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import streamlit as st
import cv2
from PIL import Image
import time
import numpy as np
# Placeholder function for analyzing images and returning descriptions
def analyze_image(image):
# Implement object recognition here
# This function should return a list of descriptions for detected objects
# For example:
return ["chair on the left", "table in the center", "cat on the right"]
def main():
st.title("Object Recognition Assistant for the Visually Impaired")
# Setup webcam capture
cap = cv2.VideoCapture(0) # Use 0 for the default webcam
FRAME_WINDOW = st.image([])
last_time = time.time()
while True:
ret, frame = cap.read()
if not ret:
continue
# Convert the image color to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img = Image.fromarray(frame)
# Display the current frame
FRAME_WINDOW.image(img)
# Check if 10 seconds have passed
if time.time() - last_time > 10:
last_time = time.time()
# Analyze the image and get descriptions
descriptions = analyze_image(img)
# Display the descriptions
st.write("Detected objects:")
for desc in descriptions:
st.write("- " + desc)
time.sleep(0.1)
if __name__ == "__main__":
main()
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