数据训练和过程但没有得到正确的结果，为什么？ [英] data train and process but dont get the correct result,why?

问题描述

火车

Train

import cv2
import numpy as np
from datetime import datetime
import os
from PIL import Image
import hashlib, os, math, time
import Image
#from PIL import Image
import ImageEnhance
from pytesser import *
from urllib import urlretrieve
import math
import random
import glob
iconset = ['0','0','0','3','3','3','3','3','3','3','3','5','5','5','5','7','7',
'7','7','7','7','7','7','7','4','4','4','4','4','4','4','4','2','2','2','2','2','2','2','2','2','2']
iconset = [0,1,20,5,6,7,8,10,11,15,17,2,12,21,22,3,4,
13,14,16,18,19,23,9,24,25,26,27,28,29,30,31,32,
33,34,35,36,37,38,39,40,41]
samples=np.empty((0,100))
responses =[]
for f in range(filen):
    img =Image.open('./iconset/'+str(f)+'.gif')
    img.convert('RGB').save ('img.jpg','JPEG')
    img= cv2.imread('img.jpg')
    im3= img.copy()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ret,thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    fg = cv2.erode(thresh,None,iterations = 2)
    bgt = cv2.dilate(thresh,None,iterations = 3)
    ret,bg = cv2.threshold(bgt,1,128,1)
    marker = cv2.add(fg,bg)
    marker32 = np.int32(marker)
    cv2.watershed(img,marker32)
    m = cv2.convertScaleAbs(marker32)
    ret,thresh = cv2.threshold(m,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    roismall = cv2.resize(thresh,(10,10))
    sample = roismall.reshape((1,100))
    samples = np.append(samples,sample,0)
    responses.append(iconset[f])
responses = np.array(responses,np.float32)
responses = responses.reshape((responses.size,1))
print "training complete"
np.savetxt('general-samples.data',samples)
np.savetxt('general-responses.data',responses)
def train():
    samples = np.loadtxt('general-samples.data',np.float32)
    responses = np.loadtxt('general-responses.data',np.float32)
    responses = responses.reshape((1,responses.size))
    model = cv2.KNearest()
    model.train(samples,responses)
    return model

破解验证码：

breaking-captcha:

import cv2
import numpy as np
samples = np.loadtxt('general-samples.data', np.float32)
responses = np.loadtxt('general-responses.data', np.float32)
responses = responses.reshape((responses.size,1))
model = cv2.KNearest()
model.train(samples, responses)

image = cv2.imread('captcha1.png')
out = np.zeros(image.shape, np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
fg = cv2.erode(thresh,None,iterations = 2)
bgt = cv2.dilate(thresh,None,iterations = 3)
ret,bg = cv2.threshold(bgt,1,128,1)
marker = cv2.add(fg,bg)
marker32 = np.int32(marker)
cv2.watershed(image,marker32)
m = cv2.convertScaleAbs(marker32)
ret,thresh = cv2.threshold(m,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
res = cv2.bitwise_and(image,image,mask = thresh)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST
        , cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
    if cv2.contourArea(contour) > 50:
        [x, y, w, h] = cv2.boundingRect(contour)
        if  h > 28:
            cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
            roi = thresh[y:y+h, x:x+w]
            roi_small = cv2.resize(roi,(10,10))
            roi_small = roi_small.reshape((1,100))
            roi_small = np.float32(roi_small)
            retval, results, neigh_resp, dists = model.find_nearest(roi_small
                    , k = 1)
            string = str(int((results[0][0])))
            cv2.putText(out, string, (x, y+h), 0, 1, (0, 255, 0))
cv2.imshow('im',image)
cv2.imshow('out',out)
cv2.waitKey(0)

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