主要是針對自如房價的爬取 。以下程式碼對房價圖片進行處理，將裡面的數字提取出來，然後用knn最近鄰演算法去對圖片上的資料進行分類。

import sys
import cv2
import numpy as np

 #######   training part    ############### 
samples = np.loadtxt('generalsamples.data',np.float32)
responses = np.loadtxt('generalresponses.data',np.float32)
responses = responses.reshape((responses.size,1))
 
model = cv2.ml.KNearest_create()
model.train(samples,cv2.ml.ROW_SAMPLE,responses)


def getNum(path):
    
    im = cv2.imread(path)
    out = np.zeros(im.shape,np.uint8)
    gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
    
    #預處理一下
    for i in range(gray.__len__()):
        for j in range(gray[0].__len__()):
            if gray[i][j] == 0:
                gray[i][j] == 255
            else:
                gray[i][j] == 0
    thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)
     
    image,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
    count = 0 
    numbers = []
    for cnt in contours:
        if cv2.contourArea(cnt)>80:
            [x,y,w,h] = cv2.boundingRect(cnt)
            if  h>25:
                cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2)
                roi = thresh[y:y+h,x:x+w]
                roismall = cv2.resize(roi,(30,30))
                roismall = roismall.reshape((1,900))
                roismall = np.float32(roismall)
                retval, results, neigh_resp, dists = model.findNearest(roismall, k = 1)
                string = str(int((results[0][0])))
                numbers.append(int((results[0][0])))
                cv2.putText(out,string,(x,y+h),0,1,(0,255,0))
                count += 1
        if count == 10:
            break
    return numbers

# numbers = getNum('1.png')
 

圖片訓練集的下載位置為： 

generalresponses.data

generalsamples.data