图像数据增强：平移、加噪、旋转、缩放（padding）

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 = imgimg2 = imgimg3 = imgimg4 = imgimg1 = 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.csdn.net/myhaspl/article/details/37693429

def noiseing(img):param = 30grayscale = 256w = 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 = 0elif fxy > grayscale - 1:fxy_val = grayscale - 1else:fxy_val = fxyif fxy1 < 0:fxy1_val = 0elif fxy1 > grayscale - 1:fxy1_val = grayscale - 1else:fxy1_val = fxy1newimg[x, y] = fxy_valnewimg[x, y + 1] = fxy1_valcv2.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 rotatedimg1 = 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.shapesize1 = (size[0], size[1] + 1)    #把img129,83 补成129,84.img_1 = 255 * np.ones(size1, np.uint8)img_1[:, :size[1]] = imgcv2.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-8import cv2import numpy as npdef 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 imgdef 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('/home/chenjia/HWDB1.1tst_gnt/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('/home/chenjia/HWDB1.1tst_gnt/img/padding.png', img)img = cv2.imread('/home/chenjia/HWDB1.1tst_gnt/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：