yolo3算法进行人体目标检测代码

下面是使用yolo3算法进行人体目标检测的Python代码，需要安装相应的库才能运行：

import cv2
import numpy as np

# 加载yolo3模型
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

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

# 设置随机颜色
colors = np.random.uniform(0, 255, size=(len(classes), 3))

# 加载图像
img = cv2.imread("test.jpg")

# 获取图像的宽度和高度
height, width, _ = img.shape

# 对图像进行预处理
blob = cv2.dnn.blobFromImage(img, 1/255, (416, 416), swapRB=True, crop=False)

# 设置输入数据
net.setInput(blob)

# 执行前向传递
outs = net.forward(net.getUnconnectedOutLayersNames())

# 初始化检测结果
class_ids = []
confidences = []
boxes = []

# 遍历每个输出层
for out in outs:
    # 遍历每个检测框
    for detection in out:
        # 获取类别概率
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        
        # 过滤掉低置信度的检测框
        if confidence > 0.5:
            # 计算检测框的位置
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            x = int(center_x - w/2)
            y = int(center_y - h/2)
            
            # 添加到检测结果中
            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([x, y, w, h])

# 对检测结果进行非最大抑制
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 绘制检测结果
for i in indices:
    i = i[0]
    box = boxes[i]
    x = box[0]
    y = box[1]
    w = box[2]
    h = box[3]
    
    # 绘制检测框和类别标签
    color = colors[class_ids[i]]
    cv2.rectangle(img, (x, y), (x+w, y+h), color, 2)
    cv2.putText(img, classes[class_ids[i]], (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

# 显示检测结果
cv2.imshow("image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，`yolov3.weights`和`yolov3.cfg`是yolo3模型的权重和配置文件，可以从官方网站下载。`coco.names`是yolo3模型的类别标签文件，也可以从官方网站下载。`test.jpg`是待检测的图像文件。执行代码后，将会在窗口中显示检测结果。