在图像的深度学习中,为了丰富图像训练集,提高模型的泛化能力,一般会对图像进行数据增强。常用的方式有:旋转、剪切、改变图像色差、扭曲图像特征、改变图像尺寸、增加图像噪声(高斯噪声、盐胶噪声)。
思考:对于人脸的数据增广来说,其是对称的,所以镜像旋转pass,然后剪切后的部分人脸应用价值不高,大多数人脸识别都会将人脸对齐,所以多种形式的剪切没有意义,但将人脸从背景中剪切出来再进行识别对于facenet来说很有用。(ps:个人愚见,欢迎讨论)
# -*- coding:utf-8 -*- """数据增强 1. 翻转变换 flip 2. 随机修剪 random crop 3. 色彩抖动 color jittering 4. 平移变换 shift 5. 尺度变换 scale 6. 对比度变换 contrast 7. 噪声扰动 noise 8. 旋转变换/反射变换 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: """ 包含数据增强的八种方式 """ def __init__(self): pass @staticmethod def openImage(image): return Image.open(image, mode="r") @staticmethod def randomRotation(image, mode=Image.BICUBIC): """ 对图像进行随机任意角度(0~360度)旋转 :param mode 邻近插值,双线性插值,双三次B样条插值(default) :param image PIL的图像image :return: 旋转转之后的图像 """ random_angle = np.random.randint(1, 360) return image.rotate(random_angle, mode) @staticmethod def randomCrop(image): """ 对图像随意剪切,考虑到图像大小范围(68,68),使用一个一个大于(36*36)的窗口进行截图 :param image: PIL的图像image :return: 剪切之后的图像 """ 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): """ 对图像进行颜色抖动 :param image: PIL的图像image :return: 有颜色色差的图像image """ random_factor = np.random.randint(0, 31) / 10. # 随机因子 color_image = ImageEnhance.Color(image).enhance(random_factor) # 调整图像的饱和度 random_factor = np.random.randint(10, 21) / 10. # 随机因子 brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor) # 调整图像的亮度 random_factor = np.random.randint(10, 21) / 10. # 随机因1子 contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor) # 调整图像对比度 random_factor = np.random.randint(0, 31) / 10. # 随机因子 return ImageEnhance.Sharpness(contrast_image).enhance(random_factor) # 调整图像锐度 @staticmethod def randomGaussian(image, mean=0.2, sigma=0.3): """ 对图像进行高斯噪声处理 :param image: :return: """ def gaussianNoisy(im, mean=0.2, sigma=0.3): """ 对图像做高斯噪音处理 :param im: 单通道图像 :param mean: 偏移量 :param sigma: 标准差 :return: """ for _i in range(len(im)): im[_i] += random.gauss(mean, sigma) return im # 将图像转化成数组 img = np.asarray(image) img.flags.writeable = True # 将数组改为读写模式 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): """ 多线程处理事务 :param src_path: 资源文件 :param des_path: 目的地文件 :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": # 读取文件并进行操作 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")