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