Есть ли способ включить графики в этот код? https://github.com/MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Python.git - PullRequest
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Есть ли способ включить графики в этот код? https://github.com/MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Python.git

0 голосов
/ 19 октября 2019

Я пытаюсь получить точность и вывести результаты КНН на следующий код: https://github.com/MicrocontrollersAndMore/OpenCV_3_License_Plate_Recognition_Python.git?

Комментированный код - это то, что я пытался сделать;) 

def recognizeCharsInPlate(imgThresh, listOfMatchingChars):
        strChars = ""               # this will be the return value, the          chars in the lic plate
    npaFlattenedImages = np.loadtxt("flattened_images.txt", np.float32)
    npaClassifications = np.loadtxt("classifications.txt", np.float32) 

    height, width = imgThresh.shape

    imgThreshColor = np.zeros((height, width, 3), np.uint8)

    listOfMatchingChars.sort(key = lambda matchingChar:   matchingChar.intCenterX)        # sort chars from left to right

    cv2.cvtColor(imgThresh, cv2.COLOR_GRAY2BGR, imgThreshColor)                     # make color version of threshold image so we can draw contours in color on it

    for currentChar in listOfMatchingChars:                                         # for each char in plate
        pt1 = (currentChar.intBoundingRectX, currentChar.intBoundingRectY)
        pt2 = ((currentChar.intBoundingRectX + currentChar.intBoundingRectWidth), (currentChar.intBoundingRectY + currentChar.intBoundingRectHeight))

        cv2.rectangle(imgThreshColor, pt1, pt2, Main.SCALAR_GREEN, 2)           # draw green box around the char

                # crop char out of threshold image
        imgROI = imgThresh[currentChar.intBoundingRectY : currentChar.intBoundingRectY + currentChar.intBoundingRectHeight,
                           currentChar.intBoundingRectX : currentChar.intBoundingRectX + currentChar.intBoundingRectWidth]

        imgROIResized = cv2.resize(imgROI, (RESIZED_CHAR_IMAGE_WIDTH, RESIZED_CHAR_IMAGE_HEIGHT))           # resize image, this is necessary for char recognition

        npaROIResized = imgROIResized.reshape((1, RESIZED_CHAR_IMAGE_WIDTH * RESIZED_CHAR_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)              # finally we can call findNearest !!!

#        print(retval, npaResults, neigh_resp, dists)
#        matches = npaResults==npaFlattenedImages
#        correct = np.count_nonzero(matches)
#        accuracy = correct*100.0/10000
#        print(accuracy)


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

        strChars = strChars + strCurrentChar                        # append current char to full string

    # end for
    # Take Red families and plot them
#    red = npaClassifications[npaFlattenedImages.ravel()==0]
#    plt.scatter(red[:,0],red[:,1],80,'r','^')
#    # Take Blue families and plot them
#    blue = npaClassifications[npaFlattenedImages.ravel()==1]
#    plt.scatter(blue[:,0],blue[:,1],80,'b','s')
#    plt.show()
#    print( "result:  {}\n".format(npaResults) )
#    print( "neighbours:  {}\n".format(neigh_resp) )
#    print( "distance:  {}\n".format(dists) )
#    plt.show()
#    
    if Main.showSteps == True: # show steps #######################################################
        cv2.imshow("10", imgThreshColor)
    # end if # show steps #########################################################################

    return strChars
# end function

Я ожидаю метрическую точность и график зависимости между K и точностью тестирования. Я спрашиваю об этом, потому что я не уверен, правильно ли я это делаю.

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