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import gradio as gr |
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import cv2, os |
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import mediapipe as mp |
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mp_hands = mp.solutions.hands |
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hands = mp_hands.Hands(max_num_hands=1) |
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if not os.path.exists("slides"): |
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os.makedirs("slides") |
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slides = sorted([f"slides/{img}" for img in os.listdir("slides")]) |
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index = 0 |
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def detect(frame): |
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global index, slides |
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slides = sorted([f"slides/{img}" for img in os.listdir("slides")]) |
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if not slides: |
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import numpy as np |
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blank = np.zeros((600,800,3),dtype=np.uint8) |
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cv2.putText(blank, "Upload slide images in /slides", (50,300), |
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cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2) |
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return blank |
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h,w,c = frame.shape |
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
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result = hands.process(frame_rgb) |
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if result.multi_hand_landmarks: |
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lm = result.multi_hand_landmarks[0] |
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x = lm.landmark[mp_hands.HandLandmark.WRIST].x * w |
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if x < w*0.3: |
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index = max(0, index-1) |
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elif x > w*0.7: |
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index = min(len(slides)-1, index+1) |
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slide = cv2.imread(slides[index]) |
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slide = cv2.cvtColor(slide, cv2.COLOR_BGR2RGB) |
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return slide |
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demo = gr.Interface(fn=detect, |
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inputs=gr.Image(source="webcam", streaming=True), |
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outputs="image", |
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live=True) |
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demo.launch() |
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