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Reference for ultralytics/solutions/object_counter.py

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This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/object_counter.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!


ultralytics.solutions.object_counter.ObjectCounter

ObjectCounter(names, reg_pts=None, count_reg_color=(255, 0, 255), count_txt_color=(0, 0, 0), count_bg_color=(255, 255, 255), line_thickness=2, track_thickness=2, view_img=False, view_in_counts=True, view_out_counts=True, draw_tracks=False, track_color=None, region_thickness=5, line_dist_thresh=15, cls_txtdisplay_gap=50)

A class to manage the counting of objects in a real-time video stream based on their tracks.

Parameters:

Name Type Description Default
names dict

Dictionary of class names.

required
reg_pts list

List of points defining the counting region.

None
count_reg_color tuple

RGB color of the counting region.

(255, 0, 255)
count_txt_color tuple

RGB color of the count text.

(0, 0, 0)
count_bg_color tuple

RGB color of the count text background.

(255, 255, 255)
line_thickness int

Line thickness for bounding boxes.

2
track_thickness int

Thickness of the track lines.

2
view_img bool

Flag to control whether to display the video stream.

False
view_in_counts bool

Flag to control whether to display the in counts on the video stream.

True
view_out_counts bool

Flag to control whether to display the out counts on the video stream.

True
draw_tracks bool

Flag to control whether to draw the object tracks.

False
track_color tuple

RGB color of the tracks.

None
region_thickness int

Thickness of the object counting region.

5
line_dist_thresh int

Euclidean distance threshold for line counter.

15
cls_txtdisplay_gap int

Display gap between each class count.

50
Source code in ultralytics/solutions/object_counter.py
def __init__(
    self,
    names,
    reg_pts=None,
    count_reg_color=(255, 0, 255),
    count_txt_color=(0, 0, 0),
    count_bg_color=(255, 255, 255),
    line_thickness=2,
    track_thickness=2,
    view_img=False,
    view_in_counts=True,
    view_out_counts=True,
    draw_tracks=False,
    track_color=None,
    region_thickness=5,
    line_dist_thresh=15,
    cls_txtdisplay_gap=50,
):
    """
    Initializes the ObjectCounter with various tracking and counting parameters.

    Args:
        names (dict): Dictionary of class names.
        reg_pts (list): List of points defining the counting region.
        count_reg_color (tuple): RGB color of the counting region.
        count_txt_color (tuple): RGB color of the count text.
        count_bg_color (tuple): RGB color of the count text background.
        line_thickness (int): Line thickness for bounding boxes.
        track_thickness (int): Thickness of the track lines.
        view_img (bool): Flag to control whether to display the video stream.
        view_in_counts (bool): Flag to control whether to display the in counts on the video stream.
        view_out_counts (bool): Flag to control whether to display the out counts on the video stream.
        draw_tracks (bool): Flag to control whether to draw the object tracks.
        track_color (tuple): RGB color of the tracks.
        region_thickness (int): Thickness of the object counting region.
        line_dist_thresh (int): Euclidean distance threshold for line counter.
        cls_txtdisplay_gap (int): Display gap between each class count.
    """

    # Mouse events
    self.is_drawing = False
    self.selected_point = None

    # Region & Line Information
    self.reg_pts = [(20, 400), (1260, 400)] if reg_pts is None else reg_pts
    self.line_dist_thresh = line_dist_thresh
    self.counting_region = None
    self.region_color = count_reg_color
    self.region_thickness = region_thickness

    # Image and annotation Information
    self.im0 = None
    self.tf = line_thickness
    self.view_img = view_img
    self.view_in_counts = view_in_counts
    self.view_out_counts = view_out_counts

    self.names = names  # Classes names
    self.annotator = None  # Annotator
    self.window_name = "Ultralytics YOLOv8 Object Counter"

    # Object counting Information
    self.in_counts = 0
    self.out_counts = 0
    self.count_ids = []
    self.class_wise_count = {}
    self.count_txt_thickness = 0
    self.count_txt_color = count_txt_color
    self.count_bg_color = count_bg_color
    self.cls_txtdisplay_gap = cls_txtdisplay_gap
    self.fontsize = 0.6

    # Tracks info
    self.track_history = defaultdict(list)
    self.track_thickness = track_thickness
    self.draw_tracks = draw_tracks
    self.track_color = track_color

    # Check if environment supports imshow
    self.env_check = check_imshow(warn=True)

    # Initialize counting region
    if len(self.reg_pts) == 2:
        print("Line Counter Initiated.")
        self.counting_region = LineString(self.reg_pts)
    elif len(self.reg_pts) >= 3:
        print("Polygon Counter Initiated.")
        self.counting_region = Polygon(self.reg_pts)
    else:
        print("Invalid Region points provided, region_points must be 2 for lines or >= 3 for polygons.")
        print("Using Line Counter Now")
        self.counting_region = LineString(self.reg_pts)

display_frames

display_frames()

Displays the current frame with annotations and regions in a window.

Source code in ultralytics/solutions/object_counter.py
def display_frames(self):
    """Displays the current frame with annotations and regions in a window."""
    if self.env_check:
        cv2.namedWindow(self.window_name)
        if len(self.reg_pts) == 4:  # only add mouse event If user drawn region
            cv2.setMouseCallback(self.window_name, self.mouse_event_for_region, {"region_points": self.reg_pts})
        cv2.imshow(self.window_name, self.im0)
        # Break Window
        if cv2.waitKey(1) & 0xFF == ord("q"):
            return

extract_and_process_tracks

extract_and_process_tracks(tracks)

Extracts and processes tracks for object counting in a video stream.

Source code in ultralytics/solutions/object_counter.py
def extract_and_process_tracks(self, tracks):
    """Extracts and processes tracks for object counting in a video stream."""

    # Annotator Init and region drawing
    self.annotator = Annotator(self.im0, self.tf, self.names)

    # Draw region or line
    self.annotator.draw_region(reg_pts=self.reg_pts, color=self.region_color, thickness=self.region_thickness)

    if tracks[0].boxes.id is not None:
        boxes = tracks[0].boxes.xyxy.cpu()
        clss = tracks[0].boxes.cls.cpu().tolist()
        track_ids = tracks[0].boxes.id.int().cpu().tolist()

        # Extract tracks
        for box, track_id, cls in zip(boxes, track_ids, clss):
            # Draw bounding box
            self.annotator.box_label(box, label=f"{self.names[cls]}#{track_id}", color=colors(int(track_id), True))

            # Store class info
            if self.names[cls] not in self.class_wise_count:
                self.class_wise_count[self.names[cls]] = {"IN": 0, "OUT": 0}

            # Draw Tracks
            track_line = self.track_history[track_id]
            track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
            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=self.track_color or colors(int(track_id), True),
                    track_thickness=self.track_thickness,
                )

            prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None

            # Count objects in any polygon
            if len(self.reg_pts) >= 3:
                is_inside = self.counting_region.contains(Point(track_line[-1]))

                if prev_position is not None and is_inside and track_id not in self.count_ids:
                    self.count_ids.append(track_id)

                    if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0:
                        self.in_counts += 1
                        self.class_wise_count[self.names[cls]]["IN"] += 1
                    else:
                        self.out_counts += 1
                        self.class_wise_count[self.names[cls]]["OUT"] += 1

            # Count objects using line
            elif len(self.reg_pts) == 2:
                if prev_position is not None and track_id not in self.count_ids:
                    distance = Point(track_line[-1]).distance(self.counting_region)
                    if distance < self.line_dist_thresh and track_id not in self.count_ids:
                        self.count_ids.append(track_id)

                        if (box[0] - prev_position[0]) * (self.counting_region.centroid.x - prev_position[0]) > 0:
                            self.in_counts += 1
                            self.class_wise_count[self.names[cls]]["IN"] += 1
                        else:
                            self.out_counts += 1
                            self.class_wise_count[self.names[cls]]["OUT"] += 1

    labels_dict = {}

    for key, value in self.class_wise_count.items():
        if value["IN"] != 0 or value["OUT"] != 0:
            if not self.view_in_counts and not self.view_out_counts:
                continue
            elif not self.view_in_counts:
                labels_dict[str.capitalize(key)] = f"OUT {value['OUT']}"
            elif not self.view_out_counts:
                labels_dict[str.capitalize(key)] = f"IN {value['IN']}"
            else:
                labels_dict[str.capitalize(key)] = f"IN {value['IN']} OUT {value['OUT']}"

    if labels_dict:
        self.annotator.display_analytics(self.im0, labels_dict, self.count_txt_color, self.count_bg_color, 10)

mouse_event_for_region

mouse_event_for_region(event, x, y, flags, params)

Handles mouse events for defining and moving the counting region in a real-time video stream.

Parameters:

Name Type Description Default
event int

The type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN, etc.).

required
x int

The x-coordinate of the mouse pointer.

required
y int

The y-coordinate of the mouse pointer.

required
flags int

Any associated event flags (e.g., cv2.EVENT_FLAG_CTRLKEY, cv2.EVENT_FLAG_SHIFTKEY, etc.).

required
params dict

Additional parameters for the function.

required
Source code in ultralytics/solutions/object_counter.py
def mouse_event_for_region(self, event, x, y, flags, params):
    """
    Handles mouse events for defining and moving the counting region in a real-time video stream.

    Args:
        event (int): The type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN, etc.).
        x (int): The x-coordinate of the mouse pointer.
        y (int): The y-coordinate of the mouse pointer.
        flags (int): Any associated event flags (e.g., cv2.EVENT_FLAG_CTRLKEY,  cv2.EVENT_FLAG_SHIFTKEY, etc.).
        params (dict): Additional parameters for the function.
    """
    if event == cv2.EVENT_LBUTTONDOWN:
        for i, point in enumerate(self.reg_pts):
            if (
                isinstance(point, (tuple, list))
                and len(point) >= 2
                and (abs(x - point[0]) < 10 and abs(y - point[1]) < 10)
            ):
                self.selected_point = i
                self.is_drawing = True
                break

    elif event == cv2.EVENT_MOUSEMOVE:
        if self.is_drawing and self.selected_point is not None:
            self.reg_pts[self.selected_point] = (x, y)
            self.counting_region = Polygon(self.reg_pts)

    elif event == cv2.EVENT_LBUTTONUP:
        self.is_drawing = False
        self.selected_point = None

start_counting

start_counting(im0, tracks)

Main function to start the object counting process.

Parameters:

Name Type Description Default
im0 ndarray

Current frame from the video stream.

required
tracks list

List of tracks obtained from the object tracking process.

required
Source code in ultralytics/solutions/object_counter.py
def start_counting(self, im0, tracks):
    """
    Main function to start the object counting process.

    Args:
        im0 (ndarray): Current frame from the video stream.
        tracks (list): List of tracks obtained from the object tracking process.
    """
    self.im0 = im0  # store image
    self.extract_and_process_tracks(tracks)  # draw region even if no objects

    if self.view_img:
        self.display_frames()
    return self.im0





Created 2023-12-02, Updated 2024-07-21
Authors: glenn-jocher (3), Burhan-Q (1), RizwanMunawar (1)