Update notebooks (#17065)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

examples/heatmaps.ipynb

@@ -96,10 +96,7 @@
    "source": [
     "import cv2\n",
     "\n",
-    "from ultralytics import YOLO, solutions\n",
-    "\n",
-    "# Load YOLO model\n",
-    "model = YOLO(\"yolo11n.pt\")\n",
+    "from ultralytics import solutions\n",
     "\n",
     "# Open video file\n",
     "cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
@@ -113,10 +110,9 @@
     "\n",
     "# Initialize heatmap object\n",
     "heatmap_obj = solutions.Heatmap(\n",
-    "    colormap=cv2.COLORMAP_PARULA,\n",
-    "    view_img=True,\n",
-    "    shape=\"circle\",\n",
-    "    names=model.names,\n",
+    "    colormap=cv2.COLORMAP_PARULA,  # Color of the heatmap\n",
+    "    show=True,  # Display the image during processing\n",
+    "    model=yolo11n.pt,  # Ultralytics YOLO11 model file\n",
     ")\n",
     "\n",
     "while cap.isOpened():\n",
@@ -125,11 +121,8 @@
     "        print(\"Video frame is empty or video processing has been successfully completed.\")\n",
     "        break\n",
     "\n",
-    "    # Perform tracking on the current frame\n",
-    "    tracks = model.track(im0, persist=True, show=False)\n",
-    "\n",
     "    # Generate heatmap on the frame\n",
-    "    im0 = heatmap_obj.generate_heatmap(im0, tracks)\n",
+    "    im0 = heatmap_obj.generate_heatmap(im0)\n",
     "\n",
     "    # Write the frame to the output video\n",
     "    video_writer.write(im0)\n",

examples/object_counting.ipynb

@@ -104,10 +104,7 @@
    "source": [
     "import cv2\n",
     "\n",
-    "from ultralytics import YOLO, solutions\n",
-    "\n",
-    "# Load the pre-trained YOLO11 model\n",
-    "model = YOLO(\"yolo11n.pt\")\n",
+    "from ultralytics import solutions\n",
     "\n",
     "# Open the video file\n",
     "cap = cv2.VideoCapture(\"path/to/video/file.mp4\")\n",
@@ -119,19 +116,15 @@
     "# Define points for a line or region of interest in the video frame\n",
     "line_points = [(20, 400), (1080, 400)]  # Line coordinates\n",
     "\n",
-    "# Specify classes to count, for example: person (0) and car (2)\n",
-    "classes_to_count = [0, 2]  # Class IDs for person and car\n",
-    "\n",
     "# Initialize the video writer to save the output video\n",
     "video_writer = cv2.VideoWriter(\"object_counting_output.avi\", cv2.VideoWriter_fourcc(*\"mp4v\"), fps, (w, h))\n",
     "\n",
     "# Initialize the Object Counter with visualization options and other parameters\n",
     "counter = solutions.ObjectCounter(\n",
-    "    view_img=True,  # Display the image during processing\n",
-    "    reg_pts=line_points,  # Region of interest points\n",
-    "    names=model.names,  # Class names from the YOLO model\n",
-    "    draw_tracks=True,  # Draw tracking lines for objects\n",
-    "    line_thickness=2,  # Thickness of the lines drawn\n",
+    "    show=True,  # Display the image during processing\n",
+    "    region=line_points,  # Region of interest points\n",
+    "    model=yolo11n.pt,  # Ultralytics YOLO11 model file\n",
+    "    line_width=2,  # Thickness of the lines and bounding boxes\n",
     ")\n",
     "\n",
     "# Process video frames in a loop\n",
@@ -141,11 +134,8 @@
     "        print(\"Video frame is empty or video processing has been successfully completed.\")\n",
     "        break\n",
     "\n",
-    "    # Perform object tracking on the current frame, filtering by specified classes\n",
-    "    tracks = model.track(im0, persist=True, show=False, classes=classes_to_count)\n",
-    "\n",
     "    # Use the Object Counter to count objects in the frame and get the annotated image\n",
-    "    im0 = counter.start_counting(im0, tracks)\n",
+    "    im0 = counter.count(im0)\n",
     "\n",
     "    # Write the annotated frame to the output video\n",
     "    video_writer.write(im0)\n",