姿态估计
姿态估计1、模型介绍2、姿态估计：图像效果预览3、姿态估计：视频效果预览4、姿态估计：实时检测4.1、USB摄像头效果预览4.2、CSI摄像头效果预览资料参考
使用Python演示Ultralytics ：Pose Estimation在图像、视频、实时检测的效果。
1、模型介绍
姿态估计是一项涉及识别图像中特定点（通常称为关键点）位置的任务。关键点可以代表物体的各个部分，如关节、地标或其他显著特征。关键点的位置通常用一组二维 [x, y] 或 3D [x, y, visible] 坐标
姿态估计模型的输出是一组代表图像中物体关键点的点，通常还包括每个点的置信度分数。当您需要识别场景中物体的特定部分及其相互之间的位置关系时，姿态估计是一个不错的选择。
在YOLO26的默认姿势模型中，有 17 个关键点，每个关键点代表人体的不同部位。以下是每个索引到相应身体关节的映射：
- 鼻子
- 左眼
- 右眼
- 左耳
- 右耳
- 左肩
- 右肩
- 左肘
- 右肘
- 左腕
- 右手腕
- 左髋关节
- 右髋关节
- 左膝
- 右膝盖
- 左脚踝
- 右脚踝
2、姿态估计：图像
使用yolo26n-pose_ncnn_model预测ultralytics26文件夹下的图片（非ultralytics自带图片）。
进入代码文件夹：
cd /home/jetson/ultralytics/yahboom_demo
运行代码：
xxxxxxxxxx python3 03.pose_image.py
效果预览
示例代码：
x from ultralytics import YOLO # Load a model model = YOLO ( "/home/jetson/ultralytics/yolo26n-pose.engine" ) # pretrained yolo26n-pose model # Run batched inference on a list of images results = model ( "/home/jetson/ultralytics/ultralytics/assets/people.jpg" ) # return a list of Results objects # Process results list for result in results : # boxes = result.boxes # Boxes object for bounding box outputs # masks = result.masks # Masks object for segmentation masks outputs keypoints = result . keypoints # Keypoints object for pose outputs # probs = result.probs # Probs object for classification outputs # obb = result.obb # Oriented boxes object for OBB outputs result . show () # display to screen result . save ( filename = "/home/jetson/ultralytics/output/people_output.jpg" ) # save to disk
3、姿态估计：视频
使用yolo26n-pose_ncnn_model预测ultralytics26文件夹下的视频（非ultralytics自带视频）。
进入代码文件夹：
xxxxxxxxxx cd /home/jetson/ultralytics/yahboom_demo
运行代码：
xxxxxxxxxx python3 03.pose_video.py
效果预览
yolo识别输出的视频位置：/home/jetson/ultralytics/output/
示例代码：
xxxxxxxxxx import cv2 from ultralytics import YOLO # Load the YOLO model model = YOLO ( "/home/jetson/ultralytics/yolo26n-pose.engine" ) # Open the video file video_path = "/home/jetson/ultralytics/ultralytics/videos/people.mp4" cap = cv2 . VideoCapture ( video_path ) # Get the video frame size and frame rate frame_width = int ( cap . get ( cv2 . CAP_PROP_FRAME_WIDTH )) frame_height = int ( cap . get ( cv2 . CAP_PROP_FRAME_HEIGHT )) fps = int ( cap . get ( cv2 . CAP_PROP_FPS )) # Define the codec and create a VideoWriter object to output the processed video output_path = "/home/jetson/ultralytics/output/03.people_animals_output.mp4" fourcc = cv2 . VideoWriter_fourcc ( * 'mp4v' ) # You can use 'XVID' or 'mp4v' depending on your platform out = cv2 . VideoWriter ( output_path , fourcc , fps , ( frame_width , frame_height )) # Loop through the video frames while cap . isOpened (): # Read a frame from the video success , frame = cap . read () if success : # Run YOLO inference on the frame results = model ( frame ) # Visualize the results on the frame annotated_frame = results [ 0 ]. plot () # Write the annotated frame to the output video file out . write ( annotated_frame ) # Display the annotated frame cv2 . imshow ( "YOLO Inference" , cv2 . resize ( annotated_frame , ( 640 , 480 ))) # Break the loop if 'q' is pressed if cv2 . waitKey ( 1 ) & 0xFF == ord ( "q" ): break else : # Break the loop if the end of the video is reached break # Release the video capture and writer objects, and close the display window cap . release () out . release () cv2 . destroyAllWindows ()
4、姿态估计：实时检测
4.1、USB摄像头
使用yolo26n-pose_ncnn_model预测USB摄像头画面。
进入代码文件夹：
xxxxxxxxxx cd /home/jetson/ultralytics/yahboom_demo
运行代码：点击预览画面，按q键可以终止程序！
xxxxxxxxxx python3 03.pose_camera_usb.py
效果预览
yolo识别输出的视频位置：/home/jetson/ultralytics/output/
示例代码：
xxxxxxxxxx import cv2 from ultralytics import YOLO # Load the YOLO model model = YOLO ( "/home/jetson/ultralytics/yolo26n-pose.engine" ) # Open the cammera cap = cv2 . VideoCapture ( 0 ) cap . set ( 6 , cv2 . VideoWriter . fourcc ( 'M' , 'J' , 'P' , 'G' )) cap . set ( cv2 . CAP_PROP_FRAME_WIDTH , 640 ) cap . set ( cv2 . CAP_PROP_FRAME_HEIGHT , 480 ) # Get the video frame size and frame rate frame_width = int ( cap . get ( cv2 . CAP_PROP_FRAME_WIDTH )) frame_height = int ( cap . get ( cv2 . CAP_PROP_FRAME_HEIGHT )) fps = int ( cap . get ( cv2 . CAP_PROP_FPS )) # Define the codec and create a VideoWriter object to output the processed video output_path = "/home/jetson/ultralytics/output/03.pose_camera_usb.mp4" fourcc = cv2 . VideoWriter_fourcc ( * 'mp4v' ) # You can use 'XVID' or 'mp4v' depending on your platform out = cv2 . VideoWriter ( output_path , fourcc , fps , ( frame_width , frame_height )) # Loop through the video frames while cap . isOpened (): # Read a frame from the video success , frame = cap . read () if success : # Run YOLO inference on the frame results = model ( frame ) # Visualize the results on the frame annotated_frame = results [ 0 ]. plot () # Write the annotated frame to the output video file out . write ( annotated_frame ) # Display the annotated frame cv2 . imshow ( "YOLO Inference" , cv2 . resize ( annotated_frame , ( 640 , 480 ))) # Break the loop if 'q' is pressed if cv2 . waitKey ( 1 ) & 0xFF == ord ( "q" ): break else : # Break the loop if the end of the video is reached break # Release the video capture and writer objects, and close the display window cap . release () out . release () cv2 . destroyAllWindows ()
4.2、CSI摄像头
使用yolo26n-pose_ncnn_model预测CSI摄像头画面。
进入代码文件夹：
xxxxxxxxxx cd /home/jetson/ultralytics/yahboom_demo
运行代码：点击预览画面，按q键可以终止程序！
xxxxxxxxxx python3 03.pose_camera_csi.py
效果预览
yolo识别输出的视频位置：/home/jetson/ultralytics/output/
示例代码：
xxxxxxxxxx import cv2 from ultralytics import YOLO from jetcam . csi_camera import CSICamera # Load the YOLO model model = YOLO ( "/home/jetson/ultralytics/yolo26n-pose.engine" ) # Open the camera (CSI Camera) cap = CSICamera ( capture_device = 0 , width = 640 , height = 480 ) # Get the video frame size and frame rate frame_width = 640 frame_height = 480 fps = 30 # Define the codec and create a VideoWriter object to output the processed video output_path = "/home/jetson/ultralytics/output/03.pose_camera_csi.mp4" fourcc = cv2 . VideoWriter_fourcc ( * 'mp4v' ) # You can use 'XVID' or 'mp4v' depending on your platform out = cv2 . VideoWriter ( output_path , fourcc , fps , ( frame_width , frame_height )) # Loop through the video frames while True : # Read a frame from the camera frame = cap . read () if frame is not None : # Run YOLO inference on the frame results = model ( frame ) # Visualize the results on the frame annotated_frame = results [ 0 ]. plot () # Write the annotated frame to the output video file out . write ( annotated_frame ) # Display the annotated frame cv2 . imshow ( "YOLO Inference" , cv2 . resize ( annotated_frame , ( 640 , 480 ))) # Break the loop if 'q' is pressed if cv2 . waitKey ( 1 ) & 0xFF == ord ( "q" ): break else : # Break the loop if no frame is received (camera error or end of stream) print ( "No frame received, breaking the loop." ) break # Release the video capture and writer objects, and close the display window cap . release () out . release () cv2 . destroyAllWindows ()