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app.py

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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()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2c6a7bd2e5cda881a364febba53bc797c5f82fbd9579005428ba94558566af8e
+size 6235363

readme.MD

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+# People Detection
+
+## How to run 
+- Step 1:
+```bash
+pip install -r requirements.txt
+```
+- Step 2:
+```bash
+streamlit run app.py
+```

requirements.txt

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+streamlit
+torch
+opencv-python
+ultralytics
+numpy
+Pillow