def chapter_augmenters_multiplybrightness(): fn_start = "color/multiplybrightness" aug = iaa.MultiplyBrightness((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 ol_aug(image, mask): # ia.seed(seed) # Example batch of images. # The array has shape (32, 64, 64, 3) and dtype uint8. images = image # B,H,W,C masks = mask # B,H,W,C # print('In Aug',images.shape,masks.shape) combo = np.concatenate((images, masks), axis=3) # print('COMBO: ',combo.shape) seq_all = iaa.Sequential([ iaa.Fliplr(0.5), # horizontal flips # iaa.PadToFixedSize(width=crop_size[0], height=crop_size[1]), # iaa.CropToFixedSize(width=crop_size[0], height=crop_size[1]), iaa.Affine( scale={"x": (0.9, 1.1), "y": (0.9, 1.1)}, # scale images to 90-110% of their size, individually per axis translate_percent={"x": (-0.1, 0.1), "y": (-0.1, 0.1)}, # translate by -10 to +10 percent (per axis) rotate=(-5, 5), # rotate by -5 to +5 degrees shear=(-3, 3), # shear by -3 to +3 degrees ), # iaa.Cutout(nb_iterations=(1, 5), size=0.2, cval=0, squared=False), ], random_order=False) # apply augmenters in random order seq_f = iaa.Sequential([ iaa.Sometimes(0.5, iaa.OneOf([ iaa.GaussianBlur((0.0, 3.0)), iaa.MotionBlur(k=(3, 20)), ]), ), iaa.Sometimes(0.5, iaa.OneOf([ iaa.Multiply((0.8, 1.2), per_channel=0.2), iaa.MultiplyBrightness((0.5, 1.5)), iaa.LinearContrast((0.5, 2.0), per_channel=0.2), iaa.BlendAlpha((0., 1.), iaa.HistogramEqualization()), iaa.MultiplyHueAndSaturation((0.5, 1.5), per_channel=0.2), ]), ), ], random_order=False) combo_aug = np.array(seq_all.augment_images(images=combo)) # print('combo_au: ', combo_aug.shape) images_aug = combo_aug[:, :, :, :3] masks_aug = combo_aug[:, :, :, 3:] images_aug = seq_f.augment_images(images=images_aug) return images_aug, masks_aug
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 __init__(self): self.aug = iaa.Sequential([ iaa.Sometimes(0.25, iaa.GaussianBlur(sigma=(0, 3.0))), # iaa.Fliplr(0.15), # iaa.Crop(px=(0, 10)), iaa.Sometimes(0.25, iaa.PerspectiveTransform(0.08)), iaa.MultiplyBrightness((0.5, 1.5)), iaa.Affine(rotate=(-10, 10), mode='symmetric'), iaa.Sometimes(0.25, iaa.OneOf([iaa.Dropout(p=(0, 0.1)), iaa.CoarseDropout(0.1, size_percent=0.5)])), iaa.AddToHueAndSaturation(value=(-10, 10), per_channel=True) ])
def __init__(self, key_source='image', key_target=None): super(RandomColorJitter, self).__init__(key_source=key_source, key_target=key_target) self.sequence = iaa.Sequential([ iaa.Sometimes( 0.8, iaa.Sequential([ iaa.MultiplyBrightness((0.8, 1.25)), iaa.MultiplyHueAndSaturation(mul_hue=(0.8, 1.25), mul_saturation=(0.8, 1.25)) ])), iaa.Sometimes(0.2, iaa.Grayscale()) ])
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 data_augmentation(path): ia.seed(2) seq = iaa.Sequential([ iaa.Sometimes(0.5, iaa.Grayscale(alpha=(0.1, 0.5))), iaa.Sometimes(0.5, iaa.Multiply((0.5, 1.5), per_channel=0.5)), iaa.Sometimes(0.5, iaa.MultiplyHueAndSaturation(mul_saturation=(0.5, 1.5))), iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 2.0))), iaa.Sometimes(0.8, iaa.MultiplyBrightness((0.5, 1.5))), iaa.AddToBrightness((-30, 30)), iaa.Sometimes(0.6, iaa.MultiplyHueAndSaturation(mul_saturation=(0.5, 1.5))) ], random_order=True) i = 0 for fname in os.listdir(path): try: img = imageio.imread(os.path.join(path, fname), pilmode="RGB") print(i) if i % 5 == 0: img_aug = seq.augment_image(img) imageio.imwrite( os.path.join(path, fname.replace(".jpg", "_imgaug.jpg")), img_aug) fname_txt = fname.replace('.jpg', '.txt') print( os.path.join(path, fname_txt.replace(".txt", "_imgaug.txt"))) shutil.copyfile( os.path.join(path, fname_txt), os.path.join(path, fname_txt.replace(".txt", "_imgaug.txt"))) except: print('Error reading img') i += 1
def __init__(self): self.aug = iaa.Sequential([ # iaa.HorizontalFlip(p = 0.5), # iaa.VerticalFlip(p = 0.5), # iaa.Affine(scale=(0.5, 1.5)), iaa.Dropout(p=(0, 0.2), per_channel=0.5), iaa.SomeOf( (1, 2), [ # iaa.Cutout(fill_mode="gaussian", fill_per_channel=True), iaa.SaltAndPepper(0.1), # iaa.Affine(rotate=(-45, 45), shear=(-16, 16)), # iaa.imgcorruptlike.GaussianNoise(severity=1), # iaa.AveragePooling(2), iaa.AddToHueAndSaturation((-60, 60)), iaa.MultiplyBrightness(mul=(0.65, 1.35)), iaa.LinearContrast((0.5, 2.0)), iaa.GaussianBlur(sigma=(0.5, 2.0)), # iaa.CoarseDropout((0.01,0.1), size_percent = 0.01) ]) ])
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
def init_augmenter(img_mode="color"): """Initializes the augmenters used in the training dataset :param config: the config object that contains all the """ ia.seed(10) if img_mode == 'color': return iaa.Sequential([ sometimes(iaa.Fliplr()), iaa.MultiplyBrightness((0.6, 1.4)), # TODO: try no ChangeColor or Brightness sometimes(iaa.ChangeColorTemperature((5000, 7000))), iaa.Crop(percent=( (0, 0.50), (0, 0.50), (0, 0.50), (0, 0.50) )) # sometimes(iaa.OneOf([ # iaa.Cutout(nb_iterations=(1, 4), size=0.2, # squared=False, cval=(0, 255), fill_mode="constant"), # iaa.Cutout(nb_iterations=(1, 4), size=0.2, squared=False, cval=( # 0, 255), fill_mode="gaussian", fill_per_channel=True), # iaa.AdditiveGaussianNoise(scale=(0, 0.1*255)) # ])) ]) else: return iaa.Sequential([ sometimes(iaa.Fliplr()), iaa.Crop(percent=( (0, 0.40), (0, 0.40), (0, 0.40), (0, 0.40) )) ])
# instantiate imgaug augmentation object sometimes = lambda aug: iaa.Sometimes(0.5, aug) AUGMENTATIONS = iaa.Sequential([ iaa.Fliplr(0.5), iaa.Flipud(0.5), sometimes(iaa.Affine( scale=(0.8, 1.2), rotate=(90), mode=ia.ALL)), sometimes(iaa.ElasticTransformation(alpha=(0.8, 1.2),\ sigma=(9.0, 11.0))), sometimes(iaa.AdditiveGaussianNoise(scale=(0, 0.1))), sometimes(iaa.GaussianBlur((0, 0.1))), sometimes(iaa.MultiplyBrightness((0.65, 1.35))), sometimes(iaa.LinearContrast((0.5, 1.5))), sometimes(iaa.MultiplyHueAndSaturation((-1, 1))) ], random_order=True) # instantiate datagen objects train_datagen = ImageDataAugmentor( # featurewise_center=True, # featurewise_std_normalization=True, augment=AUGMENTATIONS, rescale=1. / 255, preprocess_input=None) val_datagen = ImageDataAugmentor(rescale=1. / 255) # define the ImageNet mean subtraction (in RGB order)
# dataset = NewDataset() # loader = DataLoader(dataset, batch_size=2) # for x in loader: # print(x) import numpy as np from imgaug import augmenters as iaa from torch.utils.data import DataLoader, Dataset from torchvision import transforms tfs = transforms.Compose([ iaa.Sequential([ iaa.flip.Fliplr(p=0.5), iaa.flip.Flipud(p=0.5), iaa.GaussianBlur(sigma=(0.0, 0.1)), iaa.MultiplyBrightness(mul=(0.65, 1.35)), ]).augment_image, transforms.ToTensor() ]) class CustomDataset(Dataset): def __init__(self, n_images, n_classes, transform=None): self.images = np.random.randint(0, 255, (n_images, 224, 224, 3), dtype=np.uint8) self.targets = np.random.randn(n_images, n_classes) self.transform = transform def __getitem__(self, item): image = self.images[item]
iaa.BlendAlphaElementwise((0.0, 1.0), 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)),
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 )
save_model_dir = '/tmp2/wide_angel' train_type = 'wide_angle' data_version = 'v4' csv_train = Path(__file__).parent.parent.absolute().joinpath( 'datafiles', data_version, 'train.csv') csv_valid = Path(__file__).parent.parent.absolute().joinpath( 'datafiles', data_version, 'valid.csv') csv_test = Path(__file__).parent.parent.absolute().joinpath( 'datafiles', data_version, 'test.csv') iaa = iaa.Sequential([ # iaa.CropAndPad(percent=(-0.04, 0.04)), iaa.Fliplr(0.5), iaa.Flipud(0.25), iaa.GaussianBlur(sigma=(0.0, 0.3)), iaa.MultiplyBrightness(mul=(0.7, 1.3)), iaa.contrast.LinearContrast((0.7, 1.3)), iaa.Sometimes(0.9, iaa.Add((-8, 8))), iaa.Sometimes( 0.9, iaa.Affine( scale=(0.98, 1.02), translate_percent={ "x": (-0.06, 0.06), "y": (-0.06, 0.06) }, rotate=(-15, 15), )), ]) batch_size_train, batch_size_valid = 32, 64
import cv2 import os import uuid filename_csv = 'fovea.csv' image_shape = (299, 299) dir_tmp = '/tmp2/dataset_test/' iaa = iaa.Sequential([ # iaa.CropAndPad(percent=(-0.04, 0.04)), iaa.Fliplr(0.5), # horizontally flip 50% of the images iaa.Flipud(0.2), # horizontally flip 50% of the images iaa.GaussianBlur(sigma=(0.0, 0.5)), iaa.MultiplyBrightness(mul=(0.8, 1.2)), iaa.contrast.LinearContrast((0.8, 1.2)), iaa.Sometimes(0.9, iaa.Add((-8, 8))), iaa.Sometimes(0.9, iaa.Affine( scale=(0.98, 1.02), translate_percent={"x": (-0.06, 0.06), "y": (-0.06, 0.06)}, rotate=(-15, 15), )), ]) iaa = None batch_size = 32 dataset= Dataset_CSV(csv_file=filename_csv, imgaug_iaa=iaa, image_shape=image_shape) loader = DataLoader(dataset, batch_size=batch_size,
import numpy as np from imgaug import augmenters as iaa from torch.utils.data import DataLoader, Dataset from torchvision import transforms from skimage import io import os tfs = transforms.Compose([ iaa.Sequential([ iaa.Sometimes(0.5, iaa.Fliplr(1.0)), iaa.Sometimes(0.5, iaa.MultiplyBrightness((0.3, 1.3))), iaa.Sometimes(0.2, iaa.ChangeColorTemperature((4300, 6000))), #iaa.Sometimes(0.9, iaa.Affine(rotate=(-180, 180), shear=(-6, 6))), ]).augment_image, transforms.ToTensor() ]) class CustomDataset(Dataset): def __init__(self, n_images, n_classes=15, transform=None): self.images = [] self.transform = transform test_path = "/content/gdrive/My Drive/Arirang/data/test/images" file_list = os.listdir(test_path) file_list_png = [file for file in file_list if file.endswith(".png")] for idx, filename in enumerate(file_list_png): self.images.append( os.path.join(test_path, filename))
# save to local # read from local # f = open("dict.txt", 'r') # dict_ = eval(f.read()) # f.close() # print("read from local : ", dict_) def getImageVar(img): img=cv2.cvtColor(img,cv2.COLOR_RGB2GRAY) imagevar=cv2.Laplacian(img,cv2.CV_64F).var() return imagevar aug_brightness = iaa.MultiplyBrightness((0.7, 1.1)) aug_gaussian =iaa.GaussianBlur((0, 2.0)) kernel_sharpen_1 = np.array([ [-1, -1, -1], [-1, 9, -1], [-1, -1, -1]]) path='/home/ubuntu/hks/ocr/idcard_generator_project/idcard_pix2pix/data_train_with_aug/' dirs=os.listdir(path) d_dict={} for d in dirs: d_dict[d+'_0']=0 d_dict[d+'_1']=0 l=[] for i in range(1,20): for index,d in enumerate(dirs): # os.mkdir('/home/ubuntu/hks/ocr/idcard_generator_project/idcard_pix2pix/data_val_with_aug/'+d)
transformed_image = transform(image=image)['image'] elif augmentation == 'brightness': transform = iaa.imgcorruptlike.Brightness(severity=2) transformed_image = transform(image=image) elif augmentation == 'addto_hue_and_saturation': transform = iaa.AddToHueAndSaturation((-50, 50), per_channel=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']
image = tf.image.random_crop(image, [tf.shape(image)[0], *INPUT_SHAPE[:2], 3]) return image, target # resize image to 224x224 def resize_image(image, target): image = tf.image.resize(image, INPUT_SHAPE[:2]) return image, target # augmentation options AUG = iaa.Sequential([ iaa.SomeOf((0, 1), [ iaa.AddToBrightness((-30, 30)), iaa.MultiplyBrightness((0.5, 1.5)), iaa.MultiplySaturation((0.5, 1.5)), iaa.AddToSaturation((-50, 50)) ]), iaa.OneOf([ iaa.ScaleX((1.0, 1.5)), iaa.ScaleY((1.0, 1.5)), iaa.Affine(scale={ "x": (1.0, 1.2), "y": (1.0, 1.2) }), iaa.Affine(rotate=(-20, 20)), iaa.PiecewiseAffine(scale=(0.01, 0.05)), iaa.Affine(shear=(-16, 16)) ]), iaa.Fliplr(0.5),
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 augumentation(train_set): img_final_height = int(375 * 0.7) img_final_width = int(500 * 0.7) #The transform function for train data # transform_train = transforms.Compose([ # transforms.Resize((int(img_final_height*1.1),int(img_final_width*1.1))), # transforms.RandomCrop((img_final_height,img_final_width), padding=4), # # transforms.Resize(224), # transforms.ToTensor(), # #transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), # transforms.Normalize(mean=[0.485, 0.456, 0.406], # std=[0.229, 0.224, 0.225]) # ]) # transform_train = transforms.Compose([ # transforms.Resize((256,128), interpolation=transforms.InterpolationMode.BICUBIC), # transforms.RandomHorizontalFlip(0.5), # transforms.Pad(10), # transforms.RandomCrop((256,128)), # transforms.ToTensor(), # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), # RandomErasing(probability=0.5, mean=([0.485, 0.456, 0.406])) # ]) transform_train = transforms.Compose([ transforms.Resize( (int(img_final_height * 1.1), int(img_final_width * 1.1))), transforms.RandomCrop((img_final_height, img_final_width), padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), RandomErasing(probability=0.5, mean=([0.485, 0.456, 0.406])) ]) # transform_train= transforms.Compose([ # iaa.Sequential([ # iaa.flip.Fliplr(p=0.5), # iaa.flip.Flipud(p=0.5), # iaa.GaussianBlur(sigma=(0.0, 0.1)), # iaa.MultiplyBrightness(mul=(0.65, 1.35)), # ]).augment_image, # transforms.ToTensor() # ]) seq = iaa.Sequential([ iaa.flip.Flipud(p=0.5), iaa.MultiplyBrightness(mul=(0.65, 1.35)), #iaa.GammaContrast(1.5), iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5), iaa.Fliplr(p=0.5), # 水平翻轉影象 iaa.GaussianBlur(sigma=(0, 3.0)), # 使用0到3.0的sigma模糊影象 # Small gaussian blur with random sigma between 0 and 0.5. # But we only blur about 50% of all images. iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 0.5))), # Add gaussian noise. # For 50% of all images, we sample the noise once per pixel. # For the other 50% of all images, we sample the noise per pixel AND # channel. This can change the color (not only brightness) of the # pixels. iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5), # Make some images brighter and some darker. # In 20% of all cases, we sample the multiplier once per channel, # which can end up changing the color of the images. iaa.Multiply((0.8, 1.2), per_channel=0.8) # Apply affine transformations to each image. # Scale/zoom them, translate/move them, rotate them and shear them. ]) #The transform function for test data # trainset.setImgaug(seq) train_set.setTransform(transform_train) return train_set
def gen_faker_card_run(): aug_brightness = iaa.MultiplyBrightness((0.5, 1.)) aug_gaussian =iaa.GaussianBlur((0, 2.0)) # blur images with a sigma between 0 and 3.0 csv_file = open(ori_csv_file, 'r', encoding='UTF-8') csv_reader_lines = list(csv.reader(csv_file)) csv_reader_lines_train,csv_reader_lines_val=train_test_split(csv_reader_lines,test_size=0.000001, random_state=0)# 逐行读取csv文件 date = [] # 创建列表准备接收csv各行数据 cnt = 0 # 记录csv文件行数 path='/home/ubuntu/hks/ocr/idcard_generator_project/idcard_generator/template/' files = os.listdir(os.path.join(path, 'fuzhiwuxiao_mask')) for one_line in csv_reader_lines_train: date.append(one_line) image_name=date[cnt][0] result_front = [] result_back = [] result_front.append(date[cnt][1]) # 姓名 result_front.append(date[cnt][3]) # 性别 result_front.append(date[cnt][2]) # 名族 result_front.append(date[cnt][4]) # 年 result_front.append(date[cnt][5]) # 月 result_front.append(date[cnt][6]) # 日 result_front.append(date[cnt][7]) # 地址 result_front.append(date[cnt][8]) # 身份号 result_back.append(date[cnt][9]) # 签发机关 result_back.append(date[cnt][10]) # 有效日期 image1 = cv2.imread(front_img) # 读取正面模板 image2 = cv2.imread(back_img) # 读取背面模板 #img_new_white1 = img_to_white(image1) img_new_white1 = image1 # cv2.imshow('hjs',img_new_white1) # cv2.waitKey(0)# 生成画布 img_res_f = gen_card_front(img_new_white1, result_front) img_res_f = cv2.cvtColor(img_res_f,cv2.COLOR_BGR2GRAY) # 写入文字 #cv2.imwrite(result_card_path + '/{}_1.jpg'.format(image_name), img_res_f) #img_new_white2 = img_to_white(image2) img_new_white2 = image2 img_res_b = gen_card_back(img_new_white2, result_back) img_res_b = cv2.cvtColor(img_res_b, cv2.COLOR_BGR2GRAY) #cv2.imwrite(result_card_path + '/{}_0.jpg'.format(image_name), img_res_b) cnt = cnt + 1 print(cnt) for i in range(4): l = len(files) index = np.random.randint(0, l) image_gen_copy = img_res_f.copy() mask = cv2.imread(os.path.join(path + 'fuzhiwuxiao_mask', files[index]), 0) mask[mask > 150] = 255 mask[mask <= 150] = 0 image = cv2.imread(os.path.join(path + 'fuzhiwuxiao_template', files[index]), 0) image = cv2.blur(image, ksize=(5, 5)) image = image + 20 template = cv2.bitwise_and(image, image, mask=mask) h_image_gen, w_image_gen= img_res_f.shape point_x, point_y = h_image_gen, w_image_gen h, w = template.shape while point_x + h >= h_image_gen or point_y + w >= w_image_gen: point_x, point_y = np.random.randint(0, h_image_gen), np.random.randint(0, w_image_gen) rect = image_gen_copy[point_x:(h + point_x), point_y:(w + point_y)] rect1 = cv2.bitwise_and(rect, rect, mask=mask) mask = cv2.bitwise_not(mask) rect2 = cv2.bitwise_and(rect, rect, mask=mask) image_with_logo = cv2.addWeighted(rect1, 0.3, template, 0.3, 0) add_logo = image_with_logo + rect2 image_gen_copy_logo = image_gen_copy.copy() image_gen_copy_logo[point_x:(h + point_x), point_y:(w + point_y)] = add_logo image_gen_copy_logo=np.stack([image_gen_copy_logo,image_gen_copy_logo,image_gen_copy_logo],axis=2) aug_brightness_deterministic= aug_brightness.to_deterministic() aug_gaussian_deterministic=aug_gaussian.to_deterministic() image_gen_copy_logo=aug_brightness_deterministic.augment_images(images=[image_gen_copy_logo])[0] image_gen_copy_logo=aug_gaussian_deterministic.augment_images(images=[image_gen_copy_logo])[0] image_gen_copy = np.stack([image_gen_copy, image_gen_copy, image_gen_copy], axis=2) image_gen_copy = aug_brightness_deterministic.augment_images(images=[image_gen_copy])[0] image_gen_copy = aug_gaussian_deterministic.augment_images(images=[image_gen_copy])[0] #image_gen_copy=seq_nologo(image=np.stack([image_gen_copy,image_gen_copy,image_gen_copy],axis=2)) train_data = np.hstack((image_gen_copy_logo,image_gen_copy)) # cv2.imshow('image', train_data) # cv2.waitKey(300) #image_gen_copy[point_x:(h + point_x), point_y:(w + point_y)] = add_logo[:,:] if os.path.exists('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/train/' + image_name)==False: os.mkdir('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/train/' + image_name) cv2.imwrite('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/train/' +image_name+'/'+ image_name +'_'+'1'+ '-' + str(i) + '.jpg', train_data) for i in range(4): l = len(files) index = np.random.randint(0, l) image_gen_copy = img_res_b.copy() mask = cv2.imread(os.path.join(path + 'fuzhiwuxiao_mask', files[index]), 0) mask[mask > 150] = 255 mask[mask <= 150] = 0 image = cv2.imread(os.path.join(path + 'fuzhiwuxiao_template', files[index]), 0) image = cv2.blur(image, ksize=(5, 5)) image = image + 50 template = cv2.bitwise_and(image, image, mask=mask) h_image_gen, w_image_gen = img_res_b.shape point_x, point_y = h_image_gen, w_image_gen h, w = template.shape while point_x + h >= h_image_gen or point_y + w >= w_image_gen: l_point = len(point_select) l_index = np.random.randint(0, l_point) point_x, point_y = point_select[l_index] rect = image_gen_copy[point_x:(h + point_x), point_y:(w + point_y)] rect1 = cv2.bitwise_and(rect, rect, mask=mask) mask = cv2.bitwise_not(mask) rect2 = cv2.bitwise_and(rect, rect, mask=mask) image_with_logo = cv2.addWeighted(rect1, 0.3, template, 0.3, 0) add_logo = image_with_logo + rect2 image_gen_copy_logo = image_gen_copy.copy() image_gen_copy_logo[point_x:(h + point_x), point_y:(w + point_y)] = add_logo image_gen_copy_logo=np.stack([image_gen_copy_logo,image_gen_copy_logo,image_gen_copy_logo],axis=2) aug_brightness_deterministic= aug_brightness.to_deterministic() aug_gaussian_deterministic=aug_gaussian.to_deterministic() image_gen_copy_logo=aug_brightness_deterministic.augment_images(images=[image_gen_copy_logo])[0] image_gen_copy_logo=aug_gaussian_deterministic.augment_images(images=[image_gen_copy_logo])[0] image_gen_copy = np.stack([image_gen_copy, image_gen_copy, image_gen_copy], axis=2) image_gen_copy = aug_brightness_deterministic.augment_images(images=[image_gen_copy])[0] image_gen_copy = aug_gaussian_deterministic.augment_images(images=[image_gen_copy])[0] train_data = np.hstack((image_gen_copy_logo,image_gen_copy)) # cv2.imshow('template', template) # cv2.imshow('logo',image_with_logo) # cv2.imshow('image_gen',image_gen_copy) # #cv2.imshow('rect',rect2) # # cv2.imshow('image', train_data) # cv2.waitKey(300000) if os.path.exists('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/train/' + image_name) == False: os.mkdir('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/train/' + image_name) cv2.imwrite('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/train/'+image_name+'/' + image_name+'_'+'0'+ '-' + str(i) + '.jpg', train_data) del image_gen_copy_logo for one_line in csv_reader_lines_val: date.append(one_line) image_name=date[cnt][0] result_front = [] result_back = [] result_front.append(date[cnt][1]) # 姓名 result_front.append(date[cnt][3]) # 性别 result_front.append(date[cnt][2]) # 名族 result_front.append(date[cnt][4]) # 年 result_front.append(date[cnt][5]) # 月 result_front.append(date[cnt][6]) # 日 result_front.append(date[cnt][7]) # 地址 result_front.append(date[cnt][8]) # 身份号 result_back.append(date[cnt][9]) # 签发机关 result_back.append(date[cnt][10]) # 有效日期 image1 = cv2.imread(front_img) # 读取正面模板 image2 = cv2.imread(back_img) # 读取背面模板 #img_new_white1 = img_to_white(image1) img_new_white1 = image1 # cv2.imshow('hjs',img_new_white1) # cv2.waitKey(0)# 生成画布 img_res_f = gen_card_front(img_new_white1, result_front) img_res_f = cv2.cvtColor(img_res_f,cv2.COLOR_BGR2GRAY) # 写入文字 #cv2.imwrite(result_card_path + '/{}_1.jpg'.format(image_name), img_res_f) #img_new_white2 = img_to_white(image2) img_new_white2 = image2 img_res_b = gen_card_back(img_new_white2, result_back) img_res_b = cv2.cvtColor(img_res_b, cv2.COLOR_BGR2GRAY) #cv2.imwrite(result_card_path + '/{}_0.jpg'.format(image_name), img_res_b) cnt = cnt + 1 print(cnt) for i in range(4): l = len(files) index = np.random.randint(0, l) image_gen_copy = img_res_f.copy() mask = cv2.imread(os.path.join(path + 'fuzhiwuxiao_mask', files[index]), 0) mask[mask > 150] = 255 mask[mask <= 150] = 0 image = cv2.imread(os.path.join(path + 'fuzhiwuxiao_template', files[index]), 0) template = cv2.bitwise_and(image, image, mask=mask) h_image_gen, w_image_gen= img_res_f.shape point_x, point_y = h_image_gen, w_image_gen h, w = template.shape while point_x + h >= h_image_gen or point_y + w >= w_image_gen: point_x, point_y = np.random.randint(0, h_image_gen), np.random.randint(0, w_image_gen) rect = image_gen_copy[point_x:(h + point_x), point_y:(w + point_y)] rect1 = cv2.bitwise_and(rect, rect, mask=mask) mask = cv2.bitwise_not(mask) rect2 = cv2.bitwise_and(rect, rect, mask=mask) image_with_logo = cv2.addWeighted(rect1, 0.3, template, 0.3, 0) add_logo = image_with_logo + rect2 image_gen_copy_logo = image_gen_copy.copy() image_gen_copy_logo[point_x:(h + point_x), point_y:(w + point_y)] = add_logo image_gen_copy_logo = np.stack([image_gen_copy_logo, image_gen_copy_logo, image_gen_copy_logo], axis=2) aug_brightness_deterministic = aug_brightness.to_deterministic() aug_gaussian_deterministic = aug_gaussian.to_deterministic() image_gen_copy_logo = aug_brightness_deterministic.augment_images(images=[image_gen_copy_logo])[0] image_gen_copy_logo = aug_gaussian_deterministic.augment_images(images=[image_gen_copy_logo])[0] image_gen_copy = np.stack([image_gen_copy, image_gen_copy, image_gen_copy], axis=2) image_gen_copy = aug_brightness_deterministic.augment_images(images=[image_gen_copy])[0] #image_gen_copy = aug_gaussian_deterministic.augment_images(images=[image_gen_copy])[0] #image_gen_copy=seq_nologo(image=np.stack([image_gen_copy,image_gen_copy,image_gen_copy],axis=2)) train_data = np.hstack((image_gen_copy_logo,image_gen_copy)) #image_gen_copy[point_x:(h + point_x), point_y:(w + point_y)] = add_logo[:,:] if os.path.exists('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/val/' + image_name) == False: os.mkdir('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/val/' + image_name) cv2.imwrite('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/val/' +image_name+'/'+ image_name +'_'+'0'+ '-' + str(i) + '.jpg', train_data) for i in range(4): l = len(files) index = np.random.randint(0, l) image_gen_copy = img_res_b.copy() mask = cv2.imread(os.path.join(path + 'fuzhiwuxiao_mask', files[index]), 0) mask[mask > 150] = 255 mask[mask <= 150] = 0 image = cv2.imread(os.path.join(path + 'fuzhiwuxiao_template', files[index]), 0) template = cv2.bitwise_and(image, image, mask=mask) h_image_gen, w_image_gen = img_res_b.shape point_x, point_y = h_image_gen, w_image_gen h, w = template.shape while point_x + h >= h_image_gen or point_y + w >= w_image_gen: l_point = len(point_select) l_index = np.random.randint(0, l_point) point_x, point_y = point_select[l_index] rect = image_gen_copy[point_x:(h + point_x), point_y:(w + point_y)] rect1 = cv2.bitwise_and(rect, rect, mask=mask) mask = cv2.bitwise_not(mask) rect2 = cv2.bitwise_and(rect, rect, mask=mask) image_with_logo = cv2.addWeighted(rect1, 0.3, template, 0.3, 0) add_logo = image_with_logo + rect2 image_gen_copy_logo = image_gen_copy.copy() image_gen_copy_logo[point_x:(h + point_x), point_y:(w + point_y)] = add_logo image_gen_copy_logo = np.stack([image_gen_copy_logo, image_gen_copy_logo, image_gen_copy_logo], axis=2) aug_brightness_deterministic = aug_brightness.to_deterministic() aug_gaussian_deterministic = aug_gaussian.to_deterministic() image_gen_copy_logo = aug_brightness_deterministic.augment_images(images=[image_gen_copy_logo])[0] image_gen_copy_logo = aug_gaussian_deterministic.augment_images(images=[image_gen_copy_logo])[0] image_gen_copy = np.stack([image_gen_copy, image_gen_copy, image_gen_copy], axis=2) image_gen_copy = aug_brightness_deterministic.augment_images(images=[image_gen_copy])[0] #image_gen_copy = aug_gaussian_deterministic.augment_images(images=[image_gen_copy])[0] train_data = np.hstack((image_gen_copy_logo,image_gen_copy)) # cv2.imshow('template', template) # cv2.imshow('logo',image_with_logo) # cv2.imshow('image_gen',image_gen_copy) # #cv2.imshow('rect',rect2) # # cv2.imshow('image', train_data) # cv2.waitKey(300000) if os.path.exists('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/val/' + image_name) == False: os.mkdir('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/val/' + image_name) cv2.imwrite('/home/ubuntu/hks/ocr/idcard_generator_project/remove_logo_and_aug_image/val/' +image_name+'/'+ image_name+'_'+'1'+ '-' + str(i) + '.jpg', train_data) del image_gen_copy_logo
save_dir_img = 'vitamin_aug3' save_dir_seg = 'vitamin_aug3_seg' save_dir_out = 'vitamin_aug3_out' os.makedirs(save_dir_img, exist_ok=True) os.makedirs(save_dir_seg, exist_ok=True) os.makedirs(save_dir_out, exist_ok=True) file_list_img = glob('%s/*.png' % load_dir_img) file_list_seg = glob('%s/*.png' % load_dir_img) aug1 = iaa.PerspectiveTransform(scale=(0, 0.15)) aug2 = iaa.Affine(scale={"x": (0.5, 1), "y": (0.5, 1)}) aug3 = iaa.Affine(translate_percent={"x": (-0.25, 0.25), "y": (-0.25, 0.25)}) aug4 = iaa.MultiplyBrightness((0.8, 1.2)) #aug4 = iaa.Affine(rotate=(-90, 90)) seq = iaa.Sequential([aug1, aug2, aug3]) aug_num = 20 img_ind = 1 for file_path in file_list_img: print('%d/%d' % (img_ind, len(file_list_img))) file_name = os.path.basename(file_path) img = cv2.imread('%s/%s' % (load_dir_img, file_name)) seg = cv2.imread('%s/%s_0.png' % (load_dir_seg, file_name[:-4])) out = cv2.Canny(img, 30, 50) cv2.imwrite('%s/%s_aug00.png' % (save_dir_img, file_name[:-4]), img) cv2.imwrite('%s/%s_aug00.png' % (save_dir_seg, file_name[:-4]), seg) cv2.imwrite('%s/%s_aug00.png' % (save_dir_out, file_name[:-4]), out)
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
def __init__(self, Brightness_ratio=None): self.Brightness_ratio = Brightness_ratio self.seq = iaa.Sequential([iaa.MultiplyBrightness((0.95, 1.05))])
#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') plt.show() imageio.imwrite("example_segmaps.jpg", proc)
def gen_faker_card_run(): path_save = '/home/simple/mydemo/ocr_project/idcard_generator_project/split_text_idcard/' path = '/home/simple/mydemo/ocr_project/idcard_generator_project/generator_datas1/' labels = csv.reader( open( '/home/simple/mydemo/ocr_project/idcard_generator_project/idcard_generator/src/generate_labels1.csv' )) # font_template = json.load(open( # '/home/simple/mydemo/ocr_project/idcard_generator_project/idcard_generator/split_text_template/0adyypn1yq_1.json')) # xingming = font_template['shapes'][0]['points'] # xingbie = font_template['shapes'][1]['points'] # mingzhu = font_template['shapes'][2]['points'] # chusheng = font_template['shapes'][3]['points'] # dizhi = font_template['shapes'][4]['points'] # shengfengzhenghao = font_template['shapes'][5]['points'] # back_template = json.load(open( # '/home/simple/mydemo/ocr_project/idcard_generator_project/idcard_generator/image_match/back1.json')) # qianfajiguang = back_template['shapes'][0]['points'] # youxiaoqixian = back_template['shapes'][1]['points'] font_template = json.load( open( '/home/simple/mydemo/ocr_project/idcard_generator_project/idcard_generator/image_match/front.json' )) xingming = font_template['shapes'][0]['points'] xingbie = font_template['shapes'][1]['points'] mingzhu = font_template['shapes'][2]['points'] chusheng_year = font_template['shapes'][3]['points'] chusheng_month = font_template['shapes'][4]['points'] chusheng_day = font_template['shapes'][5]['points'] dizhi_line1 = font_template['shapes'][6]['points'] dizhi_line2 = font_template['shapes'][7]['points'] dizhi_line3 = font_template['shapes'][8]['points'] shengfengzhenghao = font_template['shapes'][9]['points'] back_template = json.load( open( '/home/simple/mydemo/ocr_project/idcard_generator_project/idcard_generator/image_match/back1.json' )) qianfajiguang1 = back_template['shapes'][1]['points'] qianfajiguang2 = back_template['shapes'][2]['points'] youxiaoqixian = back_template['shapes'][0]['points'] aug_brightness = iaa.MultiplyBrightness((0.5, 1.)) aug_gaussian = iaa.GaussianBlur((0, 2.0)) # blur images with a sigma between 0 and 3.0 csv_file = open(ori_csv_file, 'r', encoding='UTF-8') csv_reader_lines = list(csv.reader(csv_file)) csv_reader_lines_train, csv_reader_lines_val = train_test_split( csv_reader_lines, test_size=0.005, random_state=0) # 逐行读取csv文件 date = [] # 创建列表准备接收csv各行数据 cnt = 0 # 记录csv文件行数 path = '/home/simple/mydemo/ocr_project/idcard_generator_project/idcard_generator/template/' files = os.listdir(os.path.join(path, 'fuzhiwuxiao_mask')) for one_line in csv_reader_lines_train: date.append(one_line) image_name = date[cnt][0] result_front = [] result_back = [] result_front.append(date[cnt][1]) # 姓名 result_front.append(date[cnt][3]) # 性别 result_front.append(date[cnt][2]) # 名族 result_front.append(date[cnt][4]) # 年 result_front.append(date[cnt][5]) # 月 result_front.append(date[cnt][6]) # 日 result_front.append(date[cnt][7]) # 地址 result_front.append(date[cnt][8]) # 身份号 result_back.append(date[cnt][9]) # 签发机关 result_back.append(date[cnt][10]) # 有效日期 image1 = cv2.imread(front_img) # 读取正面模板 image2 = cv2.imread(back_img) # 读取背面模板 #img_new_white1 = img_to_white(image1) img_new_white1 = image1 # cv2.imshow('hjs',img_new_white1) # cv2.waitKey(0)# 生成画布 img_res_f = gen_card_front(img_new_white1, result_front) img_res_f = cv2.cvtColor(img_res_f, cv2.COLOR_BGR2GRAY) # 写入文字 #cv2.imwrite(result_card_path + '/{}_1.jpg'.format(image_name), img_res_f) #img_new_white2 = img_to_white(image2) img_new_white2 = image2 img_res_b = gen_card_back(img_new_white2, result_back) img_res_b = cv2.cvtColor(img_res_b, cv2.COLOR_BGR2GRAY) #cv2.imwrite(result_card_path + '/{}_0.jpg'.format(image_name), img_res_b) cnt = cnt + 1 print(cnt) # os.mkdir(path_save + image_name) for i in range(4): l = len(files) index = np.random.randint(0, l) image_gen_copy = img_res_f.copy() aug_brightness_deterministic = aug_brightness.to_deterministic() aug_gaussian_deterministic = aug_gaussian.to_deterministic() image_gen_copy = np.stack( [image_gen_copy, image_gen_copy, image_gen_copy], axis=2) image_gen_copy = aug_brightness_deterministic.augment_images( images=[image_gen_copy])[0] image_gen_copy = aug_gaussian_deterministic.augment_images( images=[image_gen_copy])[0][:, :, 0] xingming_rect = image_gen_copy[ int(xingming[0][1]):int(xingming[1][1]), int(xingming[0][0]):int(xingming[1][0])] xingbie_rect = image_gen_copy[ int(xingbie[0][1]):int(xingbie[1][1]), int(xingbie[0][0]):int(xingbie[1][0])] mingzhu_rect = image_gen_copy[ int(mingzhu[0][1]):int(mingzhu[1][1]), int(mingzhu[0][0]):int(mingzhu[1][0])] chusheng_rect_year = image_gen_copy[ int(chusheng_year[0][1]):int(chusheng_year[1][1]), int(chusheng_year[0][0]):int(chusheng_year[1][0])] chusheng_rect_month = image_gen_copy[ int(chusheng_month[0][1]):int(chusheng_month[1][1]), int(chusheng_month[0][0]):int(chusheng_month[1][0])] chusheng_rect_day = image_gen_copy[ int(chusheng_day[0][1]):int(chusheng_day[1][1]), int(chusheng_day[0][0]):int(chusheng_day[1][0])] dizhi_rect_line1 = image_gen_copy[ int(dizhi_line1[0][1]):int(dizhi_line1[1][1]), int(dizhi_line1[0][0]):int(dizhi_line1[1][0])] dizhi_rect_line2 = image_gen_copy[ int(dizhi_line2[0][1] + 2):int(dizhi_line2[1][1]), int(dizhi_line2[0][0]):int(dizhi_line2[1][0])] dizhi_rect_line3 = image_gen_copy[ int(dizhi_line3[0][1] + 3):int(dizhi_line3[1][1]), int(dizhi_line3[0][0]):int(dizhi_line3[1][0])] shengfengzhenghao_rect = image_gen_copy[ int(shengfengzhenghao[0][1]):int(shengfengzhenghao[1][1]), int(shengfengzhenghao[0][0] + 5):int(shengfengzhenghao[1][0])] cv2.imshow('xingming_roi', xingming_rect) cv2.imshow('xingbie_roi', xingbie_rect) cv2.imshow('mingzhu_roi', mingzhu_rect) cv2.imshow('chusheng_year_roi', chusheng_rect_year) cv2.imshow('chusheng_month_roi', chusheng_rect_month) cv2.imshow('chusheng_day_roi', chusheng_rect_day) cv2.imshow('dizhi_line1_roi', dizhi_rect_line1) cv2.imshow('dizhi_line2_roi', dizhi_rect_line2) cv2.imshow('dizhi_line3_roi', dizhi_rect_line3) cv2.imshow('shengfengzhenghao_roi', shengfengzhenghao_rect) cv2.waitKey(1000) # cv2.imwrite(path_save+image_name+'/'+'xingming_rect'+'-'+str(i)+'.jpg',xingming_rect) # cv2.imwrite(path_save + image_name + '/' + 'dizhi_rect' + '-' + str(i) + '.jpg', dizhi_rect) # cv2.imwrite(path_save + image_name + '/' + 'xingbie_rect' + '-' + str(i) + '.jpg', xingbie_rect) # cv2.imwrite(path_save + image_name + '/' + 'mingzhu_rect' + '-' + str(i) + '.jpg', mingzhu_rect) # cv2.imwrite(path_save + image_name + '/' + 'shengfengzhenghao_rect' + '-' + str(i) + '.jpg', shengfengzhenghao_rect) # cv2.imwrite(path_save + image_name + '/' + 'chusheng_rect_year' + '-' + str(i) + '.jpg', chusheng_rect_year) # cv2.imwrite(path_save + image_name + '/' + 'chusheng_rect_month' + '-' + str(i) + '.jpg', # chusheng_rect_month) # cv2.imwrite(path_save + image_name + '/' + 'chusheng_rect_day' + '-' + str(i) + '.jpg', # chusheng_rect_day) for i in range(4): l = len(files) index = np.random.randint(0, l) image_gen_copy = img_res_b.copy() aug_brightness_deterministic = aug_brightness.to_deterministic() aug_gaussian_deterministic = aug_gaussian.to_deterministic() image_gen_copy = np.stack( [image_gen_copy, image_gen_copy, image_gen_copy], axis=2) image_gen_copy = aug_brightness_deterministic.augment_images( images=[image_gen_copy])[0] image_gen_copy = aug_gaussian_deterministic.augment_images( images=[image_gen_copy])[0][:, :, 0] qianfajiguang1_roi = image_gen_copy[ int(qianfajiguang1[0][1]):int(qianfajiguang1[1][1]), int(qianfajiguang1[0][0]):int(qianfajiguang1[1][0])] qianfajiguang2_roi = image_gen_copy[ int(qianfajiguang2[0][1] - 3):int(qianfajiguang2[1][1] - 3), int(qianfajiguang2[0][0]):int(qianfajiguang2[1][0])] youxiaoqixian_roi = image_gen_copy[ int(youxiaoqixian[0][1]):int(youxiaoqixian[1][1]), int(youxiaoqixian[0][0]):int(youxiaoqixian[1][0])] cv2.imshow('qianfajiguang1_roi', qianfajiguang1_roi) cv2.imshow('qianfajiguang2_roi', qianfajiguang2_roi) cv2.imshow('youxiaoqixian_roi', youxiaoqixian_roi) cv2.waitKey(1000)
def __getitem__(self, index): assert index <= self.nSamples, 'index range error' index += 1 try: img_root_i = osp.join(self.data_root, str(index) + '.txt') with open(img_root_i, 'r') as f: lines = f.readlines() js_p = lines[0].strip() img_p = lines[1].strip() img = Image.open(img_p) img = img.convert("RGB") except IOError: print('Corrupted image for %d' % index) return self[index + 1] ori_w, ori_h = img.size img = img.resize((self.width, self.height)) try: pt = fileutils.red_json(js_p) pt[0] = pt[0] / ori_w * self.width pt[1] = pt[1] / ori_h * self.height except IndexError: return self[index + 1] pt_x = pt[0] pt_y = pt[1] # fileutils.test_point(img,pt[0],pt[1],index,tag='ori') if self.is_train: self.augment = iaa.Sequential([ iaa.MotionBlur(k=5, angle=45), iaa.CropAndPad(percent=(-0.2, 0.2), pad_mode="edge"), iaa.AdditiveGaussianNoise(scale=(0, 0.05 * 255)), iaa.SaltAndPepper(0.2, per_channel=True), iaa.AddToHueAndSaturation((-60, 60)), iaa.MultiplyBrightness((0.5, 1.5)), iaa.Affine(rotate=(-180, 180)), iaa.pillike.EnhanceSharpness(), iaa.pillike.EnhanceColor(), iaa.Dropout(p=(0, 0.1)) ], random_order=True) kps = [ ia.Keypoint(x=pt[0], y=pt[1]), ] if random.uniform(0, 1) < 0.2: pt_x = pt[0] pt_y = pt[1] else: img = np.asarray(img, dtype=np.uint8) kpsoi = ia.KeypointsOnImage(kps, shape=img.shape) aug_det = self.augment.to_deterministic() img = aug_det.augment_image(img) pt_aug = aug_det.augment_keypoints(kpsoi) # img = self.augment(image = img) img = Image.fromarray(img) pt_x = pt_aug[0].x_int pt_y = pt_aug[0].y_int # fileutils.test_point(img,pt_x,pt_y,index) # img.save('seeee_'+str(index)+'_.jpg') if self.transform is not None: img = self.transform(img) return img, pt_x, pt_y