OpenCV简单标准数字识别的完整实例
在学习openCV时,看到一个问答做数字识别,里面配有代码,应用到了openCV里面的ml包,很有学习价值。
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) #先转换为灰度图才能够使用图像阈值化 thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2) #自适应阈值化 ################## Now finding Contours ################### # image,contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) #边缘查找,找到数字框,但存在误判 samples = np.empty((0,900)) #将每一个识别到的数字所有像素点作为特征,储存到一个30*30的矩阵内 responses = [] #label keys = [i for i in range(48,58)] #48-58为ASCII码 count =0 for cnt in contours: if cv2.contourArea(cnt)>80: #使用边缘面积过滤较小边缘框 [x,y,w,h] = cv2.boundingRect(cnt) if h>25 and h < 30: #使用高过滤小框和大框 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: #过滤一下过多边缘框,后期可能会尝试极大抑制 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()
训练数据为:
测试数据为:
使用openCV自带的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) #预处理一下 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')
总结
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