基本数据增强处理
数据增强主要包含如下方式：
1.旋转： 可通过在原图上先放大图像，然后剪切图像得到。
2.平移：先放大图像，然后水平或垂直偏移位置剪切
3.缩放：缩放图像
4.水平翻转：以过图像中心的竖直轴为对称轴，将左、右两边像素交换
填充模式：最近邻方式
5.颜色色差（饱和度、亮度、对比度、 锐度等）
相关Python源码：
"""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, 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")