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)