def create_custom_gen(img_gen): seq = iaa.Sequential( [iaa.MultiplyHue((0.5, 1.5)), iaa.imgcorruptlike.Contrast(severity=1)]) for X_batch, y_batch in img_gen: hue = seq(images=X_batch.astype(np.uint8)) yield hue, y_batch
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_multiplyhue(): fn_start = "color/multiplyhue" aug = iaa.MultiplyHue((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 __init__(self, iaalist=None): if iaalist is None: iaalist = iaa.Sequential([ iaa.Sometimes(0.5, iaa.ChannelShuffle(0.3)), iaa.Sometimes(0.5, iaa.MultiplyHue((0.5, 1.5))), iaa.Sometimes(0.5, iaa.AddToHueAndSaturation((-50, 50), per_channel=True)), iaa.Sometimes(0.5, iaa.Fliplr(0.5)), iaa.Sometimes(0.5, iaa.Flipud(0.5)), iaa.Sometimes(0.5, iaa.Rotate((-50, 50))) ], random_order=True) self.transformSet = iaalist self.outscale = random.choice([0.8, 0.85, 0.9, 0.95])
def new_gen_train_trans(image, mask): image = np.array(image) mask = np.array(mask) h, w = mask.shape th, tw = args.train_size crop_scales = [1.0, 0.875, 0.75, 0.625, 0.5] hue_factor = 0.6 brightness_factor = 0.6 # was 0.5 p_flip = 0.5 jpeg_scale = 0, 80 # was 70 p_erase_class = 0.5 crop_scale = np.random.choice(crop_scales) ch, cw = [int(x * crop_scale) for x in (h, w)] i = np.random.randint(0, h - ch + 1) j = np.random.randint(0, w - cw + 1) image = image[i:i + ch, j:j + cw, :] mask = mask[i:i + ch, j:j + cw] brightness = iaa.MultiplyBrightness( (1 - brightness_factor, 1 + brightness_factor)) hue = iaa.MultiplyHue((1 - hue_factor, 1 + hue_factor)) jpeg = iaa.JpegCompression(compression=jpeg_scale) img_transforms = iaa.Sequential([brightness, hue, jpeg]) image = img_transforms(image=image) if np.random.rand() < p_flip: image = np.flip(image, axis=1) mask = np.flip(mask, axis=1) image = Image.fromarray(image) mask = Image.fromarray(mask) # Resize, 1 for Image.LANCZOS image = TF.resize(image, (th, tw), interpolation=1) # Resize, 0 for Image.NEAREST mask = TF.resize(mask, (th, tw), interpolation=0) # From PIL to Tensor image = TF.to_tensor(image) # Normalize image = TF.normalize(image, args.dataset_mean, args.dataset_std) # Convert ids to train_ids mask = np.array(mask, np.uint8) mask = torch.from_numpy(mask) # Numpy array to tensor return image, mask
def test_returns_correct_class(self): # this test is practically identical to # TestMultiplyToHueAndSaturation.test_returns_correct_objects__mul_hue aug = iaa.MultiplyHue((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 == [0] 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 augmentation(self, img): # 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) seq = iaa.Sequential([ sometimes(iaa.JpegCompression(compression=(1, 30))), sometimes(iaa.GaussianBlur(sigma=(0.2, 1.0))), sometimes(iaa.MultiplyHue((0.9, 1.1))), sometimes( iaa.AdditiveGaussianNoise(scale=0.01 * 255, per_channel=0.5)) ], random_order=True) img_aug = seq(image=img) #cv2.imwrite("asd.jpg", img_aug) return img_aug
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))) ])
def aug(self, img): seq = iaa.Sequential([ # iaa.Multiply((1.2, 1.5)), # change brightness, doesn't affect keypoints # iaa.Fliplr(0.5), iaa.Affine( rotate=(0, 10), # 0~360随机旋转 # scale=(0.7, 1.0),#通过增加黑边缩小图片 ), # rotate by exactly 0~360deg and scale to 70-100%, affects keypoints iaa.GaussianBlur( sigma=(0, 3.) ), # iaa.ChangeColorspace(from_colorspace="RGB", to_colorspace="HSV"), # iaa.WithChannels(channels=0, children=iaa.Add((50, 100))), # iaa.ChangeColorspace(from_colorspace="HSV", to_colorspace="RGB"), iaa.SigmoidContrast(gain=(3, 10), cutoff=(0.3, 0.9)), iaa.MultiplyHue(mul=(0.5, 1.5)) # iaa.Resize(0.5, 3) ]) seq_def = seq.to_deterministic() image_aug = seq_def.augment_image(img) return image_aug
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 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 __getitem__(self, index): co_ords = self.coords[index * BATCH_SIZE:(index + 1) * BATCH_SIZE] batch_images = np.zeros((len(co_ords), INPUT_HEIGHT, INPUT_WIDTH, 3), dtype=np.float32) batch_heatmaps = np.zeros( (len(co_ords), OUTPUT_HEIGHT, OUTPUT_WIDTH, 2), dtype=np.float32) for i, row in enumerate(co_ords): images_path, x, y = row proc_image = image.load_img(self.image_path + images_path, target_size=(INPUT_HEIGHT, INPUT_WIDTH)) proc_image = image.img_to_array(proc_image, dtype='uint8') heatmap = heatmap_splat(y, x) # y is height and x is width!! heatmap = np.expand_dims(heatmap, axis=0) aug_list = iaa.OneOf([ iaa.Dropout([0.05, 0.1]), iaa.Sharpen((0.0, 1.0)), iaa.MultiplyHue((0.7, 1.4)), iaa.MultiplyBrightness((0.7, 1.4)), ]) aug = iaa.Sequential([aug_list, iaa.Fliplr(0.5)], random_order=True) proc_image, heatmap = aug.augment(image=proc_image, heatmaps=heatmap) proc_image = np.expand_dims(proc_image, axis=0) proc_image = proc_image / 255. #just for now try without normalising batch_images[i] = proc_image batch_heatmaps[i] = heatmap return batch_images, batch_heatmaps
elif augmentation == 'glass_blur': transform = GlassBlur(always_apply=True) transformed_image = transform(image=image)['image'] elif augmentation == 'defocus_blur': transform = iaa.imgcorruptlike.DefocusBlur(severity=2) transformed_image = transform(image=image) 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':
def main(): try: config_dirs_file = sys.argv[1] # directories file config_file = sys.argv[2] # main params file except: print("Config file names not specified, setting them to default namess") config_dirs_file = "config_dirs.json" config_file = "config760.json" print(f'USING CONFIG FILES: config dirs:{config_dirs_file} main config:{config_file}') #print(type(feature_directory)) C = cs760.loadas_json('config760.json') print("Running with parameters:", C) Cdirs = cs760.loadas_json(config_dirs_file) print("Directories:", Cdirs) C['dirs'] = Cdirs video_directory = C['dirs']['indir'] feature_directory = C['dirs']['outdir'] print(f'Creating feature file Dir: {feature_directory}') os.makedirs(feature_directory, exist_ok=True) #if dir already exists will continue and WILL NOT delete existing files in that directory sometimes = lambda aug: iaa.Sometimes(C["augmentation_chance"][0], aug) sequential_list = [iaa.Sequential([sometimes(iaa.Fliplr(1.0))]), # horizontal flip iaa.Sequential([sometimes(iaa.Rotate(-5, 5))]), # rotate 5 degrees +/- iaa.Sequential([sometimes(iaa.CenterCropToAspectRatio(1.15))]), iaa.Sequential([sometimes(iaa.MultiplyBrightness((2.0, 2.0)))]), # increase brightness iaa.Sequential([sometimes(iaa.MultiplyHue((0.5, 1.5)))]), # change hue random iaa.Sequential([sometimes(iaa.RemoveSaturation(1.0))]), # effectively greyscale iaa.Sequential([sometimes(iaa.pillike.FilterContour())]), # edge detection iaa.Sequential([sometimes(iaa.AdditiveLaplaceNoise(scale=0.05*255, per_channel=True))]), # add colourful noise iaa.Sequential([sometimes(iaa.Invert(1))]) # invert colours ] print("Reading videos from " + video_directory) print("Outputting features to " + feature_directory) print("Loading pretrained CNN...") model = hub.KerasLayer(C["module_url"]) # can be used like any other kera layer including in other layers... print("Pretrained CNN Loaded OK") vids = cs760.list_files_pattern(video_directory, C["vid_type"]) print(f'Processing {len(vids)} videos...') for i, vid in enumerate(vids): print(f'{i} Processing: {vid}') vid_np = cs760.get_vid_frames(vid, video_directory, writejpgs=False, writenpy=False, returnnp=True) (framecount, frameheight, framewidth, channels) = vid_np.shape res_key = str(frameheight) + "-" + str(framewidth) #print(vid, vid_np.shape) outfile = os.path.splitext(vid)[0] print(f"Vid frames, h, w, c = {(framecount, frameheight, framewidth, channels)}") if C["crop_by_res"].get(res_key) is not None: vid_np_top = cs760.crop_image(vid_np, C["crop_by_res"][res_key]) print(f"Cropped by resolution to {C['crop_by_res'][res_key]}") else: vid_np_top = cs760.crop_image(vid_np, C["crop_top"]) print(f"Cropped by default to {C['crop_top']}") outfile_top = outfile + "__TOP.pkl" for n in range((len(sequential_list) + 1)): if n != 0: vid_aug = sequential_list[n - 1](images=vid_np_top) # augments frames if type(vid_aug) is list: vid_aug = np.asarray(vid_aug) batch = cs760.resize_batch(vid_aug, width=C["expect_img_size"], height=C["expect_img_size"], pad_type='L', inter=cv2.INTER_CUBIC, BGRtoRGB=False, simplenormalize=True, imagenetmeansubtract=False) temp_outfile = outfile_top[:-4] + C["augmentation_type"][n - 1] + ".pkl" features = extract(C, model, batch) cs760.saveas_pickle(features, os.path.join(feature_directory, temp_outfile)) else: batch = cs760.resize_batch(vid_np_top, width=C["expect_img_size"], height=C["expect_img_size"], pad_type='L', inter=cv2.INTER_CUBIC, BGRtoRGB=False, simplenormalize=True, imagenetmeansubtract=False) features = extract(C, model, batch) cs760.saveas_pickle(features, os.path.join(feature_directory, outfile_top)) print(f'Features output shape: {features.shape}') if C["crop_type"] == 'B': # only for boston vids vid_np_bot = cs760.crop_image(vid_np, C["crop_bottom"]) outfile_bot = outfile + "__BOT.pkl" batch = cs760.resize_batch(vid_np_bot, width=C["expect_img_size"], height=C["expect_img_size"], pad_type='L', inter=cv2.INTER_CUBIC, BGRtoRGB=False, simplenormalize=True, imagenetmeansubtract=False) features = extract(C, model, batch) cs760.saveas_pickle(features, os.path.join(feature_directory, outfile_bot)) print('Finished outputting features!!')
def train_trans(self, image, mask, index): image_scale_x = self.epoch_image_scales_x[index] image_scale_y = self.epoch_image_scales_y[index] hue_factor = 0.6 brightness_factor = 0.6 # was 0.5 p_flip = 0.5 p_imgaug = 0.7 p_jpeg = 0.4 jpeg_scale = 0, 70 # was 70 p_pixel_attack = 0.0 pixel_attack_density = 0.05 rotation_angle = (np.random.rand() - 0.5) * 20 image = TF.resize(image, (self.image_size[0] * image_scale_x, self.image_size[1] * image_scale_y), interpolation=1) mask = TF.resize(mask, (self.image_size[0] * image_scale_x, self.image_size[1] * image_scale_y), interpolation=0) image = np.array(image) mask = np.array(mask) image = image[int(self.epoch_image_main_direcs[ index, 0]):int(self.epoch_image_main_direcs[index, 0] + self.train_size[0]), int(self.epoch_image_main_direcs[ index, 1]):int(self.epoch_image_main_direcs[index, 1] + self.train_size[1]), :] mask = mask[int(self.epoch_image_main_direcs[ index, 0]):int(self.epoch_image_main_direcs[index, 0] + self.train_size[0]), int(self.epoch_image_main_direcs[ index, 1]):int(self.epoch_image_main_direcs[index, 1] + self.train_size[1])] hue = iaa.MultiplyHue((1 - hue_factor, 1 + hue_factor)) jpeg = iaa.JpegCompression(compression=jpeg_scale) rotator = iaa.Affine(rotate=rotation_angle) if np.random.rand() < p_imgaug: img_transforms = iaa.Sequential([jpeg, hue, rotator]) image = img_transforms(image=image) rotator = iaa.Affine(rotate=rotation_angle, order=0, cval=19) mask_transforms = iaa.Sequential([rotator]) mask = mask_transforms(image=mask) if np.random.rand() < p_flip: image = np.flip(image, axis=1) mask = np.flip(mask, axis=1) if np.random.rand() < p_pixel_attack: sel_pixels = np.random.choice( np.arange(self.train_size[0] * self.train_size[1]), int((self.train_size[0] * self.train_size[1] * pixel_attack_density) // 1)) rand_pixels = np.random.randint(0, 255, (sel_pixels.shape[0], 3)) image = image.reshape((self.train_size[0] * self.train_size[1], 3)) image[sel_pixels] = rand_pixels image = image.reshape((self.train_size[0], self.train_size[1], 3)) mask = mask.reshape((self.train_size[0] * self.train_size[1], 1)) mask[sel_pixels] = 19 mask = mask.reshape((self.train_size[0], self.train_size[1])) image = Image.fromarray(image) mask = Image.fromarray(mask) # From PIL to Tensor image = TF.to_tensor(image) # Normalize image = TF.normalize(image, self.dataset_mean, self.dataset_std) # Convert ids to train_ids mask = np.array(mask, np.uint8) mask = torch.from_numpy(mask) # Numpy array to tensor return image, mask
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)), iaa.GaussianBlur((0.4, 1.2)),
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 )
''' 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))) ### add the augmenters ### seq = iaa.Sequential([ ## 0.5 is the probability, horizontally flip 50% of the images iaa.Fliplr(0.5), #iaa.Flipud(0.5), ## crop images from each side by 0 to 16px(randomly chosen)
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
import cv2 import numpy as np from config import config import misc_utils from pycocotools.coco import COCO import torch import imgaug as ia import imgaug.augmenters as iaa aug_seq = iaa.Sequential([ iaa.AdditiveGaussianNoise(scale=(0, 0.025 * 255)), iaa.MultiplyHue((0.75, 1.25)), iaa.Add((-80, 80)) ]) heavy_aug_seq = iaa.Sequential([ iaa.AdditiveGaussianNoise(scale=(0, 0.025 * 255)), iaa.MultiplyHue((0.75, 1.25)), iaa.Add((-80, 80)), iaa.Dropout(p=(0, 0.3)), iaa.imgcorruptlike.MotionBlur(severity=(1, 3)), iaa.GammaContrast((0.0, 2.0), per_channel=True) ]) category_map = ['background', 'person'] def load_coco_json_lines(fpath, image_folder=None): cocoGt = COCO(fpath)
proc_image = image.img_to_array(proc_image, dtype='uint8') #imageio.imwrite("example_segmaps.jpg", proc_image) #proc_image = np.expand_dims(proc_image, axis=0) heatmap = np.zeros((1, OUTPUT_HEIGHT, OUTPUT_WIDTH, 1), dtype=np.float32) heatmap[0, 20, 140, 0] = 1. #heatmap =heatmap[:,:,:,0] #plt.imshow(heatmap, cmap='gray') #plt.show() aug_list = iaa.OneOf([ <<<<<<< Updated upstream #iaa.Dropout([0.02, 0.1]), #iaa.Sharpen((0.0, 1.0)), iaa.MultiplyHue((0.7, 1.4)), #iaa.MultiplyBrightness((0.7, 1.4)) ======= iaa.Dropout([0.02, 0.1]), iaa.Sharpen((0.0, 1.0)), iaa.MultiplyHue((0.7, 1.4)), iaa.MultiplyBrightness((0.7, 1.4)) >>>>>>> Stashed changes ]) aug = iaa.Sequential([aug_list, iaa.Fliplr(0.5)], random_order=True) proc, hm= aug.augment(image=proc_image, heatmaps=heatmap) hm = hm[0,:,:,0] plt.imshow(hm, cmap='gray')