Upload app.py

app.py

@@ -0,0 +1,66 @@
+import cv2
+import numpy as np
+import streamlit as st
+from PIL import Image
+from ultralytics import YOLO
+from ultralytics.utils.plotting import Annotator, colors
+from collections import defaultdict
+
+track_history = defaultdict(lambda: [])
+model = YOLO("last_1.pt")
+names = model.model.names
+
+# Function to perform object tracking
+def perform_object_tracking(video_path):
+    cap = cv2.VideoCapture(video_path)
+    assert cap.isOpened(), "Error reading video file"
+
+    while cap.isOpened():
+        success, frame = cap.read()
+        if success:
+            results = model.track(frame, persist=True, verbose=False)
+            boxes = results[0].boxes.xyxy.cpu()
+
+            if results[0].boxes.id is not None:
+                clss = results[0].boxes.cls.cpu().tolist()
+                track_ids = results[0].boxes.id.int().cpu().tolist()
+
+                annotator = Annotator(frame, line_width=2)
+
+                for box, cls, track_id in zip(boxes, clss, track_ids):
+                    annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)])
+
+                    track = track_history[track_id]
+                    track.append((int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)))
+
+                    if len(track) > 30:
+                        track.pop(0)
+
+                    points = np.array(track, dtype=np.int32).reshape((-1, 1, 2))
+                    cv2.circle(frame, (track[-1]), 7, colors(int(cls), True), -1)
+                    cv2.polylines(frame, [points], isClosed=False, color=colors(int(cls), True), thickness=2)
+
+                # Display the frame with tracking annotations
+                st.image(frame, channels="BGR")
+
+        else:
+            break
+
+    # Release video capture
+    cap.release()
+
+# Streamlit app
+def main():
+    st.title("Object Tracking with Streamlit")
+
+    uploaded_file = st.file_uploader("Upload a video file", type=["mp4"])
+
+    if uploaded_file is not None:
+        video_path = f"uploaded_video.{uploaded_file.name.split('.')[-1]}"
+        with open(video_path, "wb") as f:
+            f.write(uploaded_file.read())
+
+        perform_object_tracking(video_path)
+
+if __name__ == "__main__":
+    main()