This is an old revision of the document!
plate
plate detection with neural network
http://www.pyimagesearch.com/2017/02/13/recognizing-digits-with-opencv-and-python/
# load the example image image = cv2.imread("example.jpg") # pre-process the image by resizing it, converting it to # graycale, blurring it, and computing an edge map image = imutils.resize(image, height=500) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Applying Gaussian blurring with a 5×5 kernel to reduce high-frequency noise blurred = cv2.GaussianBlur(gray, (5, 5), 0) # Computing the edge map via the Canny edge detector. edged = cv2.Canny(blurred, 50, 200, 255) # find contours in the edge map, then sort them by their # size in descending order cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if imutils.is_cv2() else cnts[1] cnts = sorted(cnts, key=cv2.contourArea, reverse=True) displayCnt = None # loop over the contours for c in cnts: # approximate the contour peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.02 * peri, True) # if the contour has four vertices, then we have found # the thermostat display if len(approx) == 4: displayCnt = approx break # extract the plate, apply a perspective transform to it # Applying this perspective transform gives us a top-down, birds-eye-view of plate warped = four_point_transform(gray, displayCnt.reshape(4, 2)) output = four_point_transform(image, displayCnt.reshape(4, 2)) # threshold the warped image, then apply a series of morphological # operations to cleanup the thresholded image thresh = cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 5)) thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel) # find contours in the thresholded image, then initialize the # digit contours lists cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = cnts[0] if imutils.is_cv2() else cnts[1] digitCnts = [] # loop over the digit area candidates for c in cnts: # compute the bounding box of the contour (x, y, w, h) = cv2.boundingRect(c) # if the contour is sufficiently large, it must be a digit if w >= 15 and (h >= 30 and h <= 40): digitCnts.append(c) # TODO display contour # cv2.rectangle(output, (x, y), (x + w, y + h), (0, 255, 0), 1) # sort the contours from left-to-right, then initialize the # actual digits themselves digitCnts = contours.sort_contours(digitCnts, method="left-to-right")[0] digits = [] # cv2.putText(output, str(digit), (x - 10, y - 10), # cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 255, 0), 2)