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import streamlit as st
import torch
import cv2
import numpy as np
from PIL import Image
import tempfile
import os
from ultralytics import YOLO
# Load the YOLOv8 model from the local path
model = YOLO('best.pt') # Directly reference the best.pt file in the same directory as app.py
def detect_people(image):
results = model(image)
return results
def process_results(results, image):
annotated_frame = image.copy()
person_detected = False
for result in results:
boxes = result.boxes.xyxy.cpu().numpy().astype(int) # Bounding boxes
if len(boxes) > 0:
person_detected = True
for box in boxes:
cv2.rectangle(annotated_frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
if person_detected:
cv2.putText(annotated_frame, 'Canh bao! Co nguoi! Co nguoi', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
return annotated_frame
def main():
st.title("People Detection Application using YOLOv8")
option = st.sidebar.selectbox("Choose input type", ("Upload Image/Video", "Use Camera"))
if option == "Upload Image/Video":
uploaded_file = st.file_uploader("Upload Image or Video", type=['jpg', 'jpeg', 'png', 'mp4', 'avi', 'mov'])
if uploaded_file is not None:
if uploaded_file.type.startswith('image'):
file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
image = cv2.imdecode(file_bytes, 1)
st.image(image, caption='Uploaded Image.', use_column_width=True)
results = detect_people(image)
processed_frame = process_results(results, image)
st.image(processed_frame, caption='Processed Image.', use_column_width=True)
elif uploaded_file.type.startswith('video'):
tfile = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
tfile.write(uploaded_file.read())
tfile.close()
st.write(f'Temporary file path: {tfile.name}')
vidcap = cv2.VideoCapture(tfile.name)
stframe = st.empty()
while vidcap.isOpened():
success, frame = vidcap.read()
if not success:
break
results = detect_people(frame)
processed_frame = process_results(results, frame)
stframe.image(processed_frame, channels="BGR")
vidcap.release()
os.remove(tfile.name)
elif option == "Use Camera":
stframe = st.empty()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
results = detect_people(frame)
processed_frame = process_results(results, frame)
stframe.image(processed_frame, channels="BGR")
cap.release()
if __name__ == '__main__':
main()
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