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，很有学习代价。https://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python#
import sysimport numpy as npimport 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 = [] #labelkeys = #48-58为ASCII码count =0for cnt in contours: if cv2.contourArea(cnt)>80: #使用边缘面积过滤较小边缘框 = cv2.boundingRect(cnt) ifh>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 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 breakresponses = 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：
http://img.jbzj.com/file_images/article/202109/202199113717654.png?202189113735
测试数据为：
http://img.jbzj.com/file_images/article/202109/202199113747886.png?202189113753
使用openCV自带的ML包，KNearest算法
import sysimport cv2import 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) #预处置惩罚一下 for i in range(gray.__len__()): for j in range(gray.__len__()): if gray == 0: gray == 255 else: gray == 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: = cv2.boundingRect(cnt) ifh>25: cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),2) roi = thresh 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))) numbers.append(int((results))) cv2.putText(out,string,(x,y+h),0,1,(0,255,0)) count += 1 if count == 10: break return numbers numbers = getNum('1.png')http://img.jbzj.com/file_images/article/202109/202199113823745.png?202189113829
总结
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