首先是平移



path = '/home/chenjia/HWDB1.1tst_gnt/img/'




#左右、上下平移



#a为平移的尺度,这里设置为10.



def moving(img, a, size, path):



	img1 = img



	img2 = img



	img3 = img



	img4 = img



	img1 = np.concatenate((img1[:, a:], img1[:, :a]), axis=1)  #左




	cv2.imwrite(path + 'mov_zuo.png', img1)



	img2 = np.concatenate((img2[:, size[1] - a:], img2[:, :size[1] - a]), axis=1)  # 右




	cv2.imwrite(path + 'mov_you.png', img2)



	img3 = np.concatenate((img3[a:, :], img3[:a, :]), axis=0)   #上




	cv2.imwrite(path + 'mov_shang.png', img3)



	img4 = np.concatenate((img4[size[0] - a:, :], img4[:size[0] -a, :]), axis=0)   #下




	cv2.imwrite(path + 'mov_xia.png', img4)



moving(img_1, 10, size1, path)

加噪，参考链接：http://blog.****.net/myhaspl/article/details/37693429

def noiseing(img):



	param = 30




	grayscale = 256




	w = img.shape[1]



	h = img.shape[0]



	newimg = np.zeros((h, w), np.uint8)



 



	for x in xrange(0, h):



		for y in xrange(0, w, 2):



			r1 = np.random.random_sample()



			r2 = np.random.random_sample()



			z1 = param * np.cos(2 * np.pi * r2) * np.sqrt((-2) * np.log(r1))



			z2 = param * np.sin(2 * np.pi * r2) * np.sqrt((-2) * np.log(r1))



 



			fxy = int(img[x, y] + z1)



			fxy1 = int(img[x, y + 1] + z2)



			if fxy < 0:



				fxy_val = 0




			elif fxy > grayscale - 1:



				fxy_val = grayscale - 1




			else:



				fxy_val = fxy



			if fxy1 < 0:



				fxy1_val = 0




			elif fxy1 > grayscale - 1:



				fxy1_val = grayscale - 1




			else:



				fxy1_val = fxy1



			newimg[x, y] = fxy_val



			newimg[x, y + 1] = fxy1_val



 



	cv2.destroyAllWindows()



	return newimg

旋转

def rotate(image, angle, center=None, scale=1.0):



    (h, w) = image.shape[:2]



    # 若未指定旋转中心，则将图像中心设为旋转中心




    if center is None:



        center = (w / 2, h / 2)



    M = cv2.getRotationMatrix2D(center, angle, scale)



    rotated = cv2.warpAffine(image, M, (w, h))



    return rotated



img1 = rotate(img, 5) #5 -5 10 -10等角度均可 正负值表示顺逆时针

img = cv2.imread(path + '1.png', -1)



size = img.shape



#im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)   #转换了灰度化



size1 = (size[0], size[1] + 1)    #把img129,83 补成129,84.




img_1 = 255 * np.ones(size1, np.uint8)



img_1[:, :size[1]] = img



cv2.imshow('preview', img_1)



cv2.waitKey()    #显示图像

缩放  +    以下代码是把图像缩小后，又进行边缘padding的操作。  主要采用边缘镜像padding和边缘点像素值padding两种思想：

#coding=utf-8




import cv2




import numpy as np



 



def ImageScale(img, scale):



    size = img.shape



    SIZE1 = size[0]



    SIZE2 = size[1]



    if scale > 1:



        size1 = int(SIZE1 * scale) + 1




        size2 = int(SIZE2 * scale)    #是否加1,根据具体图像尺寸的奇偶决定




        #这里需要注意，对于w h 不等的图像, w h 的顺序值需要调整好.




        img = cv2.resize(img, (size2, size1), interpolation = cv2.INTER_CUBIC)  #双三次线性插值法.




        a1 = (size1 - SIZE1) / 2




        b1 = size1 - a1



        a2 = (size2 - SIZE2) / 2




        b2 = size2 - a2



        #print a1,b1,a2,b2




        img = img[a1:b1, a2:b2]



        #print img.shape




    else:   #即scale<1




        size1 = int(SIZE1 * scale)



        size2 = int(SIZE2 * scale) + 1   # 是否加1,根据具体图像尺寸的奇偶决定




        img = cv2.resize(img, (size2, size1), interpolation=cv2.INTER_CUBIC)  # 双三次线性插值法.




 



    return img



 



def mirrpadding(img, s1, s2):



    orgsize = img.shape



    size1 = orgsize[0]



    size2 = orgsize[1]



    a1 = (s1 - size1) / 2   #例：(129-103)/2 = 13




    b1 = size1 - a1           #103-13




    a2 = (s2 - size2) / 2     #例：(84-68)/2 = 8




    b2 = size2 - a2           # 68-8




    #print a1,b1,a2,b2




    img1 = np.rot90((np.rot90(img[:a1, :a2].T)).T, 3)



    print img1.shape, '1'




    img2 = np.rot90(img[:a1,:].T)



    print img2.shape, '2'




    img3 = np.rot90((np.rot90(img[:a1, b2:].T)).T, 3)



    print img3.shape, '3'




    img4 = np.rot90(img[:,:a2].T, 3)



    print img4.shape, '4'




    img5 = np.rot90(img[:, b2:].T, 3)



    print img5.shape, '5'




    img6 = np.rot90((np.rot90(img[b1:, :a2].T)).T, 3)



    print img6.shape, '6'




    img7 = np.rot90(img[b1:, :].T)



    print img7.shape, '7'




    img8 = np.rot90((np.rot90(img[b1:, b2:].T)).T, 3)



    print img8.shape, '8'




    img = np.concatenate((img4, img, img5), axis=1)  #concatenate拼接函数,axis=1即在第二个维度上进行拼接.




    img1 = np.concatenate((img1, img2, img3), axis=1)



    img6 = np.concatenate((img6, img7, img8), axis=1)



    img = np.concatenate((img1, img, img6), axis=0)



    print img.shape, 'img'




    cv2.imwrite('/.../img/mirror.png', img)



 



 



 



#关于填充什么像素值，可以根据图像特点进行修改.




def padding(img, s1, s2):   #s1 s2为原图的w h值




    img_1 = img



    orgsize = img.shape



    size1 = orgsize[0]



    size2 = orgsize[1]



    a1 = (s1 - size1) / 2   #例：(129-103)/2 = 13




    b1 = size1 - a1           #103-13




    a2 = (s2 - size2) / 2     #例：(84-68)/2 = 8




    b2 = size2 - a2           # 68-8




    #print a1,b1,a2,b2




 



    img1 = np.zeros([a1, a2],np.uint)



    size = img1.shape



    for i in range(size[0]):



        for j in range(size[1]):



            img1[i, j] = img[0, 0]   #padding为最上角的像素值.




    print img1.shape, '1'




    img2 = img_1[:a1,:]



    size = img2.shape



    for i in range(size[0]):



        for j in range(size[1]):



            img2[i, j] = img[0, 0]   #  得视情况而定...




    print img2.shape, '2'




    img3 = img_1[:a1, b2:]



    size = img3.shape



    for i in range(size[0]):



        for j in range(size[1]):



            img3[i, j] = img[0, 0]  # 




    print img3.shape, '3'




    img4 = img_1[:,:a2]



    size = img4.shape



    for i in range(size[0]):



        for j in range(size[1]):



            img4[i, j] = img[0, 0]  # 




    print img4.shape, '4'




    img5 = img_1[:, b2:]



    size = img5.shape



    for i in range(size[0]):



        for j in range(size[1]):



            img5[i, j] = img[0, 0]  # 




    print img5.shape, '5'




    img6 = img_1[b1:, :a2]



    size = img6.shape



    for i in range(size[0]):



        for j in range(size[1]):



            img6[i, j] = img[0, 0]  # 




    print img6.shape, '6'




    img7 = img_1[b1:, :]



    size = img7.shape



    for i in range(size[0]):



        for j in range(size[1]):



            img7[i, j] = img[0, 0]  # 




    print img7.shape, '7'




    img8 = img_1[b1:, b2:]



    size = img8.shape



    for i in range(size[0]):



        for j in range(size[1]):



            img8[i, j] = img[0, 0]  # 




    print img8.shape, '8'




    img = np.concatenate((img4, img, img5), axis=1)  #concatenate拼接函数,axis=1即在第二个维度上进行拼接.




    img1 = np.concatenate((img1, img2, img3), axis=1)



    img6 = np.concatenate((img6, img7, img8), axis=1)



    img = np.concatenate((img1, img, img6), axis=0)



    cv2.imwrite('.../img/padding.png', img)



 



 



img = cv2.imread('.../img/1_1.png', -1)



s1 = img.shape[0]



s2 = img.shape[1]



img1 = ImageScale(img, 0.8)



cv2.imwrite('/home/chenjia/HWDB1.1tst_gnt/img/0.8.png', img1)



# img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)



# img = cv2.resize(img, (102,102), interpolation = cv2.INTER_CUBIC)



# cv2.imwrite('/home/lenovo/2Tdisk/face/code/test/gray.jpg', img)



mirrpadding(img1, s1, s2)



padding(img1, s1, s2)

镜像padding的手稿：