60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
#!/usr/bin/python3
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import cv2
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from picamera2 import Picamera2
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import mediapipe as mp
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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_hands = mp.solutions.hands
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# Grab images as numpy arrays and leave everything else to OpenCV.
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cv2.startWindowThread()
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picam2 = Picamera2()
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picam2.configure(picam2.create_preview_configuration(main={"format": 'XRGB8888', "size": (1280, 720)}))
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picam2.start()
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cv2.namedWindow("Camera", cv2.WND_PROP_FULLSCREEN)
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cv2.setWindowProperty("Camera", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
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with mp_hands.Hands(
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model_complexity=0,
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5,
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max_num_hands=20) as hands:
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while True:
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image = picam2.capture_array()
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image.flags.writeable = False
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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results = hands.process(image)
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# Draw the hand annotations 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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if results.multi_hand_landmarks:
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for hand_landmarks in results.multi_hand_landmarks:
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x = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x
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y = hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y
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mp_drawing.draw_landmarks(
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image,
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hand_landmarks,
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mp_hands.HAND_CONNECTIONS,
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mp_drawing_styles.get_default_hand_landmarks_style(),
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mp_drawing_styles.get_default_hand_connections_style())
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# Flip the image horizontally for a selfie-view display.
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cv2.imshow("Camera", image)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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