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| import cv2 | |
| import numpy as np | |
| import mediapipe as mp | |
| from mediapipe.tasks import python | |
| from mediapipe.tasks.python import vision | |
| from mediapipe.framework.formats import landmark_pb2 | |
| # Model available to download here: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker#models | |
| model_path = "pose_landmarker_full.task" | |
| video_source = 0 | |
| num_poses = 4 | |
| min_pose_detection_confidence = 0.5 | |
| min_pose_presence_confidence = 0.5 | |
| min_tracking_confidence = 0.5 | |
| def draw_landmarks_on_image(rgb_image, detection_result): | |
| pose_landmarks_list = detection_result.pose_landmarks | |
| annotated_image = np.copy(rgb_image) | |
| # Loop through the detected poses to visualize. | |
| for idx in range(len(pose_landmarks_list)): | |
| pose_landmarks = pose_landmarks_list[idx] | |
| pose_landmarks_proto = landmark_pb2.NormalizedLandmarkList() | |
| pose_landmarks_proto.landmark.extend([ | |
| landmark_pb2.NormalizedLandmark( | |
| x=landmark.x, | |
| y=landmark.y, | |
| z=landmark.z) for landmark in pose_landmarks | |
| ]) | |
| mp.solutions.drawing_utils.draw_landmarks( | |
| annotated_image, | |
| pose_landmarks_proto, | |
| mp.solutions.pose.POSE_CONNECTIONS, | |
| mp.solutions.drawing_styles.get_default_pose_landmarks_style()) | |
| return annotated_image | |
| to_window = None | |
| last_timestamp_ms = 0 | |
| def print_result(detection_result: vision.PoseLandmarkerResult, output_image: mp.Image, | |
| timestamp_ms: int): | |
| global to_window | |
| global last_timestamp_ms | |
| if timestamp_ms < last_timestamp_ms: | |
| return | |
| last_timestamp_ms = timestamp_ms | |
| # print("pose landmarker result: {}".format(detection_result)) | |
| to_window = cv2.cvtColor( | |
| draw_landmarks_on_image(output_image.numpy_view(), detection_result), cv2.COLOR_RGB2BGR) | |
| base_options = python.BaseOptions(model_asset_path=model_path) | |
| options = vision.PoseLandmarkerOptions( | |
| base_options=base_options, | |
| running_mode=vision.RunningMode.LIVE_STREAM, | |
| num_poses=num_poses, | |
| min_pose_detection_confidence=min_pose_detection_confidence, | |
| min_pose_presence_confidence=min_pose_presence_confidence, | |
| min_tracking_confidence=min_tracking_confidence, | |
| output_segmentation_masks=False, | |
| result_callback=print_result | |
| ) | |
| with vision.PoseLandmarker.create_from_options(options) as landmarker: | |
| # Use OpenCV’s VideoCapture to start capturing from the webcam. | |
| cap = cv2.VideoCapture(video_source) | |
| # Create a loop to read the latest frame from the camera using VideoCapture#read() | |
| while cap.isOpened(): | |
| success, image = cap.read() | |
| if not success: | |
| print("Image capture failed.") | |
| break | |
| # Convert the frame received from OpenCV to a MediaPipe’s Image object. | |
| mp_image = mp.Image( | |
| image_format=mp.ImageFormat.SRGB, | |
| data=cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
| timestamp_ms = int(cv2.getTickCount() / cv2.getTickFrequency() * 1000) | |
| landmarker.detect_async(mp_image, timestamp_ms) | |
| if to_window is not None: | |
| cv2.imshow("MediaPipe Pose Landmark", to_window) | |
| if cv2.waitKey(1) & 0xFF == ord('q'): | |
| break | |
| cap.release() | |
| cv2.destroyAllWindows() |
You are welcome! Thank you for stopping by :)
Really appreciate :)
If you want, you can write a medium tutorial on how to use mediapipe pose estimation with multi people and I will add your post to jetson-docs.com's blog :)
If you want, you can write a medium tutorial on how to use mediapipe pose estimation with multi people and I will add your post to jetson-docs.com's blog :)
It’s an interesting thing!, cuz I knew blazed pose detection model from mediapipe document was made of single person detection. so i want to know why this model is operating from multiple person. Did you know how it detect multiple person? if you know, Can you reply for me?
Cool! I'm curious too! I don't know how mediapipe does it, but let me know if you find something interesting :)
I find how to using this model with multiple person, Blaze Pose model from mediapipe pose model is operating only Pose Detect (two types of Modules from this model). then If we set all component of multiple pose(option num pose >= 2), 1st module(detector) were detect multi pose landmark at first video frame and 2nd module(Tracker) were using from frist frame pose landmark. so we can use multiple pose detection model
+) if you want to know detail of model. Enter my post link of multiple pose detetion model architecture.
Link : https://dablro12.tistory.com/12
In order to widely inform Koreans about this met
hod, I wrote it in the Korean version.
It's a hassle, but please understand.
I find how to using this model with multiple person, Blaze Pose model from mediapipe pose model is operating only Pose Detect (two types of Modules from this model). then If we set all component of multiple pose(option num pose >= 2), 1st module(detector) were detect multi pose landmark at first video frame and 2nd module(Tracker) were using from frist frame pose landmark. so we can use multiple pose detection model
+) if you want to know detail of model. Enter my post link of multiple pose detetion model architecture.
Link : https://dablro12.tistory.com/12
In order to widely inform Koreans about this met hod, I wrote it in the Korean version. It's a hassle, but please understand.
did it work? I'm gonna convert it into mediapipe flugin unity
hey @lanzani ,
Thanks for sharing the script! I tried running it on my mac and when I have 2 people in camera frame, the skeletal landmarks keep switching between the 2 :(
based on what I read from this post and also the model info document , Pose Landmarker from mediapipe cannot detect multiple people in a single image. Even though you can set the num_poses parameter in options, the model itself only works when there is a single person present in the image.
just curious if the script worked on your end to output more than 1 person's skeletal landmarks?
Hi Federico,
Many thanks for posting this. Really helpful. Just to be on the safe side (as you do not specify a licence) - do I have your permission to re-use and extend this code in my own application? I will of course credit yourself as the originator. Would you also like me to clarify which parts of the code are yours and which I added (as per Apache 2.0)?
My extensions concern outputting landmark data to a .csv file and dealing with cases where two people are close together and only one is detected,
hey @lanzani , Thanks for sharing the script! I tried running it on my mac and when I have 2 people in camera frame, the skeletal landmarks keep switching between the 2 :(
I had the same problem with OpenPose. I ended up writing a postprocessor which addressed the switching by matching the keypoints for each person to the best fit in the previous frame, among other tweaks. I will share this when I have tidied it up!
based on what I read from this post and also the model info document , Pose Landmarker from mediapipe cannot detect multiple people in a single image. Even though you can set the num_poses parameter in options, the model itself only works when there is a single person present in the image.
just curious if the script worked on your end to output more than 1 person's skeletal landmarks?
i believe Mediapipe can detect multiple people in a single image but, as with video, only if they are far enough apart. I haven't quantified 'far enough' but it seems to be about the width of a person in the image.
hey @lanzani , Thanks for sharing the script! I tried running it on my mac and when I have 2 people in camera frame, the skeletal landmarks keep switching between the 2 :(
based on what I read from this post and also the model info document , Pose Landmarker from mediapipe cannot detect multiple people in a single image. Even though you can set the num_poses parameter in options, the model itself only works when there is a single person present in the image.
just curious if the script worked on your end to output more than 1 person's skeletal landmarks?
Hello! From what I remember, it worked. Keep in mind that I'm not developing with mediapipe since then, so probably something has changed :)
Hi Federico, Many thanks for posting this. Really helpful. Just to be on the safe side (as you do not specify a licence) - do I have your permission to re-use and extend this code in my own application? I will of course credit yourself as the originator. Would you also like me to clarify which parts of the code are yours and which I added (as per Apache 2.0)? My extensions concern outputting landmark data to a .csv file and dealing with cases where two people are close together and only one is detected,
Sure
hey @lanzani , Thanks for sharing the script! I tried running it on my mac and when I have 2 people in camera frame, the skeletal landmarks keep switching between the 2 :(
I had the same problem with OpenPose. I ended up writing a postprocessor which addressed the switching by matching the keypoints for each person to the best fit in the previous frame, among other tweaks. I will share this when I have tidied it up!
based on what I read from this post and also the model info document , Pose Landmarker from mediapipe cannot detect multiple people in a single image. Even though you can set the num_poses parameter in options, the model itself only works when there is a single person present in the image.
just curious if the script worked on your end to output more than 1 person's skeletal landmarks?i believe Mediapipe can detect multiple people in a single image but, as with video, only if they are far enough apart. I haven't quantified 'far enough' but it seems to be about the width of a person in the image.
Unfortunately I'm not developing with mediapipe anymore, so I'm not much help :)
Hi Federico, Many thanks for posting this. Really helpful. Just to be on the safe side (as you do not specify a licence) - do I have your permission to re-use and extend this code in my own application? I will of course credit yourself as the originator. Would you also like me to clarify which parts of the code are yours and which I added (as per Apache 2.0)? My extensions concern outputting landmark data to a .csv file and dealing with cases where two people are close together and only one is detected,
Sure
Great, thanks :)
Thank your for hardly sarching. If I did't find your medium post, I have a long time to make multi-person algorithms