Reference for ultralytics/solutions/heatmap.py

def display_frames(self):
    """Display frame."""
    cv2.imshow("Ultralytics Heatmap", self.im0)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        return

def extract_results(self, tracks):
    """
    Extracts results from the provided data.

    Args:
        tracks (list): List of tracks obtained from the object tracking process.
    """
    if tracks[0].boxes.id is not None:
        self.boxes = tracks[0].boxes.xyxy.cpu()
        self.clss = tracks[0].boxes.cls.tolist()
        self.track_ids = tracks[0].boxes.id.int().tolist()

def generate_heatmap(self, im0, tracks):
    """
    Generate heatmap based on tracking data.

    Args:
        im0 (nd array): Image
        tracks (list): List of tracks obtained from the object tracking process.
    """
    self.im0 = im0

    # Initialize heatmap only once
    if not self.initialized:
        self.heatmap = np.zeros((int(self.im0.shape[0]), int(self.im0.shape[1])), dtype=np.float32)
        self.initialized = True

    self.heatmap *= self.decay_factor  # decay factor

    self.extract_results(tracks)
    self.annotator = Annotator(self.im0, self.tf, None)

    if self.track_ids:
        # Draw counting region
        if self.count_reg_pts is not None:
            self.annotator.draw_region(
                reg_pts=self.count_reg_pts, color=self.region_color, thickness=self.region_thickness
            )

        for box, cls, track_id in zip(self.boxes, self.clss, self.track_ids):
            # Store class info
            if self.names[cls] not in self.class_wise_count:
                self.class_wise_count[self.names[cls]] = {"IN": 0, "OUT": 0}

            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]
            track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
            if len(track_line) > 30:
                track_line.pop(0)

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

            if self.count_reg_pts is not None:
                # Count objects in any polygon
                if len(self.count_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.count_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

    else:
        for box, cls in zip(self.boxes, self.clss):
            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

    if self.count_reg_pts is not None:
        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 is not None:
            self.annotator.display_analytics(self.im0, labels_dict, self.count_txt_color, self.count_bg_color, 10)

    # Normalize, apply colormap to heatmap and combine with original image
    heatmap_normalized = cv2.normalize(self.heatmap, None, 0, 255, cv2.NORM_MINMAX)
    heatmap_colored = cv2.applyColorMap(heatmap_normalized.astype(np.uint8), self.colormap)
    self.im0 = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)

    if self.env_check and self.view_img:
        self.display_frames()

    return self.im0