Python实现车牌定位及分割

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

gaussian = cv2.GaussianBlur(gray, (3, 3), 0, 0, cv2.BORDER_DEFAULT)median = cv2.medianBlur(gaussian, 5)

sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0,  ksize = 3)

ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY)

# 膨胀和腐蚀操作的核函数element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1))element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (8, 6))# 膨胀一次，让轮廓突出dilation = cv2.dilate(binary, element2, iterations = 1)# 腐蚀一次，去掉细节erosion = cv2.erode(dilation, element1, iterations = 1)# 再次膨胀，让轮廓明显一些dilation2 = cv2.dilate(erosion, element2,iterations = 3)

def findPlateNumberRegion(img):    region = []    # 查找轮廓    contours,hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)    # 筛选面积小的    for i in range(len(contours)):        cnt = contours[i]        # 计算该轮廓的面积        area = cv2.contourArea(cnt)        # 面积小的都筛选掉        if (area < 2000):            continue        # 轮廓近似，作用很小        epsilon = 0.001 * cv2.arcLength(cnt,True)        approx = cv2.approxPolyDP(cnt, epsilon, True)        # 找到最小的矩形，该矩形可能有方向        rect = cv2.minAreaRect(cnt)        print "rect is: "        print rect        # box是四个点的坐标        box = cv2.cv.BoxPoints(rect)        box = np.int0(box)        # 计算高和宽        height = abs(box[0][1] - box[2][1])        width = abs(box[0][0] - box[2][0])        # 车牌正常情况下长高比在2.7-5之间        ratio =float(width) / float(height)        if (ratio > 5 or ratio < 2):            continue        region.append(box)    return region

    # 用绿线画出这些找到的轮廓    for box in region:        cv2.drawContours(img, [box], 0, (0, 255, 0), 2)    ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]    xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]    ys_sorted_index = np.argsort(ys)    xs_sorted_index = np.argsort(xs)    x1 = box[xs_sorted_index[0], 0]    x2 = box[xs_sorted_index[3], 0]    y1 = box[ys_sorted_index[0], 1]    y2 = box[ys_sorted_index[3], 1]    img_org2 = img.copy()    img_plate = img_org2[y1:y2, x1:x2]