车牌识别pycharm代码

以下是一个基于PyCharm的车牌识别代码，基于OpenCV和Tesseract OCR库实现：

import cv2
import pytesseract

# 读取图片
img = cv2.imread('car.jpg')

# 调整图片大小
img = cv2.resize(img, (620, 480))

# 转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 对图像进行滤波处理
gray = cv2.bilateralFilter(gray, 11, 17, 17)

# 边缘检测
edged = cv2.Canny(gray, 30, 200)

# 查找轮廓
contours, _ = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 选择最大的轮廓
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
screenCnt = None

# 遍历轮廓
for c in contours:
    # 近似轮廓
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.018 * peri, True)

    # 如果近似轮廓有4个点，就认为是车牌区域
    if len(approx) == 4:
        screenCnt = approx
        break

if screenCnt is None:
    detected = 0
    print("No contour detected")
else:
    detected = 1

if detected == 1:
    # 在图像上绘制车牌区域
    cv2.drawContours(img, [screenCnt], -1, (0, 255, 0), 3)

    # 裁剪车牌区域
    mask = np.zeros(gray.shape, np.uint8)
    new_image = cv2.drawContours(mask, [screenCnt], 0, 255, -1, )
    new_image = cv2.bitwise_and(img, img, mask=mask)

    # 转换为灰度图像
    new_image_gray = cv2.cvtColor(new_image, cv2.COLOR_BGR2GRAY)

    # 图像二值化处理
    _, thresh = cv2.threshold(new_image_gray, 110, 255, cv2.THRESH_BINARY)

    # 使用Tesseract OCR库识别文字
    text = pytesseract.image_to_string(thresh, config='--psm 11')

    # 输出识别结果
    print("Detected license plate Number is:", text)

    # 显示处理后的图像
    cv2.imshow('image', img)
    cv2.imshow('Cropped Image', thresh)

# 等待按键
cv2.waitKey(0)

您可以将此代码复制并粘贴到PyCharm中，然后运行代码以进行测试。请注意，您需要安装OpenCV和Tesseract OCR库才能运行此代码。