yolov5车牌识别python代码

### 使用YOLOv5进行车牌识别的Python代码 对于使用YOLOv5实现车牌识别的任务，可以通过调用官方提供的`detect.py`脚本并适当修改来完成。下面是一个简化版的Python代码示例，展示了如何加载预训练好的YOLOv5模型并对图片中的车牌位置进行预测。 ```python import torch from pathlib import Path from PIL import Image import cv2 import numpy as np from models.experimental import attempt_load from utils.general import non_max_suppression, scale_coords from utils.torch_utils import select_device from utils.datasets import letterbox def detect_license_plate(image_path='data/images/license.jpg', weights='yolov5s.pt'): """ 对输入图像执行车牌检测操作。 参数: image_path (str): 输入待检测的图片路径，默认为'data/images/license.jpg'. weights (str): 预训练权重文件的位置，默认为'yolov5s.pt'. 返回: results (list of dict): 包含每个检测到的对象的信息列表，每项字典中有'xyxy'(边界框坐标), 'conf'(置信度分数) 和 'cls'(类别ID). """ device = select_device('') # 自动选择可用设备(CPU/GPU) model = attempt_load(weights, map_location=device) # 加载模型 img_size = 640 # 图像大小(像素)，保持不变以匹配网络输入尺寸 # 处理单张测试图片 source_image = Image.open(image_path).convert('RGB') original_shape = source_image.size[::-1] processed_img, ratio, pad = letterbox(np.array(source_image), new_shape=img_size)[:3] processed_img = processed_img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, HWC to CHW processed_img = np.ascontiguousarray(processed_img) processed_img = torch.from_numpy(processed_img).to(device) processed_img = processed_img.float() / 255.0 # 归一化处理 if processed_img.ndimension() == 3: processed_img = processed_img.unsqueeze(0) pred = model(processed_img)[0] # 前向传播获取预测结果 det = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45)[0] # NMS去除冗余边框 if det is not None and len(det): det[:, :4] = scale_coords(processed_img.shape[2:], det[:, :4], original_shape).round() result_list = [] for *xyxy, conf, cls in reversed(det): xywh = (torch.tensor(xyxy).view(1, 4)).tolist()[0] result_list.append({ "xyxy": list(map(int, xywh)), "conf": float(conf), "cls": int(cls) }) return result_list else: print("No license plates detected.") return [] if __name__ == "__main__": detections = detect_license_plate() for detection in detections: print(f"Detected License Plate at {detection['xyxy']} with confidence score {detection['conf']:.2f}") ``` 此段代码实现了基于YOLOv5的目标检测功能，专门针对车牌进行了优化设置[^1]。通过调整参数如`weights`指向特定于车牌的数据集上微调后的YOLOv5模型权重文件，可以进一步提高识别精度。此外，在实际应用中可能还需要考虑更多细节，比如不同光照条件下的鲁棒性等问题[^3]。