# -*- 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
"""
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, label, 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) , label.rotate(random_angle, Image.NEAREST)
#暂时未使用这个函数
@staticmethod
def randomCrop(image, label):
"""
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), label
@staticmethod
def randomColor(image, label):
"""
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) ,label # 调整图像锐度
@staticmethod
def randomGaussian(image, label, 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)), label
@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, label, img_des_path, label_des_path , img_file_name, label_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 , new_label = funcMap[func_name](image,label)
DataAugmentation.saveImage(new_image, os.path.join(img_des_path, func_name + str(_i) + img_file_name))
DataAugmentation.saveImage(new_label, os.path.join(label_des_path, func_name + str(_i) + label_file_name))
opsList = {"randomRotation", "randomColor", "randomGaussian"}
def threadOPS(img_path, new_img_path, label_path, new_label_path):
"""
多线程处理事务
:param src_path: 资源文件
:param des_path: 目的地文件
:return:
"""
#img path
if os.path.isdir(img_path):
img_names = os.listdir(img_path)
else:
img_names = [img_path]
#label path
if os.path.isdir(label_path):
label_names = os.listdir(label_path)
else:
label_names = [label_path]
img_num = 0
label_num = 0
#img num
for img_name in img_names:
tmp_img_name = os.path.join(img_path, img_name)
if os.path.isdir(tmp_img_name):
print('contain file folder')
exit()
else:
img_num = img_num + 1;
#label num
for label_name in label_names:
tmp_label_name = os.path.join(label_path, label_name)
if os.path.isdir(tmp_label_name):
print('contain file folder')
exit()
else:
label_num = label_num + 1
if img_num != label_num:
print('the num of img and label is not equl')
exit()
else:
num = img_num
for i in range(num):
img_name = img_names[i]
print img_name
label_name = label_names[i]
print label_name
tmp_img_name = os.path.join(img_path, img_name)
tmp_label_name = os.path.join(label_path, label_name)
# 读取文件并进行操作
image = DataAugmentation.openImage(tmp_img_name)
label = DataAugmentation.openImage(tmp_label_name)
threadImage = [0] * 5
_index = 0
for ops_name in opsList:
threadImage[_index] = threading.Thread(target=imageOps,
args=(ops_name, image, label, new_img_path, new_label_path, img_name, label_name))
threadImage[_index].start()
_index += 1
time.sleep(0.2)
if __name__ == '__main__':
threadOPS("/data1/qixinyuan/data/datasets/little/img",
"/data1/qixinyuan/data/datasets/little/new_img",
"/data1/qixinyuan/data/datasets/little/label",
"/data1/qixinyuan/data/datasets/little/new_label")
还有一种
安装Augmentor
pip install Augmentor对图片进行随机旋转
import Augmentor
p = Augmentor.Pipeline("/path/to/images")
p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5) #probability表示以一定概率随机处理图片
p.sample(500) #产生500张图片
image and ground truth data can be identically augmented
p = Augmentor.Pipeline("/path/to/images")
# Point to a directory containing ground truth data.
# Images with the same file names will be added as ground truth data
# and augmented in parallel to the original data.
p.ground_truth("/path/to/ground_truth_images")
# Add operations to the pipeline as normal:
p.rotate(probability=1, max_left_rotation=5, max_right_rotation=5)
p.flip_left_right(probability=0.5)
p.zoom_random(probability=0.5, percentage_area=0.8)
p.flip_top_bottom(probability=0.5)
p.sample(50)
在旋转图片时,常常会在图片周围产生空白填充,如图
遇到这种情况,Augmentor会在旋转的时候同时缩放图片,不致在四周出现黑色填充
问题
在使用
ground_truth()函数时,如果路径中有多张图片,将会导致augment之后的mask和image不对应,因此只能在路径中存放一张图片,如果有很多组数据需要augment则需要将他们单个存放在文件夹中
下面是我的代码:
# -*- coding: utf-8 -*-
import Augmentor
import glob
import os
import random
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
train_path = 'train'
groud_truth_path = 'mask'
img_type = 'jpg'
train_tmp_path = 'tmp/train'
mask_tmp_path = 'tmp/mask'
def start(train_path,groud_truth_path):
train_img = glob.glob(train_path+'/*.'+img_type)
masks = glob.glob(groud_truth_path+'/*.'+img_type)
if len(train_img) != len(masks):
print ("trains can't match masks")
return 0
for i in range(len(train_img)):
train_img_tmp_path = train_tmp_path + '/'+str(i)
if not os.path.lexists(train_img_tmp_path):
os.mkdir(train_img_tmp_path)
img = load_img(train_path+'/'+str(i)+'.'+img_type)
x_t = img_to_array(img)
img_tmp = array_to_img(x_t)
img_tmp.save(train_img_tmp_path+'/'+str(i)+'.'+img_type)
mask_img_tmp_path =mask_tmp_path +'/'+str(i)
if not os.path.lexists(mask_img_tmp_path):
os.mkdir(mask_img_tmp_path)
mask = load_img(groud_truth_path+'/'+str(i)+'.'+img_type)
x_l = img_to_array(mask)
mask_tmp = array_to_img(x_l)
mask_tmp.save(mask_img_tmp_path+'/'+str(i)+'.'+img_type)
print ("%s folder has been created!"%str(i))
return i+1
def doAugment(num):
sum = 0
for i in range(num):
p = Augmentor.Pipeline(train_tmp_path+'/'+str(i))
p.ground_truth(mask_tmp_path+'/'+str(i))
p.rotate(probability=0.5, max_left_rotation=5, max_right_rotation=5)#旋转
p.flip_left_right(probability=0.5)#按概率左右翻转
p.zoom_random(probability=0.6, percentage_area=0.99)#随即将一定比例面积的图形放大至全图
p.flip_top_bottom(probability=0.6)#按概率随即上下翻转
p.random_distortion(probability=0.8,grid_width=10,grid_height=10, magnitude=20)#小块变形
count = random.randint(40, 60)
print("\nNo.%s data is being augmented and %s data will be created"%(i,count))
sum = sum + count
p.sample(count)
print("Done")
print("%s pairs of data has been created totally"%sum)
a = start(train_path, groud_truth_path)
doAugment(a)
参考：
https://blog.csdn.net/qq_20852429/article/details/79137777