diff --git a/data_augmentation/img_aug.py b/data_augmentation/img_aug.py
deleted file mode 100755
index 48ef279..0000000
--- a/data_augmentation/img_aug.py
+++ /dev/null
@@ -1,462 +0,0 @@
-import random
-import numpy as np
-import cv2
-import matplotlib.pyplot as plt
-import sys
-import os
-import math
-import imgaug.augmenters as iaa
-import torch
-
-from utils.utils import get_rotated_coors
-
-
-class HSV(object):
-    def __init__(self , saturation=0, brightness=0, p=0.5):
-        self.saturation = saturation 
-        self.brightness = brightness
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)  # hue, sat, val
-            S = img_hsv[:, :, 1].astype(np.float32)  # saturation
-            V = img_hsv[:, :, 2].astype(np.float32)  # value
-            a = random.uniform(-1, 1) * self.saturation + 1
-            b = random.uniform(-1, 1) * self.brightness + 1
-            S *= a
-            V *= b
-            img_hsv[:, :, 1] = S if a < 1 else S.clip(None, 255)
-            img_hsv[:, :, 2] = V if b < 1 else V.clip(None, 255)
-            cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
-        return img, labels
-    
-
-class Blur(object):
-    def __init__(self, sigma=0 ,p=0.5):
-        self.sigma = sigma 
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            blur_aug = iaa.GaussianBlur(sigma=(0,self.sigma))
-            img = blur_aug.augment_image(img)
-        return img, labels
-
-
-class Grayscale(object):
-    def __init__(self, grayscale=0. ,p=0.5):
-        self.alpha = random.uniform(grayscale,1.0)
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            gray_aug = iaa.Grayscale(alpha=(self.alpha, 1.0))
-            img = gray_aug.augment_image(img)
-        return img, labels
-
-
-class Gamma(object):
-    def __init__(self, intensity=0 ,p=0.5):
-        self.intensity = intensity 
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            gm = random.uniform(1-self.intensity,1+self.intensity)
-            img = np.uint8(np.power(img/float(np.max(img)), gm)*np.max(img))
-        return img, labels
-
-
-class Noise(object):
-    def __init__(self, intensity=0 ,p=0.5):
-        self.intensity = intensity 
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            noise_aug = iaa.AdditiveGaussianNoise(scale=(0, self.intensity * 255))
-            img = noise_aug.augment_image(img)
-        return img, labels
-
-
-
-class Sharpen(object):
-    def __init__(self, intensity=0 ,p=0.5):
-        self.intensity = intensity 
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            sharpen_aug = iaa.Sharpen(alpha=(0.0, 1.0), lightness=(1 - self.intensity,1 + self.intensity))
-            img = sharpen_aug.augment_image(img)
-        return img, labels
-
-
-class Contrast(object):
-    def __init__(self, intensity=0 ,p=0.5):
-        self.intensity = intensity 
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            contrast_aug = aug = iaa.contrast.LinearContrast((1 - self.intensity, 1 + self.intensity))
-            img=contrast_aug.augment_image(img)
-        return img, labels
-
-
-
-
-class HorizontalFlip(object):
-    def __init__(self, p=0.5):
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            img = np.fliplr(img)
-            if len(labels):
-                labels[:, 1] = img.shape[1] - labels[:, 1]
-                labels[:, 5] = -labels[:, 5]
-        return img, labels        
-
-
-class VerticalFlip(object):
-    def __init__(self ,p=0.5):
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            img = np.flipud(img)
-            if len(labels):
-                labels[:, 2] = img.shape[0] - labels[:, 2]
-                labels[:, 5] = -labels[:, 5]
-        return img, labels 
-
-
-class Affine(object):
-    def __init__(self, degrees = 0., translate = 0., scale = 0., shear = 0., p=0.5):
-        self.degrees = degrees 
-        self.translate = translate
-        self.scale = scale
-        self.shear = shear
-        self.p = p
-
-    def __call__(self, img, labels):
-        if random.random() < self.p:
-            img, labels = random_affine(img, labels,
-                                        degrees=self.degrees,
-                                        translate=self.translate,
-                                        scale=self.scale,
-                                        shear=self.shear)
-        return img, labels 
-
-
-
-class CopyPaste(object):
-    def __init__(self, mean = 0 , sigma = 0, p=0.5):
-        self.mean = mean
-        self.sigma = sigma
-        self.p = np.clip(p, 0, 0.5)
-        # 遵循3sigma原则，在船体侧边一个h位置为mean=0，偏移的范围约束在0+3*sigma内(2*sigma就够了)
-        self.pos = abs(np.random.normal(self.mean, self.sigma))
-
-
-    def __call__(self, img, labels):
-        boxes_w = labels[:,3]
-        boxes_h = labels[:,4]
-        boxes_a = labels[:,5]
-        areas = boxes_w * boxes_h
-        object_coors = [get_rotated_coors(i).reshape(-1,2).astype(np.int32)  for i in labels[:,1:]]
-        pasted_img=img.copy()
-        for i,coor in enumerate(object_coors):
-            a=boxes_a[i]; w=boxes_w[i]; h=boxes_h[i]; area = areas[i]
-            area_ratio = areas[i]/img.shape[0]/img.shape[1]
-            # vis验证bbox计算无误
-            # img = cv2.polylines(img,[coor],True,(0,0,255),2)	# 后三个参数为：是否封闭/color/thickness
-            # cv2.imshow('drawbox',img)
-            # cv2.moveWindow('drawbox',100,100)
-            # cv2.waitKey(0)
-            # cv2.destroyAllWindows()
-            M_up   = np.float32([[1, 0, -h*(1+self.pos)*np.cos(math.pi*0.5+a)], [0, 1, -h*(1+self.pos)*np.sin(math.pi*0.5+a)]])
-            M_down = np.float32([[1, 0,  h*(1+self.pos)*np.cos(math.pi*0.5+a)], [0, 1,  h*(1+self.pos)*np.sin(math.pi*0.5+a)]])
-            # 分别获得bbox上下邻域的梯度
-            sobel_up  , up_masked_img   , up_pos_mask   = cal_sobel(M_up, coor,img)
-            sobel_down, down_masked_img , down_pos_mask = cal_sobel(M_down, coor,img)
-            up_masked_img   = cv2.cvtColor(up_masked_img,   cv2.COLOR_BGR2GRAY)
-            down_masked_img = cv2.cvtColor(down_masked_img, cv2.COLOR_BGR2GRAY)
-            # 获取gt_mask
-            gt_mask = np.zeros(img.shape[:-1], np.uint8)
-            cv2.fillConvexPoly(gt_mask, coor, (1, 1))
-
-            if  area_ratio<0.01:   self.p *= 1.2  # 小目标尤其丢的厉害，加倍加倍
-            # 两侧都不越界，为了考虑海面反光导致的梯度骤增采用作差法。适合场景：陆海/海面/陆地
-            if not sobel_up.all() and not sobel_down.all() and random.random() < self.p:	
-                grad_diff = ((sobel_up>20).sum()-(sobel_down>20).sum())/area	# thre: 0.1
-                pix_diff = abs((up_masked_img).sum()/(area*255) - (down_masked_img).sum()/(area*255)) # 防止模糊图像的梯度都平滑带来误操作
-                if grad_diff < 0.15 and pix_diff < 0.15:	# 两侧环境一致，均为海面，两边等概率paste
-                    if random.random()<0.7:
-                        pasted_img = copy_paste(pasted_img,gt_mask,up_pos_mask)
-                        labels = np.row_stack((labels,generate_label(M_up,labels[i])))
-                    if random.random()<0.7:
-                        pasted_img = copy_paste(pasted_img,gt_mask,down_pos_mask)
-                        labels = np.row_stack((labels,generate_label(M_down,labels[i])))
-                else: 		# 半海半陆地，选海面paste
-                    if up_masked_img.sum()<down_masked_img.sum() : 
-                        pos_mask = up_pos_mask
-                        M = M_up
-                    else:
-                        pos_mask = down_pos_mask
-                        M = M_down
-                    pasted_img = copy_paste(pasted_img,gt_mask,pos_mask)
-                    labels = np.row_stack((labels,generate_label(M,labels[i])))
-            else:		# 越界增强有风险，没有差分对比，容易误判，暂时不做增强
-                pass
-
-
-        # vis:可视化检查正确性
-        # fig = plt.figure(figsize=(10, 10))   
-        # ax1 = fig.add_subplot(121)
-        # ax1.imshow(img)
-        # plt.title('img')
-        # plt.axis('off')
-
-        # ax4 = fig.add_subplot(122)
-        # ax4.imshow(pasted_img)
-        # plt.title('pasted_img')
-        # plt.axis('off')
-        # plt.show()
-
-        return pasted_img, labels
-
-# 注意labels是 c xywha
-class Transform(object):
-    # probs设定整体的增强概率，也可以单独再设置，默认均1
-    def __init__(self, augmentations, probs = 1):
-        self.augmentations = augmentations
-        self.probs = probs
-        
-    def __call__(self, img, labels):
-        for i, augmentation in enumerate(self.augmentations):
-            if type(self.probs) == list:
-                prob = self.probs[i]
-            else:
-                prob = self.probs
-                
-            if random.random() < prob:
-                img, labels = augmentation(img, labels)
-        return img, labels
-
-
-
-# 一次传入的是一张图像 target是xyxya的格式
-# 注意：仅支持box无形变的augment
-def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10):
-    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
-    # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
-    
-    if targets is None:
-        targets = []
-    border = 0  # width of added border (optional)
-    height = img.shape[0] + border * 2
-    width = img.shape[1] + border * 2
-
-    # Rotation and Scale
-    R = np.eye(3)
-    a = random.uniform(-degrees, degrees)
-    # # # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
-    s = random.uniform(1 - scale, 1 + scale)
-    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
-
-    # Translation
-    T = np.eye(3)
-    T[0, 2] = random.uniform(-translate, translate) * img.shape[0] + border  # x translation (pixels)
-    T[1, 2] = random.uniform(-translate, translate) * img.shape[1] + border  # y translation (pixels)
-
-
-    M =  T @ R  # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
-    imw = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA,
-                         borderValue=(128, 128, 128))  # BGR order borderValue
-
-    # Return warped points also
-    if len(targets) > 0:
-        n = targets.shape[0]
-
-        targets[:,5]-=a/180*math.pi # 逆时针
-        targets[:,5][targets[:,5]> 0.5*math.pi] -= math.pi
-        targets[:,5][targets[:,5]<-0.5*math.pi] += math.pi
-
-        transcx = targets[:,1] * M[0,0] + targets[:,2] * M[0,1] + M[0,2]
-        transcy = targets[:,1] * M[1,0] + targets[:,2] * M[1,1] + M[1,2]
-        targets[:,1] = transcx
-        targets[:,2] = transcy
-        targets[:,[3,4]] *= s
-
-        # reject warped points outside of image
-        targets[:,1] = targets[:,1].clip(0, width)
-        targets[:,2] = targets[:,2].clip(0, height)
-        w = targets[:,3]
-        h = targets[:,4]
-        ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
-        coors = torch.stack([get_rotated_coors(i) for i in torch.from_numpy(targets[:,1:])],0)
-        i = (w > 4) & \
-            (h > 4) & \
-            (ar < 15) & \
-            torch.stack([((i[::2] <img.shape[1]) * (i[::2] >0)).all() for i in coors],0).numpy() & \
-            torch.stack([((i[1::2]<img.shape[0]) * (i[1::2]>0)).all() for i in coors],0).numpy()
-       
-        targets = targets[i]
-    return imw, targets
-
-
-
-def cutout(image, labels):
-    # https://arxiv.org/abs/1708.04552
-    # https://github.com/hysts/pytorch_cutout/blob/master/dataloader.py
-    # https://towardsdatascience.com/when-conventional-wisdom-fails-revisiting-data-augmentation-for-self-driving-cars-4831998c5509
-    h, w = image.shape[:2]
-
-    def bbox_ioa(box1, box2, x1y1x2y2=True):
-        # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
-        box2 = box2.transpose()
-
-        # Get the coordinates of bounding boxes
-        # x1, y1, x2, y2 = box1
-        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
-        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
-
-        # Intersection area
-        inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
-                     (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
-
-        # box2 area
-        box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16
-
-        # Intersection over box2 area
-        return inter_area / box2_area
-
-    # random mask_size up to 50% image size
-    mask_h = random.randint(1, int(h * 0.5))
-    mask_w = random.randint(1, int(w * 0.5))
-
-    # box center
-    cx = random.randint(0, h)
-    cy = random.randint(0, w)
-
-    xmin = max(0, cx - mask_w // 2)
-    ymin = max(0, cy - mask_h // 2)
-    xmax = min(w, xmin + mask_w)
-    ymax = min(h, ymin + mask_h)
-
-    # apply random color mask
-    mask_color = [random.randint(0, 255) for _ in range(3)]
-    image[ymin:ymax, xmin:xmax] = mask_color
-
-    # return unobscured labels
-    if len(labels):
-        box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
-        ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
-        labels = labels[ioa < 0.90]  # remove >90% obscured labels
-    return labels
-
-
-##########  copy-paste实现相关
-def coor_trans(M,coor):
-    tcoor = [0 for i in range(8)]
-    coor_x = coor[:,0]
-    coor_y = coor[:,1]
-    tx = M[0,0]*coor_x + M[0,1]*coor_y + M[0,2]
-    ty = M[1,0]*coor_x + M[1,1]*coor_y + M[1,2]
-    tcoor[::2] = tx
-    tcoor[1::2] = ty
-    return np.array(tcoor).reshape(4,2).astype(np.int32)
-
-def cal_sobel(M,coor,img):
-    mask = np.zeros(img.shape[:-1], np.uint8)
-    tcoor = coor_trans(M,coor)
-    # 校验变换
-    if (tcoor>0).all() and (tcoor[:,0]<img.shape[1]).all() and (tcoor[:,1]<img.shape[0]).all() :
-        cv2.fillConvexPoly(mask, tcoor, (1, 1))
-        masked_img = img * np.expand_dims(mask,-1)	# 找到目标区域的mask
-        sobel = filter(masked_img)[...,0]	# 三通道，没必要
-        pos_mask = mask.copy()
-        cv2.fillConvexPoly(pos_mask, tcoor, (1, 1))
-
-        return sobel,masked_img,pos_mask
-    else:
-        mask.fill(255)
-        return mask, img,mask
-
-def filter(img):
-	img_gry = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-
-	x = cv2.Sobel(img_gry, cv2.CV_16S, 1, 0)
-	y = cv2.Sobel(img_gry, cv2.CV_16S, 0, 1)
-	xy = cv2.Sobel(img_gry,cv2.CV_16S, 1 , 1)
-	absX = cv2.convertScaleAbs(x)
-	absY = cv2.convertScaleAbs(y)
-	xy = cv2.convertScaleAbs(xy)
-	sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
-	sobel = cv2.cvtColor(sobel, cv2.COLOR_GRAY2RGB)
-
-	return sobel
-
-def copy_paste(img,gt_mask,pos_mask):
-    # gt_mask包含paste的内容；pos_mask提供位置（0-1 mask）
-	pasted = img.copy() 
-	pasted[pos_mask!=0]=img[gt_mask!=0]  
-	return pasted 
-
-def generate_label(M,label):
-    new_label = label.copy()
-    cx = label[1]; cy = label[2]; 
-    tx = M[0,0]*cx + M[0,1]*cy + M[0,2]
-    ty = M[1,0]*cx + M[1,1]*cy + M[1,2]
-    new_label[1] = tx
-    new_label[2] = ty
-    return new_label
-
-
-
-
-# # ---------------------
-# def get_rotated_coors(box):
-#         assert len(box) > 0 , 'Input valid box!'
-#         cx = box[0]; cy = box[1]; w = box[2]; h = box[3]; a = box[4]
-#         xmin = cx - w*0.5; xmax = cx + w*0.5; ymin = cy - h*0.5; ymax = cy + h*0.5
-#         t_x0=xmin; t_y0=ymin; t_x1=xmin; t_y1=ymax; t_x2=xmax; t_y2=ymax; t_x3=xmax; t_y3=ymin
-#         R = np.eye(3)
-#         R[:2] = cv2.getRotationMatrix2D(angle=-a*180/math.pi, center=(cx,cy), scale=1)
-#         x0 = t_x0*R[0,0] + t_y0*R[0,1] + R[0,2] 
-#         y0 = t_x0*R[1,0] + t_y0*R[1,1] + R[1,2] 
-#         x1 = t_x1*R[0,0] + t_y1*R[0,1] + R[0,2] 
-#         y1 = t_x1*R[1,0] + t_y1*R[1,1] + R[1,2] 
-#         x2 = t_x2*R[0,0] + t_y2*R[0,1] + R[0,2] 
-#         y2 = t_x2*R[1,0] + t_y2*R[1,1] + R[1,2] 
-#         x3 = t_x3*R[0,0] + t_y3*R[0,1] + R[0,2] 
-#         y3 = t_x3*R[1,0] + t_y3*R[1,1] + R[1,2] 
-#         if isinstance(x0,torch.Tensor):
-#             r_box=torch.cat([x0.unsqueeze(0),y0.unsqueeze(0),
-#                             x1.unsqueeze(0),y1.unsqueeze(0),
-#                             x2.unsqueeze(0),y2.unsqueeze(0),
-#                             x3.unsqueeze(0),y3.unsqueeze(0)], 0)
-#         else:
-#             r_box = np.array([x0,y0,x1,y1,x2,y2,x3,y3])
-#         return r_box
-
-
-# if __name__ == "__main__":
-    
-
-#     path = '/py/datasets/HRSC2016/yolo-dataset/train'
-#     img_files = os.listdir(path)
-#     img_files = [i for i in img_files if i.endswith('jpg')]
-#     for img_file in img_files:
-#         img = cv2.imread(os.path.join(path,img_file),1)
-#         labels = np.loadtxt(os.path.join(path,img_file)[:-4]+'.txt')
-#         if len(labels.shape)<2:
-#             labels = np.array([labels])
-#         labels[:,[1,3]] *= img.shape[1]
-#         labels[:,[2,4]] *= img.shape[0]
-
-#         cp = CopyPaste(sigma= 0.1)
-#         cp(img,labels)
\ No newline at end of file
diff --git a/data_augmentation/smart_augment.py b/data_augmentation/smart_augment.py
index 73fa882..5adbf83 100644
--- a/data_augmentation/smart_augment.py
+++ b/data_augmentation/smart_augment.py
@@ -397,7 +397,7 @@ def random_affine(img,  targets=(), degree=10, translate=.1, scale=.1, shear=10)
                                     # Noise(0.02, p=0.2),
                                     # Blur(1.3, p=0.5),
                                     ], box_mode = 'xyxyxyxy')
-            im, bboxes = transform(img, _bboxes)
+            im, bboxes = transform(img, _bboxes.copy())
             # validation judgement
             area_flag = True
             for box in bboxes: