本文轉載自：http://www.cnblogs.com/gongxijun/p/6117588.html

在影象的深度學習中，為了豐富影象訓練集，更好的提取影象特徵，泛化模型（防止模型過擬合），一般都會對資料影象進行資料增強，資料增強，常用的方式，就是：旋轉影象，剪下影象，改變影象色差,扭曲影象特徵，改變影象尺寸大小，增強影象噪音（一般使用高斯噪音，鹽椒噪音）等。但是需要注意，不要加入其他影象輪廓的噪音。對於常用的影象的資料增強的實現，如下：

# -*- coding:utf-8 -*-
"""資料增強
   1. 翻轉變換 flip
   2. 隨機修剪 random crop
   3. 色彩抖動 color jittering
   4. 平移變換 shift
   5. 尺度變換 scale
   6. 對比度變換 contrast
   7. 噪聲擾動 noise
   8. 旋轉變換/反射變換 Rotation/reflection
   author: XiJun.Gong
   date:2016-11-29
"""

from PIL import Image, ImageEnhance, ImageOps, ImageFile
import numpy as np
import random
import threading, os, time
import logging

logger = logging.getLogger(__name__)
ImageFile.LOAD_TRUNCATED_IMAGES = True


class DataAugmentation:
    """
    包含資料增強的八種方式
    """


    def __init__(self):
        pass

    @staticmethod
    def openImage(image):
        return Image.open(image, mode="r")

    @staticmethod
    def randomRotation(image, mode=Image.BICUBIC):
        """
         對影象進行隨機任意角度(0~360度)旋轉
        :param mode 鄰近插值,雙線性插值,雙三次B樣條插值(default)
        :param image PIL的影象image
        :return: 旋轉轉之後的影象
        """
        random_angle = np.random.randint(1, 360)
        return image.rotate(random_angle, mode)

    @staticmethod
    def randomCrop(image):
        """
        對影象隨意剪下,考慮到影象大小範圍(68,68),使用一個一個大於(36*36)的視窗進行截圖
        :param image: PIL的影象image
        :return: 剪下之後的影象

        """
        image_width = image.size[0]
        image_height = image.size[1]
        crop_win_size = np.random.randint(40, 68)
        random_region = (
            (image_width - crop_win_size) >> 1, (image_height - crop_win_size) >> 1, (image_width + crop_win_size) >> 1,
            (image_height + crop_win_size) >> 1)
        return image.crop(random_region)

    @staticmethod
    def randomColor(image):
        """
        對影象進行顏色抖動
        :param image: PIL的影象image
        :return: 有顏色色差的影象image
        """
        random_factor = np.random.randint(0, 31) / 10.  # 隨機因子
        color_image = ImageEnhance.Color(image).enhance(random_factor)  # 調整影象的飽和度
        random_factor = np.random.randint(10, 21) / 10.  # 隨機因子
        brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor)  # 調整影象的亮度
        random_factor = np.random.randint(10, 21) / 10.  # 隨機因1子
        contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor)  # 調整影象對比度
        random_factor = np.random.randint(0, 31) / 10.  # 隨機因子
        return ImageEnhance.Sharpness(contrast_image).enhance(random_factor)  # 調整影象銳度

    @staticmethod
    def randomGaussian(image, mean=0.2, sigma=0.3):
        """
         對影象進行高斯噪聲處理
        :param image:
        :return:
        """

        def gaussianNoisy(im, mean=0.2, sigma=0.3):
            """
            對影象做高斯噪音處理
            :param im: 單通道影象
            :param mean: 偏移量
            :param sigma: 標準差
            :return:
            """
            for _i in range(len(im)):
                im[_i] += random.gauss(mean, sigma)
            return im

        # 將影象轉化成陣列
        img = np.asarray(image)
        img.flags.writeable = True  # 將陣列改為讀寫模式
        width, height = img.shape[:2]
        img_r = gaussianNoisy(img[:, :, 0].flatten(), mean, sigma)
        img_g = gaussianNoisy(img[:, :, 1].flatten(), mean, sigma)
        img_b = gaussianNoisy(img[:, :, 2].flatten(), mean, sigma)
        img[:, :, 0] = img_r.reshape([width, height])
        img[:, :, 1] = img_g.reshape([width, height])
        img[:, :, 2] = img_b.reshape([width, height])
        return Image.fromarray(np.uint8(img))

    @staticmethod
    def saveImage(image, path):
        image.save(path)


def makeDir(path):
    try:
        if not os.path.exists(path):
            if not os.path.isfile(path):
                # os.mkdir(path)
                os.makedirs(path)
            return 0
        else:
            return 1
    except Exception, e:
        print str(e)
        return -2


def imageOps(func_name, image, des_path, file_name, times=5):
    funcMap = {"randomRotation": DataAugmentation.randomRotation,
               "randomCrop": DataAugmentation.randomCrop,
               "randomColor": DataAugmentation.randomColor,
               "randomGaussian": DataAugmentation.randomGaussian
               }
    if funcMap.get(func_name) is None:
        logger.error("%s is not exist", func_name)
        return -1

    for _i in range(0, times, 1):
        new_image = funcMap[func_name](image)
        DataAugmentation.saveImage(new_image, os.path.join(des_path, func_name + str(_i) + file_name))


opsList = {"randomRotation", "randomCrop", "randomColor", "randomGaussian"}


def threadOPS(path, new_path):
    """
    多執行緒處理事務
    :param src_path: 資原始檔
    :param des_path: 目的地檔案
    :return:
    """
    if os.path.isdir(path):
        img_names = os.listdir(path)
    else:
        img_names = [path]
    for img_name in img_names:
        print img_name
        tmp_img_name = os.path.join(path, img_name)
        if os.path.isdir(tmp_img_name):
            if makeDir(os.path.join(new_path, img_name)) != -1:
                threadOPS(tmp_img_name, os.path.join(new_path, img_name))
            else:
                print 'create new dir failure'
                return -1
                # os.removedirs(tmp_img_name)
        elif tmp_img_name.split('.')[1] != "DS_Store":
            # 讀取檔案並進行操作
            image = DataAugmentation.openImage(tmp_img_name)
            threadImage = [0] * 5
            _index = 0
            for ops_name in opsList:
                threadImage[_index] = threading.Thread(target=imageOps,
                                                       args=(ops_name, image, new_path, img_name,))
                threadImage[_index].start()
                _index += 1
                time.sleep(0.2)


if __name__ == '__main__':
    threadOPS("/home/pic-image/train/12306train",
              "/home/pic-image/train/12306train3")