【进迭时空 K1 MUSE Pi Pro】车牌识别

本文介绍了进迭时空 K1 MUSE Pi Pro 开发板结合 LPRNet 算法和 Ultralytics 库实现物车牌识别的项目设计，包括环境部署、软件包安装、模型获取、关键代码以及板端推理等相关流程。

项目介绍

准备工作：OpenCV 安装、Ultralytics 软件包安装、预训练模型下载等；
车牌识别：采用 LPRNet 算法及 ONNX 模型实现车牌识别的板端推理；

硬件连接

使用网线连接路由器和 LAN 接口，或连接 Wi-Fi 无线网；
连接鼠标键盘至 USB 端口；
使用 5V 直流电源 Type-C 供电；

环境搭建

为了快速实现图像分类，需完成 OpenCV 部署和 Ultralytics 软件包的安装等操作。

终端执行如下指令，搭建虚拟环境并安装所需软件包

sudo apt update
sudo apt install python3-venv # 安装虚拟环境软件包
python3 -m venv .venv # 创建虚拟环境
pip config set global.extra-index-url https://git.spacemit.com/api/v4/projects/33/packages/pypi/simple # 更改pip镜像源
source .venv/bin/activate # 激活虚拟环境
pip install opencv-python # 安装 opencv
pip install pillow opencv-python numpy matplotlib tqdm requests psutil pyparsing
pip install torch torchvision ultralytics

安装完成后，执行下列指令进行验证

python3 -c "import ultralytics, sys, torch; print('ultralytics', ultralytics.__version__, '| torch', torch.__version__, '| Python', sys.version.split()[0])"

输出 ultralytics 版本号

详见：【进迭时空 K1 MUSE Pi Pro】YOLO26 部署测试 - 华秋开源硬件社区 .

字体安装

为了方便显示中文车牌，安装 CJK 字体

sudo apt install fonts-noto-cjk
fc-list | grep -i "Noto Sans CJK" | head -3

记录字体所在路径，如 /usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc 以便调用。

模型获取

终端执行如下指令

wget https://github.com/h030162/PlateRecognition/blob/main/ocr_rec.py
cd ./model # 模型存放在 model 文件夹
wget https://github.com/h030162/PlateRecognition/blob/main/license_models/y11n-pose_plate_best.onnx
wget https://github.com/h030162/PlateRecognition/blob/main/license_models/y11n-pose_plate_best.pt
wget https://github.com/h030162/PlateRecognition/blob/main/license_models/license_ocr.onnx
wget https://github.com/h030162/PlateRecognition/blob/main/license_models/dict.txt

详见：sirius-ai/LPRNet_Pytorch , h030162/PlateRecognition .

文件目录

~/lpr $ tree
.
├── img
│   ├── ji.jpg
│   ├── ...
│   └── yue.jpg
├── lpr_demo.py
├── model
│   ├── dict.txt
│   ├── license_ocr.onnx
│   ├── y11n-pose_plate_best.onnx
│   └── y11n-pose_plate_best.pt
└── ocr_rec.py

流程图

工程代码

终端执行 touch lpr_demo.py 新建文件，添加如下代码

import numpy as np
import cv2
from ocr_rec import TextRecognizer, init_args
from PIL import Image, ImageDraw, ImageFont
from ultralytics import YOLO
import warnings
warnings.filterwarnings("ignore")
class PlateRecognizer:
    def __init__(self, det_model_path="./model/y11n-pose_plate_best.onnx"):
        self.model_det = YOLO(det_model_path)
        parser = init_args().parse_args()
        self.model_ocr = TextRecognizer(parser)
    def recognize(self, img):
        plate_objs=[]
        # detect plates
        plates = self.model_det(img, verbose=False)
        for plate, conf in zip(plates[0].boxes.xyxy, plates[0].boxes.conf):
            x1, y1, x2, y2 = plate.cpu()
            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
            plate_img = img[y1:y2, x1:x2]
            # recognize text
            try:
                rec_res, _ = self.model_ocr([plate_img])
            except Exception as E:
                print(E)
                exit()
            if len(rec_res[0])>0:
                obj = {}
                obj['text'] = rec_res[0][0]
                obj['score_text'] = rec_res[0][1]
                obj['bbox'] = [x1, y1, x2, y2]
                obj['score_bbox'] = conf.cpu().numpy().item()
                plate_objs.append(obj)
        return plate_objs
      

def DrawPlateNum(img, palte_num, x1, y1):
    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img_pil = Image.fromarray(img_rgb)
    draw = ImageDraw.Draw(img_pil)
    font = ImageFont.truetype("/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc", 40)
    # ===== 文字位于车牌上方 =====
    try:
        _, top, _, bottom = draw.textbbox((0, 0), palte_num, font=font)
        text_h = bottom - top
    except AttributeError:                       # 旧版本 Pillow
        text_h = font.getsize(palte_num)[1]

    ty = y1 - text_h - 6                         # 6 为间隙，避免贴框
    if ty < 0 and y2 is not None:                # 若车牌靠上，则下方画框
        ty = y2 + 6
    # =====================================
    draw.text((x1, ty), palte_num, font=font, fill=(255, 255, 0))
    img_bgr = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
    return img_bgr

def demo():
    parser = init_args().parse_args()
    img = cv2.imread(parser.image_path)
    if img is None:
        print("Error: no image found")
        return
    plate_rec = PlateRecognizer()
    plate_objs = plate_rec.recognize(img)
    print(plate_objs)
    for bbox in plate_objs:
        x1, y1, x2, y2 = bbox['bbox']
        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
        img = DrawPlateNum(img, bbox['text'], x1, y1)
    cv2.imshow("LPR", img)
    cv2.waitKey(0)
    # cv2.imwrite("result.jpg", img)  

if __name__ == "__main__":
    parser = init_args().parse_args()      
    demo()