yolov12推理代码

### YOLOv12 推理代码实例 对于YOLOv12模型的推理过程，通常遵循加载预训练权重、设置输入图像尺寸以及执行预测的标准流程。下面提供了一个基于Python环境下的YOLOv12推理代码示例： ```python import torch from yolov12.models.experimental import attempt_load from yolov12.utils.general import non_max_suppression, scale_coords from yolov12.utils.torch_utils import select_device from PIL import Image import numpy as np def load_model(weights_path='yolov12.pt', device=''): # 加载模型并将其移动到指定设备上(CPU/GPU)[^3] device = select_device(device) model = attempt_load(weights_path, map_location=device) # 自动下载或加载本地权重文件 return model, device def preprocess_image(image_path, img_size=640): # 图像预处理函数，调整大小至img_size×img_size，并转换成tensor格式[^3] image = Image.open(image_path).convert('RGB') ratio = min(img_size / max(image.size), img_size / min(image.size)) new_shape = (int(round(shape * ratio)) for shape in reversed(image.size)) resized_img = image.resize(new_shape, resample=Image.BILINEAR) padded_img = np.full((img_size, img_size, 3), 114, dtype=np.uint8) pad_w, pad_h = (img_size - new_shape[0]) // 2, (img_size - new_shape[1]) // 2 padded_img[pad_h:pad_h + new_shape[1], pad_w:pad_w + new_shape[0]] = resized_img img_tensor = torch.from_numpy(padded_img).to(torch.float32) / 255. img_tensor = img_tensor.permute(2, 0, 1).unsqueeze_(0) return img_tensor, ratio, (pad_w, pad_h) def run_inference(model, image_tensor, conf_thres=0.25, iou_thres=0.45, device=''): pred = model(image_tensor.to(device))[0] det = non_max_suppression(pred, conf_thres, iou_thres)[0] if det is not None and len(det): det[:, :4] = scale_coords(image_tensor.shape[2:], det[:, :4], original_shape).round() return det.cpu().numpy() if __name__ == '__main__': weights = 'path_to_your_weights_file' source_image = 'your_test_image.jpg' model, device = load_model(weights_path=weights, device='') input_tensor, _, _ = preprocess_image(source_image) detections = run_inference(model, input_tensor) print(detections) ``` 此段代码展示了如何利用`attempt_load()`方法来加载YOLOv12模型及其对应的预训练参数；通过定义辅助性的`preprocess_image()`来进行数据准备；最后调用核心的`run_inference()`完成目标检测任务。