在opencv中裁剪圖像

Question

我有一張圖像，裏面有一些文字。我想發送圖像到OCR，但圖像中有一些白色的噪音，所以OCR結果並不是那麼好。我試圖腐蝕/擴大圖像，但無法達到完美的工作門檻。由於圖像中的所有文字都是完全水平的，我嘗試了Hough變換。

下面是當我運行與OpenCV捆綁在一起的樣本hough變換程序時，圖像的樣子。

問題

• 我怎麼能黑掉一切除了其中紅色線是？ 或如何爲紅線突出顯示的每個區域劃出單獨的圖像？

• 我只想專注於水平線，我可以放棄對角線。

任何一個選項在發送到OCR時都適用於我。但是，我想嘗試兩種方法來查看哪個取得最佳結果。

Answer 1

HOWTO/s的輸出

• 我怎麼能黑掉一切，除了其中紅色線是？
  • dotess2()
  • ['Footel text goes he: e\n', 'Some mole hele\n', 'Some Text Here\n']
• 或如何可以裁剪出一個單獨的圖像每個由紅色線條強調的領域嗎？
  • dotess1()
  • ['Foolel text goes he: e\n', 'Some mole hele\n', 'Some Text Here\n', 'Directions\n']

代碼

# -*- coding: utf-8 -*- 
import cv2 
import numpy as np 
import math 
import subprocess 
import os 
import operator 

#some clean up/init blah blah 
junk='\/,-‘’「 」?.\';!{§_~!@#$%^&*()_+-|:}»£[]¢€¥°><' 
tmpdir='./tmp' 
if not os.path.exists(tmpdir): 
    os.makedirs(tmpdir) 
for path, subdirs, files in os.walk(tmpdir): 
    for name in files: 
     os.remove(os.path.join(path, name))  

#when the preprocessor is not pefect, there will be junk in the result. this is a crude mean of ridding them off 
def resfilter(res): 
    rd = dict() 
    for l in set(res): 
     rd[l]=0. 

    for l in rd: 
     for i in l: 
      if i in junk: 
       rd[l]-=1 
      elif i.isdigit(): 
       rd[l]+=.5 
      else: 
       rd[l]+=1 
    ret=[] 
    for v in sorted(rd.iteritems(), key=operator.itemgetter(1), reverse=True): 
     ret.append(v[0]) 
    return ret 

def dotess1(): 
    res =[] 
    for path, subdirs, files in os.walk(tmpdir): 
     for name in files: 
      fpath = os.path.join(path, name) 
      img = cv2.imread(fpath) 
      gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 

      ''' 
      #if the text is too small/contains noise etc, resize and maintain aspect ratio 
      if gray.shape[1]<100: 
       gray=cv2.resize(gray,(int(100/gray.shape[0]*gray.shape[1]),100)) 
      '''  
      cv2.imwrite('tmp.jpg',gray) 
      args = ['tesseract.exe','tmp.jpg','tessres','-psm','7', '-l','eng'] 
      subprocess.call(args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 
      with open('tessres.txt') as f: 
        for line in f: 
         if line.strip() != '': 
          res.append(line) 
    print resfilter(res) 


def dotess2(): 
    res =[] 
    args = ['tesseract.exe','clean.jpg','tessres','-psm','3', '-l','eng'] 
    subprocess.call(args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) 
    with open('tessres.txt') as f: 
      for line in f: 
       if line.strip() != '': 
        res.append(line) 
    print resfilter(res) 

''' 
start of code 
''' 
img = cv2.imread('c:/data/ocr3.png') 
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 
canny=cv2.Canny(gray,50,200,3) 
cv2.imshow('canny',canny) 

#remove the actual horizontal lines so that hough wont detect them 
linek = np.zeros((11,11),dtype=np.uint8) 
linek[5,...]=1 
x=cv2.morphologyEx(canny, cv2.MORPH_OPEN, linek ,iterations=1) 
canny-=x 
cv2.imshow('canny no horizontal lines',canny) 

#draw a fat line so that you can box it up 
lines = cv2.HoughLinesP(canny, 1, math.pi/2, 50,50, 50, 20) 
linemask = np.zeros(gray.shape,gray.dtype) 
for line in lines[0]: 
    if line[1]==line[3]:#check horizontal 
     pt1 = (line[0],line[1]) 
     pt2 = (line[2],line[3]) 
     cv2.line(linemask, pt1, pt2, (255), 30) 

cv2.imshow('linemask',linemask) 

''' 
* two methods of doing ocr,line mode and page mode 
* boxmask is used to so that a clean image can be saved for page mode 
* for every detected boxes, the roi are cropped and saved so that tess3 can be run in line mode 
''' 

boxmask = np.zeros(gray.shape,gray.dtype) 
contours,hierarchy = cv2.findContours(linemask,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) 
idx=0 
for cnt in contours: 
    idx+=1 
    area = cv2.contourArea(cnt) 
    x,y,w,h = cv2.boundingRect(cnt) 
    roi=img[y:y+h,x:x+w].copy() 
    cv2.imwrite('%s/%s.jpg'%(tmpdir,str(idx)),roi) 
    cv2.rectangle(boxmask,(x,y),(x+w,y+h),(255),-1) 


cv2.imshow('clean',img&cv2.cvtColor(boxmask,cv2.COLOR_GRAY2BGR)) 
cv2.imwrite('clean.jpg',img&cv2.cvtColor(boxmask,cv2.COLOR_GRAY2BGR)) 
cv2.imshow('img',img) 

dotess1() 
dotess2() 
cv2.waitKey(0)