49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
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import cv2
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import mediapipe as mp
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mp_drawing = mp.solutions.drawing_utils # type: ignore
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mp_drawing_styles = mp.solutions.drawing_styles # type: ignore
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mp_holistic = mp.solutions.holistic # type: ignore
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# open webcam
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cap = cv2.VideoCapture(0)
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with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
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while cap.isOpened():
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# read webcam
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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 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 = holistic.process(image)
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# draw landmark 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.face_landmarks,
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mp_holistic.FACEMESH_CONTOURS,
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landmark_drawing_spec=None,
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connection_drawing_spec=mp_drawing_styles.get_default_face_mesh_contours_style(),
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)
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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_holistic.POSE_CONNECTIONS,
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landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style(),
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)
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# flip the image horizontally for a selfie-view display.
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cv2.imshow("MediaPipe Holistic", cv2.flip(image, 1))
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if cv2.waitKey(5) & 0xFF == 27:
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break
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# close webcam
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cap.release()
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