# Ultralytics YOLO 🚀, AGPL-3.0 license from collections import defaultdict import cv2 import numpy as np from ultralytics.utils.checks import check_imshow, check_requirements from ultralytics.utils.plotting import Annotator check_requirements("shapely>=2.0.0") from shapely.geometry import LineString, Point, Polygon class Heatmap: """A class to draw heatmaps in real-time video stream based on their tracks.""" def __init__( self, names, imw=0, imh=0, colormap=cv2.COLORMAP_JET, heatmap_alpha=0.5, view_img=False, view_in_counts=True, view_out_counts=True, count_reg_pts=None, count_txt_color=(0, 0, 0), count_bg_color=(255, 255, 255), count_reg_color=(255, 0, 255), region_thickness=5, line_dist_thresh=15, line_thickness=2, decay_factor=0.99, shape="circle", ): """Initializes the heatmap class with default values for Visual, Image, track, count and heatmap parameters.""" # Visual information self.annotator = None self.view_img = view_img self.shape = shape self.initialized = False self.names = names # Classes names # Image information self.imw = imw self.imh = imh self.im0 = None self.tf = line_thickness self.view_in_counts = view_in_counts self.view_out_counts = view_out_counts # Heatmap colormap and heatmap np array self.colormap = colormap self.heatmap = None self.heatmap_alpha = heatmap_alpha # Predict/track information self.boxes = [] self.track_ids = [] self.clss = [] self.track_history = defaultdict(list) # Region & Line Information self.counting_region = None self.line_dist_thresh = line_dist_thresh self.region_thickness = region_thickness self.region_color = count_reg_color # Object Counting Information self.in_counts = 0 self.out_counts = 0 self.count_ids = [] self.class_wise_count = {} self.count_txt_color = count_txt_color self.count_bg_color = count_bg_color self.cls_txtdisplay_gap = 50 # Decay factor self.decay_factor = decay_factor # Check if environment supports imshow self.env_check = check_imshow(warn=True) # Region and line selection self.count_reg_pts = count_reg_pts print(self.count_reg_pts) if self.count_reg_pts is not None: if len(self.count_reg_pts) == 2: print("Line Counter Initiated.") self.counting_region = LineString(self.count_reg_pts) elif len(self.count_reg_pts) >= 3: print("Polygon Counter Initiated.") self.counting_region = Polygon(self.count_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.count_reg_pts) # 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): """ 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 def display_frames(self): """Display frame.""" cv2.imshow("Ultralytics Heatmap", self.im0) if cv2.waitKey(1) & 0xFF == ord("q"): return if __name__ == "__main__": classes_names = {0: "person", 1: "car"} # example class names heatmap = Heatmap(classes_names)