NumberPlateRecognitionSystem/TrainAndTest.py at master · venkster11/NumberPlateRecognitionSystem · GitHub

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# TrainAndTest.py

import cv2
import numpy as np
import operator
import os

MIN_CONTOUR_AREA = 100

RESIZED_IMAGE_WIDTH = 20
RESIZED_IMAGE_HEIGHT = 30


class ContourWithData():


    npaContour = None
    boundingRect = None
    intRectX = 0
    intRectY = 0
    intRectWidth = 0
    intRectHeight = 0
    fltArea = 0.0

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

    def checkIfContourIsValid(self):
        if self.fltArea < MIN_CONTOUR_AREA: return False
        return True


def main():
    allContoursWithData = []
    validContoursWithData = []

    try:
        npaClassifications = np.loadtxt("classifications1.txt", np.float32)
    except:
        print ("error, unable to open classifications.txt, exiting program\n")
        os.system("pause")
        return
    # end try

    try:
        npaFlattenedImages = np.loadtxt("flattened_images1.txt", np.float32)
    except:
        print ("error, unable to open flattened_images.txt, exiting program\n")
        os.system("pause")
        return
    # end try

    npaClassifications = npaClassifications.reshape((npaClassifications.size, 1))

    kNearest = cv2.ml.KNearest_create()

    kNearest.train(npaFlattenedImages, cv2.ml.ROW_SAMPLE, npaClassifications)
    nCropped_img_loc = 'Cropped Images-Text/8.png'

    imgTestingNumbers = cv2.imread(nCropped_img_loc)

    if imgTestingNumbers is None:
        print ("error: image not read from file \n\n")
        os.system("pause")
        return
    # end if

    imgGray = cv2.cvtColor(imgTestingNumbers, cv2.COLOR_BGR2GRAY)
    imgBlurred = cv2.GaussianBlur(imgGray, (5,5), 0)


    imgThresh = cv2.adaptiveThreshold(imgBlurred,
                                      255,
                                      cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                      cv2.THRESH_BINARY_INV,
                                      11,
                                      2)

    imgThreshCopy = imgThresh.copy()

    npaContours, npaHierarchy = cv2.findContours(imgThreshCopy,
                                                 cv2.RETR_EXTERNAL,
                                                 cv2.CHAIN_APPROX_SIMPLE)

    for npaContour in npaContours:
        contourWithData = ContourWithData()
        contourWithData.npaContour = npaContour
        contourWithData.boundingRect = cv2.boundingRect(contourWithData.npaContour)
        contourWithData.calculateRectTopLeftPointAndWidthAndHeight()
        contourWithData.fltArea = cv2.contourArea(contourWithData.npaContour)
        allContoursWithData.append(contourWithData)
    # end for

    for contourWithData in allContoursWithData:
        if contourWithData.checkIfContourIsValid():
            validContoursWithData.append(contourWithData)
        # end if
    # end for

    validContoursWithData.sort(key = operator.attrgetter("intRectX"))

    strFinalString = ""

    for contourWithData in validContoursWithData:

        cv2.rectangle(imgTestingNumbers,
                      (contourWithData.intRectX, contourWithData.intRectY),
                      (contourWithData.intRectX + contourWithData.intRectWidth, contourWithData.intRectY + contourWithData.intRectHeight),
                      (0, 255, 0),
                      2)

        imgROI = imgThresh[contourWithData.intRectY : contourWithData.intRectY + contourWithData.intRectHeight,
                           contourWithData.intRectX : contourWithData.intRectX + contourWithData.intRectWidth]

        imgROIResized = cv2.resize(imgROI, (RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT))

        npaROIResized = imgROIResized.reshape((1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT))

        npaROIResized = np.float32(npaROIResized)

        retval, npaResults, neigh_resp, dists = kNearest.findNearest(npaROIResized, k = 5)

        strCurrentChar = str(chr(int(npaResults[0][0])))

        strFinalString = strFinalString + strCurrentChar
    # end for


    print("\nCharacter recognition using KNN Algorithm")
    print ("\n" + strFinalString[2:] + "\n")

    cv2.imshow("Char recognition", imgTestingNumbers)

    cv2.waitKey(0)

    cv2.destroyAllWindows()

    return


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