Add line counting and circular heatmaps in Ultralytics Solutions (#7113)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/en/guides/heatmaps.md

@@ -20,14 +20,19 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
 |                                                                 Transportation                                                                  |                                                                 Retail                                                                  |
 |:-----------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:|
-| ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/50d197b8-c7f6-4ecf-a664-3d4363b073de) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/ffd0649f-5ff5-48d2-876d-6bdffeff5c54) |
+| ![Ultralytics YOLOv8 Transportation Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/288d7053-622b-4452-b4e4-1f41aeb764aa) | ![Ultralytics YOLOv8 Retail Heatmap](https://github.com/RizwanMunawar/ultralytics/assets/62513924/a9139af0-2cb7-41fe-a0d5-29a300dee768) |
 |                                                    Ultralytics YOLOv8 Transportation Heatmap                                                    |                                                    Ultralytics YOLOv8 Retail Heatmap                                                    |
 
 ???+ tip "heatmap_alpha"
 
     heatmap_alpha value should be in range (0.0 - 1.0)
 
-!!! Example "Heatmap Example"
+???+ tip "decay_factor"
+
+    Used for removal of heatmap after object removed from frame, value should be in range (0.0 - 1.0)
+
+
+!!! Example "Heatmaps using Ultralytics YOLOv8 Example"
 
     === "Heatmap"
         ```python
@@ -35,31 +40,126 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
         from ultralytics.solutions import heatmap
         import cv2
 
-        model = YOLO("yolov8s.pt")   # YOLOv8 custom/pretrained model
+        model = YOLO("yolov8n.pt")
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
 
-        # Heatmap Init
+        # Video writer
+        video_writer = cv2.VideoWriter("heatmap_output.avi",
+                                       cv2.VideoWriter_fourcc(*'mp4v'),
+                                       int(cap.get(5)),
+                                       (int(cap.get(3)), int(cap.get(4))))
+
+        # Init heatmap
         heatmap_obj = heatmap.Heatmap()
-        heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
+        heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
-                             imw=cap.get(4),  # should same as cap width
+                             imw=cap.get(4),  # should same as cap height
-                             imh=cap.get(3),  # should same as cap height
+                             imh=cap.get(3),  # should same as cap width
                              view_img=True,
-                             decay_factor=0.99)
+                             shape="circle")
 
         while cap.isOpened():
             success, im0 = cap.read()
             if not success:
                 print("Video frame is empty or video processing has been successfully completed.")
                 break
+            tracks = model.track(im0, persist=True, show=False)
 
-            results = model.track(im0, persist=True)
+            im0 = heatmap_obj.generate_heatmap(im0, tracks)
-            im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
+            video_writer.write(im0)
 
+        cap.release()
+        video_writer.release()
+        cv2.destroyAllWindows()
+
+        ```
+
+    === "Line Counting"
+        ```python
+        from ultralytics import YOLO
+        from ultralytics.solutions import heatmap
+        import cv2
+
+        model = YOLO("yolov8n.pt")
+        cap = cv2.VideoCapture("path/to/video/file.mp4")
+        assert cap.isOpened(), "Error reading video file"
+
+        # Video writer
+        video_writer = cv2.VideoWriter("heatmap_output.avi",
+                                       cv2.VideoWriter_fourcc(*'mp4v'),
+                                       int(cap.get(5)),
+                                       (int(cap.get(3)), int(cap.get(4))))
+
+        line_points = [(256, 409), (694, 532)]  # line for object counting
+
+        # Init heatmap
+        heatmap_obj = heatmap.Heatmap()
+        heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
+                             imw=cap.get(4),  # should same as cap height
+                             imh=cap.get(3),  # should same as cap width
+                             view_img=True,
+                             shape="circle",
+                             count_reg_pts=line_points)
+
+        while cap.isOpened():
+            success, im0 = cap.read()
+            if not success:
+                print("Video frame is empty or video processing has been successfully completed.")
+                break
+            tracks = model.track(im0, persist=True, show=False)
+
+            im0 = heatmap_obj.generate_heatmap(im0, tracks)
+            video_writer.write(im0)
+
+        cap.release()
+        video_writer.release()
         cv2.destroyAllWindows()
         ```
 
-    === "Heatmap with im0"
+    === "Region Counting"
+        ```python
+        from ultralytics import YOLO
+        from ultralytics.solutions import heatmap
+        import cv2
+
+        model = YOLO("yolov8n.pt")
+        cap = cv2.VideoCapture("path/to/video/file.mp4")
+        assert cap.isOpened(), "Error reading video file"
+
+        # Video writer
+        video_writer = cv2.VideoWriter("heatmap_output.avi",
+                                       cv2.VideoWriter_fourcc(*'mp4v'),
+                                       int(cap.get(5)),
+                                       (int(cap.get(3)), int(cap.get(4))))
+
+        # Define region points
+        region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
+
+        # Init heatmap
+        heatmap_obj = heatmap.Heatmap()
+        heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
+                             imw=cap.get(4),  # should same as cap height
+                             imh=cap.get(3),  # should same as cap width
+                             view_img=True,
+                             shape="circle",
+                             count_reg_pts=region_points)
+
+        while cap.isOpened():
+            success, im0 = cap.read()
+            if not success:
+                print("Video frame is empty or video processing has been successfully completed.")
+                break
+            tracks = model.track(im0, persist=True, show=False)
+
+            im0 = heatmap_obj.generate_heatmap(im0, tracks)
+            video_writer.write(im0)
+
+        cap.release()
+        video_writer.release()
+        cv2.destroyAllWindows()
+        ```
+
+    === "Im0"
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import heatmap
@@ -71,10 +171,11 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
         # Heatmap Init
         heatmap_obj = heatmap.Heatmap()
-        heatmap_obj.set_args(colormap=cv2.COLORMAP_JET,
+        heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
-                             imw=im0.shape[0],  # should same as im0 width
+                                     imw=cap.get(4),  # should same as cap height
-                             imh=im0.shape[1],  # should same as im0 height
+                                     imh=cap.get(3),  # should same as cap width
-                             view_img=True)
+                                     view_img=True,
+                                     shape="circle")
 
 
         results = model.track(im0, persist=True)
@@ -82,43 +183,13 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
         cv2.imwrite("ultralytics_output.png", im0)
         ```
 
-    === "Heatmap with Specific Classes"
+    === "Specific Classes"
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import heatmap
         import cv2
 
-        model = YOLO("yolov8s.pt")   # YOLOv8 custom/pretrained model
+        model = YOLO("yolov8n.pt")
-        cap = cv2.VideoCapture("path/to/video/file.mp4")
-        assert cap.isOpened(), "Error reading video file"
-
-        classes_for_heatmap = [0, 2]
-
-        # Heatmap init
-        heatmap_obj = heatmap.Heatmap()
-        heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
-                              imw=cap.get(4),  # should same as cap width
-                              imh=cap.get(3),  # should same as cap height
-                              view_img=True)
-
-        while cap.isOpened():
-            success, im0 = cap.read()
-            if not success:
-              print("Video frame is empty or video processing has been successfully completed.")
-              break
-            results = model.track(im0, persist=True, classes=classes_for_heatmap)
-            im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
-
-        cv2.destroyAllWindows()
-        ```
-
-    === "Heatmap with Save Output"
-        ```python
-        from ultralytics import YOLO
-        from ultralytics.solutions import heatmap
-        import cv2
-
-        model = YOLO("yolov8s.pt")   # YOLOv8 custom/pretrained model
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
 
@@ -128,64 +199,36 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
                                        int(cap.get(5)),
                                        (int(cap.get(3)), int(cap.get(4))))
 
-        # Heatmap init
+        classes_for_heatmap = [0, 2]  # classes for heatmap
+
+        # Init heatmap
         heatmap_obj = heatmap.Heatmap()
-        heatmap_obj.set_args(colormap=cv2.COLORMAP_CIVIDIS,
+        heatmap_obj.set_args(colormap=cv2.COLORMAP_PARULA ,
-                             imw=cap.get(4),  # should same as cap width
+                             imw=cap.get(4),  # should same as cap height
-                             imh=cap.get(3),  # should same as cap height
+                             imh=cap.get(3),  # should same as cap width
-                             view_img=True)
+                             view_img=True,
+                             shape="circle")
 
         while cap.isOpened():
             success, im0 = cap.read()
             if not success:
                 print("Video frame is empty or video processing has been successfully completed.")
                 break
-            results = model.track(im0, persist=True)
+            tracks = model.track(im0, persist=True, show=False,
-            im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
+                                 classes=classes_for_heatmap)
+
+            im0 = heatmap_obj.generate_heatmap(im0, tracks)
             video_writer.write(im0)
 
+        cap.release()
         video_writer.release()
         cv2.destroyAllWindows()
         ```
 
-    === "Heatmap with Object Counting"
-        ```python
-        from ultralytics import YOLO
-        from ultralytics.solutions import heatmap
-        import cv2
-
-        model = YOLO("yolov8s.pt")   # YOLOv8 custom/pretrained model
-
-        cap = cv2.VideoCapture("path/to/video/file.mp4")  # Video file Path, webcam 0
-        assert cap.isOpened(), "Error reading video file"
-
-        # Region for object counting
-        count_reg_pts = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
-
-        # Heatmap Init
-        heatmap_obj = heatmap.Heatmap()
-        heatmap_obj.set_args(colormap=cv2.COLORMAP_JET,
-                              imw=cap.get(4),  # should same as cap width
-                              imh=cap.get(3),  # should same as cap height
-                              view_img=True,
-                              count_reg_pts=count_reg_pts)
-
-        while cap.isOpened():
-            success, im0 = cap.read()
-            if not success:
-              print("Video frame is empty or video processing has been successfully completed.")
-              break
-            results = model.track(im0, persist=True)
-            im0 = heatmap_obj.generate_heatmap(im0, tracks=results)
-
-        cv2.destroyAllWindows()
-
-        ```
-
 ### Arguments `set_args`
 
 | Name                | Type           | Default           | Description                                               |
-|---------------------|----------------|-----------------|-----------------------------------------------------------|
+|---------------------|----------------|-------------------|-----------------------------------------------------------|
 | view_img            | `bool`         | `False`           | Display the frame with heatmap                            |
 | colormap            | `cv2.COLORMAP` | `None`            | cv2.COLORMAP for heatmap                                  |
 | imw                 | `int`          | `None`            | Width of Heatmap                                          |
@@ -193,9 +236,13 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 | heatmap_alpha       | `float`        | `0.5`             | Heatmap alpha value                                       |
 | count_reg_pts       | `list`         | `None`            | Object counting region points                             |
 | count_txt_thickness | `int`          | `2`               | Count values text size                                    |
-| count_reg_color     | `tuple`        | `(255, 0, 255)` | Counting region color                                     |
+| count_txt_color     | `RGB Color`    | `(0, 0, 0)`       | Foreground color for Object counts text                   |
+| count_color         | `RGB Color`    | `(255, 255, 255)` | Background color for Object counts text                   |
+| count_reg_color     | `RGB Color`    | `(255, 0, 255)`   | Counting region color                                     |
 | region_thickness    | `int`          | `5`               | Counting region thickness value                           |
 | decay_factor        | `float`        | `0.99`            | Decay factor for heatmap area removal after specific time |
+| shape               | `str`          | `circle`          | Heatmap shape for display "rect" or "circle" supported    |
+| line_dist_thresh    | `int`          | `15`              | Euclidean Distance threshold for line counter             |
 
 ### Arguments `model.track`
 

docs/en/guides/object-counting.md

@@ -34,9 +34,9 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 | ![Conveyor Belt Packets Counting Using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/70e2d106-510c-4c6c-a57a-d34a765aa757) | ![Fish Counting in Sea using Ultralytics YOLOv8](https://github.com/RizwanMunawar/ultralytics/assets/62513924/c60d047b-3837-435f-8d29-bb9fc95d2191) |
 |                                                    Conveyor Belt Packets Counting Using Ultralytics YOLOv8                                                    |                                                    Fish Counting in Sea using Ultralytics YOLOv8                                                    |
 
-!!! Example "Object Counting Example"
+!!! Example "Object Counting using YOLOv8 Example"
 
-    === "Object Counting"
+    === "Region"
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import object_counter
@@ -46,8 +46,17 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
 
-        counter = object_counter.ObjectCounter()  # Init Object Counter
+        # Define region points
         region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
+
+        # Video writer
+        video_writer = cv2.VideoWriter("object_counting_output.avi",
+                               cv2.VideoWriter_fourcc(*'mp4v'),
+                               int(cap.get(5)),
+                               (int(cap.get(3)), int(cap.get(4))))
+
+        # Init Object Counter
+        counter = object_counter.ObjectCounter()
         counter.set_args(view_img=True,
                          reg_pts=region_points,
                          classes_names=model.names,
@@ -59,12 +68,17 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
                 print("Video frame is empty or video processing has been successfully completed.")
                 break
             tracks = model.track(im0, persist=True, show=False)
-            im0 = counter.start_counting(im0, tracks)
 
+            im0 = counter.start_counting(im0, tracks)
+            video_writer.write(im0)
+
+        cap.release()
+        video_writer.release()
         cv2.destroyAllWindows()
+
         ```
 
-    === "Object Counting with Specific Classes"
+    === "Line"
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import object_counter
@@ -74,11 +88,60 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         cap = cv2.VideoCapture("path/to/video/file.mp4")
         assert cap.isOpened(), "Error reading video file"
 
-        classes_to_count = [0, 2]
+        # Define line points
-        counter = object_counter.ObjectCounter()  # Init Object Counter
+        line_points = [(20, 400), (1080, 400)]
-        region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
+
+        # Video writer
+        video_writer = cv2.VideoWriter("object_counting_output.avi",
+                               cv2.VideoWriter_fourcc(*'mp4v'),
+                               int(cap.get(5)),
+                               (int(cap.get(3)), int(cap.get(4))))
+
+        # Init Object Counter
+        counter = object_counter.ObjectCounter()
         counter.set_args(view_img=True,
-                          reg_pts=region_points,
+                         reg_pts=line_points,
+                         classes_names=model.names,
+                         draw_tracks=True)
+
+        while cap.isOpened():
+            success, im0 = cap.read()
+            if not success:
+                print("Video frame is empty or video processing has been successfully completed.")
+                break
+            tracks = model.track(im0, persist=True, show=False)
+
+            im0 = counter.start_counting(im0, tracks)
+            video_writer.write(im0)
+
+        cap.release()
+        video_writer.release()
+        cv2.destroyAllWindows()
+        ```
+
+    === "Specific Classes"
+        ```python
+        from ultralytics import YOLO
+        from ultralytics.solutions import object_counter
+        import cv2
+
+        model = YOLO("yolov8n.pt")
+        cap = cv2.VideoCapture("path/to/video/file.mp4")
+        assert cap.isOpened(), "Error reading video file"
+
+        line_points = [(20, 400), (1080, 400)]  # line or region points
+        classes_to_count = [0, 2]  # person and car classes for count
+
+        # Video writer
+        video_writer = cv2.VideoWriter("object_counting_output.avi",
+                               cv2.VideoWriter_fourcc(*'mp4v'),
+                               int(cap.get(5)),
+                               (int(cap.get(3)), int(cap.get(4))))
+
+        # Init Object Counter
+        counter = object_counter.ObjectCounter()
+        counter.set_args(view_img=True,
+                         reg_pts=line_points,
                          classes_names=model.names,
                          draw_tracks=True)
 
@@ -89,42 +152,11 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
                 break
             tracks = model.track(im0, persist=True, show=False,
                                  classes=classes_to_count)
-            im0 = counter.start_counting(im0, tracks)
 
-        cv2.destroyAllWindows()
-        ```
-
-    === "Object Counting with Save Output"
-        ```python
-        from ultralytics import YOLO
-        from ultralytics.solutions import object_counter
-        import cv2
-
-        model = YOLO("yolov8n.pt")
-        cap = cv2.VideoCapture("path/to/video/file.mp4")
-        assert cap.isOpened(), "Error reading video file"
-
-        video_writer = cv2.VideoWriter("object_counting.avi",
-                                        cv2.VideoWriter_fourcc(*'mp4v'),
-                                        int(cap.get(5)),
-                                        (int(cap.get(3)), int(cap.get(4))))
-
-        counter = object_counter.ObjectCounter()  # Init Object Counter
-        region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
-        counter.set_args(view_img=True,
-                          reg_pts=region_points,
-                          classes_names=model.names,
-                          draw_tracks=True)
-
-        while cap.isOpened():
-            success, im0 = cap.read()
-            if not success:
-                print("Video frame is empty or video processing has been successfully completed.")
-                break
-            tracks = model.track(im0, persist=True, show=False)
             im0 = counter.start_counting(im0, tracks)
             video_writer.write(im0)
 
+        cap.release()
         video_writer.release()
         cv2.destroyAllWindows()
         ```
@@ -135,15 +167,22 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 
 ### Optional Arguments `set_args`
 
+
 | Name                | Type        | Default                    | Description                                   |
-|-----------------|---------|--------------------------------------------------|---------------------------------------|
+|---------------------|-------------|----------------------------|-----------------------------------------------|
-| view_img        | `bool`  | `False`                                          | Display the frame with counts         |
+| view_img            | `bool`      | `False`                    | Display frames with counts                    |
-| line_thickness  | `int`   | `2`                                              | Increase the thickness of count value |
+| line_thickness      | `int`       | `2`                        | Increase bounding boxes thickness             |
-| reg_pts         | `list`  | `(20, 400), (1080, 404), (1080, 360), (20, 360)` | Region Area Points                    |
+| reg_pts             | `list`      | `[(20, 400), (1260, 400)]` | Points defining the Region Area               |
-| classes_names   | `dict`  | `model.model.names`                              | Classes Names Dict                    |
+| classes_names       | `dict`      | `model.model.names`        | Dictionary of Class Names                     |
-| region_color    | `tuple` | `(0, 255, 0)`                                    | Region Area Color                     |
+| region_color        | `RGB Color` | `(255, 0, 255)`            | Color of the Object counting Region or Line   |
-| track_thickness | `int`   | `2`                                              | Tracking line thickness               |
+| track_thickness     | `int`       | `2`                        | Thickness of Tracking Lines                   |
-| draw_tracks     | `bool`  | `False`                                          | Draw Tracks lines                     |
+| draw_tracks         | `bool`      | `False`                    | Enable drawing Track lines                    |
+| track_color         | `RGB Color` | `(0, 255, 0)`              | Color for each track line                     |
+| line_dist_thresh    | `int`       | `15`                       | Euclidean Distance threshold for line counter |
+| count_txt_thickness | `int`       | `2`                        | Thickness of Object counts text               |
+| count_txt_color     | `RGB Color` | `(0, 0, 0)`                | Foreground color for Object counts text       |
+| count_color         | `RGB Color` | `(255, 255, 255)`          | Background color for Object counts text       |
+| region_thickness    | `int`       | `5`                        | Thickness for object counter region or line   |
 
 ### Arguments `model.track`
 
@@ -155,3 +194,4 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 | `conf`    | `float` | `0.3`          | Confidence Threshold                                        |
 | `iou`     | `float` | `0.5`          | IOU Threshold                                               |
 | `classes` | `list`  | `None`         | filter results by class, i.e. classes=0, or classes=[0,2,3] |
+| `verbose` | `bool`  | `True`         | Display the object tracking results                         |

ultralytics/solutions/heatmap.py

@@ -10,8 +10,7 @@ from ultralytics.utils.plotting import Annotator
 
 check_requirements('shapely>=2.0.0')
 
-from shapely.geometry import Polygon
+from shapely.geometry import LineString, Point, Polygon
-from shapely.geometry.point import Point
 
 
 class Heatmap:
@@ -23,6 +22,7 @@ class Heatmap:
         # Visual information
         self.annotator = None
         self.view_img = False
+        self.shape = 'circle'
 
         # Image information
         self.imw = None
@@ -38,17 +38,22 @@ class Heatmap:
         self.boxes = None
         self.track_ids = None
         self.clss = None
-        self.track_history = None
+        self.track_history = defaultdict(list)
 
-        # Counting info
+        # Region & Line Information
         self.count_reg_pts = None
-        self.count_region = None
+        self.counting_region = None
+        self.line_dist_thresh = 15
+        self.region_thickness = 5
+        self.region_color = (255, 0, 255)
+
+        # Object Counting Information
         self.in_counts = 0
         self.out_counts = 0
-        self.count_list = []
+        self.counting_list = []
         self.count_txt_thickness = 0
-        self.count_reg_color = (0, 255, 0)
+        self.count_txt_color = (0, 0, 0)
-        self.region_thickness = 5
+        self.count_color = (255, 255, 255)
 
         # Decay factor
         self.decay_factor = 0.99
@@ -64,9 +69,13 @@ class Heatmap:
                  view_img=False,
                  count_reg_pts=None,
                  count_txt_thickness=2,
+                 count_txt_color=(0, 0, 0),
+                 count_color=(255, 255, 255),
                  count_reg_color=(255, 0, 255),
                  region_thickness=5,
-                 decay_factor=0.99):
+                 line_dist_thresh=15,
+                 decay_factor=0.99,
+                 shape='circle'):
         """
         Configures the heatmap colormap, width, height and display parameters.
 
@@ -78,27 +87,55 @@ class Heatmap:
             view_img (bool): Flag indicating frame display
             count_reg_pts (list): Object counting region points
             count_txt_thickness (int): Text thickness for object counting display
+            count_txt_color (RGB color): count text color value
+            count_color (RGB color): count text background color value
             count_reg_color (RGB color): Color of object counting region
             region_thickness (int): Object counting Region thickness
+            line_dist_thresh (int): Euclidean Distance threshold for line counter
             decay_factor (float): value for removing heatmap area after object passed
+            shape (str): Heatmap shape, rect or circle shape supported
         """
         self.imw = imw
         self.imh = imh
-        self.colormap = colormap
         self.heatmap_alpha = heatmap_alpha
         self.view_img = view_img
+        self.colormap = colormap
 
-        self.heatmap = np.zeros((int(self.imw), int(self.imh)), dtype=np.float32)  # Heatmap new frame
+        # Region and line selection
-
         if count_reg_pts is not None:
-            self.track_history = defaultdict(list)
-            self.count_reg_pts = count_reg_pts
-            self.count_region = Polygon(self.count_reg_pts)
 
-        self.count_txt_thickness = count_txt_thickness  # Counting text thickness
+            if len(count_reg_pts) == 2:
-        self.count_reg_color = count_reg_color
+                print('Line Counter Initiated.')
+                self.count_reg_pts = count_reg_pts
+                self.counting_region = LineString(count_reg_pts)
+
+            elif len(count_reg_pts) == 4:
+                print('Region Counter Initiated.')
+                self.count_reg_pts = count_reg_pts
+                self.counting_region = Polygon(self.count_reg_pts)
+
+            else:
+                print('Region or line points Invalid, 2 or 4 points supported')
+                print('Using Line Counter Now')
+                self.counting_region = Polygon([(20, 400), (1260, 400)])  # dummy points
+
+        # Heatmap new frame
+        self.heatmap = np.zeros((int(self.imw), int(self.imh)), dtype=np.float32)
+
+        self.count_txt_thickness = count_txt_thickness
+        self.count_txt_color = count_txt_color
+        self.count_color = count_color
+        self.region_color = count_reg_color
         self.region_thickness = region_thickness
         self.decay_factor = decay_factor
+        self.line_dist_thresh = line_dist_thresh
+        self.shape = shape
+
+        # shape of heatmap, if not selected
+        if self.shape not in ['circle', 'rect']:
+            print("Unknown shape value provided, 'circle' & 'rect' supported")
+            print('Using Circular shape now')
+            self.shape = 'circle'
 
     def extract_results(self, tracks):
         """
@@ -128,13 +165,26 @@ class Heatmap:
         self.annotator = Annotator(self.im0, self.count_txt_thickness, None)
 
         if self.count_reg_pts is not None:
+
             # Draw counting region
             self.annotator.draw_region(reg_pts=self.count_reg_pts,
-                                       color=self.count_reg_color,
+                                       color=self.region_color,
                                        thickness=self.region_thickness)
 
             for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
-                self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 1
+
+                if self.shape == 'circle':
+                    center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
+                    radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2
+
+                    y, x = np.ogrid[0:self.heatmap.shape[0], 0:self.heatmap.shape[1]]
+                    mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius ** 2
+
+                    self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += \
+                        (2 * mask[int(box[1]):int(box[3]), int(box[0]):int(box[2])])
+
+                else:
+                    self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 2
 
                 # Store tracking hist
                 track_line = self.track_history[track_id]
@@ -143,16 +193,39 @@ class Heatmap:
                     track_line.pop(0)
 
                 # Count objects
-                if self.count_region.contains(Point(track_line[-1])):
+                if len(self.count_reg_pts) == 4:
-                    if track_id not in self.count_list:
+                    if self.counting_region.contains(Point(track_line[-1])):
-                        self.count_list.append(track_id)
+                        if track_id not in self.counting_list:
-                        if box[0] < self.count_region.centroid.x:
+                            self.counting_list.append(track_id)
+                            if box[0] < self.counting_region.centroid.x:
+                                self.out_counts += 1
+                            else:
+                                self.in_counts += 1
+
+                elif len(self.count_reg_pts) == 2:
+                    distance = Point(track_line[-1]).distance(self.counting_region)
+                    if distance < self.line_dist_thresh:
+                        if track_id not in self.counting_list:
+                            self.counting_list.append(track_id)
+                            if box[0] < self.counting_region.centroid.x:
                                 self.out_counts += 1
                             else:
                                 self.in_counts += 1
         else:
             for box, cls in zip(self.boxes, self.clss):
-                self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 1
+
+                if self.shape == 'circle':
+                    center = (int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2))
+                    radius = min(int(box[2]) - int(box[0]), int(box[3]) - int(box[1])) // 2
+
+                    y, x = np.ogrid[0:self.heatmap.shape[0], 0:self.heatmap.shape[1]]
+                    mask = (x - center[0]) ** 2 + (y - center[1]) ** 2 <= radius ** 2
+
+                    self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += \
+                        (2 * mask[int(box[1]):int(box[3]), int(box[0]):int(box[2])])
+
+                else:
+                    self.heatmap[int(box[1]):int(box[3]), int(box[0]):int(box[2])] += 2
 
         # Normalize, apply colormap to heatmap and combine with original image
         heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
@@ -161,7 +234,11 @@ class Heatmap:
         if self.count_reg_pts is not None:
             incount_label = 'InCount : ' + f'{self.in_counts}'
             outcount_label = 'OutCount : ' + f'{self.out_counts}'
-            self.annotator.count_labels(in_count=incount_label, out_count=outcount_label)
+            self.annotator.count_labels(in_count=incount_label,
+                                        out_count=outcount_label,
+                                        count_txt_size=self.count_txt_thickness,
+                                        txt_color=self.count_txt_color,
+                                        color=self.count_color)
 
         im0_with_heatmap = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)
 

ultralytics/solutions/object_counter.py

@@ -9,8 +9,7 @@ from ultralytics.utils.plotting import Annotator, colors
 
 check_requirements('shapely>=2.0.0')
 
-from shapely.geometry import Polygon
+from shapely.geometry import LineString, Point, Polygon
-from shapely.geometry.point import Point
 
 
 class ObjectCounter:
@@ -23,10 +22,12 @@ class ObjectCounter:
         self.is_drawing = False
         self.selected_point = None
 
-        # Region Information
+        # Region & Line Information
-        self.reg_pts = None
+        self.reg_pts = [(20, 400), (1260, 400)]
+        self.line_dist_thresh = 15
         self.counting_region = None
-        self.region_color = (255, 255, 255)
+        self.region_color = (255, 0, 255)
+        self.region_thickness = 5
 
         # Image and annotation Information
         self.im0 = None
@@ -40,11 +41,15 @@ class ObjectCounter:
         self.in_counts = 0
         self.out_counts = 0
         self.counting_list = []
+        self.count_txt_thickness = 0
+        self.count_txt_color = (0, 0, 0)
+        self.count_color = (255, 255, 255)
 
         # Tracks info
         self.track_history = defaultdict(list)
         self.track_thickness = 2
         self.draw_tracks = False
+        self.track_color = (0, 255, 0)
 
         # Check if environment support imshow
         self.env_check = check_imshow(warn=True)
@@ -52,11 +57,17 @@ class ObjectCounter:
     def set_args(self,
                  classes_names,
                  reg_pts,
-                 region_color=None,
+                 count_reg_color=(255, 0, 255),
                  line_thickness=2,
                  track_thickness=2,
                  view_img=False,
-                 draw_tracks=False):
+                 draw_tracks=False,
+                 count_txt_thickness=2,
+                 count_txt_color=(0, 0, 0),
+                 count_color=(255, 255, 255),
+                 track_color=(0, 255, 0),
+                 region_thickness=5,
+                 line_dist_thresh=15):
         """
         Configures the Counter's image, bounding box line thickness, and counting region points.
 
@@ -65,18 +76,43 @@ class ObjectCounter:
             view_img (bool): Flag to control whether to display the video stream.
             reg_pts (list): Initial list of points defining the counting region.
             classes_names (dict): Classes names
-            region_color (tuple): color for region line
             track_thickness (int): Track thickness
             draw_tracks (Bool): draw tracks
+            count_txt_thickness (int): Text thickness for object counting display
+            count_txt_color (RGB color): count text color value
+            count_color (RGB color): count text background color value
+            count_reg_color (RGB color): Color of object counting region
+            track_color (RGB color): color for tracks
+            region_thickness (int): Object counting Region thickness
+            line_dist_thresh (int): Euclidean Distance threshold for line counter
         """
         self.tf = line_thickness
         self.view_img = view_img
         self.track_thickness = track_thickness
         self.draw_tracks = draw_tracks
+
+        # Region and line selection
+        if len(reg_pts) == 2:
+            print('Line Counter Initiated.')
+            self.reg_pts = reg_pts
+            self.counting_region = LineString(self.reg_pts)
+        elif len(reg_pts) == 4:
+            print('Region Counter Initiated.')
             self.reg_pts = reg_pts
             self.counting_region = Polygon(self.reg_pts)
+        else:
+            print('Invalid Region points provided, region_points can be 2 or 4')
+            print('Using Line Counter Now')
+            self.counting_region = LineString(self.reg_pts)
+
         self.names = classes_names
-        self.region_color = region_color if region_color else self.region_color
+        self.track_color = track_color
+        self.count_txt_thickness = count_txt_thickness
+        self.count_txt_color = count_txt_color
+        self.count_color = count_color
+        self.region_color = count_reg_color
+        self.region_thickness = region_thickness
+        self.line_dist_thresh = line_dist_thresh
 
     def mouse_event_for_region(self, event, x, y, flags, params):
         """
@@ -113,11 +149,14 @@ class ObjectCounter:
         clss = tracks[0].boxes.cls.cpu().tolist()
         track_ids = tracks[0].boxes.id.int().cpu().tolist()
 
+        # Annotator Init and region drawing
         self.annotator = Annotator(self.im0, self.tf, self.names)
-        self.annotator.draw_region(reg_pts=self.reg_pts, color=(0, 255, 0))
+        self.annotator.draw_region(reg_pts=self.reg_pts, color=self.region_color, thickness=self.region_thickness)
 
+        # Extract tracks
         for box, track_id, cls in zip(boxes, track_ids, clss):
-            self.annotator.box_label(box, label=self.names[cls], color=colors(int(cls), True))  # Draw bounding box
+            self.annotator.box_label(box, label=str(track_id) + ':' + self.names[cls],
+                                     color=colors(int(cls), True))  # Draw bounding box
 
             # Draw Tracks
             track_line = self.track_history[track_id]
@@ -125,12 +164,14 @@ class ObjectCounter:
             if len(track_line) > 30:
                 track_line.pop(0)
 
+            # Draw track trails
             if self.draw_tracks:
                 self.annotator.draw_centroid_and_tracks(track_line,
-                                                        color=(0, 255, 0),
+                                                        color=self.track_color,
                                                         track_thickness=self.track_thickness)
 
             # Count objects
+            if len(self.reg_pts) == 4:
                 if self.counting_region.contains(Point(track_line[-1])):
                     if track_id not in self.counting_list:
                         self.counting_list.append(track_id)
@@ -139,12 +180,27 @@ class ObjectCounter:
                         else:
                             self.in_counts += 1
 
-        incount_label = 'InCount : ' + f'{self.in_counts}'
+            elif len(self.reg_pts) == 2:
+                distance = Point(track_line[-1]).distance(self.counting_region)
+                if distance < self.line_dist_thresh:
+                    if track_id not in self.counting_list:
+                        self.counting_list.append(track_id)
+                        if box[0] < self.counting_region.centroid.x:
+                            self.out_counts += 1
+                        else:
+                            self.in_counts += 1
+
+        incount_label = 'In Count : ' + f'{self.in_counts}'
         outcount_label = 'OutCount : ' + f'{self.out_counts}'
-        self.annotator.count_labels(in_count=incount_label, out_count=outcount_label)
+        self.annotator.count_labels(in_count=incount_label,
+                                    out_count=outcount_label,
+                                    count_txt_size=self.count_txt_thickness,
+                                    txt_color=self.count_txt_color,
+                                    color=self.count_color)
 
         if self.env_check and self.view_img:
             cv2.namedWindow('Ultralytics YOLOv8 Object Counter')
+            if len(self.reg_pts) == 4:  # only add mouse event If user drawn region
                 cv2.setMouseCallback('Ultralytics YOLOv8 Object Counter', self.mouse_event_for_region,
                                      {'region_points': self.reg_pts})
             cv2.imshow('Ultralytics YOLOv8 Object Counter', self.im0)
@@ -161,6 +217,7 @@ class ObjectCounter:
             tracks (list): List of tracks obtained from the object tracking process.
         """
         self.im0 = im0  # store image
+
         if tracks[0].boxes.id is None:
             return
         self.extract_and_process_tracks(tracks)

ultralytics/utils/plotting.py

@@ -260,19 +260,41 @@ class Annotator:
 
     # Object Counting Annotator
     def draw_region(self, reg_pts=None, color=(0, 255, 0), thickness=5):
-        # Draw region line
+        """
+        Draw region line
+        Args:
+            reg_pts (list): Region Points (for line 2 points, for region 4 points)
+            color (tuple): Region Color value
+            thickness (int): Region area thickness value
+        """
         cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=thickness)
 
     def draw_centroid_and_tracks(self, track, color=(255, 0, 255), track_thickness=2):
-        # Draw region line
+        """
+        Draw centroid point and track trails
+        Args:
+            track (list): object tracking points for trails display
+            color (tuple): tracks line color
+            track_thickness (int): track line thickness value
+        """
         points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
         cv2.polylines(self.im, [points], isClosed=False, color=color, thickness=track_thickness)
         cv2.circle(self.im, (int(track[-1][0]), int(track[-1][1])), track_thickness * 2, color, -1)
 
-    def count_labels(self, in_count=0, out_count=0, color=(255, 255, 255), txt_color=(0, 0, 0)):
+    def count_labels(self, in_count=0, out_count=0, count_txt_size=2, color=(255, 255, 255), txt_color=(0, 0, 0)):
+        """
+        Plot counts for object counter
+        Args:
+            in_count (int): in count value
+            out_count (int): out count value
+            count_txt_size (int): text size for counts display
+            color (tuple): background color of counts display
+            txt_color (tuple): text color of counts display
+        """
+        self.tf = count_txt_size
         tl = self.tf or round(0.002 * (self.im.shape[0] + self.im.shape[1]) / 2) + 1
         tf = max(tl - 1, 1)
-        gap = int(24 * tl)  # Calculate the gap between in_count and out_count based on line_thickness
+        gap = int(24 * tl)  # gap between in_count and out_count based on line_thickness
 
         # Get text size for in_count and out_count
         t_size_in = cv2.getTextSize(str(in_count), 0, fontScale=tl / 2, thickness=tf)[0]
@@ -306,14 +328,13 @@ class Annotator:
                     thickness=self.tf,
                     lineType=cv2.LINE_AA)
 
-    # AI GYM Annotator
+    @staticmethod
-    def estimate_pose_angle(self, a, b, c):
+    def estimate_pose_angle(a, b, c):
         """Calculate the pose angle for object
         Args:
             a (float) : The value of pose point a
             b (float): The value of pose point b
             c (float): The value o pose point c
-
         Returns:
             angle (degree): Degree value of angle between three points
         """
@@ -325,7 +346,15 @@ class Annotator:
         return angle
 
     def draw_specific_points(self, keypoints, indices=[2, 5, 7], shape=(640, 640), radius=2):
-        """Draw specific keypoints for gym steps counting."""
+        """
+        Draw specific keypoints for gym steps counting.
+
+        Args:
+            keypoints (list): list of keypoints data to be plotted
+            indices (list): keypoints ids list to be plotted
+            shape (tuple): imgsz for model inference
+            radius (int): Keypoint radius value
+        """
         nkpts, ndim = keypoints.shape
         nkpts == 17 and ndim == 3
         for i, k in enumerate(keypoints):
@@ -340,8 +369,17 @@ class Annotator:
         return self.im
 
     def plot_angle_and_count_and_stage(self, angle_text, count_text, stage_text, center_kpt, line_thickness=2):
-        """Plot the pose angle, count value and step stage."""
+        """
-        angle_text, count_text, stage_text = f' {angle_text:.2f}', 'Steps : ' + f'{count_text}', f' {stage_text}'
+        Plot the pose angle, count value and step stage.
+
+        Args:
+            angle_text (str): angle value for workout monitoring
+            count_text (str): counts value for workout monitoring
+            stage_text (str): stage decision for workout monitoring
+            center_kpt (int): centroid pose index for workout monitoring
+            line_thickness (int): thickness for text display
+        """
+        angle_text, count_text, stage_text = (f' {angle_text:.2f}', 'Steps : ' + f'{count_text}', f' {stage_text}')
         font_scale = 0.6 + (line_thickness / 10.0)
 
         # Draw angle
@@ -378,18 +416,38 @@ class Annotator:
         cv2.putText(self.im, stage_text, stage_text_position, 0, font_scale, (0, 0, 0), line_thickness)
 
     def seg_bbox(self, mask, mask_color=(255, 0, 255), det_label=None, track_label=None):
-        """Function for drawing segmented object in bounding box shape."""
+        """
+        Function for drawing segmented object in bounding box shape.
+
+        Args:
+            mask (list): masks data list for instance segmentation area plotting
+            mask_color (tuple): mask foreground color
+            det_label (str): Detection label text
+            track_label (str): Tracking label text
+        """
         cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)
 
         label = f'Track ID: {track_label}' if track_label else det_label
         text_size, _ = cv2.getTextSize(label, 0, 0.7, 1)
+
         cv2.rectangle(self.im, (int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
                       (int(mask[0][0]) + text_size[0] // 2 + 5, int(mask[0][1] + 5)), mask_color, -1)
+
         cv2.putText(self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1]) - 5), 0, 0.7, (255, 255, 255),
                     2)
 
     def visioneye(self, box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255), thickness=2, pins_radius=10):
-        """Function for pinpoint human-vision eye mapping and plotting."""
+        """
+        Function for pinpoint human-vision eye mapping and plotting.
+
+        Args:
+            box (list): Bounding box coordinates
+            center_point (tuple): center point for vision eye view
+            color (tuple): object centroid and line color value
+            pin_color (tuple): visioneye point color value
+            thickness (int): int value for line thickness
+            pins_radius (int): visioneye point radius value
+        """
         center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
         cv2.circle(self.im, center_point, pins_radius, pin_color, -1)
         cv2.circle(self.im, center_bbox, pins_radius, color, -1)