原文链接：https://blog.csdn.net/wei_guo_xd/article/details/74199729
常用的图像扩充方式有：
水平翻转，裁剪，视角变换，jpeg压缩，尺度变换，颜色变换，旋转
当用于分类数据集时，这些变换方法可以全部被使用，然而考虑到目标检测标注框的变换，我们选择如下几种方式用于目标检测数据集扩充：
jpeg压缩，尺度变换，颜色变换
这里，我们介绍一个图象变换包
这是项目主页，里面介绍了用于图像变换的基本方法，以及如何组合它们可以得到最好的效果，项目主页里同时带python程序。
里面的图像变换程序如下（用于windows下，用于目标检测时，做了一些修改）：
import os, sys, pdb, numpy
from PIL import Image,ImageChops,ImageOps,ImageDraw
#parameters used for the CVPR paper
NCROPS = 10
NHOMO = 8
JPG=[70,50,30]
ROTS = [3,6,9,12,15]
SCALES=[1.5**0.5,1.5,1.5**1.5,1.5**2,1.5**2.5]
#parameters computed on ILSVRC10 dataset
lcolor = [ 381688.61379382 , 4881.28307136, 2316.10313483]
pcolor = [[-0.57848371, -0.7915924, 0.19681989],
[-0.5795621 , 0.22908373, -0.78206676],
[-0.57398987 , 0.56648223 , 0.59129816]]
#pre-generated gaussian values
alphas = [[0.004894 , 0.153527, -0.012182],
[-0.058978, 0.114067, -0.061488],
[0.002428, -0.003576, -0.125031]]
def gen_colorimetry(i):
p1r = pcolor[0][0]
p1g = pcolor[1][0]
p1b = pcolor[2][0]
p2r = pcolor[0][1]
p2g = pcolor[1][1]
p2b = pcolor[2][1]
p3r = pcolor[0][2]
p3g = pcolor[1][2]
p3b = pcolor[2][2]
l1 = numpy.sqrt(lcolor[0])
l2 = numpy.sqrt(lcolor[1])
l3 = numpy.sqrt(lcolor[2])
if i<=3:
alpha = alphas[i]
else:
numpy.random.seed(i*3)
alpha = numpy.random.randn(3,0,0.01)
a1 = alpha[0]
a2 = alpha[1]
a3 = alpha[2]
return (a1*l1*p1r + a2*l2*p2r + a3*l3*p3r,
a1*l1*p1g + a2*l2*p2g + a3*l3*p3g,
a1*l1*p1b + a2*l2*p2b + a3*l3*p3b)
def gen_crop(i,w,h):
numpy.random.seed(4*i)
x0 = numpy.random.random()*(w/4)
y0 = numpy.random.random()*(h/4)
x1 = w - numpy.random.random()*(w/4)
y1 = h - numpy.random.random()*(h/4)
return (int(x0),int(y0),int(x1),int(y1))
def gen_homo(i,w,h):
if i==0:
return (0,0,int(0.125*w),h,int(0.875*w),h,w,0)
elif i==1:
return (0,0,int(0.25*w),h,int(0.75*w),h,w,0)
elif i==2:
return (0,int(0.125*h),0,int(0.875*h),w,h,w,0)
elif i==3:
return (0,int(0.25*h),0,int(0.75*h),w,h,w,0)
elif i==4:
return (int(0.125*w),0,0,h,w,h,int(0.875*w),0)
elif i==5:
return (int(0.25*w),0,0,h,w,h,int(0.75*w),0)
elif i==6:
return (0,0,0,h,w,int(0.875*h),w,int(0.125*h))
elif i==7:
return (0,0,0,h,w,int(0.75*h),w,int(0.25*h))
else:
assert False
def rot(image,angle,fname):
white = Image.new('L',image.size,"white")
wr = white.rotate(angle,Image.NEAREST,expand=0)
im = image.rotate(angle,Image.BILINEAR,expand=0)
try:
image.paste(im,wr)
except ValueError:
print >>sys.stderr, 'error: image do not match '+fname
return image
def gen_corner(n, w, h):
x0 = 0
x1 = w
y0 = 0
y1 = h
rat = 256 - 227
if n == 0: #center
x0 = (rat*w)/(2*256.0)
y0 = (rat*h)/(2*256.0)
x1 = w - (rat*w)/(2*256.0)
y1 = h - (rat*h)/(2*256.0)
elif n == 1:
x0 = (rat*w)/256.0
y0 = (rat*h)/256.0
elif n == 2:
x1 = w - (rat*w)/256.0
y0 = (rat*h)/256.0
elif n == 3:
x1 = w - (rat*w)/256.0
y1 = h - (rat*h)/256.0
else:
assert n==4
x0 = (rat*w)/256.0
y1 = h - (rat*h)/256.0
return (int(x0),int(y0),int(x1),int(y1))
#the main fonction to call
#takes a image input path, a transformation and an output path and does the transformation
def gen_trans(imgfile,trans,outfile):
for trans in trans.split('*'):
image = Image.open(imgfile)
w,h = image.size
if trans=="plain":
image.save(outfile,"JPEG",quality=100)
elif trans=="flip":
ImageOps.mirror(image).save(outfile,"JPEG",quality=100)
elif trans.startswith("crop"):
c = int(trans[4:])
image.crop(gen_crop(c,w,h)).save(outfile,"JPEG",quality=100)
elif trans.startswith("homo"):
c = int(trans[4:])
image.transform((w,h),Image.QUAD,
gen_homo(c,w,h),
Image.BILINEAR).save(outfile,"JPEG",quality=100)
elif trans.startswith("jpg"):
image.save(outfile,quality=int(trans[3:]))
elif trans.startswith("scale"):
scale = SCALES[int(trans.replace("scale",""))]
image.resize((int(w/scale),int(h/scale)),Image.BILINEAR).save(outfile,"JPEG",quality=100)
elif trans.startswith('color'):
(dr,dg,db) = gen_colorimetry(int(trans[5]))
table = numpy.tile(numpy.arange(256),(3))
table[ :256]+= (int)(dr)
table[256:512]+= (int)(dg)
table[512: ]+= (int)(db)
image.convert("RGB").point(table).save(outfile,"JPEG",quality=100)
elif trans.startswith('rot-'):
angle =int(trans[4:])
for i in range(angle):
image = rot(image,-1,outfile)
image.save(outfile,"JPEG",quality=100)
elif trans.startswith('rot'):
angle =int(trans[3:])
for i in range(angle):
image = rot(image,1,outfile)
image.save(outfile,"JPEG",quality=100)
elif trans.startswith('corner'):
i = int(trans[6:])
image.crop(gen_corner(i,w,h)).save(outfile,"JPEG",quality=100)
else:
assert False, "Unrecognized transformation: "+trans
imgfile = outfile # in case we iterate
#Our 41 transformations used in the CVPR paper
def get_all_trans():
# transformations = (["plain","flip"]
# # +["crop%d"%i for i in range(NCROPS)]
# # +["homo%d"%i for i in range(NHOMO)]
# +["jpg%d"%i for i in JPG]
# +["scale0","scale1","scale2","scale3","scale4"]
# +["color%d"%i for i in range(3)]
# # +["rot-%d"%i for i in ROTS]
# # +["rot%d"%i for i in ROTS]
# )+["scale0","scale1","scale2","scale3","scale4"]
transformations=(["plain"]
+ ["jpg%d" % i for i in JPG]
+ ["scale0", "scale1", "scale2", "scale3", "scale4"]
+ ["color%d" % i for i in range(3)])
return transformations
#transformations used at test time in deep architectures
def get_deep_trans():
return ['corner0','corner1','corner2','corner3','corner4','corner0*flip','corner1*flip','corner2*flip','corner3*flip','corner4*flip']
if __name__=="__main__":
inputpath = sys.argv[1]
name = [name for name in os.listdir(inputpath) if os.path.isfile(os.path.join(inputpath,name))]
#img_input = sys.argv[1]
outpath = sys.argv[2]
if len(sys.argv)>= 4:
trans = sys.argv[3]
if not trans.startswith("["):
trans = [trans]
else:
trans = eval(trans)
else:
trans = get_all_trans()
print "Generating transformations and storing in %s"%(outpath)
for k in name:
for t in trans:
img_input=inputpath+'\\'+k
gen_trans(img_input,t,outpath+'\\%s_%s.jpg'%(".".join(img_input.split("\\")[-1].split(".")[:-1]),t))
#gen_trans(k, t, outpath + '\\%s_%s.jpg' % (".".join(k.split(".")[:-1]), t))
print "Finished. Transformations generated: %s"%(" ".join(trans))修改xml文件的程序如下;
# -*- coding=utf-8 -*-
import os
import sys
import shutil
from xml.dom.minidom import Document
from xml.etree.ElementTree import ElementTree,Element
import xml.dom.minidom
JPG=[70,50,30]
SCALES=[1.5**0.5,1.5,1.5**1.5,1.5**2,1.5**2.5]
#产生变换后的xml文件
def gen_xml(xml_input,trans,outfile):
for trans in trans.split('*'):
if trans=="plain" or trans.startswith("jpg") or trans.startswith('color'):#如果是这几种变换，直接修改xml文件名就好
dom = xml.dom.minidom.parse(xml_input)
root = dom.documentElement
filenamelist = root.getElementsByTagName('filename')
filename = filenamelist[0]
c = str(filename.firstChild.data)
d = ".".join(outfile.split("\\")[-1].split(".")[:-1]) + '.jpg'
filename.firstChild.data = d
f = open(outfile, 'w')
dom.writexml(f, encoding='utf-8')
elif trans.startswith("scale"):#对于尺度变换，xml文件信息也需要改变
scale = SCALES[int(trans.replace("scale", ""))]
dom=xml.dom.minidom.parse(xml_input)
root=dom.documentElement
filenamelist=root.getElementsByTagName('filename')
filename=filenamelist[0]
c=str(filename.firstChild.data)
d=".".join(outfile.split("\\")[-1].split(".")[:-1])+'.jpg'
filename.firstChild.data=d
heightlist = root.getElementsByTagName('height')
height = heightlist[0]
a = int(height.firstChild.data)
b = str(int(a / scale))
height.firstChild.data = b
widthlist=root.getElementsByTagName('width')
width=widthlist[0]
a = int(width.firstChild.data)
b = str(int(a / scale))
width.firstChild.data=b
objectlist=root.getElementsByTagName('xmin')
for object in objectlist:
a=int(object.firstChild.data)
b=str(int(a/scale))
object.firstChild.data=b
objectlist = root.getElementsByTagName('ymin')
for object in objectlist:
a = int(object.firstChild.data)
b = str(int(a / scale))
object.firstChild.data = b
objectlist = root.getElementsByTagName('xmax')
for object in objectlist:
a = int(object.firstChild.data)
b = str(int(a / scale))
object.firstChild.data = b
objectlist = root.getElementsByTagName('ymax')
for object in objectlist:
a = int(object.firstChild.data)
b = str(int(a / scale))
object.firstChild.data = b
f=open(outfile,'w')
dom.writexml(f,encoding='utf-8')
else:
assert False, "Unrecognized transformation: "+trans
#产生各种变换名
def get_all_trans():
transformations=(["plain"]
+ ["jpg%d" % i for i in JPG]
+ ["scale0", "scale1", "scale2", "scale3", "scale4"]
+ ["color%d" % i for i in range(3)])
return transformations
if __name__=="__main__":
inputpath = sys.argv[1]
name = [name for name in os.listdir(inputpath) if os.path.isfile(os.path.join(inputpath,name))]
outpath = sys.argv[2]
if len(sys.argv)>= 4:
trans = sys.argv[3]
if not trans.startswith("["):
trans = [trans]
else:
trans = eval(trans)
else:
trans = get_all_trans()
print "Generating transformations and storing in %s"%(outpath)
for k in name:
for t in trans:
xml_input=inputpath+'\\'+k
gen_xml(xml_input,t,outpath+'\\%s_%s.xml'%(".".join(xml_input.split("\\")[-1].split(".")[:-1]),t))