我现在有个onnx模型，请帮我写一个加载模型，预处理图像归一，后处理iou和nms以及画框后输出某个类别的目标坐标的代码

在Python中，特别是使用PyTorch和OpenCV库，你可以按照以下步骤来实现这个功能：

import torch
from PIL import Image
import cv2
import numpy as np
from torchvision.transforms import ToTensor, Normalize
from detectron2.config import get_cfg
from detectron2.modeling import build_model
from detectron2.engine import DefaultPredictor
from detectron2.structures import Boxes

# 1. 加载ONNX模型
model = torch.jit.load('your_onnx_model.onnx')  # 替换为你的模型路径
model.eval()  # 设置模型为推理模式

# 2. 预处理图像
def preprocess_image(image_path):
    img = Image.open(image_path)
    transform = transforms.Compose([ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
    img_tensor = transform(img).unsqueeze(0)  # 添加维度用于网络输入
    return img_tensor

# 3. 计算IoU和NMS
def compute_iou(box1, box2):
    # 定义计算IoU的函数，这里是一个简化版示例，实际应用可能需要根据你的需求调整
    x_min = max(box1[0], box2[0])
    y_min = max(box1[1], box2[1])
    x_max = min(box1[2], box2[2])
    y_max = min(box1[3], box2[3])

    intersection_area = (x_max - x_min) * (y_max - y_min)
    union_area = (box1[2] - box1[0]) * (box1[3] - box1[1]) + (box2[2] - box2[0]) * (box2[3] - box2[1]) - intersection_area
    iou = intersection_area / union_area
    return iou

def nms(boxes, scores, threshold=0.5):
    keep = []
    while boxes.shape[0]:
        max_score_idx = np.argmax(scores)
        keep.append(max_score_idx)
        if len(keep) == boxes.shape[0]: break
        boxes_to_remove = [i for i in range(len(boxes)) if i != max_score_idx and compute_iou(boxes[max_score_idx], boxes[i]) > threshold]
        boxes = np.delete(boxes, boxes_to_remove, axis=0)
        scores = np.delete(scores, boxes_to_remove)
    return keep

# 4. 绘制边框并输出特定类别坐标
def draw_boxes_and_output(image, prediction, category_id):
    boxes = prediction["instances"].pred_boxes.tensor
    scores = prediction["instances"].scores
    class_labels = prediction["instances"].pred_classes.tolist()

    indices = [idx for idx, label in enumerate(class_labels) if label == category_id]
    filtered_boxes = boxes[indices]
    
    for box in filtered_boxes:
        top_left, bottom_right = box.tolist()
        cv2.rectangle(image, (top_left[0], top_left[1]), (bottom_right[0], bottom_right[1]), color=(0, 255, 0), thickness=2)

    return image, filtered_boxes

# 示例使用Detectron2模型预测
cfg = get_cfg()  # 获取默认配置
cfg.MODEL.WEIGHTS = 'detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849600/model_final.pth'  # 替换为你需要的预训练权重
cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
predictor = DefaultPredictor(cfg)

image_path = 'input_image.jpg'  # 输入图像路径
img_preprocessed = preprocess_image(image_path)
outputs = model(img_preprocessed.to(device))

# 预测和后处理
predictions = predictor(outputs)
drawn_image, output_boxes = draw_boxes_and_output(cv2.imread(image_path), predictions, category_id=your_category_id)  # 替换为你的类别ID
cv2.imshow("Output", drawn_image)
cv2.waitKey(0)