pytorch进阶学习（三）：在数据集数量不够时如何进行数据增强

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pytorch进阶学习（三）：在数据集数量不够时如何进行数据增强

#pytorch进阶学习（三）：在数据集数量不够时如何进行数据增强| 来源: 网络整理| 查看: 265

对图片数据增强，可以对图片实现：

1. 尺寸放大缩小

2. 旋转（任意角度，如45°，90°，180°，270°）

3. 翻转（水平翻转，垂直翻转）

4. 明亮度改变（变亮，变暗）

5. 像素平移（往一个方向平移像素，空出部分自动填补黑色）

6. 添加噪声（椒盐噪声，高斯噪声）

一、放大缩小

二、水平/垂直翻转

三、旋转

四、明亮度

五、平移

六、添加噪声

七、模糊

八、对一张图片进行单种变换

九、对一张图片进行多种变换

十、对数据集中所有类别的图片进行变换

数据集文件夹名为data4，第一个分类daisy中只有五张图片。

一、放大缩小 import os import numpy as np import cv2 # 放大缩小 def Scale(image, scale): return cv2.resize(image,None,fx=scale,fy=scale,interpolation=cv2.INTER_LINEAR) def TestOnePic(): test_jpg_loc = r"data4/daisy/1.jpg" test_jpg = cv2.imread(test_jpg_loc) cv2.imshow("ShowImg", test_jpg) img1 = Scale(test_jpg,0.5) cv2.imshow("Img1", img1) img2 = Scale(test_jpg,2) cv2.imshow("Img2", img2) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": TestOnePic()

二、水平/垂直翻转

import os import numpy as np import cv2 # flipcode=1为水平翻转,flipcode=0为垂直翻转 def Horizontal(image): return cv2.flip(image,1,dst=None) def Vertical(image): return cv2.flip(image,0,dst=None) def TestOnePic(): test_jpg_loc = r"data4/daisy/1.jpg" test_jpg = cv2.imread(test_jpg_loc) cv2.imshow("ShowImg", test_jpg) img1 = Horizontal(test_jpg) cv2.imshow("Img1", img1) img2 = Vertical(test_jpg) cv2.imshow("Img2", img2) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": TestOnePic()

三、旋转

image.shape[0], 图片垂直尺寸

image.shape[1], 图片水平尺寸

image.shape[2], 图片通道数

cv2.getRotationMatrix2D()经常被使用到的参数有三个：

旋转中心旋转角度旋转后的缩放比例

利用opencv实现仿射变换一般会涉及到warpAffine和getRotationMatrix2D两个函数，其中warpAffine可以实现一些简单的重映射，而getRotationMatrix2D可以获得旋转矩阵。cv2.warpAffine()主要有以下参数：

src: 输入图像dst: 输出图像，尺寸由dsize指定，图像类型与原图像一致M: 2X3的变换矩阵dsize: 指定图像输出尺寸flags: 插值算法标识符，有默认值INTER_LINEAR，如果插值算法WARP_INVERSE_MAP, warpAffine函数使用如下矩阵进行图像转换 import os import numpy as np import cv2 #旋转 def Rotate(image,angle,scale): w = image.shape[1] h = image.shape[0] # 第一个参数旋转中心，第二个参数旋转角度，第三个参数：缩放比例 M = cv2.getRotationMatrix2D((w/2,h/2),angle,scale) # 得到矩阵后得用到图像的仿射变换函数才可以进行最终图像的变化 image = cv2.warpAffine(image,M,(w,h)) return image def TestOnePic(): test_jpg_loc = r"data4/daisy/1.jpg" test_jpg = cv2.imread(test_jpg_loc) cv2.imshow("ShowImg", test_jpg) img1 = Rotate(test_jpg,45,2) cv2.imshow("Img1", img1) img2 = Rotate(test_jpg,90,1) cv2.imshow("Img2", img2) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": TestOnePic()

旋转后如果图片大小不够大的话会填充黑色。

四、明亮度

把图片变亮或者变暗。

import os import numpy as np import cv2 ''' 明亮度 ''' # 变暗 def Darker(image,percetage): image_copy = image.copy() w = image.shape[1] h = image.shape[0] #get darker for xi in range(0,w): for xj in range(0,h): image_copy[xj,xi,0] = int(image[xj,xi,0]*percetage) image_copy[xj,xi,1] = int(image[xj,xi,1]*percetage) image_copy[xj,xi,2] = int(image[xj,xi,2]*percetage) return image_copy # 明亮 def Brighter(image, percetage): image_copy = image.copy() w = image.shape[1] h = image.shape[0] #get brighter for xi in range(0,w): for xj in range(0,h): image_copy[xj,xi,0] = np.clip(int(image[xj,xi,0]*percetage),a_max=255,a_min=0) image_copy[xj,xi,1] = np.clip(int(image[xj,xi,1]*percetage),a_max=255,a_min=0) image_copy[xj,xi,2] = np.clip(int(image[xj,xi,2]*percetage),a_max=255,a_min=0) return image_copy def TestOnePic(): test_jpg_loc = r"data4/daisy/1.jpg" test_jpg = cv2.imread(test_jpg_loc) cv2.imshow("ShowImg", test_jpg) img1 = Darker(test_jpg,0.9) cv2.imshow("Img1", img1) img2 = Brighter(test_jpg,1.1) cv2.imshow("Img2", img2) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": TestOnePic()

五、平移 import os import numpy as np import cv2 def Move(img,x,y): img_info=img.shape height=img_info[0] width=img_info[1] mat_translation=np.float32([[1,0,x],[0,1,y]]) #变换矩阵：设置平移变换所需的计算矩阵：2行3列 #[[1,0,20],[0,1,50]] 表示平移变换：其中x表示水平方向上的平移距离，y表示竖直方向上的平移距离。 dst=cv2.warpAffine(img,mat_translation,(width,height)) #变换函数 return dst def TestOnePic(): test_jpg_loc = r"data4/daisy/1.jpg" test_jpg = cv2.imread(test_jpg_loc) cv2.imshow("ShowImg", test_jpg) img1 = Move(test_jpg,10,20) cv2.imshow("Img1", img1) img2 = Move(test_jpg,-20,-10) cv2.imshow("Img2", img2) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": TestOnePic()

六、添加噪声 import os import numpy as np import cv2 ''' 增加噪声 ''' # 椒盐噪声 def SaltAndPepper(src,percetage): SP_NoiseImg=src.copy() SP_NoiseNum=int(percetage*src.shape[0]*src.shape[1]) for i in range(SP_NoiseNum): randR=np.random.randint(0,src.shape[0]-1) randG=np.random.randint(0,src.shape[1]-1) randB=np.random.randint(0,3) if np.random.randint(0,1)==0: SP_NoiseImg[randR,randG,randB]=0 else: SP_NoiseImg[randR,randG,randB]=255 return SP_NoiseImg # 高斯噪声 def GaussianNoise(image,percetage): G_Noiseimg = image.copy() w = image.shape[1] h = image.shape[0] G_NoiseNum=int(percetage*image.shape[0]*image.shape[1]) for i in range(G_NoiseNum): temp_x = np.random.randint(0,h) temp_y = np.random.randint(0,w) G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0] return G_Noiseimg def TestOnePic(): test_jpg_loc = r"data4/daisy/1.jpg" test_jpg = cv2.imread(test_jpg_loc) cv2.imshow("ShowImg", test_jpg) img1 = SaltAndPepper(test_jpg,0.05) cv2.imshow("Img1", img1) img2 = GaussianNoise(test_jpg,0.05) cv2.imshow("Img2", img2) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": TestOnePic()

七、模糊

高斯模糊：

cv2.GaussianBlur(图像，卷积核，标准差） import os import numpy as np import cv2 def Blur(img): blur = cv2.GaussianBlur(img, (7, 7), 1.5) # # cv2.GaussianBlur(图像，卷积核，标准差） return blur def TestOnePic(): test_jpg_loc = r"data4/daisy/1.jpg" test_jpg = cv2.imread(test_jpg_loc) cv2.imshow("ShowImg", test_jpg) img1 = Blur(test_jpg) cv2.imshow("Img1", img1) cv2.waitKey(0) cv2.destroyAllWindows() if __name__ == "__main__": TestOnePic()

八、对一张图片进行单种变换 ''' 这是图片数据增强的代码，可以对图片实现： 1. 尺寸放大缩小 2. 旋转（任意角度，如45°，90°，180°，270°） 3. 翻转（水平翻转，垂直翻转） 4. 明亮度改变（变亮，变暗） 5. 像素平移（往一个方向平移像素，空出部分自动填补黑色） 6. 添加噪声（椒盐噪声，高斯噪声） ''' import os import numpy as np import cv2 ''' 缩放 ''' # 放大缩小 def Scale(image, scale): return cv2.resize(image,None,fx=scale,fy=scale,interpolation=cv2.INTER_LINEAR) ''' 翻转 ''' # 水平翻转 def Horizontal(image): return cv2.flip(image,1,dst=None) #水平镜像 # 垂直翻转 def Vertical(image): return cv2.flip(image,0,dst=None) #垂直镜像 # 旋转，R可控制图片放大缩小 def Rotate(image, angle=15, scale=0.9): w = image.shape[1] h = image.shape[0] #rotate matrix M = cv2.getRotationMatrix2D((w/2,h/2), angle, scale) #rotate image = cv2.warpAffine(image,M,(w,h)) return image ''' 明亮度 ''' # 变暗 def Darker(image,percetage=0.9): image_copy = image.copy() w = image.shape[1] h = image.shape[0] #get darker for xi in range(0,w): for xj in range(0,h): image_copy[xj,xi,0] = int(image[xj,xi,0]*percetage) image_copy[xj,xi,1] = int(image[xj,xi,1]*percetage) image_copy[xj,xi,2] = int(image[xj,xi,2]*percetage) return image_copy # 明亮 def Brighter(image, percetage=1.1): image_copy = image.copy() w = image.shape[1] h = image.shape[0] #get brighter for xi in range(0,w): for xj in range(0,h): image_copy[xj,xi,0] = np.clip(int(image[xj,xi,0]*percetage),a_max=255,a_min=0) image_copy[xj,xi,1] = np.clip(int(image[xj,xi,1]*percetage),a_max=255,a_min=0) image_copy[xj,xi,2] = np.clip(int(image[xj,xi,2]*percetage),a_max=255,a_min=0) return image_copy # 平移 def Move(img,x,y): img_info=img.shape height=img_info[0] width=img_info[1] mat_translation=np.float32([[1,0,x],[0,1,y]]) #变换矩阵：设置平移变换所需的计算矩阵：2行3列 #[[1,0,20],[0,1,50]] 表示平移变换：其中x表示水平方向上的平移距离，y表示竖直方向上的平移距离。 dst=cv2.warpAffine(img,mat_translation,(width,height)) #变换函数 return dst ''' 增加噪声 ''' # 椒盐噪声 def SaltAndPepper(src,percetage=0.05): SP_NoiseImg=src.copy() SP_NoiseNum=int(percetage*src.shape[0]*src.shape[1]) for i in range(SP_NoiseNum): randR=np.random.randint(0,src.shape[0]-1) randG=np.random.randint(0,src.shape[1]-1) randB=np.random.randint(0,3) if np.random.randint(0,1)==0: SP_NoiseImg[randR,randG,randB]=0 else: SP_NoiseImg[randR,randG,randB]=255 return SP_NoiseImg # 高斯噪声 def GaussianNoise(image,percetage=0.05): G_Noiseimg = image.copy() w = image.shape[1] h = image.shape[0] G_NoiseNum=int(percetage*image.shape[0]*image.shape[1]) for i in range(G_NoiseNum): temp_x = np.random.randint(0,h) temp_y = np.random.randint(0,w) G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0] return G_Noiseimg def Blur(img): blur = cv2.GaussianBlur(img, (7, 7), 1.5) # # cv2.GaussianBlur(图像，卷积核，标准差） return blur def TestOneDir(): root_path = "data4/daisy" save_path = root_path for a, b, c in os.walk(root_path): for file_i in c: file_i_path = os.path.join(a, file_i) print(file_i_path) img_i = cv2.imread(file_i_path) img_scale = Scale(img_i,1.5) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_scale.jpg"), img_scale) img_horizontal = Horizontal(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_horizontal.jpg"), img_horizontal) img_vertical = Vertical(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_vertical.jpg"), img_vertical) img_rotate = Rotate(img_i,90) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate90.jpg"), img_rotate) img_rotate = Rotate(img_i, 180) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate180.jpg"), img_rotate) img_rotate = Rotate(img_i, 270) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate270.jpg"), img_rotate) img_move = Move(img_i,15,15) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_move.jpg"), img_move) img_darker = Darker(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_darker.jpg"), img_darker) img_brighter = Brighter(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_brighter.jpg"), img_brighter) img_blur = Blur(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_blur.jpg"), img_blur) img_salt = SaltAndPepper(img_i,0.05) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_salt.jpg"), img_salt) if __name__ == "__main__": TestOneDir() # root_path = "data4/" # AllData(root_path)

可以看到daisy中由原来的5张图变为了60张，每张图都经过了11种变换。

九、对一张图片进行多种变换

想要对一张图片进行翻转、平移、模糊等叠加操作，就依次对所需代码进行执行。

1. 一开始我们有五张图片。

2. 先对图片进行scale操作，把其他代码注释掉，现在变为10张图片。

import os import numpy as np import cv2 ''' 缩放 ''' # 放大缩小 def Scale(image, scale): return cv2.resize(image,None,fx=scale,fy=scale,interpolation=cv2.INTER_LINEAR) ''' 翻转 ''' # 水平翻转 def Horizontal(image): return cv2.flip(image,1,dst=None) #水平镜像 # 垂直翻转 def Vertical(image): return cv2.flip(image,0,dst=None) #垂直镜像 # 旋转，R可控制图片放大缩小 def Rotate(image, angle=15, scale=0.9): w = image.shape[1] h = image.shape[0] #rotate matrix M = cv2.getRotationMatrix2D((w/2,h/2), angle, scale) #rotate image = cv2.warpAffine(image,M,(w,h)) return image ''' 明亮度 ''' # 变暗 def Darker(image,percetage=0.9): image_copy = image.copy() w = image.shape[1] h = image.shape[0] #get darker for xi in range(0,w): for xj in range(0,h): image_copy[xj,xi,0] = int(image[xj,xi,0]*percetage) image_copy[xj,xi,1] = int(image[xj,xi,1]*percetage) image_copy[xj,xi,2] = int(image[xj,xi,2]*percetage) return image_copy # 明亮 def Brighter(image, percetage=1.1): image_copy = image.copy() w = image.shape[1] h = image.shape[0] #get brighter for xi in range(0,w): for xj in range(0,h): image_copy[xj,xi,0] = np.clip(int(image[xj,xi,0]*percetage),a_max=255,a_min=0) image_copy[xj,xi,1] = np.clip(int(image[xj,xi,1]*percetage),a_max=255,a_min=0) image_copy[xj,xi,2] = np.clip(int(image[xj,xi,2]*percetage),a_max=255,a_min=0) return image_copy # 平移 def Move(img,x,y): img_info=img.shape height=img_info[0] width=img_info[1] mat_translation=np.float32([[1,0,x],[0,1,y]]) #变换矩阵：设置平移变换所需的计算矩阵：2行3列 #[[1,0,20],[0,1,50]] 表示平移变换：其中x表示水平方向上的平移距离，y表示竖直方向上的平移距离。 dst=cv2.warpAffine(img,mat_translation,(width,height)) #变换函数 return dst ''' 增加噪声 ''' # 椒盐噪声 def SaltAndPepper(src,percetage=0.05): SP_NoiseImg=src.copy() SP_NoiseNum=int(percetage*src.shape[0]*src.shape[1]) for i in range(SP_NoiseNum): randR=np.random.randint(0,src.shape[0]-1) randG=np.random.randint(0,src.shape[1]-1) randB=np.random.randint(0,3) if np.random.randint(0,1)==0: SP_NoiseImg[randR,randG,randB]=0 else: SP_NoiseImg[randR,randG,randB]=255 return SP_NoiseImg # 高斯噪声 def GaussianNoise(image,percetage=0.05): G_Noiseimg = image.copy() w = image.shape[1] h = image.shape[0] G_NoiseNum=int(percetage*image.shape[0]*image.shape[1]) for i in range(G_NoiseNum): temp_x = np.random.randint(0,h) temp_y = np.random.randint(0,w) G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0] return G_Noiseimg def Blur(img): blur = cv2.GaussianBlur(img, (7, 7), 1.5) # # cv2.GaussianBlur(图像，卷积核，标准差） return blur def TestOneDir(): root_path = "data4/daisy" save_path = root_path for a, b, c in os.walk(root_path): for file_i in c: file_i_path = os.path.join(a, file_i) print(file_i_path) img_i = cv2.imread(file_i_path) img_scale = Scale(img_i,1.5) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_scale.jpg"), img_scale) # # img_horizontal = Horizontal(img_i) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_horizontal.jpg"), img_horizontal) # # img_vertical = Vertical(img_i) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_vertical.jpg"), img_vertical) # # img_rotate = Rotate(img_i,90) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate90.jpg"), img_rotate) # # img_rotate = Rotate(img_i, 180) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate180.jpg"), img_rotate) # # img_rotate = Rotate(img_i, 270) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate270.jpg"), img_rotate) # # img_move = Move(img_i,15,15) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_move.jpg"), img_move) # # img_darker = Darker(img_i) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_darker.jpg"), img_darker) # # img_brighter = Brighter(img_i) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_brighter.jpg"), img_brighter) # # img_blur = Blur(img_i) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_blur.jpg"), img_blur) # # img_salt = SaltAndPepper(img_i,0.05) # cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_salt.jpg"), img_salt) # # if __name__ == "__main__": TestOneDir() # root_path = "data4/" # AllData(root_path)

十、对数据集中所有类别的图片进行变换 ''' 这是图片数据增强的代码，可以对图片实现： 1. 尺寸放大缩小 2. 旋转（任意角度，如45°，90°，180°，270°） 3. 翻转（水平翻转，垂直翻转） 4. 明亮度改变（变亮，变暗） 5. 像素平移（往一个方向平移像素，空出部分自动填补黑色） 6. 添加噪声（椒盐噪声，高斯噪声） ''' import os import numpy as np import cv2 # envpath = '/home/zhaolei/anaconda3/envs/maweiyi/lib/python3.8/site-packages/cv2/qt/plugins/platforms' # os.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = envpath ''' 缩放 ''' # 放大缩小 def Scale(image, scale): return cv2.resize(image,None,fx=scale,fy=scale,interpolation=cv2.INTER_LINEAR) ''' 翻转 ''' # 水平翻转 def Horizontal(image): return cv2.flip(image,1,dst=None) #水平镜像 # 垂直翻转 def Vertical(image): return cv2.flip(image,0,dst=None) #垂直镜像 # 旋转，R可控制图片放大缩小 def Rotate(image, angle=15, scale=0.9): w = image.shape[1] h = image.shape[0] #rotate matrix M = cv2.getRotationMatrix2D((w/2,h/2), angle, scale) #rotate image = cv2.warpAffine(image,M,(w,h)) return image ''' 明亮度 ''' # 变暗 def Darker(image,percetage=0.9): image_copy = image.copy() w = image.shape[1] h = image.shape[0] #get darker for xi in range(0,w): for xj in range(0,h): image_copy[xj,xi,0] = int(image[xj,xi,0]*percetage) image_copy[xj,xi,1] = int(image[xj,xi,1]*percetage) image_copy[xj,xi,2] = int(image[xj,xi,2]*percetage) return image_copy # 明亮 def Brighter(image, percetage=1.1): image_copy = image.copy() w = image.shape[1] h = image.shape[0] #get brighter for xi in range(0,w): for xj in range(0,h): image_copy[xj,xi,0] = np.clip(int(image[xj,xi,0]*percetage),a_max=255,a_min=0) image_copy[xj,xi,1] = np.clip(int(image[xj,xi,1]*percetage),a_max=255,a_min=0) image_copy[xj,xi,2] = np.clip(int(image[xj,xi,2]*percetage),a_max=255,a_min=0) return image_copy # 平移 def Move(img,x,y): img_info=img.shape height=img_info[0] width=img_info[1] mat_translation=np.float32([[1,0,x],[0,1,y]]) #变换矩阵：设置平移变换所需的计算矩阵：2行3列 #[[1,0,20],[0,1,50]] 表示平移变换：其中x表示水平方向上的平移距离，y表示竖直方向上的平移距离。 dst=cv2.warpAffine(img,mat_translation,(width,height)) #变换函数 return dst ''' 增加噪声 ''' # 椒盐噪声 def SaltAndPepper(src,percetage=0.05): SP_NoiseImg=src.copy() SP_NoiseNum=int(percetage*src.shape[0]*src.shape[1]) for i in range(SP_NoiseNum): randR=np.random.randint(0,src.shape[0]-1) randG=np.random.randint(0,src.shape[1]-1) randB=np.random.randint(0,3) if np.random.randint(0,1)==0: SP_NoiseImg[randR,randG,randB]=0 else: SP_NoiseImg[randR,randG,randB]=255 return SP_NoiseImg # 高斯噪声 def GaussianNoise(image,percetage=0.05): G_Noiseimg = image.copy() w = image.shape[1] h = image.shape[0] G_NoiseNum=int(percetage*image.shape[0]*image.shape[1]) for i in range(G_NoiseNum): temp_x = np.random.randint(0,h) temp_y = np.random.randint(0,w) G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0] return G_Noiseimg def Blur(img): blur = cv2.GaussianBlur(img, (7, 7), 1.5) # # cv2.GaussianBlur(图像，卷积核，标准差） return blur def AllData(rootpath): root_path = "data4/" save_loc = root_path for a,b,c in os.walk(root_path): for file_i in c: file_i_path = os.path.join(a,file_i) print(file_i_path) split = os.path.split(file_i_path) dir_loc = os.path.split(split[0])[1] save_path = os.path.join(save_loc,dir_loc) img_i = cv2.imread(file_i_path) img_scale = Scale(img_i,1.5) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_scale.jpg"), img_scale) img_horizontal = Horizontal(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_horizontal.jpg"), img_horizontal) img_vertical = Vertical(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_vertical.jpg"), img_vertical) img_rotate = Rotate(img_i, 90) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate90.jpg"), img_rotate) img_rotate = Rotate(img_i, 180) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate180.jpg"), img_rotate) img_rotate = Rotate(img_i, 270) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate270.jpg"), img_rotate) img_move = Move(img_i, 15, 15) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_move.jpg"), img_move) img_darker = Darker(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_darker.jpg"), img_darker) img_brighter = Brighter(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_brighter.jpg"), img_brighter) img_blur = Blur(img_i) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_blur.jpg"), img_blur) img_salt = SaltAndPepper(img_i, 0.05) cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_salt.jpg"), img_salt) if __name__ == "__main__": root_path = "data4/" AllData(root_path)

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