yolo跌倒行为检测的代码

以下是使用YOLOv3实现跌倒行为检测的代码示例：

import cv2
import numpy as np

# 加载YOLOv3模型
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

# 加载类别标签文件
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# 获取输出层信息
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 加载视频流
cap = cv2.VideoCapture(0)

while True:
    # 读取一帧图像
    ret, frame = cap.read()
    if not ret:
        break

    # 对图像进行预处理
    blob = cv2.dnn.blobFromImage(frame, scalefactor=0.00392, size=(416, 416), mean=(0, 0, 0), swapRB=True, crop=False)

    # 将预处理后的图像输入到网络中进行推理
    net.setInput(blob)
    outs = net.forward(output_layers)

    # 解析网络的输出结果，获取识别结果
    class_ids = []
    confidences = []
    boxes = []
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                # 检测到了物体，记录其类别、置信度和位置信息
                center_x = int(detection[0] * frame.shape[1])
                center_y = int(detection[1] * frame.shape[0])
                w = int(detection[2] * frame.shape[1])
                h = int(detection[3] * frame.shape[0])
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                class_ids.append(class_id)
                confidences.append(float(confidence))
                boxes.append([x, y, w, h])

    # 非最大值抑制，对重叠的物体进行去重
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

    # 在图像中绘制识别结果
    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = f"{classes[class_ids[i]]}: {confidences[i]:.2f}"
            color = (0, 255, 0)
            if classes[class_ids[i]] == "person":
                # 检测到了人，判断是否跌倒
                if h > 1.2 * w:
                    label = "Fall Detected"
                    color = (0, 0, 255)
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

    # 显示图像
    cv2.imshow("Detection", frame)

    # 按下 q 键退出程序
    if cv2.waitKey(1) == ord('q'):
        break

# 释放资源
cap.release()
cv2.destroyAllWindows()

注：以上代码仅用于示例，具体实现可能与实际情况有所不同，需要根据具体需求进行调整。同时，为了使模型能够检测到跌倒行为，需要在训练数据中包含跌倒行为的样本，并重新训练YOLOv3模型。