bak

13.摄像头实时检测.py

@@ -3,7 +3,9 @@ import time
 import cv2
 from ultralytics import YOLO
 
-model = YOLO('./runs/detect/train6/weights/best.pt')
+# model = YOLO('./runs/detect/train6/weights/best.pt')
+model = YOLO('models/best.pt')
+
 # 打开默认摄像头（通常是0）
 cap = cv2.VideoCapture(1)
 # 检查摄像头是否成功打开

15.模型检测-姿态估计.py

@@ -3,7 +3,9 @@ from ultralytics import YOLO
 # 加载训练好的模型
 # best.pt: 最佳模型，适用于生产
 # last.pt: 最后一轮训练的模型，适用于继续训练
-yolo = YOLO('runs/pose/train/weights/best.pt')
-# yolo = YOLO('models/yolo11n-pose.pt')
-# yolo('resources/input.mp4', show=True, save=True)
-yolo.predict('resources/姿态估计-自定义', show=False, save=True)
+# model = YOLO('runs/pose/train/weights/best.pt')
+model = YOLO('models/best.pt')
+# model = YOLO('models/yolo11n-pose.pt')
+# model('resources/input.mp4', show=True, save=True)
+# model.predict('resources/姿态估计-自定义', show=False, save=True)
+model.predict('dataset3/train/images', show=False, save=True)

dataset_tiger/train.yaml

@@ -1,4 +1,4 @@
-path: ./dataset1 # 数据集根目录
+path: ./dataset_tiger # 数据集根目录
 train: train/images # 训练集目录名
 val: valid/images # 验证集目录名
 task: pose #任务类型为 pose