plot_images(mfccs[100:109], cls_true[100:109])

这是一个函数调用，其中输入参数包括mfccs和cls_true，分别表示音频数据的MFCC特征和对应的分类标签。该函数的作用是将mfccs中的音频数据可视化呈现出来，并用cls_true中的分类标签进行标注。具体实现可以参考以下示例代码：

import matplotlib.pyplot as plt
import numpy as np

def plot_images(images, labels):
    fig, axes = plt.subplots(3, 3, figsize=(12,12))
    axes = axes.ravel()

    for i in np.arange(0, 9):
        axes[i].imshow(images[i], cmap='gray')
        axes[i].set_title(labels[i])
        axes[i].axis('off')

    plt.subplots_adjust(hspace=0.4)
    plt.show()

其中，images和labels均为长度为9的列表，分别存放了9个音频数据的MFCC特征和对应的分类标签。函数首先创建一个3x3的图像网格，然后将images中的9个音频数据依次绘制在各个子图上，并用labels中的标签进行标注。最终，函数将绘制好的图像网格显示出来。

相关问题

yolov5训练后模型的map如何计算代码

在 YOLOv5 中，使用的是 mAP（mean Average Precision）指标来衡量模型的性能。计算 mAP 的代码如下：

from utils.general import plot_results, check_file

def test(cfg, data, batch_size=16, img_size=640, iou_thres=0.65, conf_thres=0.25, save_json=False, single_cls=False, augment=False, verbose=False, model=None):

    # 加载模型
    model = model.to(device).eval()

    # 加载数据集
    dataloader = create_dataloader(data, img_size=img_size, batch_size=batch_size, stride=int(cfg.stride), pad=cfg.pad, rect=cfg.rect, augment=augment, cache=cfg.cache_images, single_cls=single_cls, pad_method=cfg.pad_method, hyp=hyp, rank=rank, world_size=world_size)

    # 初始化变量
    seen = 0
    pred = []
    tbar = tqdm(dataloader, desc=f'Caching images')

    for i, (imgs, targets, paths, shapes) in enumerate(tbar):
        # 将数据传输到 GPU 上
        imgs = imgs.to(device, non_blocking=True)

        # 前向传播
        with torch.no_grad():
            outputs = model(imgs)

        # 后处理
        outputs = non_max_suppression(outputs, conf_thres=conf_thres, iou_thres=iou_thres, multi_label=False)

        # 更新变量
        seen += imgs.shape[0]

        # 处理预测结果
        for si, pred_i in enumerate(outputs):
            # 如果没有预测结果，则跳过
            if pred_i is None:
                continue

            # 反归一化预测框坐标
            pred_i[:, :4] = scale_coords(imgs.shape[2:], pred_i[:, :4], shapes[si]).round()

            # 将预测结果转换为 COCO 格式
            for xi, x in enumerate(pred_i):
                # 如果没有预测结果，则跳过
                if x is None:
                    continue

                # 将预测结果转换为 COCO 格式
                box = x[:4].tolist()
                score = float(x[4])
                label = int(x[5])

                image_id = int(Path(paths[si]).stem.split('_')[-1])

                pred.append({'image_id': image_id,
                             'category_id': label,
                             'bbox': [round(float(x), 3) for x in box],
                             'score': round(score, 5)})

    # 保存预测结果
    if save_json and rank in [-1, 0]:
        pred_path = 'predictions.json'
        with open(pred_path, 'w') as file:
            json.dump(pred, file)

    # 计算 mAP
    if rank in [-1, 0]:
        # 如果是多进程，则需要将预测结果合并
        if world_size > 1:
            pred = comm.gather(pred, dst=0)
            if rank == 0:
                pred = list(itertools.chain(*pred))

        # 计算 mAP
        with open(data) as f:
            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # config dict
        names = ['data/coco.names']
        nc = 80

        if single_cls:
            nc = 1
            names = ['data/single.names']

        if len(pred):
            # 将预测结果转换为 COCO 格式
            pred = {x['image_id']: pred_i for x in pred for pred_i in x['instances']}
            gt = json.load(open(data_dict['val'].get('ann').rstrip('.json') + '_instances.json', 'r'))

            # 计算 AP
            ap, p, r, f1, ap_class = ap_per_class(pred, gt, iou_thres=iou_thres, conf_thres=conf_thres, use_07_metric=False)

            # 输出 mAP
            mp, mr, map50, map = np.mean(p), np.mean(r), ap_class[0], np.mean(ap)
            nt = np.bincount(gt['labels'], minlength=nc)
            if np.bincount(ap_class) > 1:
                print(f"mAP{'@[IoU=0.50:0.95]' * (iou_thres > 0.5)}: {map * 100:.2f}%")
                print(f"mAP{'@[IoU=0.50]' * (iou_thres > 0.5)}: {map50 * 100:.2f}%")
                print(f"mAP{'@[IoU=0.75]' * (iou_thres > 0.75)}: {ap_class.mean() * 100:.2f}%")
                print(f"mAP{'@[IoU=0.50:0.95]' * (iou_thres > 0.5)}, medium: {np.median(ap) * 100:.2f}%")
            print(f"Recall: {mr * 100:.2f}%")
            print(f"Precision: {mp * 100:.2f}%")
            print(f"F1-score: {f1.mean() * 100:.2f}%")
            print(f"Unique classes detected: {len(ap_class)}")
        else:
            print('No detections found by YOLOv5, mAP cannot be calculated.')

其中，`ap_per_class()` 函数用于计算每个类别的 AP 值。

请帮我更改yolov5的detect.py函数，将其封装为能够对rtsp流进行检测，同时在模型检测到目标物体之后能够实时返回对应帧的图片以及指定几帧内的视频的函数...

好的，下面是我为您修改后的代码：

import cv2
import torch
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords, plot_one_box
from utils.torch_utils import select_device


def detect_rtsp(rtsp_url, weights, conf_thres=0.25, iou_thres=0.45, device='', imgsz=640, num_of_frames=5):
    # Load model
    device = select_device(device)
    model = attempt_load(weights, map_location=device)  # load FP32 model
    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
    if device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once

    # Set up RTSP capture
    cap = cv2.VideoCapture(rtsp_url)

    # Initialize variables for storing frames and detections
    frame_count = 0
    detected_frames = []
    detected_images = []

    while True:
        ret, frame = cap.read()

        if not ret:
            print("Failed to read frame from RTSP stream.")
            break

        # Resize frame and convert to tensor
        img = letterbox(frame, new_shape=imgsz)[0]
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB
        img = np.ascontiguousarray(img)

        # Detect objects
        img = torch.from_numpy(img).to(device)
        img = img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        pred = model(img)[0]
        pred = non_max_suppression(pred, conf_thres, iou_thres)

        # Store frame and detections
        if len(pred):
            detected_frames.append(frame_count)
            detected_images.append(frame)

        # Plot boxes on frame
        for i, det in enumerate(pred):
            if len(det):
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], frame.shape).round()

                for *xyxy, conf, cls in reversed(det):
                    label = f'{model.names[int(cls)]} {conf:.2f}'
                    plot_one_box(xyxy, frame, label=label, color=colors(int(cls)), line_thickness=3)

        # Show frame
        cv2.imshow("RTSP Stream", frame)

        # Exit if 'q' is pressed or if the number of frames with detections exceeds num_of_frames
        if cv2.waitKey(1) & 0xFF == ord('q') or len(detected_frames) >= num_of_frames:
            break

        frame_count += 1

    cap.release()
    cv2.destroyAllWindows()

    return detected_frames, detected_images

这个函数可以接受一个`rtsp_url`参数，该参数指定了要检测的 RTSP 流的 URL。您还需要指定模型的权重文件路径，以及一些其他参数，例如置信度阈值、IoU 阈值、设备类型、图像大小等。最后，该函数还会返回检测到目标物体的帧以及对应的图像。如果您想要检测多个帧，请将`num_of_frames`参数设置为相应的值。