def onPreview(self):
f=open(self.compare_url.get(),'r')
g=open(self.output_url.get(),'w')
img=cv2.imread(self.source_url.get(),0)
if(img==None):
print url+' does\'nt exist'
exit()
img = pp.preprocess(img)
im,rot = pp.skew_correction(img)
line = pp.find_lines(im.copy())
# print len(linene)
label_list=it.train.label_unicode()
q=f.readlines()
i=0
num=[]
for l in line:
for w in l.word_list:
for c in w.char_list:
tup=label_list[int(c.label)]
if(q[i][:-1]!=tup):
tup=q[i][:-1]
g.write(tup)
i+=1
g.write(' ')
g.write('\n')
f.close()
g.close()
self.draw_frame3()
def onPreview(self):
f = open(self.compare_url.get(), 'r')
g = open(self.output_url.get(), 'w')
img = cv2.imread(self.source_url.get(), 0)
if (img == None):
print url + ' does\'nt exist'
exit()
img = pp.preprocess(img)
im, rot = pp.skew_correction(img)
line = pp.find_lines(im.copy())
# print len(linene)
label_list = it.train.label_unicode()
q = f.readlines()
i = 0
num = []
for l in line:
for w in l.word_list:
for c in w.char_list:
tup = label_list[int(c.label)]
if (q[i][:-1] != tup):
tup = q[i][:-1]
g.write(tup)
i += 1
g.write(' ')
g.write('\n')
f.close()
g.close()
self.draw_frame3()
def make_modified_file():
f=open('./compare_list.txt','r')
g=open('./output_file.txt','w')
img=cv2.imread('./Example/dc_books_page.png',0)
if(img==None):
print url+' does\'nt exist'
exit()
img = pp.preprocess(img)
im,rot = pp.skew_correction(img)
line = pp.find_lines(im.copy())
# print len(linene)
label_list=train.label_unicode()
q=f.readlines()
i=0
num=[]
for l in line:
for w in l.word_list:
for c in w.char_list:
# num.append((str(i),label_list[int(c.label)]))
tup=label_list[int(c.label)]
if(q[i][:-1]!=tup):
print tup
# f.write(tup+'\n')
g.write(tup)
# cv2.imwrite('samp/'+str(i)+'.png',c.data)
i+=1
g.write(' ')
g.write('\n')
f.close()
g.close()
def make_modified_file():
f = open('./compare_list.txt', 'r')
g = open('./output_file.txt', 'w')
img = cv2.imread('./Example/dc_books_page.png', 0)
if (img == None):
print url + ' does\'nt exist'
exit()
img = pp.preprocess(img)
im, rot = pp.skew_correction(img)
line = pp.find_lines(im.copy())
# print len(linene)
label_list = train.label_unicode()
q = f.readlines()
i = 0
num = []
for l in line:
for w in l.word_list:
for c in w.char_list:
# num.append((str(i),label_list[int(c.label)]))
tup = label_list[int(c.label)]
if (q[i][:-1] != tup):
print tup
# f.write(tup+'\n')
g.write(tup)
# cv2.imwrite('samp/'+str(i)+'.png',c.data)
i += 1
g.write(' ')
g.write('\n')
f.close()
g.close()
def gen_train_sample(im):
# train.classifierclassifier.load('svm_class.xml')
img = pp.preprocess(im.copy())
img,rot = pp.skew_correction(img)
hight,width=im.shape
M = cv2.getRotationMatrix2D((hight/2,width/2),rot-90,1)
im = cv2.warpAffine(im,M,(width,hight))
cv2.imwrite('skew correct.png',im)
contours2, hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
contours = []
for cnt in contours2:
# print (cv2.contourArea(cnt))
if(cv2.contourArea(cnt)>20):
contours.append(cnt)
X = [cv2.contourArea(C) for C in contours]
# print len(contours),len(X)
t=[i for i in range (0,len(contours))]
X,t = zip(*sorted(zip(X,t)))
i=0
for j in t:
x,y,w,h=cv2.boundingRect(contours[j])
box = im[y-1:y+h+1,x-1:x+w+1]
char = pp.preprocess(box.copy())
try:
f = train.find_feature(char)
fu= train.np.array(f,train.np.float32)
# print len(fu)
t = train.classifier.predict(fu)
print t
except IndexError:
t = 0
cv2.imwrite('samp/zsamp31_'+str(int(t))+'_'+str(i)+'.png',box)
i+=1
# -*- coding: utf-8 -*- """ Created on Thu Sep 17 10:25:26 2015 Project Malayalam OCR - Lekha ----------------------------- Sponsored by ICFOSS contributers : james, jithin """ import cv2 import sys import preprocess as pp import training as train import random url = sys.argv[1] #geting file url from command prompt img=cv2.imread(url,0) if(img.data==None): print url+' does\'nt exist' exit() img = pp.preprocess(img) im=img im,rot = pp.skew_correction(img) # Add Layout analysis here print pp.recognize_block(im)
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 17 10:25:26 2015
Project Malayalam OCR - Lekha
-----------------------------
Sponsored by ICFOSS
contributers : james, jithin
"""
import cv2
import sys
import preprocess as pp
import training as train
import random
url = sys.argv[1] #geting file url from command prompt
img = cv2.imread(url, 0)
if (img.data == None):
print url + ' does\'nt exist'
exit()
img = pp.preprocess(img)
im = img
im, rot = pp.skew_correction(img)
# Add Layout analysis here
print pp.recognize_block(im)
