ultralytics 8.2.56 Streamlit tracking app (#14269)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Nguyễn Anh Bình <sometimesocrazy@gmail.com>
Co-authored-by: Johnny <johnnynuca14@gmail.com>

examples/YOLOv8-ONNXRuntime-Rust/README.md

@@ -7,7 +7,7 @@ This repository provides a Rust demo for performing YOLOv8 tasks like `Classific
 - Add YOLOv8-OBB demo
 - Update ONNXRuntime to 1.17.x
 
-Newly updated YOLOv8 example code is located in this repository (https://github.com/jamjamjon/usls/tree/main/examples/yolov8)
+Newly updated YOLOv8 example code is located in this repository (https://github.com/jamjamjon/usls/tree/main/examples/yolo)
 
 ## Features
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.55"
+__version__ = "8.2.56"
 
 import os
 

ultralytics/solutions/streamlit_inference.py

@@ -6,6 +6,8 @@ import time
 import cv2
 import torch
 
+from ultralytics.utils.downloads import GITHUB_ASSETS_STEMS
+
 
 def inference():
     """Runs real-time object detection on video input using Ultralytics YOLOv8 in a Streamlit application."""
@@ -65,28 +67,12 @@ def inference():
         vid_file_name = 0
 
     # Add dropdown menu for model selection
-    yolov8_model = st.sidebar.selectbox(
+    available_models = (x.replace("yolo", "YOLO") for x in GITHUB_ASSETS_STEMS if x.startswith("yolov8"))
-        "Model",
+    selected_model = st.sidebar.selectbox("Model", available_models)
-        (
+    with st.spinner("Model is downloading..."):
-            "YOLOv8n",
+        model = YOLO(f"{selected_model.lower()}.pt")  # Load the YOLO model
-            "YOLOv8s",
+        class_names = list(model.names.values())  # Convert dictionary to list of class names
-            "YOLOv8m",
+    st.success("Model loaded successfully!")
-            "YOLOv8l",
-            "YOLOv8x",
-            "YOLOv8n-Seg",
-            "YOLOv8s-Seg",
-            "YOLOv8m-Seg",
-            "YOLOv8l-Seg",
-            "YOLOv8x-Seg",
-            "YOLOv8n-Pose",
-            "YOLOv8s-Pose",
-            "YOLOv8m-Pose",
-            "YOLOv8l-Pose",
-            "YOLOv8x-Pose",
-        ),
-    )
-    model = YOLO(f"{yolov8_model.lower()}.pt")  # Load the yolov8 model
-    class_names = list(model.names.values())  # Convert dictionary to list of class names
 
     # Multiselect box with class names and get indices of selected classes
     selected_classes = st.sidebar.multiselect("Classes", class_names, default=class_names[:3])
@@ -95,8 +81,9 @@ def inference():
     if not isinstance(selected_ind, list):  # Ensure selected_options is a list
         selected_ind = list(selected_ind)
 
-    conf_thres = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.25, 0.01)
+    enable_trk = st.sidebar.radio("Enable Tracking", ("Yes", "No"))
-    nms_thres = st.sidebar.slider("NMS Threshold", 0.0, 1.0, 0.45, 0.01)
+    conf = float(st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.25, 0.01))
+    iou = float(st.sidebar.slider("IoU Threshold", 0.0, 1.0, 0.45, 0.01))
 
     col1, col2 = st.columns(2)
     org_frame = col1.empty()
@@ -124,7 +111,10 @@ def inference():
             prev_time = curr_time
 
             # Store model predictions
-            results = model(frame, conf=float(conf_thres), iou=float(nms_thres), classes=selected_ind)
+            if enable_trk:
+                results = model.track(frame, conf=conf, iou=iou, classes=selected_ind, persist=True)
+            else:
+                results = model(frame, conf=conf, iou=iou, classes=selected_ind)
             annotated_frame = results[0].plot()  # Add annotations on frame
 
             # display frame