# -*- coding: utf-8 -*-
#zhangtao 2016/06/14
#QQ 406878851
from PIL import *
import Image
import numpy as np
import os
########################################################################################
########################################################################################
class PixHelper(object):
def __init__(self ,(r ,g ,b)):
self. r =r
self. g =g
self. b =b
def grayVal(self):
return (self.r * 30 +self.g * 59 + self.b * 11) / 100
def validPix(self):
if self.isBackgroundPix():
if self.grayVal() != 255:
pass
# print self.grayVal()
return 255
return 0
def isBackgroundPix(self):
background = PixHelper((150, 150, 150))
background_grayVal = background.grayVal()
return self.grayVal() > background_grayVal
def isNeetPix(self):
return False == self.isBackgroundPix()
########################################################################################
########################################################################################
class ImageHelper(object):
model_file="svm.model"
def __init__(self, image):
self.image = image
self.width = image.size[0]
self.height = image.size[1]
self.subWidth=16
self.subHeight=self.height
#二值图
def convertGrayImage(self):
for x in xrange(self.width):
for y in xrange(self.height):
pix = self.image.getpixel((x, y))
pixhelper = PixHelper(pix)
pix2 = pixhelper.validPix()
self.image.putpixel((x, y), (pix2, pix2, pix2))
def fixCropSubImages(self):
boxList = []
box0 = (7, 0, 20, self.height)
box1 = (20, 0, 34, self.height)
box2 = (34, 0, 46, self.height)
box3 = (46, 0, 59, self.height)
box4 = (59, 0, 72, self.height)
box5 = (72, 0, 88, self.height)
boxList.append(box0)
boxList.append(box1)
boxList.append(box2)
boxList.append(box3)
boxList.append(box4)
boxList.append(box5)
subList = []
for box in boxList:
sub = self.image.crop(box)
subList.append(sub)
return subList
def getRect(self,block):
minx = self.width
maxx = 0
miny = self.height
maxy = 0
for p in block:
# print p
minx = min(minx, p[0])
maxx = max(maxx, p[0])
miny = min(miny, p[1])
maxy = max(maxy, p[1])
return (minx, miny, maxx - minx + 1, maxy - miny + 1)
def getValidPoints(self):
points = list()
for x in xrange(self.width):
for y in xrange(self.height):
p = (x, y)
pix = self.image.getpixel(p)
pixhelper = PixHelper(pix)
if pixhelper.isNeetPix():
points.append(p)
pass
return points
def getDataListWithOffset(self):
points = self.getValidPoints()
(left, top, width, height) = self.getRect(points)
dataList = list()
# if 1 == 0:
# data = self.getData()
# dataList.append(data)
# return dataList
xBegin = 0 - left
xEnd = self.subWidth - (left + width)
yBegin = 0 - top
yEnd = self.subHeight - (top + height)
h_i = 0
w_i = 0
# print "######################"
for h_i in xrange(yBegin, yEnd, 1):
for w_i in xrange(xBegin, xEnd, 1):
data = [0] * (self.subWidth * self.subHeight)
for p in points:
(x, y) = p
(x2, y2) = (x + w_i, y + h_i)
if x2 >= self.subWidth or y2 >= self.subHeight:
continue
idx = x2 + y2 * self.subWidth
data[idx] = 1
dataList.append(data)
return dataList
def getData(self):
points = self.getValidPoints()
(left, top, width, height) = self.getRect(points)
data = [0] * (self.subWidth * self.subHeight)
for p in points:
(x, y) = p
idx = x + y * self.subWidth
data[idx] = 1
return data
----
# -*- coding: utf-8 -*-
#zhangtao 2016/06/14
#QQ 406878851
import os
import numpy as np
import mlpy
import sys
import time
from PIL
import Image
from ImageHelper
import *
from common
import *
def checkSampleSucc(train_label, test_result):
if len(train_label) != len(test_result):
print "checkSampleSucc shape no match" return total = len(train_label)
succ = 0
for i
in xrange(total):
item1 = test_result[i]
item2 = train_label[i]
if int(item1) == int(item2):
succ += 1
succRate = float(succ) / float(total)
print "checkSampleSucc : ", (succ, total), succRate
def getDataListFromFile(srcfilepath):
im = Image.open(srcfilepath).convert(
'RGB')
helper = ImageHelper(im)
return helper.getDataListWithOffset()
def loadSampleFromDir(label, srcdir, x, y):
print "load ..", srcdir
srcfiles = os.listdir(srcdir)
tm = int(time.time())
k = 0
for srcfilename
in srcfiles:
srcfilepath = os.path.join(srcdir, srcfilename)
if srcfilename[0] ==
'a':
pass # continue
# print srcfilename
dataList = getDataListFromFile(srcfilepath)
idx = 0
for data
in dataList:
x.append(data)
y.append(label)
# createImageFromData(srcdir,data,idx)
idx += 1
newfilename =
"{0}\\{1}.png".format(srcdir, k + tm)
os.rename(srcfilepath,newfilename)
k += 1
def loadSampleFiles():
x = []
y = []
label_count = 0
for c
in xrange(ord(
'a'), ord(
'z') + 1, 1):
if c == ord(
'i'):
pass #continue
srcdir = r
'sample\{0}'.format(chr(c))
#mkdir(srcdir)
loadSampleFromDir(c, srcdir, x, y)
label_count += 1
for i
in xrange(1,10,1):
srcdir=r
'sample\{0}'.format( i )
#mkdir(srcdir)
loadSampleFromDir(i,srcdir,x,y)
label_count += 1
print "label_count:",label_count
return (x, y)
def train():
(x, y) = loadSampleFiles()
train_data = np.array(x, dtype=
'float32')
train_label = np.array(y, dtype=
'int32')
svm = mlpy.LibSvm(svm_type=
'c_svc', kernel_type=
'poly', gamma=10, probability=True)
print "svm learn 
." print "len(x):",len(x)
svm.learn(train_data, train_label)
print "svm learn done!" result = svm.pred(train_data)
print result
checkSampleSucc(y, result)
svm.save_model(ImageHelper.model_file)
# print result
def main():
begin = time.time()
train()
#test()
end = time.time()
t = end - begin
print "done:", t, t / (60)
# test2()
if __name__ ==
"__main__":
print __file__ main()
----
# -*- coding: utf-8 -*-
#zhangtao 2016/06/14
#QQ 406878851
import os
import numpy as np
import mlpy
import sys
import time
from PIL import Image
from ImageHelper import *
from common import *
testDir="test"
def processOne(i):
dataList=[]
fn=r'img/{0}.jpg'.format(i)
fn2=r'img2/{0}.bmp'.format(i)
im = Image.open(fn)
helper = ImageHelper(im)
helper.convertGrayImage()
im.save(fn2)
subList=helper.fixCropSubImages()
count = len(subList)
for j in xrange(count):
fn3=r'img3/{0}_{1}.bmp'.format(i,j)
sub = subList[j]
sub.save(fn3)
subHelper=ImageHelper(sub)
data=subHelper.getData()
dataList.append(data)
return dataList
def savePredictResult(result_list):
global testDir
print "savePredictResult"
count=len(result_list)
if count > 0:
with open("data.txt",'w') as file:
for i in xrange(0,count,1):
item=result_list[i]
file.write(str(i)+":")
file.write(item)
file.write("\n")
#print i, item
lines1=[]
lines2=[]
with open("data.txt",'r') as f:
lines1=f.readlines()
with open(r"{0}/data.txt".format(testDir),'r') as f:
lines2=f.readlines()
count1=len(lines1)
count2=len(lines2)
if count1 != count2 :
print "no match :",count1,count2
return
succ=0
for i in xrange(count1):
line1=lines1[i].strip()
line2=lines2[i].strip()
if line1 != line2:
print "!=",line1,line2
else:
succ += 1
rate= float(succ)/float(count1)
print "result:",(succ,count1),rate
def main():
print "-----------test---------------------"
if False == os.path.exists(ImageHelper.model_file):
print "s.path.exists ,faild ", ImageHelper.model_file
return
svm = mlpy.LibSvm.load_model(ImageHelper.model_file)
#print 'nclasses:', svm.nclasses()
#print 'labels:', svm.labels()
test_list=[]
for i in xrange(100):
dataList=processOne(i)
labels=[]
for data in dataList:
input_data = np.array(data, dtype='float32')
result_label = int(svm.pred(input_data))
labels.append(result_label)
result_string=parseLabels(labels)
print "{0}:{1}".format(i,result_string)
test_list.append(result_string)
savePredictResult(test_list)
if __name__ == "__main__":
print __file__
main()
---
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