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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
import os
###################################### Helper functions ##############################
# Read the image file and encode it as base64
with open('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) |