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import cv2 |
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import gradio as gr |
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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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def fun(img): |
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print(type(img)) |
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with mp_hands.Hands( model_complexity=0,min_detection_confidence=0.5,min_tracking_confidence=0.5) as hands: |
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img.flags.writeable = False |
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image = cv2.flip(img[:,:,::-1], 1) |
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results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) |
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image.flags.writeable = True |
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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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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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return cv2.flip(image[:,:,::-1],1) |
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with gr.Blocks(title="Realtime Keypoint Detection | Data Science Dojo", css="footer {display:none !important} .output-markdown{display:none !important}") as demo: |
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with gr.Row(): |
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with gr.Column(): |
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input = gr.Webcam(streaming=True) |
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with gr.Column(): |
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output = gr.outputs.Image() |
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input.stream(fn=fun, |
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inputs = input, |
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outputs = output) |
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demo.launch(debug=True) |
