Python 属性错误：'；模块'；对象没有属性'；ml'；

我使用以下代码进行字符识别，这段python代码支持openCV-3，但我有openCV-2.4.9。当我尝试执行时，我得到以下错误

File "TrainAndTest.py", line 143, in <module>
main()
File "TrainAndTest.py", line 60, in main
kNearest = cv2.ml.KNearest_create()   # instantiate KNN object             
AttributeError: 'module' object has no attribute 'ml'

文件“TrainAndTest.py”，第143行，在
main（）
文件“TrainAndTest.py”，第60行，主
kNearest=cv2.ml.kNearest_create（）#实例化KNN对象
AttributeError:“模块”对象没有属性“ml”

Python代码

import cv2
import numpy as np
import operator
import os

# module level variables 
MIN_CONTOUR_AREA = 100
RESIZED_IMAGE_WIDTH = 20
RESIZED_IMAGE_HEIGHT = 30

class ContourWithData():

# member variables 
npaContour = None           # contour
boundingRect = None         # bounding rect for contour
intRectX = 0                # bounding rect top left corner x location
intRectY = 0                # bounding rect top left corner y location
intRectWidth = 0            # bounding rect width
intRectHeight = 0           # bounding rect height
fltArea = 0.0               # area of contour

def calculateRectTopLeftPointAndWidthAndHeight(self):  # calculate bounding rect info
    [intX, intY, intWidth, intHeight] = self.boundingRect
    self.intRectX = intX
    self.intRectY = intY
    self.intRectWidth = intWidth
    self.intRectHeight = intHeight

def checkIfContourIsValid(self):                            # this is oversimplified, for a production grade program
    if self.fltArea < MIN_CONTOUR_AREA: return False        # much better validity checking would be necessary
    return True

def main():
allContoursWithData = []                # declare empty lists,
validContoursWithData = []              # we will fill these shortly

try:
    npaClassifications = np.loadtxt("classifications.txt", np.float32)                  # read in training classifications
except:
    print "error, unable to open classifications.txt, exiting program\n"
    os.system("pause")
    return
# end try

try:
    npaFlattenedImages = np.loadtxt("flattened_images.txt", np.float32)                 # read in training images
except:
    print "error, unable to open flattened_images.txt, exiting program\n"
    os.system("pause")
    return
# end try

npaClassifications = npaClassifications.reshape((npaClassifications.size, 1))       # reshape numpy array to 1d, necessary to pass to call to train

kNearest = cv2.ml.KNearest_create()                   # instantiate KNN object

kNearest.train(npaFlattenedImages, cv2.ml.ROW_SAMPLE, npaClassifications)

imgTestingNumbers = cv2.imread("1.jpg")          # read in testing numbers image

if imgTestingNumbers is None:                           # if image was not read successfully
    print "error: image not read from file \n\n"        # print error message to std out
    os.system("pause")                                  # pause so user can see error message
    return                                              # and exit function (which exits program)
# end if

imgGray = cv2.cvtColor(imgTestingNumbers, cv2.COLOR_BGR2GRAY)       # get grayscale image
imgBlurred = cv2.GaussianBlur(imgGray, (5,5), 0)                    # blur

                                                    # filter image from grayscale to black and white
imgThresh = cv2.adaptiveThreshold(imgBlurred,                           # input image
                                  255,                                  # make pixels that pass the threshold full white
                                  cv2.ADAPTIVE_THRESH_GAUSSIAN_C,       # use gaussian rather than mean, seems to give better results
                                  cv2.THRESH_BINARY_INV,                # invert so foreground will be white, background will be black
                                  11,                                   # size of a pixel neighborhood used to calculate threshold value
                                  2)                                    # constant subtracted from the mean or weighted mean

imgThreshCopy = imgThresh.copy()        # make a copy of the thresh image, this in necessary b/c findContours modifies the image

imgContours, npaContours, npaHierarchy = cv2.findContours(imgThreshCopy,             # input image, make sure to use a copy since the function will modify this image in the course of finding contours
                                             cv2.RETR_EXTERNAL,         # retrieve the outermost contours only
                                             cv2.CHAIN_APPROX_SIMPLE)   # compress horizontal, vertical, and diagonal segments and leave only their end points

for npaContour in npaContours:                             # for each contour
    contourWithData = ContourWithData()                                             # instantiate a contour with data object
    contourWithData.npaContour = npaContour                                         # assign contour to contour with data
    contourWithData.boundingRect = cv2.boundingRect(contourWithData.npaContour)     # get the bounding rect
    contourWithData.calculateRectTopLeftPointAndWidthAndHeight()                    # get bounding rect info
    contourWithData.fltArea = cv2.contourArea(contourWithData.npaContour)           # calculate the contour area
    allContoursWithData.append(contourWithData)                                     # add contour with data object to list of all contours with data
# end for

for contourWithData in allContoursWithData:                 # for all contours
    if contourWithData.checkIfContourIsValid():             # check if valid
        validContoursWithData.append(contourWithData)       # if so, append to valid contour list
    # end if
# end for

validContoursWithData.sort(key = operator.attrgetter("intRectX"))         # sort contours from left to right

strFinalString = ""         # declare final string, this will have the final number sequence by the end of the program

for contourWithData in validContoursWithData:            # for each contour
                                            # draw a green rect around the current char
    cv2.rectangle(imgTestingNumbers,                                        # draw rectangle on original testing image
                  (contourWithData.intRectX, contourWithData.intRectY),     # upper left corner
                  (contourWithData.intRectX + contourWithData.intRectWidth, contourWithData.intRectY + contourWithData.intRectHeight),      # lower right corner
                  (0, 255, 0),              # green
                  2)                        # thickness

    imgROI = imgThresh[contourWithData.intRectY : contourWithData.intRectY + contourWithData.intRectHeight,     # crop char out of threshold image
                       contourWithData.intRectX : contourWithData.intRectX + contourWithData.intRectWidth]

    imgROIResized = cv2.resize(imgROI, (RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT))             # resize image, this will be more consistent for recognition and storage

    npaROIResized = imgROIResized.reshape((1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT))      # flatten image into 1d numpy array

    npaROIResized = np.float32(npaROIResized)       # convert from 1d numpy array of ints to 1d numpy array of floats

    retval, npaResults, neigh_resp, dists = kNearest.findNearest(npaROIResized, k = 1)     # call KNN function find_nearest

    strCurrentChar = str(chr(int(npaResults[0][0])))                                             # get character from results

    strFinalString = strFinalString + "\0" + strCurrentChar            # append current char to full string
# end for

print "\n"  + strFinalString + "\n"                  # show the full string

cv2.imshow("imgTestingNumbers", imgTestingNumbers)      # show input image with green boxes drawn around found digits
cv2.waitKey(0)                                          # wait for user key press

cv2.destroyAllWindows()             # remove windows from memory

return


if __name__ == "__main__":
main()
# end if

导入cv2
将numpy作为np导入
进口经营者
导入操作系统
#模块级变量
最小轮廓面积=100
调整大小的图像宽度=20
调整大小的图像高度=30
使用数据（）类：
#成员变量
NPA轮廓=无#轮廓
boundingRect=无#轮廓的边界矩形
intRectX=0#边界矩形左上角x位置
intRectY=0#边界矩形左上角y位置
intRectWidth=0#边界矩形宽度
intRectHeight=0#边界矩形高度
fltArea=0.0#轮廓面积
def CalculateRectopLeftPointandWidthandHeight（self）：#计算边界矩形信息
[intX，intY，intWidth，inthight]=self.boundingRect
self.intRectX=intX
self.intRectY=intY
self.intRectWidth=intWidth
self.intrecthheight=intHeight
def checkIfContourIsValid（自我）：#这对于生产级计划来说过于简单
如果self.fltArea