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 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)