GenData.py

# GenData.py

import sys
import numpy as np
import cv2
import os


MIN_CONTOUR_AREA = 100

RESIZED_IMAGE_WIDTH = 20
RESIZED_IMAGE_HEIGHT = 30


def main(fileName):
    imgTrainingNumbers = cv2.imread(fileName)           #samples file

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

    imgGray = cv2.cvtColor(imgTrainingNumbers, cv2.COLOR_BGR2GRAY)      #Negative Filter
    imgBlurred = cv2.GaussianBlur(imgGray, (5,5), 0)                    #Blurring Filter

    imgThresh = cv2.adaptiveThreshold(imgBlurred,                       #Passing Only White
                                      255,
                                      cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                      cv2.THRESH_BINARY_INV,
                                      11,
                                      2)

    cv2.imshow("imgThresh", imgThresh)

    imgThreshCopy = imgThresh.copy()

    imgContours, npaContours, npaHierarchy = cv2.findContours(imgThreshCopy,    #Finding Contours in the last image
                                                 cv2.RETR_EXTERNAL,
                                                 cv2.CHAIN_APPROX_SIMPLE)



    npaFlattenedImages =  np.empty((0, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT))

    intClassifications = []

    intValidChars = [ord('0'), ord('1'), ord('2'), ord('3'), ord('4'), ord('5'), ord('6'), ord('7'), ord('8'), ord('9'),
                     ord('A'), ord('B'), ord('C'), ord('D'), ord('E'), ord('F'), ord('G'), ord('H'), ord('I'), ord('J'),
                     ord('K'), ord('L'), ord('M'), ord('N'), ord('O'), ord('P'), ord('Q'), ord('R'), ord('S'), ord('T'),
                     ord('U'), ord('V'), ord('W'), ord('X'), ord('Y'), ord('Z')]

    for npaContour in npaContours:
        if cv2.contourArea(npaContour) > MIN_CONTOUR_AREA:
            [intX, intY, intW, intH] = cv2.boundingRect(npaContour)

            cv2.rectangle(imgTrainingNumbers,
                          (intX, intY),
                          (intX+intW,intY+intH),
                          (0, 0, 255),
                          2)

            imgROI = imgThresh[intY:intY+intH, intX:intX+intW]
            imgROIResized = cv2.resize(imgROI, (RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT))

            cv2.imshow("imgROI", imgROI)
            cv2.imshow("imgROIResized", imgROIResized)
            cv2.imshow("training_numbers.png", imgTrainingNumbers)

            intChar = cv2.waitKey(0)

            if intChar == 27:
                sys.exit()
            elif intChar in intValidChars:
                intClassifications.append(intChar)          #passing the key to classification.txt
                npaFlattenedImage = imgROIResized.reshape((1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT))

                # passing the image matrix to the FlattenedImages.txt
                npaFlattenedImages = np.append(npaFlattenedImages, npaFlattenedImage, 0)

    fltClassifications = np.array(intClassifications, np.float32)

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

    print("\n\ntraining complete !!\n")

    with open('classifications.txt', 'ab') as file1:
        np.savetxt(file1, npaClassifications)

    with open('flattened_images.txt', 'ab') as file2:
        np.savetxt(file2, npaFlattenedImages)

    cv2.destroyAllWindows()

    return


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