Delete hand_tracking.py

hand_tracking.py

@@ -1,87 +0,0 @@
-# https://medium.com/mlearning-ai/live-webcam-with-streamlit-f32bf68945a4
-
-from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration
-import streamlit as st
-import cv2
-import numpy as np
-import av
-import mediapipe as mp
-import base64
-
-
-###################################### Helper functions ##############################
-# Read the image file and encode it as base64
-
-with open('/mount/src/rock_paper_scissors/Resources/ai_face.jpg', 'rb') as aiface:
-    image_data = base64.b64encode(aiface.read()).decode('utf-8') 
-
-# Set up MediaPipe Hands
-mp_drawing = mp.solutions.drawing_utils
-mp_drawing_styles = mp.solutions.drawing_styles
-mp_hands = mp.solutions.hands
-hands = mp_hands.Hands(
-    model_complexity=0,
-    min_detection_confidence=0.5,
-    min_tracking_confidence=0.5
-)
-
-# Function to process video frames
-def process(image):
-    image.flags.writeable = False
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    results = hands.process(image)
-    
-    # Draw hand landmarks on the image
-    image.flags.writeable = True
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    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)
-
-# Define RTC Configuration
-RTC_CONFIGURATION = RTCConfiguration(
-    {"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}
-)
-
-
-# Create Streamlit web app
-scores = [0, 0] # [AI, Player]
-    
-st.set_page_config(page_title="RPS", page_icon="🤖", layout="wide",)
-
-col1, col2 = st.columns(2)
-
-# Add content to the right column (video stream)
-with col1:
-    st.info(f"Player{scores[1]}")
-    # Define a video processor class
-    class VideoProcessor:
-        def recv(self, frame):
-            img = frame.to_ndarray(format="bgr24")
-            img = process(img)
-            return av.VideoFrame.from_ndarray(img, format="bgr24")
-    
-    # Create the WebRTC streamer
-    webrtc_ctx = webrtc_streamer(
-        key="hand-tracking",
-        mode=WebRtcMode.SENDRECV,
-        rtc_configuration=RTC_CONFIGURATION,
-        media_stream_constraints={"video": True, "audio": False},
-        video_processor_factory=VideoProcessor,
-        async_processing=True,
-    )
-
-# Add content to the left column (app description)
-with col2:
-    st.info(f"AI {scores[0]}")
-    img_tag = f'<img src="data:image/png;base64,{image_data}" style="border: 2px solid green; border-radius: 15px;">'
-    # Create a Streamlit component to render the HTML
-    st.components.v1.html(img_tag, height=400)