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datasciencedojo
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Parent(s):
1a3c0f2
Upload 2 files
Browse files- app.py +44 -0
- requirements.txt +1 -0
app.py
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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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# Convert the BGR image to RGB before processing.
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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() 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)
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requirements.txt
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mediapipe
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