写一段python的，基于yolo的人体识别代码

### 回答1：
好的，这是一段基于 yolo 的人体检测代码的示例：

import cv2
import numpy as np

# 载入 yolo 预训练权重和配置文件
net = cv2.dnn.readNetFromDarknet("yolov3.cfg", "yolov3.weights")

# 读取输入图像
image = cv2.imread("input.jpg")
height, width, _ = image.shape

# 建立一个 blob，将输入图像转换为 yolo 输入所需的尺寸
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
                             swapRB=True, crop=False)

# 将 blob 输入网络并进行推理
net.setInput(blob)
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
outputs = net.forward(output_layers)

# 初始化检测框列表和置信度列表
boxes = []
confidences = []

# 遍历每一个检测层的输出
for output in outputs:
    # 遍历每一个检测
    for detection in output:
        # 获取检测的类别和置信度
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        # 确保置信度大于设定阈值
        if confidence > 0.5:
            # 计算检测框的大小
            center_x, center_y, width, height = (detection[0:4] * np.array([width, height, width, height])).astype("int")
            # 获取左上角和右下角的坐标
            x = int(center_x - width / 2)
            y = int(center_y - height / 2)
            # 将检测框的坐标添加到列表中
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))

# 使用非最大值抑制来过  

### 回答2：
下面是一个基于YOLO的人体识别的Python代码段，用于从图像中检测和识别人体。

import cv2
import numpy as np

# 加载YOLO权重和配置文件
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

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

# 设置层的名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 加载图像
image = cv2.imread("image.jpg")
height, width, channels = image.shape

# 进行目标检测
blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

# 初始化参数
class_ids = []
confidences = []
boxes = []
conf_threshold = 0.5 # 置信度阈值
nms_threshold = 0.4 # 非最大抑制阈值

# 解析输出
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > conf_threshold:
# 检测框位置和大小
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)

# 矩形框的左上角坐标
x = int(center_x - w / 2)
y = int(center_y - h / 2)

boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)

# 应用非最大抑制，去除重叠的检测框
indexes = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

# 在图像上绘制框和标签
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
confidence = confidences[i]
color = (0, 255, 0)
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
cv2.putText(image, label + " " + str(round(confidence, 2)), (x, y + 30), font, 1, color, 2)

# 显示结果图像
cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

以上代码接收一个名为image.jpg的图像作为输入，并使用YOLO进行人体识别。输出图像将展示检测到的人体，并在每个检测框上显示类别和置信度。