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# 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,
classes_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 = classes_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 = None
self.track_ids = None
self.clss = None
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, _intialized=False):
"""
Extracts results from the provided data.
Args:
tracks (list): List of tracks obtained from the object tracking process.
"""
self.boxes = tracks[0].boxes.xyxy.cpu()
self.clss = tracks[0].boxes.cls.cpu().tolist()
self.track_ids = tracks[0].boxes.id.int().cpu().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 is not None:
# 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)
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