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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() |
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
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 :)