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Runtime error
| import cv2 | |
| import gradio as gr | |
| import mediapipe as mp | |
| mp_drawing = mp.solutions.drawing_utils | |
| mp_drawing_styles = mp.solutions.drawing_styles | |
| mp_hands = mp.solutions.hands | |
| def fun(img): | |
| print(type(img)) | |
| with mp_hands.Hands( model_complexity=0,min_detection_confidence=0.5,min_tracking_confidence=0.5) as hands: | |
| img.flags.writeable = False | |
| image = cv2.flip(img[:,:,::-1], 1) | |
| # Convert the BGR image to RGB before processing. | |
| results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) | |
| image.flags.writeable = True | |
| if results.multi_hand_landmarks: | |
| for hand_landmarks in results.multi_hand_landmarks: | |
| mp_drawing.draw_landmarks( | |
| image, | |
| hand_landmarks, | |
| mp_hands.HAND_CONNECTIONS, | |
| mp_drawing_styles.get_default_hand_landmarks_style(), | |
| mp_drawing_styles.get_default_hand_connections_style()) | |
| return cv2.flip(image[:,:,::-1],1) | |
| with gr.Blocks(title="Realtime Keypoint Detection | Data Science Dojo", css="footer {display:none !important} .output-markdown{display:none !important}") as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| input = gr.Webcam(streaming=True) | |
| with gr.Column(): | |
| output = gr.outputs.Image() | |
| input.stream(fn=fun, | |
| inputs = input, | |
| outputs = output) | |
| demo.launch(debug=True) |