opencv车牌识别代码

以下是一个基于OpenCV的车牌识别代码示例，包含车牌定位、字符分割和字符识别三个部分：

import cv2
import numpy as np

# 车牌定位
def locate_car_plate(img):
    # 预处理：灰度化、高斯模糊、边缘检测
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    edged = cv2.Canny(blurred, 100, 200)

    # 车牌定位：颜色过滤、形态学变换
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    lower_blue = np.array([100, 50, 50])
    upper_blue = np.array([140, 255, 255])
    mask = cv2.inRange(hsv, lower_blue, upper_blue)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (17, 5))
    closed = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
    opened = cv2.morphologyEx(closed, cv2.MORPH_OPEN, kernel)
    contours, hierarchy = cv2.findContours(opened, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 找到最大的轮廓
    maxContour = None
    maxArea = 0
    for contour in contours:
        area = cv2.contourArea(contour)
        if area > maxArea:
            maxArea = area
            maxContour = contour

    # 找到包含车牌的最小矩形
    rect = cv2.minAreaRect(maxContour)
    box = np.int0(cv2.boxPoints(rect))

    return box

# 字符分割
def segment_characters(img):
    # 预处理：灰度化、二值化、去噪
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    _, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    opened = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)

    # 字符分割：垂直投影
    h, w = opened.shape[:2]
    x_axis = np.sum(opened, axis=0)
    x_axis = x_axis / h
    x_axis = np.uint8(x_axis)
    x_axis = cv2.resize(x_axis, (w, 1))
    _, x_axis = cv2.threshold(x_axis, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    contours, hierarchy = cv2.findContours(x_axis, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 分割出每个字符
    boxes = []
    for contour in contours:
        x, y, w, h = cv2.boundingRect(contour)
        if w > h and w > 10 and h > 20:
            boxes.append([x, y, w, h])

    # 对字符按照位置进行排序
    boxes = sorted(boxes, key=lambda x: x[0])

    return boxes

# 字符识别
def recognize_characters(img, boxes, model):
    result = ""
    for box in boxes:
        x, y, w, h = box
        char_img = img[y:y + h, x:x + w]
        char_img = cv2.resize(char_img, (20, 20))
        char_img = char_img.reshape((1, -1))
        char_img = np.float32(char_img)
        _, result_, _, _ = model.predict(char_img)
        result += str(chr(int(result_[0][0])))

    return result

# 主函数
if __name__ == "__main__":
    # 加载模型
    model = cv2.ml.KNearest_create()
    model.load("knn_model.xml")

    # 加载图片
    img = cv2.imread("car_plate.jpg")

    # 定位车牌
    box = locate_car_plate(img)
    cv2.drawContours(img, [box], -1, (0, 255, 0), 2)

    # 分割字符
    x, y, w, h = cv2.boundingRect(box)
    plate_img = img[y:y + h, x:x + w]
    boxes = segment_characters(plate_img)

    # 识别字符
    result = recognize_characters(plate_img, boxes, model)

    # 显示结果
    cv2.imshow("Image", img)
    print("车牌号码：", result)
    cv2.waitKey(0)

需要注意的是，这里使用了KNN模型进行字符识别，需要在运行代码之前训练好模型并保存为XML文件。具体训练过程可以参考OpenCV官方文档。