本文目录导读：
我来详细介绍Python中使用YOLO进行目标检测的几种主流方法：
使用Ultralytics YOLOv8（推荐）
这是最现代的YOLO实现,安装和使用都很简单：
# 安装
# pip install ultralytics
from ultralytics import YOLO
# 加载预训练模型
model = YOLO('yolov8n.pt') # 可选: yolov8n/s/m/l/x
# 对图片进行检测
results = model('image.jpg')
# 对视频进行检测
results = model('video.mp4', stream=True)
# 实时摄像头检测
results = model(0) # 0代表第一个摄像头
# 处理检测结果
for r in results:
boxes = r.boxes # 边界框
for box in boxes:
x1, y1, x2, y2 = box.xyxy[0] # 坐标
conf = box.conf[0] # 置信度
cls = box.cls[0] # 类别ID
print(f"检测到: {model.names[int(cls)]}, 置信度: {conf:.2f}")
使用YOLOv5 (官方实现)
# 安装
# pip install yolov5
import torch
from PIL import Image
# 加载模型
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
# 检测图片
img = 'image.jpg'
results = model(img)
# 显示结果
results.show()
# 获取检测信息
results.print() # 打印检测结果
print(results.pandas().xyxy[0]) # 以DataFrame格式输出
使用YOLOv3/v4 (Darknet)
# 使用OpenCV DNN模块加载
import cv2
import numpy as np
# 加载模型
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
with open('coco.names', 'r') as f:
classes = [line.strip() for line in f.readlines()]
# 加载图片
image = cv2.imread('image.jpg')
height, width = image.shape[:2]
# 预处理
blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
# 前向传播
outputs = net.forward(net.getUnconnectedOutLayersNames())
# 解析结果
boxes, confidences, class_ids = [], [], []
for output in outputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
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)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
完整示例：YOLOv8摄像头实时检测
import cv2
from ultralytics import YOLO
# 加载模型
model = YOLO('yolov8n.pt')
# 打开摄像头
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
# 检测
results = model(frame)
# 绘制结果
annotated_frame = results[0].plot()
# 显示
cv2.imshow('YOLOv8 Detection', annotated_frame)
# 按键退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
自定义模型训练后的调用
from ultralytics import YOLO
# 加载自定义训练模型
model = YOLO('path/to/your/best.pt')
# 预测
results = model('test.jpg')
# 提取特定类别
for r in results:
for box in r.boxes:
cls = int(box.cls[0])
if cls == 0: # 假设类别0是person
x1, y1, x2, y2 = map(int, box.xyxy[0])
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
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推荐使用Ultralytics YOLOv8 - 最简单、功能最全
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模型选择:
yolov8n.pt(最轻量)yolov8s.pt(小型)yolov8m.pt(中型)yolov8l.pt(大型)yolov8x.pt(最准确但最慢)
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硬件要求:
- GPU推荐：CUDA支持的NVIDIA显卡
- CPU也可运行,但速度较慢
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安装命令:
pip install ultralytics
这样你就可以快速开始使用YOLO进行目标检测了！
