ultralytics/ultralytics/solutions/heatmap.py

# Ultralytics YOLO 🚀, AGPL-3.0 license

import cv2
import numpy as np

from ultralytics.solutions.object_counter import ObjectCounter
from ultralytics.utils.plotting import Annotator


class Heatmap(ObjectCounter):
    """
    A class to draw heatmaps in real-time video streams based on object tracks.

    This class extends the ObjectCounter class to generate and visualize heatmaps of object movements in video
    streams. It uses tracked object positions to create a cumulative heatmap effect over time.

    Attributes:
        initialized (bool): Flag indicating whether the heatmap has been initialized.
        colormap (int): OpenCV colormap used for heatmap visualization.
        heatmap (np.ndarray): Array storing the cumulative heatmap data.
        annotator (Annotator): Object for drawing annotations on the image.

    Methods:
        heatmap_effect: Calculates and updates the heatmap effect for a given bounding box.
        generate_heatmap: Generates and applies the heatmap effect to each frame.

    Examples:
        >>> from ultralytics.solutions import Heatmap
        >>> heatmap = Heatmap(model="yolov8n.pt", colormap=cv2.COLORMAP_JET)
        >>> frame = cv2.imread("frame.jpg")
        >>> processed_frame = heatmap.generate_heatmap(frame)
    """

    def __init__(self, **kwargs):
        """Initializes the Heatmap class for real-time video stream heatmap generation based on object tracks."""
        super().__init__(**kwargs)

        self.initialized = False  # bool variable for heatmap initialization
        if self.region is not None:  # check if user provided the region coordinates
            self.initialize_region()

        # store colormap
        self.colormap = cv2.COLORMAP_PARULA if self.CFG["colormap"] is None else self.CFG["colormap"]
        self.heatmap = None

    def heatmap_effect(self, box):
        """
        Efficiently calculates heatmap area and effect location for applying colormap.

        Args:
            box (List[float]): Bounding box coordinates [x0, y0, x1, y1].

        Examples:
            >>> heatmap = Heatmap()
            >>> box = [100, 100, 200, 200]
            >>> heatmap.heatmap_effect(box)
        """
        x0, y0, x1, y1 = map(int, box)
        radius_squared = (min(x1 - x0, y1 - y0) // 2) ** 2

        # Create a meshgrid with region of interest (ROI) for vectorized distance calculations
        xv, yv = np.meshgrid(np.arange(x0, x1), np.arange(y0, y1))

        # Calculate squared distances from the center
        dist_squared = (xv - ((x0 + x1) // 2)) ** 2 + (yv - ((y0 + y1) // 2)) ** 2

        # Create a mask of points within the radius
        within_radius = dist_squared <= radius_squared

        # Update only the values within the bounding box in a single vectorized operation
        self.heatmap[y0:y1, x0:x1][within_radius] += 2

    def generate_heatmap(self, im0):
        """
        Generate heatmap for each frame using Ultralytics.

        Args:
            im0 (np.ndarray): Input image array for processing.

        Returns:
            (np.ndarray): Processed image with heatmap overlay and object counts (if region is specified).

        Examples:
            >>> heatmap = Heatmap()
            >>> im0 = cv2.imread("image.jpg")
            >>> result = heatmap.generate_heatmap(im0)
        """
        if not self.initialized:
            self.heatmap = np.zeros_like(im0, dtype=np.float32) * 0.99
        self.initialized = True  # Initialize heatmap only once

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

        # 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.heatmap_effect(box)

            if self.region is not None:
                self.annotator.draw_region(reg_pts=self.region, color=(104, 0, 123), thickness=self.line_width * 2)
                self.store_tracking_history(track_id, box)  # Store track history
                self.store_classwise_counts(cls)  # store classwise counts in dict
                current_centroid = ((box[0] + box[2]) / 2, (box[1] + box[3]) / 2)
                # Store tracking previous position and perform 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

        if self.region is not None:
            self.display_counts(im0)  # Display the counts on the frame

        # Normalize, apply colormap to heatmap and combine with original image
        if self.track_data.id is not None:
            im0 = cv2.addWeighted(
                im0,
                0.5,
                cv2.applyColorMap(
                    cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8), self.colormap
                ),
                0.5,
                0,
            )

        self.display_output(im0)  # display output with base class function
        return im0  # return output image for more usage