43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
import cv2
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import mediapipe as mp
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import cv2
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import mediapipe as mp
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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mp_drawing = mp.solutions.drawing_utils
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mp_drawing_styles = mp.solutions.drawing_styles
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mp_pose = mp.solutions.pose
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cap = cv2.VideoCapture(0)
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with mp_pose.Pose(
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5) as pose:
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while cap.isOpened():
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success, image = cap.read()
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if not success:
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print("Ignoring empty camera frame.")
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# If loading a video, use 'break' instead of 'continue'.
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continue
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# To improve performance, optionally mark the image as not writeable to
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# pass by reference.
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image.flags.writeable = False
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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results = pose.process(image)
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print(f"\r{results.pose_world_landmarks[0]}", end="")
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# Draw the pose annotation on the image.
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image.flags.writeable = True
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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mp_drawing.draw_landmarks(
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image,
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results.pose_landmarks,
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mp_pose.POSE_CONNECTIONS,
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landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
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# Flip the image horizontally for a selfie-view display.
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landmarks = results.pose_world_landmarks.landmark
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print(landmark)
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cap.release() |