Upload app.py

app.py

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+import streamlit as st
+import cv2
+import tempfile
+from ultralytics import YOLO
+import imageio
+import os
+
+# ---------------- YOLO Model ----------------
+st.title("🎥 YOLOv8 Object Tracking on Video")
+model = YOLO("yolov8n.pt")  # Pretrained YOLOv8n model
+
+# ---------------- Function to Process Video ----------------
+def detect_objects_in_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    fps = int(cap.get(cv2.CAP_PROP_FPS)) or 25
+
+    # Temporary output file
+    temp_output = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    output_path = temp_output.name
+    temp_output.close()
+
+    writer = imageio.get_writer(output_path, fps=fps, codec="libx264")
+
+    stframe = st.empty()
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    processed_frames = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        results = model(frame, imgsz=1280, conf=0.25)
+        annotated_frame = results[0].plot()
+
+        writer.append_data(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))
+
+        # Progress bar
+        processed_frames += 1
+        stframe.text(f"Processing frame {processed_frames}/{total_frames}...")
+
+    cap.release()
+    writer.close()
+
+    # Read video as bytes for Streamlit
+    with open(output_path, "rb") as f:
+        video_bytes = f.read()
+
+    try:
+        os.remove(output_path)
+    except PermissionError:
+        pass
+
+    return video_bytes
+
+# ---------------- Streamlit UI ----------------
+st.write("Upload a video and see detections in MP4 format.")
+
+uploaded_file = st.file_uploader("Upload a video", type=["mp4", "mov", "avi"])
+
+if uploaded_file is not None:
+    # Save uploaded video to temp file
+    temp_input = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    temp_input.write(uploaded_file.read())
+    temp_input.close()
+
+    st.subheader("📥 Original Video")
+    st.video(temp_input.name)  # Display original video
+
+    st.write("🔄 Processing video with YOLOv8... please wait")
+    video_bytes = detect_objects_in_video(temp_input.name)
+
+    st.subheader("✅ Processed Video")
+    st.video(video_bytes)  # Display processed video
+
+    st.download_button(
+        label="⬇️ Download Processed Video",
+        data=video_bytes,
+        file_name="processed_output.mp4",
+        mime="video/mp4"
+    )
+
+    # Cleanup
+    try:
+        os.remove(temp_input.name)
+    except PermissionError:
+        pass