pytorch进阶学习（三）：在数据集数量不够时如何进行数据增强
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）
目录
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 imagedef 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 darkerfor 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 brighterfor 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_copydef 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 cv2def Move(img,x,y):img_info=img.shapeheight=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 dstdef 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]=0else:SP_NoiseImg[randR,randG,randB]=255return 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_Noiseimgdef 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 cv2def Blur(img):blur = cv2.GaussianBlur(img, (7, 7), 1.5)# # cv2.GaussianBlur(图像，卷积核，标准差）return blurdef 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 matrixM = cv2.getRotationMatrix2D((w/2,h/2), angle, scale)#rotateimage = 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 darkerfor 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 brighterfor 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.shapeheight=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]=0else:SP_NoiseImg[randR,randG,randB]=255return 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_Noiseimgdef Blur(img):blur = cv2.GaussianBlur(img, (7, 7), 1.5)# # cv2.GaussianBlur(图像，卷积核，标准差）return blurdef TestOneDir():root_path = "data4/daisy"save_path = root_pathfor 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 matrixM = cv2.getRotationMatrix2D((w/2,h/2), angle, scale)#rotateimage = 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 darkerfor 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 brighterfor 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.shapeheight=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]=0else:SP_NoiseImg[randR,randG,randB]=255return 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_Noiseimgdef Blur(img):blur = cv2.GaussianBlur(img, (7, 7), 1.5)# # cv2.GaussianBlur(图像，卷积核，标准差）return blurdef TestOneDir():root_path = "data4/daisy"save_path = root_pathfor 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 matrixM = cv2.getRotationMatrix2D((w/2,h/2), angle, scale)#rotateimage = 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 darkerfor 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 brighterfor 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.shapeheight=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]=0else:SP_NoiseImg[randR,randG,randB]=255return 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_Noiseimgdef Blur(img):blur = cv2.GaussianBlur(img, (7, 7), 1.5)# # cv2.GaussianBlur(图像，卷积核，标准差）return blurdef AllData(rootpath):root_path = "data4/"save_loc = root_pathfor 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)
