python语义分割任务数据增强，对图片和label做同样的增强变换

# -*- 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")