本文主要是介绍YOLOv9摄像头或视频实时检测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
1、下载yolov9的项目
地址：YOLOv9
2、使用下面代码进行检测
import torch
import cv2
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_boxes
from utils.plots import plot_one_box# 加载预训练的YOLOv9模型
model = attempt_load(r'./yolov9-t-converted.pt', device='cpu') # 使用CPU进行推理，如果有GPU可以切换到'cuda'# 获取摄像头内容，参数 0 表示使用默认的摄像头
cap = cv2.VideoCapture(0,cv2.CAP_DSHOW)# 打开视频文件
# cap = cv2.VideoCapture('video.mp4')# 获取视频的宽度和高度
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))# 定义视频写入对象
out = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'XVID'), 30, (frame_width, frame_height))while cap.isOpened():ret, frame = cap.read()if not ret:break# 转换为RGB格式并进行检测img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)img_tensor = torch.from_numpy(img).to('cpu').permute(2, 0, 1).float() / 255.0 # 转换为Tensor并归一化img_tensor = img_tensor.unsqueeze(0)# 推理results = model(img_tensor)[0]# 后处理：非极大值抑制results = non_max_suppression(results, 0.4, 0.5)# 绘制检测框for det in results:if det is not None and len(det):det[:, :4] = scale_boxes(img_tensor.shape[2:], det[:, :4], frame.shape).round()for *xyxy, conf, cls in det:label = f'{model.names[int(cls)]} {conf:.2f}'plot_one_box(xyxy, frame, label=label, color=(255, 0, 0), line_thickness=2)# 写入视频文件out.write(frame)# 显示结果cv2.imshow('frame', frame)if cv2.waitKey(1) & 0xFF == ord('q'):breakcap.release()
out.release()
cv2.destroyAllWindows()3、在from utils.plots中添加plot_one_box
源码没有这个函数，直接在plots里面添加一个新的plot_one_box方法。否则会报错
def plot_one_box(x, img, color=None, label=None, line_thickness=None):# Plots one bounding box on image imgtl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thicknesscolor = color or [random.randint(0, 255) for _ in range(3)]c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)if label:tf = max(tl - 1, 1) # font thicknesst_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filledcv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)这篇关于YOLOv9摄像头或视频实时检测的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
