在學習openCV時，看到一個問答做數字識別，里面配有代碼，應用到了openCV里面的ml包，很有學習價值。
https://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python#
import sys
import numpy as np
import cv2
im = cv2.imread('t.png')
im3 = im.copy()
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY) #先轉換為灰度圖才能夠使用圖像閾值化
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2) #自適應閾值化
################## Now finding Contours ###################
#
image,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
#邊緣查找，找到數字框，但存在誤判
samples = np.empty((0,900)) #將每一個識別到的數字所有像素點作為特征，儲存到一個30*30的矩陣內
responses = [] #label
keys = [i for i in range(48,58)] #48-58為ASCII碼
count =0
for cnt in contours:
if cv2.contourArea(cnt)>80: #使用邊緣面積過濾較小邊緣框
[x,y,w,h] = cv2.boundingRect(cnt)
if h>25 and h 30: #使用高過濾小框和大框
count+=1
cv2.rectangle(im,(x,y),(x+w,y+h),(0,0,255),2)
roi = thresh[y:y+h,x:x+w]
roismall = cv2.resize(roi,(30,30))
cv2.imshow('norm',im)
key = cv2.waitKey(0)
if key == 27: # (escape to quit)
sys.exit()
elif key in keys:
responses.append(int(chr(key)))
sample = roismall.reshape((1,900))
samples = np.append(samples,sample,0)
if count == 100: #過濾一下過多邊緣框，后期可能會嘗試極大抑制
break
responses = np.array(responses,np.float32)
responses = responses.reshape((responses.size,1))
print ("training complete")
np.savetxt('generalsamples.data',samples)
np.savetxt('generalresponses.data',responses)
#
cv2.waitKey()
cv2.destroyAllWindows()
訓練數據為：
測試數據為：
使用openCV自帶的ML包，KNearest算法
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')
總結
到此這篇關于OpenCV簡單標準數字識別的文章就介紹到這了,更多相關OpenCV標準數字識別內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
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