First, ensure you have the necessary dependencies installed by running the following command:
!pip install tensorflow==2.4.1 tensorflow-gpu==2.4.1 opencv-python mediapipe sklearn matplotlib
Utilize the MP Holistic model to detect keypoints in images.
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mediapipe_detection(image, model):
- Convert image from BGR to RGB.
- Make predictions using the model.
- Convert image back to BGR.
-
draw_landmarks(image, results):
- Draw face, pose, left hand, and right hand connections on the image.
-
draw_styled_landmarks(image, results):
- Customize the styling of landmarks for face, pose, left hand, and right hand connections.
cap = cv2.VideoCapture(0)
with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
while cap.isOpened():
ret, frame = cap.read()
image, results = mediapipe_detection(frame, holistic)
draw_styled_landmarks(image, results)
cv2.imshow('OpenCV Feed', image)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
This README provides an overview of how to set up dependencies, use the MP Holistic model for keypoint detection, and process video feeds to visualize landmarks.[1]