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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(**kwargs)

Bases: BaseSolution

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

This class extends the BaseSolution class and provides functionality for counting objects moving in and out of a specified region in a video stream. It supports both polygonal and linear regions for counting.

Attributes:

Name Type Description
in_count int

Counter for objects moving inward.

out_count int

Counter for objects moving outward.

counted_ids List[int]

List of IDs of objects that have been counted.

classwise_counts Dict[str, Dict[str, int]]

Dictionary for counts, categorized by object class.

region_initialized bool

Flag indicating whether the counting region has been initialized.

show_in bool

Flag to control display of inward count.

show_out bool

Flag to control display of outward count.

Methods:

Name Description
count_objects

Counts objects within a polygonal or linear region.

store_classwise_counts

Initializes class-wise counts if not already present.

display_counts

Displays object counts on the frame.

count

Processes input data (frames or object tracks) and updates counts.

Examples:

>>> counter = ObjectCounter()
>>> frame = cv2.imread("frame.jpg")
>>> processed_frame = counter.count(frame)
>>> print(f"Inward count: {counter.in_count}, Outward count: {counter.out_count}")
Source code in ultralytics/solutions/object_counter.py
def __init__(self, **kwargs):
    """Initializes the ObjectCounter class for real-time object counting in video streams."""
    super().__init__(**kwargs)

    self.in_count = 0  # Counter for objects moving inward
    self.out_count = 0  # Counter for objects moving outward
    self.counted_ids = []  # List of IDs of objects that have been counted
    self.classwise_counts = {}  # Dictionary for counts, categorized by object class
    self.region_initialized = False  # Bool variable for region initialization

    self.show_in = self.CFG["show_in"]
    self.show_out = self.CFG["show_out"]

count

count(im0)

Processes input data (frames or object tracks) and updates object counts.

This method initializes the counting region, extracts tracks, draws bounding boxes and regions, updates object counts, and displays the results on the input image.

Parameters:

Name Type Description Default
im0 ndarray

The input image or frame to be processed.

required

Returns:

Type Description
ndarray

The processed image with annotations and count information.

Examples:

>>> counter = ObjectCounter()
>>> frame = cv2.imread("path/to/image.jpg")
>>> processed_frame = counter.count(frame)
Source code in ultralytics/solutions/object_counter.py
def count(self, im0):
    """
    Processes input data (frames or object tracks) and updates object counts.

    This method initializes the counting region, extracts tracks, draws bounding boxes and regions, updates
    object counts, and displays the results on the input image.

    Args:
        im0 (numpy.ndarray): The input image or frame to be processed.

    Returns:
        (numpy.ndarray): The processed image with annotations and count information.

    Examples:
        >>> counter = ObjectCounter()
        >>> frame = cv2.imread("path/to/image.jpg")
        >>> processed_frame = counter.count(frame)
    """
    if not self.region_initialized:
        self.initialize_region()
        self.region_initialized = True

    self.annotator = Annotator(im0, line_width=self.line_width)  # Initialize annotator
    self.extract_tracks(im0)  # Extract tracks

    self.annotator.draw_region(
        reg_pts=self.region, color=(104, 0, 123), thickness=self.line_width * 2
    )  # Draw region

    # Iterate over bounding boxes, track ids and classes index
    for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
        # Draw bounding box and counting region
        self.annotator.box_label(box, label=self.names[cls], color=colors(cls, True))
        self.store_tracking_history(track_id, box)  # Store track history
        self.store_classwise_counts(cls)  # store classwise counts in dict

        # Draw tracks of objects
        self.annotator.draw_centroid_and_tracks(
            self.track_line, color=colors(int(cls), True), track_thickness=self.line_width
        )
        current_centroid = ((box[0] + box[2]) / 2, (box[1] + box[3]) / 2)
        # store previous position of track for object counting
        prev_position = None
        if len(self.track_history[track_id]) > 1:
            prev_position = self.track_history[track_id][-2]
        self.count_objects(current_centroid, track_id, prev_position, cls)  # Perform object counting

    self.display_counts(im0)  # Display the counts on the frame
    self.display_output(im0)  # display output with base class function

    return im0  # return output image for more usage

count_objects

count_objects(current_centroid, track_id, prev_position, cls)

Counts objects within a polygonal or linear region based on their tracks.

Parameters:

Name Type Description Default
current_centroid Tuple[float, float]

Current centroid values in the current frame.

required
track_id int

Unique identifier for the tracked object.

required
prev_position Tuple[float, float]

Last frame position coordinates (x, y) of the track.

required
cls int

Class index for classwise count updates.

required

Examples:

>>> counter = ObjectCounter()
>>> track_line = {1: [100, 200], 2: [110, 210], 3: [120, 220]}
>>> box = [130, 230, 150, 250]
>>> track_id = 1
>>> prev_position = (120, 220)
>>> cls = 0
>>> counter.count_objects(current_centroid, track_id, prev_position, cls)
Source code in ultralytics/solutions/object_counter.py
def count_objects(self, current_centroid, track_id, prev_position, cls):
    """
    Counts objects within a polygonal or linear region based on their tracks.

    Args:
        current_centroid (Tuple[float, float]): Current centroid values in the current frame.
        track_id (int): Unique identifier for the tracked object.
        prev_position (Tuple[float, float]): Last frame position coordinates (x, y) of the track.
        cls (int): Class index for classwise count updates.

    Examples:
        >>> counter = ObjectCounter()
        >>> track_line = {1: [100, 200], 2: [110, 210], 3: [120, 220]}
        >>> box = [130, 230, 150, 250]
        >>> track_id = 1
        >>> prev_position = (120, 220)
        >>> cls = 0
        >>> counter.count_objects(current_centroid, track_id, prev_position, cls)
    """
    if prev_position is None or track_id in self.counted_ids:
        return

    if len(self.region) == 2:  # Linear region (defined as a line segment)
        line = self.LineString(self.region)  # Check if the line intersects the trajectory of the object
        if line.intersects(self.LineString([prev_position, current_centroid])):
            # Determine orientation of the region (vertical or horizontal)
            if abs(self.region[0][0] - self.region[1][0]) < abs(self.region[0][1] - self.region[1][1]):
                # Vertical region: Compare x-coordinates to determine direction
                if current_centroid[0] > prev_position[0]:  # Moving right
                    self.in_count += 1
                    self.classwise_counts[self.names[cls]]["IN"] += 1
                else:  # Moving left
                    self.out_count += 1
                    self.classwise_counts[self.names[cls]]["OUT"] += 1
            # Horizontal region: Compare y-coordinates to determine direction
            elif current_centroid[1] > prev_position[1]:  # Moving downward
                self.in_count += 1
                self.classwise_counts[self.names[cls]]["IN"] += 1
            else:  # Moving upward
                self.out_count += 1
                self.classwise_counts[self.names[cls]]["OUT"] += 1
            self.counted_ids.append(track_id)

    elif len(self.region) > 2:  # Polygonal region
        polygon = self.Polygon(self.region)
        if polygon.contains(self.Point(current_centroid)):
            # Determine motion direction for vertical or horizontal polygons
            region_width = max(p[0] for p in self.region) - min(p[0] for p in self.region)
            region_height = max(p[1] for p in self.region) - min(p[1] for p in self.region)

            if (
                region_width < region_height
                and current_centroid[0] > prev_position[0]
                or region_width >= region_height
                and current_centroid[1] > prev_position[1]
            ):  # Moving right
                self.in_count += 1
                self.classwise_counts[self.names[cls]]["IN"] += 1
            else:  # Moving left
                self.out_count += 1
                self.classwise_counts[self.names[cls]]["OUT"] += 1
            self.counted_ids.append(track_id)

display_counts

display_counts(im0)

Displays object counts on the input image or frame.

Parameters:

Name Type Description Default
im0 ndarray

The input image or frame to display counts on.

required

Examples:

>>> counter = ObjectCounter()
>>> frame = cv2.imread("image.jpg")
>>> counter.display_counts(frame)
Source code in ultralytics/solutions/object_counter.py
def display_counts(self, im0):
    """
    Displays object counts on the input image or frame.

    Args:
        im0 (numpy.ndarray): The input image or frame to display counts on.

    Examples:
        >>> counter = ObjectCounter()
        >>> frame = cv2.imread("image.jpg")
        >>> counter.display_counts(frame)
    """
    labels_dict = {
        str.capitalize(key): f"{'IN ' + str(value['IN']) if self.show_in else ''} "
        f"{'OUT ' + str(value['OUT']) if self.show_out else ''}".strip()
        for key, value in self.classwise_counts.items()
        if value["IN"] != 0 or value["OUT"] != 0
    }

    if labels_dict:
        self.annotator.display_analytics(im0, labels_dict, (104, 31, 17), (255, 255, 255), 10)

store_classwise_counts

store_classwise_counts(cls)

Initialize class-wise counts for a specific object class if not already present.

Parameters:

Name Type Description Default
cls int

Class index for classwise count updates.

required

This method ensures that the 'classwise_counts' dictionary contains an entry for the specified class, initializing 'IN' and 'OUT' counts to zero if the class is not already present.

Examples:

>>> counter = ObjectCounter()
>>> counter.store_classwise_counts(0)  # Initialize counts for class index 0
>>> print(counter.classwise_counts)
{'person': {'IN': 0, 'OUT': 0}}
Source code in ultralytics/solutions/object_counter.py
def store_classwise_counts(self, cls):
    """
    Initialize class-wise counts for a specific object class if not already present.

    Args:
        cls (int): Class index for classwise count updates.

    This method ensures that the 'classwise_counts' dictionary contains an entry for the specified class,
    initializing 'IN' and 'OUT' counts to zero if the class is not already present.

    Examples:
        >>> counter = ObjectCounter()
        >>> counter.store_classwise_counts(0)  # Initialize counts for class index 0
        >>> print(counter.classwise_counts)
        {'person': {'IN': 0, 'OUT': 0}}
    """
    if self.names[cls] not in self.classwise_counts:
        self.classwise_counts[self.names[cls]] = {"IN": 0, "OUT": 0}



📅 Created 1 year ago ✏️ Updated 2 months ago