python对图像数据增强，包括翻转、镜像、加噪。

import cv2
import numpy as np
import os.path
import copy


def rotate(image, angle, center=None, scale=1.0):
    (h, w) = image.shape[:2]
    # If no rotation center is specified, the center of the image is set as the rotation center
    if center is None:
        center = (w / 2, h / 2)
    M = cv2.getRotationMatrix2D(center, angle, scale)
    rotated = cv2.warpAffine(image, M, (w, h))
    return rotated


def noiseing(img):
    #img = cv2.cvtColor(rgbimg, cv2.COLOR_BGR2GRAY)
    param = 30
    grayscale = 256
    w = img.shape[1]
    h = img.shape[0]
    newimg = np.zeros((h, w, 3), np.uint8)
    #row and col
    for x in xrange(0, h):
        for y in xrange(0, w, 2): #Avoid exceeding boundaries
            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_0 = int(img[x, y, 0] + z1)
            fxy_1 = int(img[x, y, 1] + z1)
            fxy_2 = int(img[x, y, 2] + z1)
            fxy1_0 = int(img[x, y + 1, 0] + z2)
            fxy1_1 = int(img[x, y + 1, 1] + z2)
            fxy1_2 = int(img[x, y + 1, 2] + z2)
            # f(x,y)
            if fxy_0 < 0:
                fxy_val_0 = 0
            elif fxy_0 > grayscale - 1:
                fxy_val_0 = grayscale - 1
            else:
                fxy_val_0 = fxy_0
            if fxy_1 < 0:
                fxy_val_1 = 0
            elif fxy_1 > grayscale - 1:
                fxy_val_1 = grayscale - 1
            else:
                fxy_val_1 = fxy_1
            if fxy_2 < 0:
                fxy_val_2 = 0
            elif fxy_2 > grayscale - 1:
                fxy_val_2 = grayscale - 1
            else:
                fxy_val_2 = fxy_2
            # f(x,y+1)
            if fxy1_0 < 0:
                fxy1_val_0 = 0
            elif fxy1_0 > grayscale - 1:
                fxy1_val_0 = grayscale - 1
            else:
                fxy1_val_0 = fxy1_0
            if fxy1_1 < 0:
                fxy1_val_1 = 0
            elif fxy1_1 > grayscale - 1:
                fxy1_val_1 = grayscale - 1
            else:
                fxy1_val_1 = fxy1_1
            if fxy1_2 < 0:
                fxy1_val_2 = 0
            elif fxy1_2 > grayscale - 1:
                fxy1_val_2 = grayscale - 1
            else:
                fxy1_val_2 = fxy1_2

            newimg[x, y, 0] = fxy_val_0
            newimg[x, y, 1] = fxy_val_1
            newimg[x, y, 2] = fxy_val_2
            newimg[x, y + 1, 0] = fxy1_val_0
            newimg[x, y + 1, 1] = fxy1_val_1
            newimg[x, y + 1, 2] = fxy1_val_2

        #newimg = cv2.cvtColor(newimg, cv2.COLOR_GRAY2RGB)
    cv2.destroyAllWindows()
    return newimg



#i = 0
file_dir = "/home/xn/caffe/examples/facetestquestions/ImageDatainc/"
for class_name in os.listdir(file_dir):
#for index,name in enumerate(classes):
    class_path = file_dir+class_name+"/"
    for img_name in os.listdir(class_path):
        img_path = class_path + img_name
        image = cv2.imread(img_path)

        #Simple rotation 90 degrees
        rotated = rotate(image, 90)
        cv2.imwrite(class_path + '/' + img_name[0:7] +'_ro90.jpg', rotated)

        #Rotate 180 degrees and add Gaussian noise
        rotated = rotate(image, 180)
#        if __name__ == '__main__':
            #print 'load %s ...' % fn
            #img = cv2.imread(rotated)
#            coutn = 100000
#            for k in xrange(0, coutn):
                # get the random point
#                xi = int(np.random.uniform(0, rotated.shape[1]))
#                xj = int(np.random.uniform(0, rotated.shape[0]))
#                # add noise
#                if rotated.ndim == 2:
#                    rotated[xj, xi] = 255
#                elif rotated.ndim == 3:
#                    rotated[xj, xi, 0] = 25
#                    rotated[xj, xi, 1] = 20
#                    rotated[xj, xi, 2] = 20
            #cv2.namedWindow('img')
            #cv2.imshow('img', img)
            #cv2.waitKey()
#            cv2.destroyAllWindows()
        #newimg = skimage.util.random_noise(rotated, mode='salt', seed=None, clip=False)
        newimg = noiseing(rotated)
        #newimg = cv2.cvtColor(newing, cv2.COLOR_GRAY2BGR)
        cv2.imwrite(class_path + '/' + img_name[0:7] + '_rono.jpg', newimg)

        #Image processing
        size = image.shape
        #Get an image that is the same as the original image, note this to use deep copy
        iLR = copy.deepcopy(image)
        h = size[0]
        w = size[1]
        for i in range(h):  # row and col
            for j in range(w):
                iLR[i, w - 1 - j] = image[i, j]  # Mirror formula
        cv2.imwrite(class_path + '/' + img_name[0:7] + '_mirr.jpg', iLR)