(128, 0.4 * 128), per_channel=0.5), iaa.Dropout(p=(0, 0.05), per_channel=0.5), ])), # Brightness + Color + Contrast iaa.Sometimes( 0.5, iaa.OneOf([ iaa.Add(iap.Normal(iap.Choice([-30, 30]), 10)), iaa.Multiply((0.75, 1.25)), iaa.AddToBrightness((-35, 35)), iaa.MultiplyBrightness((0.85, 1.15)), iaa.MultiplyAndAddToBrightness(mul=(0.85, 1.15), add=(-10, 10)), iaa.BlendAlphaHorizontalLinearGradient(iaa.Add( iap.Normal(iap.Choice([-40, 40]), 10)), start_at=(0, 0.2), end_at=(0.8, 1)), iaa.BlendAlphaHorizontalLinearGradient(iaa.Add( iap.Normal(iap.Choice([-40, 40]), 10)), start_at=(0.8, 1), end_at=(0, 0.2)), iaa.BlendAlphaVerticalLinearGradient(iaa.Add( iap.Normal(iap.Choice([-40, 40]), 10)), start_at=(0.8, 1), end_at=(0, 0.2)), iaa.BlendAlphaVerticalLinearGradient(iaa.Add( iap.Normal(iap.Choice([-40, 40]), 10)), start_at=(0, 0.2),
import imgaug.augmenters as iaa import random import numpy as np import cv2 from PIL import Image aug_transform = iaa.SomeOf((0, None), [ iaa.OneOf([ iaa.MultiplyAndAddToBrightness(mul=(0.3, 1.6), add=(-50, 50)), iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=True), iaa.ChannelShuffle(0.5), iaa.RemoveSaturation(), iaa.Grayscale(alpha=(0.0, 1.0)), iaa.ChangeColorTemperature((1100, 35000)), ]), iaa.OneOf([ iaa.MedianBlur(k=(3, 7)), iaa.BilateralBlur( d=(3, 10), sigma_color=(10, 250), sigma_space=(10, 250)), iaa.MotionBlur(k=(3, 9), angle=[-45, 45]), iaa.MeanShiftBlur(spatial_radius=(5.0, 10.0), color_radius=(5.0, 10.0)), iaa.AllChannelsCLAHE(clip_limit=(1, 10)), iaa.AllChannelsHistogramEqualization(), iaa.GammaContrast((0.5, 1.5), per_channel=True), iaa.GammaContrast((0.5, 1.5)), iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6), per_channel=True), iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.4, 0.6)), iaa.HistogramEqualization(), iaa.Sharpen(alpha=0.5)
def train(name, df, resume=False): now = datetime.now() dt_string = now.strftime("%d|%m_%H|%M|%S") print("Starting -->", dt_string) os.makedirs(OUTPUT_DIR, exist_ok=True) wandb.init( project="imanip", config=config_defaults, name=f"{name},{dt_string}", ) config = wandb.config model = SRM_Classifer(num_classes=312) print("Parameters : ", sum(p.numel() for p in model.parameters() if p.requires_grad)) wandb.save('segmentation/merged_net.py') wandb.save('pretrain_dataset.py') ##################################################################################################################### train_imgaug = iaa.Sequential( [ iaa.SomeOf((0, 5), [ iaa.OneOf([ iaa.JpegCompression(compression=(10, 60)), iaa.GaussianBlur((0, 1.75)), # 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, 7)), # 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.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images # iaa.Sometimes(0.3, 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 iaa.LinearContrast((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast # # either change the brightness of the whole image (sometimes # # per channel) or change the brightness of subareas iaa.Sometimes(0.5, iaa.OneOf([ iaa.Multiply((0.5, 1.5), per_channel=0.5), iaa.MultiplyAndAddToBrightness(mul=(0.5, 2.5), add=(-10,10)), iaa.MultiplyHueAndSaturation(), # iaa.BlendAlphaFrequencyNoise( # exponent=(-4, 0), # foreground=iaa.Multiply((0.5, 1.5), per_channel=True), # background=iaa.LinearContrast((0.5, 2.0)) # ) ]) ), ], random_order=True ) ], random_order=True ) train_geo_aug = albumentations.Compose( [ albumentations.HorizontalFlip(p=0.5), albumentations.VerticalFlip(p=0.5), albumentations.RandomRotate90(p=0.1), albumentations.ShiftScaleRotate(shift_limit=0.01, scale_limit=0.04, rotate_limit=35, p=0.25), # albumentations.OneOf([ # albumentations.ElasticTransform(p=0.5, alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03), # albumentations.GridDistortion(p=0.5), # albumentations.OpticalDistortion(p=0.5, distort_limit=2, shift_limit=0.5) # ], p=0.7), ], additional_targets={'ela':'image'} ) #################################################################################################################### normalize = { "mean": [0.4535408213875562, 0.42862278450748387, 0.41780105499276865], "std": [0.2672804038612597, 0.2550410416463668, 0.29475415579144293], } transforms_normalize = albumentations.Compose( [ albumentations.Normalize(mean=normalize['mean'], std=normalize['std'], always_apply=True, p=1), albumentations.pytorch.transforms.ToTensorV2() ], additional_targets={'ela':'image'} ) # -------------------------------- CREATE DATASET and DATALOADER -------------------------- df_train, df_val, df_test = stratified_train_val_test_split(df, stratify_colname='class_idx', frac_train=0.96, frac_val=0.02, frac_test=0.02) train_dataset = DATASET( dataframe=df_train, mode="train", transforms_normalize=transforms_normalize, imgaug_augment=None, geo_augment=train_geo_aug ) train_loader = DataLoader(train_dataset, batch_size=config.train_batch_size, shuffle=True, num_workers=12, pin_memory=True, drop_last=False) valid_dataset = DATASET( dataframe=df_val, mode="val", transforms_normalize=transforms_normalize, ) valid_loader = DataLoader(valid_dataset, batch_size=config.valid_batch_size, shuffle=True, num_workers=12, pin_memory=True, drop_last=False) test_dataset = DATASET( dataframe=df_test, mode="test", transforms_normalize=transforms_normalize, ) test_loader = DataLoader(test_dataset, batch_size=config.valid_batch_size, shuffle=True, num_workers=12, pin_memory=True, drop_last=False) optimizer = get_optimizer(model, config.optimizer, config.learning_rate, config.weight_decay) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( optimizer, patience=config.schedule_patience, mode="min", factor=config.schedule_factor, ) model = nn.DataParallel(model).to(device) criterion = nn.CrossEntropyLoss() es = EarlyStopping(patience=15, mode="min") start_epoch = 0 if resume: checkpoint = torch.load('checkpoint/pretrain_[28|03_21|47|58].pt') scheduler.load_state_dict(checkpoint['scheduler_state_dict']) model.load_state_dict(checkpoint['model_state_dict']) optimizer.load_state_dict(checkpoint['optimizer_state_dict']) start_epoch = checkpoint['epoch'] + 1 print("-----------> Resuming <------------") for epoch in range(start_epoch, config.epochs): print(f"Epoch = {epoch}/{config.epochs-1}") print("------------------") train_metrics = train_epoch(model, train_loader, optimizer, criterion, epoch) valid_metrics = valid_epoch(model, valid_loader, criterion, epoch) scheduler.step(valid_metrics['valid_loss']) print( "TRAIN_ACC = %.5f, TRAIN_LOSS = %.5f" % (train_metrics['train_acc5_manual'], train_metrics['train_loss']) ) print( "VALID_ACC = %.5f, VALID_LOSS = %.5f" % (valid_metrics['valid_acc5_manual'], valid_metrics['valid_loss']) ) print("New LR", optimizer.param_groups[0]['lr']) checkpoint = { 'epoch': epoch, 'model_state_dict': model.state_dict(), 'optimizer_state_dict' : optimizer.state_dict(), 'scheduler_state_dict': scheduler.state_dict(), } os.makedirs('checkpoint', exist_ok=True) torch.save(checkpoint, os.path.join('checkpoint', f"{name}_[{dt_string}].pt")) os.makedirs(OUTPUT_DIR, exist_ok=True) es( valid_metrics['valid_loss'], model, model_path=os.path.join(OUTPUT_DIR, f"{name}_[{dt_string}].h5"), ) if es.early_stop: print("Early stopping") break if os.path.exists(os.path.join(OUTPUT_DIR, f"{name}_[{dt_string}].h5")): print(model.load_state_dict(torch.load(os.path.join(OUTPUT_DIR, f"{name}_[{dt_string}].h5")))) print("LOADED FOR TEST") wandb.save(os.path.join(OUTPUT_DIR, f"{name}_[{dt_string}].h5")) test_metrics = test(model, test_loader, criterion) return test_metrics
def compute_cluster(image_path): """each cluster can be computed independently""" image_path_data = Path(image_path) stem = image_path_data.stem basename = image_path_data.name image_annotation_path = get_image_annotation_path(stem) image_annotation_path_data = Path(image_annotation_path) print("Processing {}...".format(stem)) if image_annotation_path: labels_df = decode_image_annotation(image_annotation_path) shutil.copy(image_path, "{}/{}".format(DATASET_AUGM_IMAGES_DIR, basename)) shutil.copy( image_annotation_path, "{}/{}".format(DATASET_AUGM_ANNOTS_DIR, image_annotation_path_data.name)) for i in range(0, AUG_NB_AUGMENTATION_PER_IMAGE): # This setup of augmentation parameters will pick 1 to 4 # of the given augmenters and apply them in random order. sometimes = lambda aug: augmenters.Sometimes(0.5, aug) aug_config = augmenters.SomeOf( (1, 4), [ augmenters.Affine(scale=(0.1, 1.5)), augmenters.Affine(rotate=(-45, 45)), augmenters.Affine(translate_percent={ "x": (-0.3, 0.3), "y": (-0.3, 0.3) }), # augmenters.Affine(shear=(-16, 16)), augmenters.OneOf([ augmenters.Fliplr(1), augmenters.Flipud(1), ]), augmenters.Rot90(1), # sometimes( # augmenters.Superpixels( # p_replace=(0, 1.0), # n_segments=(20, 200) # ) # ), augmenters.OneOf([ augmenters.GaussianBlur((0, 3.0)), augmenters.AverageBlur(k=(2, 7)), augmenters.MedianBlur(k=(3, 11)), augmenters.MotionBlur(k=3), ]), augmenters.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sometimes(augmenters.OneOf([ # augmenters.EdgeDetect(alpha=(0, 0.7)), # augmenters.DirectedEdgeDetect( # alpha=(0, 0.7), direction=(0.0, 1.0) # ), # ])), augmenters.AdditiveGaussianNoise( loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5), augmenters.OneOf([ augmenters.Dropout((0.05, 0.3), per_channel=0.5), augmenters.CoarseDropout((0.02, 0.05), size_percent=(0.01, 0.02), per_channel=0.2), ]), augmenters.OneOf([ augmenters.pillike.EnhanceColor(), augmenters.Add((-10, 10)), augmenters.Multiply((0.5, 1.5)), augmenters.LinearContrast((0.5, 2.0), per_channel=0.5), augmenters.MultiplyAndAddToBrightness(mul=(0.5, 1.5), add=(-30, 30)), # augmenters.Grayscale(alpha=(0.0, 1.0)), ]), sometimes( augmenters.ElasticTransformation(alpha=(0.2, 1.5), sigma=0.25)), sometimes(augmenters.PiecewiseAffine(scale=(0.01, 0.05))) ], random_order=True) aug_file_suffix = '_aug_{}'.format(i) try: augmented_image_df = augment_image(labels_df, DATASET_IMAGES_DIR, DATASET_AUGM_IMAGES_DIR, aug_file_suffix, aug_config) image_annotation_dest_path = "{}/{}{}{}".format( DATASET_AUGM_ANNOTS_DIR, stem, aug_file_suffix, image_annotation_path_data.suffix) if (len(augmented_image_df.index)): xml_annotations_et = get_xml_from_dataframe( image_annotation_path, augmented_image_df) xml_annotations_et.write(image_annotation_dest_path) except: print("Failed to augment {}".format(aug_file_suffix)) os.remove(image_annotation_path) os.remove(image_path) print("Removed: {}".format(image_path.split('/')[-1]))
import json import webcolors import imgaug.augmenters as iaa classes=os.getcwd()+'/yolov3_tf2/'+'data/voc2012.names' weights=os.getcwd()+'/yolov3_tf2/'+'/checkpoints/yolov3_train_e2_8.tf' tiny=False, size= 416 image= './data/girl.png' tfrecord= None output='./output.jpg' num_classes= 9 class_names=[] yolo=None first_run_flag=True aug = iaa.HistogramEqualization() brightless = iaa.MultiplyAndAddToBrightness(mul=(0.9, 1.0), add=(-40, -50)) brightmore = iaa.MultiplyAndAddToBrightness(mul=(1.6, 1.8), add=(10, 20)) from numpy.linalg import norm class_mappings={ 'rayban01':'Rayban Wayfarer', 'Oo9343':'Oakley Men\'s Oo9343 M2 Frame Xl Shield Sunglasses', 'ck01': 'CK One Eau De Toilette', 'oakleySun2':'Oakley Sunglasses 2', 'dhl_envelope' : 'DHL Envelope as per the demo kits', 'poloshirt': 'Polo Shirt x 3', 'hoodie': 'Hoodie x 2', 'listerine': 'Listerine', 'Everydrop': 'Filter cartridge'} # flags = tf.compat.v1.flags
# plt.figure(figsize=(6.66,10)) # plt.imshow( (20*np.log10( 0.1 + F2)).astype(int), cmap=plt.cm.gray) # plt.show() # im1 = fftpack.ifft2(fftpack.ifftshift(F2)).real # plt.figure(figsize=(10,10)) # plt.imshow(im1, cmap='gray') # plt.axis('off') # plt.show() # exit() aug_seq = iaa.Sequential([ iaa.Resize({ "height": 72 * 5, "width": 128 * 5 }), iaa.MultiplyAndAddToBrightness(mul=(0.9, 1.1), add=0), # 가우시안 필터는 scale 0이 정상 0~15사이인데 정상이 중간값으로 진행되지 않습니다. iaa.SigmoidContrast(gain=5, cutoff=0.35), iaa.GammaContrast((0.9, 1.1), per_channel=True), iaa.ChangeColorTemperature(kelvin=(8000, 12000)), iaa.MultiplyHueAndSaturation((0.8, 1.1), per_channel=True), iaa.Resize({ "height": 720, "width": 1280 }), iaa.Sometimes( 0.5, iaa.Sequential([ iaa.MotionBlur(k=15, angle=[-90, 90]), iaa.AdditiveGaussianNoise(scale=(0, 15)), ]))