Create app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from tempfile import NamedTemporaryFile
+import os
+
+# Initialize YOLOv8 model
+model = YOLO("yolov8n.pt")
+
+# Streamlit app title and creator information
+st.markdown("[Created by Engr. Hamesh Raj](https://www.linkedin.com/in/datascientisthameshraj/)")
+st.title("🎥 YOLOv8 Object Detection on Videos")
+
+# Sidebar for video upload
+st.sidebar.header("Upload Video")
+uploaded_video = st.sidebar.file_uploader("Choose a video...", type=["mp4", "mov", "avi", "mkv"])
+
+if uploaded_video is not None:
+    # Save the uploaded video to a temporary file
+    temp_video = NamedTemporaryFile(delete=False)
+    temp_video.write(uploaded_video.read())
+    video_path = temp_video.name
+
+    # Display the uploaded video
+    st.sidebar.video(uploaded_video)
+
+    # Submit button to process the video
+    if st.sidebar.button("Submit"):
+        st.subheader("Processing Video...")
+
+        # Open the video file
+        cap = cv2.VideoCapture(video_path)
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+
+        # Create a temporary file to save the output video
+        temp_output_video = NamedTemporaryFile(delete=False, suffix='.mp4')
+        output_video_path = temp_output_video.name
+
+        # Define codec and create VideoWriter object
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_video_path, fourcc, fps, (frame_width, frame_height))
+
+        # Process each frame of the video
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            # Perform object detection
+            results = model(frame)
+
+            # Draw bounding boxes on the frame
+            for result in results:
+                for box in result.boxes:
+                    x1, y1, x2, y2 = map(int, box.xyxy[0])
+                    conf = box.conf[0]
+                    cls = box.cls[0]
+                    label = f'{model.names[int(cls)]} {conf:.2f}'
+
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
+                    cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
+
+            out.write(frame)
+
+        cap.release()
+        out.release()
+
+        # Display the processed video
+        st.subheader("Processed Video")
+        st.video(output_video_path)
+
+        # Download button for the processed video
+        with open(output_video_path, "rb") as file:
+            st.download_button(
+                label="Download Processed Video",
+                data=file,
+                file_name="processed_video.mp4",
+                mime="video/mp4"
+            )
+
+        # Clean up temporary files
+        os.remove(video_path)
+        os.remove(output_video_path)