转载/zhw864680355/article/details/89317741
# -*- coding:utf-8 -*-"""数据增强1. 翻转变换 flip2. 随机修剪 random crop3. 色彩抖动 color jittering4. 平移变换 shift5. 尺度变换 scale6. 对比度变换 contrast7. 噪声扰动 noise8. 旋转变换/反射变换 Rotation/reflection"""# -*- coding:utf-8 -*-"""数据增强1. 翻转变换 flip2. 随机修剪 random crop3. 色彩抖动 color jittering4. 平移变换 shift5. 尺度变换 scale6. 对比度变换 contrast7. 噪声扰动 noise8. 旋转变换/反射变换 Rotation/reflection"""from PIL import Image, ImageEnhance, ImageOps, ImageFileimport numpy as npimport randomimport threading, os, timeimport loggingimport mathimport shutillogger = logging.getLogger(__name__)ImageFile.LOAD_TRUNCATED_IMAGES = Trueclass DataAugmentation:"""包含数据增强的八种方式"""def __init__(self):pass@staticmethoddef openImage(image):return Image.open(image, mode="r")@staticmethoddef randomFlip(image, mode=Image.FLIP_LEFT_RIGHT):"""对图像进行上下左右四个方面的随机翻转:param image: PIL的图像image:param model: 水平或者垂直方向的随机翻转模式,默认右向翻转:return: 翻转之后的图像model- Image.FLIP_LEFT_RIGHT,表示将图像左右翻转- Image.FLIP_TOP_BOTTOM,表示将图像上下翻转- Image.ROTATE_90,表示将图像逆时针旋转90°- Image.ROTATE_180,表示将图像逆时针旋转180°- Image.ROTATE_270,表示将图像逆时针旋转270°- Image.TRANSPOSE,表示将图像进行转置(相当于顺时针旋转90°)- Image.TRANSVERSE,表示将图像进行转置,再水平翻转"""# random_model = np.random.randint(0, 2)# flip_model = [Image.FLIP_LEFT_RIGHT, Image.FLIP_TOP_BOTTOM]# return image.transpose(flip_model[random_model])return image.transpose(mode)@staticmethoddef randomShift(image):# def randomShift(image, xoffset, yoffset=None):"""对图像进行平移操作:param image: PIL的图像image:param xoffset: x方向向右平移:param yoffset: y方向向下平移:return: 翻转之后的图像"""random_xoffset = np.random.randint(0, math.ceil(image.size[0] * 0.2))random_yoffset = np.random.randint(0, math.ceil(image.size[1] * 0.2))# return image.offset(xoffset = random_xoffset, yoffset = random_yoffset)return image.offset(random_xoffset)@staticmethoddef 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)--PIL.Image.BICUBIC--PIL.Image.LANCZOS--PIL.Image.BILINEAR--PIL.Image.NEAREST: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)@staticmethoddef randomCrop(image):"""对图像随意剪切,裁剪图像大小宽和高的2/3: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_image_width = math.ceil(image_width * 2 / 3)crop_image_height = math.ceil(image_height * 2 / 3)x = np.random.randint(0, image_width - crop_image_width)y = np.random.randint(0, image_height - crop_image_height)random_region = (x, y, x + crop_image_width, y + crop_image_height)return image.crop(random_region)@staticmethoddef 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@staticmethoddef 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]try: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])except:img = imgreturn Image.fromarray(np.uint8(img))@staticmethoddef saveImage(image, path):try:image.save(path)except:print('not save img: ', path)passfiles = []def get_files(dir_path):global filesif os.path.exists(dir_path):parents = os.listdir(dir_path)for parent in parents:child = os.path.join(dir_path, parent)if os.path.exists(child) and os.path.isfile(child):# child = child.split('/')[4:]# str_child = '/'.join(child)files.append(child)elif os.path.isdir(child):get_files(child)return fileselse:return Noneif __name__ == '__main__':times = 1 # 重复次数imgs_dir = '/home/huadian/图片/12'new_imgs_dir = '/home/huadian/图片/13/'# if os.path.exists(new_imgs_dir):# shutil.rmtree(new_imgs_dir)funcMap = {"flip": DataAugmentation.randomFlip,"rotation": DataAugmentation.randomRotation,"crop": DataAugmentation.randomCrop,"color": DataAugmentation.randomColor,"gaussian": DataAugmentation.randomGaussian}funcLists = {"flip", "rotation", "crop", "color", "gaussian"}# funcLists = {"flip", "rotation", "crop", "gaussian"}# funcLists = {"rotation", "crop", "gaussian"}global _indeximgs_list = get_files(imgs_dir)for index_img, img in enumerate(imgs_list):if index_img != 0 and index_img % 50 == 0:print('now is dealing %d image' % (index_img))tmp_img_dir_list = img.split('/')[:-1]tmp_img_dir_list[0:len(new_imgs_dir.split('/'))] = new_imgs_dir.split('/')new_img_dir = '/'.join(tmp_img_dir_list)if not os.path.exists(new_img_dir):os.makedirs(new_img_dir)try:shutil.copy(img, os.path.join(new_img_dir, img.split('/')[-1]))except:passimg_name = img.split('/')[-1].split('.')[0]postfix = img.split('.')[1] # 后缀if postfix.lower() in ['jpg', 'jpeg', 'png', 'bmp']:image = DataAugmentation.openImage(img)_index = 1for func in funcLists:if func == 'flip':flip_model = [Image.FLIP_LEFT_RIGHT, Image.FLIP_TOP_BOTTOM]for model_index in range(len(flip_model)):new_image = DataAugmentation.randomFlip(image, flip_model[model_index])img_path = os.path.join(new_img_dir, img_name + '_' + str(_index) + '.' + postfix)DataAugmentation.saveImage(new_image, img_path)_index += 1elif func == 'gaussian':new_image = DataAugmentation.randomGaussian(image)img_path = os.path.join(new_img_dir, img_name + '_' + str(_index) + '.' + postfix)DataAugmentation.saveImage(new_image, img_path)_index += 1else:for _i in range(0, times, 1):new_image = funcMap[func](image)img_path = os.path.join(new_img_dir, img_name + '_' + str(_index) + '.' + postfix)DataAugmentation.saveImage(new_image, img_path)_index += 1
