"--char-classifier",
required=True,
help="path to the output character classifier")
ap.add_argument("-d",
"--digit-classifier",
required=True,
help="path to the output digit classifier")
args = vars(ap.parse_args())
# load the character and digit classifiers
charModel = cPickle.loads(open(args["char_classifier"]).read())
digitModel = cPickle.loads(open(args["digit_classifier"]).read())
# initialize the descriptor
blockSizes = ((5, 5), (5, 10), (10, 5), (10, 10))
desc = BlockBinaryPixelSum(targetSize=(30, 15), blockSizes=blockSizes)
# loop over the images
for imagePath in sorted(list(paths.list_images(args["images"]))):
# load the image
print(imagePath[imagePath.rfind("/") + 1:])
image = cv2.imread(imagePath)
# if the width is greater than 640 pixels, then resize the image
if image.shape[1] > 640:
image = imutils.resize(image, width=640)
# initialize the license plate detector and detect the license plates and characters
lpd = LicensePlateDetector(image, numChars=7)
plates = lpd.detect()
help="path to the output digit classifier")
args = vars(ap.parse_args())
# initialize characters string
alphabet = "abcdefghijklmnopqrstuvwxyz0123456789"
# initialize the data and labels for the alphabet and digits
alphabetData = []
digitsData = []
alphabetLabels = []
digitsLabels = []
# initialize the descriptor
print("[INFO] describing font examples...")
blockSizes = ((5, 5), (5, 10), (10, 5), (10, 10))
desc = BlockBinaryPixelSum(targetSize=(30, 15), blockSizes=blockSizes)
# loop over the font paths
for fontPath in paths.list_images(args["fonts"]):
# load the font image, convert it to grayscale and threshold it
font = cv2.imread(fontPath)
font = cv2.cvtColor(font, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(font, 128, 255, cv2.THRESH_BINARY_INV)[1]
# detect contours in the thresholded image and sort them from left to right
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = sorted(cnts,
key=lambda c:
(cv2.boundingRect(c)[0] + cv2.boundingRect(c)[1]))