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237 lines (206 loc) · 6.46 KB
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#!/usr/bin/env python
#encoding=utf-8
import sys,os,random
from PIL import Image,ImageEnhance,ImageFilter,ImageDraw
from StringIO import StringIO
import copy
import json,urllib2
def calcThreshold(im):
L = im.convert('L').histogram()
sum = 0
threshold = 0
for i in xrange(len(L)):
sum += L[i]
if sum >= 450:
threshold = i
break
return threshold
def binaryzation(im,threshold = 90):
table = []
for i in range(256):
if i < threshold:
table.append(0)
else:
table.append(1)
imgry = im.convert('L')
imout = imgry.point(table,'1')
return imout
'''
抽取出字符矩阵 列表
'''
def extractChar(im):
OFFSETLIST = [(1,0),(0,1),(-1,0),(0,-1),(1,1),(-1,1),(1,-1),(-1,-1)]
pixelAccess = im.load()
num = 1
queue = []
ff = [[0]*im.size[1] for i in xrange(im.size[0])]
'''
floodfill 提出块
'''
for i in xrange(im.size[0]):
for j in xrange(im.size[1]):
'''
pixelAccess[i,j] == 0 表示是黑点
'''
if pixelAccess[i,j] == 0 and ff[i][j] == 0:
ff[i][j] = num
queue.append((i,j))
while len(queue) > 0 :
a,b = queue[0]
queue = queue[1:]
for offset1,offset2 in OFFSETLIST:
x,y = a + offset1, b + offset2
if x < 0 or x >= im.size[0]:continue
if y < 0 or y >= im.size[1]:continue
if pixelAccess[x,y] == 0 and ff[x][y] == 0:
ff[x][y] = num
queue.append((x,y))
num += 1
'''
字符点阵的坐标列表，对齐到 (0,0)
eg: [(1,2),(3,24),(54,23)]
'''
#初始化字符数组
info = {
"x_min":im.size[0],
"y_min":im.size[1],
"x_max":0,
"y_max":0,
"width":0,
"height":0,
"number":0,
"points":[]
}
charList = [copy.deepcopy(info) for i in xrange(num)]
#统计
for i in xrange(im.size[0]):
for j in xrange(im.size[1]):
if ff[i][j] == 0:
continue
id = ff[i][j]
if i < charList[id]['x_min']:charList[id]['x_min'] = i
if j < charList[id]['y_min']:charList[id]['y_min'] = j
if i > charList[id]['x_max']:charList[id]['x_max'] = i
if j > charList[id]['y_max']:charList[id]['y_max'] = j
charList[id]['number'] += 1
charList[id]['points'].append((i,j))
for i in xrange(num):
charList[i]['width'] = charList[i]['x_max'] - charList[i]['x_min'] + 1
charList[i]['height'] = charList[i]['y_max'] - charList[i]['y_min'] + 1
#修正偏移
charList[i]['points'] = [(x-charList[i]['x_min'], y-charList[i]['y_min']) for x,y in charList[i]['points'] ]
#过滤杂点
ret = [one for one in charList if one['number'] > 6]
#排序
ret.sort(lambda a,b:a['x_min'] < b['x_min'])
return ret
'''
获取模型
'''
def getCharList():
arr = {
'char' : '',
'data' : ''
}
trainfiles = os.listdir('json')
charList = [copy.deepcopy(arr) for i in xrange(len(trainfiles))]
i = 0
for trainfile in trainfiles:
(filepath,tempfilename) = os.path.split(trainfile);
(shotname,extension) = os.path.splitext(tempfilename);
if extension != '.json':
continue
f = open('json/'+trainfile, 'r')
str = f.read()
ret = json.loads(str.decode("utf8"))
f.close()
charList[i]['data'] = ret
charList[i]['char'] = shotname
i += 1
return charList
'''
识别字符
'''
def charSimilarity(charA,charB):
s2 = set([(one[0],one[1]) for one in charB['points']])
sumlen = len(charA['points']) + len(charB['points'])
max = 0
# 晃动匹配
i_adjust = 1 if charB['width'] - charA['width'] >= 0 else -1
j_adjust = 1 if charB['height'] - charA['height'] >= 0 else -1
for i in xrange(0,charB['width'] - charA['width'] + i_adjust,i_adjust):
for j in xrange(0,charB['height'] - charA['height'] + j_adjust,j_adjust):
s1 = set([(one[0]+i,one[1]+j) for one in charA['points']])
sim = len(s1&s2) *2.0 / sumlen
if sim > max:
max = sim
return max
def recognise(one):
trainfiles = os.listdir('json')
i = 0
max = 0
char = None;
for trainfile in trainfiles:
(filepath,tempfilename) = os.path.split(trainfile);
(shotname,extension) = os.path.splitext(tempfilename);
if extension != '.json':
continue
f = open('json/'+trainfile, 'r')
str = f.read()
ret = json.loads(str.decode("utf8"))
f.close()
s = charSimilarity(one,ret)
if s > max:
char = shotname[0]
max = s
i += 1
return char
'''
训练获取字形模具
'''
def train():
trainfiles = os.listdir('png')
for trainfile in trainfiles:
(filepath,tempfilename) = os.path.split(trainfile);
(shotname,extension) = os.path.splitext(tempfilename);
if extension != '.png':
continue
im = Image.open('png/'+trainfile)
threshold = calcThreshold(im)
imout = binaryzation(im,threshold)
imout.show()
ret = extractChar(imout)
if len(ret) != len(shotname):
continue
for i in range(len(ret)):
dump(shotname[i],ret[i])
'''
获取字模
'''
def dump(char,dic):
number = random.randint(0, 100)
with open('json/'+ char + '_'+bytes(number)+'.json','wb') as f:
f.write(json.dumps(dic))
'''
下载图片保存到本地
'''
def downImg(flink):
number = random.randint(1000, 10000)
content2 = urllib2.urlopen(flink).read()
with open(u'imgs'+'/temp_'+bytes(number)+'.png','wb') as code:
code.write(content2)
return 'imgs'+'/temp_'+bytes(number)+'.png'
def dowork(image_url):
image_name = downImg(image_url)
ans = []
im = Image.open(image_name)
threshold = calcThreshold(im)
imout = binaryzation(im,threshold)
imout.show()
rets = extractChar(imout)
for one in rets:
ans.append(recognise(one))
print ans
if __name__ == '__main__':
dowork('http://www.zbjj.gov.cn:5555/imageServlet?0.08824421651661396')
#train()