# 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):
"""Initializes the heatmap class with default values for Visual, Image, track, count and heatmap parameters."""
# Visual information
self.annotator = None
self.view_img = False
self.shape = "circle"
# Image information
self.imw = None
self.imh = None
self.im0 = None
self.view_in_counts = True
self.view_out_counts = True
# Heatmap colormap and heatmap np array
self.colormap = None
self.heatmap = None
self.heatmap_alpha = 0.5
# Predict/track information
self.boxes = None
self.track_ids = None
self.clss = None
self.track_history = defaultdict(list)
# Region & Line Information
self.count_reg_pts = None
self.counting_region = None
self.line_dist_thresh = 15
self.region_thickness = 5
self.region_color = (255, 0, 255)
# Object Counting Information
self.in_counts = 0
self.out_counts = 0
self.counting_list = []
self.count_txt_thickness = 0
self.count_txt_color = (0, 0, 0)
self.count_color = (255, 255, 255)
# Decay factor
self.decay_factor = 0.99
# Check if environment support imshow
self.env_check = check_imshow(warn=True)
def set_args(
self,
imw,
imh,
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_thickness=2,
count_txt_color=(0, 0, 0),
count_color=(255, 255, 255),
count_reg_color=(255, 0, 255),
region_thickness=5,
line_dist_thresh=15,
decay_factor=0.99,
shape="circle",
):
"""
Configures the heatmap colormap, width, height and display parameters.
Args:
colormap (cv2.COLORMAP): The colormap to be set.
imw (int): The width of the frame.
imh (int): The height of the frame.
heatmap_alpha (float): alpha value for heatmap display
view_img (bool): Flag indicating frame display
view_in_counts (bool): Flag to control whether to display the incounts on video stream.
view_out_counts (bool): Flag to control whether to display the outcounts on video stream.
count_reg_pts (list): Object counting region points
count_txt_thickness (int): Text thickness for object counting display
count_txt_color (RGB color): count text color value
count_color (RGB color): count text background color value
count_reg_color (RGB color): Color of object counting region
region_thickness (int): Object counting Region thickness
line_dist_thresh (int): Euclidean Distance threshold for line counter
decay_factor (float): value for removing heatmap area after object passed
shape (str): Heatmap shape, rect or circle shape supported
"""
self.imw = imw
self.imh = imh
self.heatmap_alpha = heatmap_alpha
self.view_img = view_img
self.view_in_counts = view_in_counts
self.view_out_counts = view_out_counts
self.colormap = colormap
# Region and line selection
if count_reg_pts is not None:
if len(count_reg_pts) == 2:
print("Line Counter Initiated.")
self.count_reg_pts = count_reg_pts
self.counting_region = LineString(count_reg_pts)
elif len(count_reg_pts) == 4:
print("Region Counter Initiated.")
self.count_reg_pts = count_reg_pts
self.counting_region = Polygon(self.count_reg_pts)
else:
print("Region or line points Invalid, 2 or 4 points supported")
print("Using Line Counter Now")
self.counting_region = Polygon([(20, 400), (1260, 400)]) # dummy points
# Heatmap new frame
self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)
self.count_txt_thickness = count_txt_thickness
self.count_txt_color = count_txt_color
self.count_color = count_color
self.region_color = count_reg_color
self.region_thickness = region_thickness
self.decay_factor = decay_factor
self.line_dist_thresh = line_dist_thresh
self.shape = shape
# 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.
"""
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
if tracks[0].boxes.id is None:
self.heatmap = np.zeros((int(self.imh), int(self.imw)), dtype=np.float32)
if self.view_img and self.env_check:
self.display_frames()
return im0
self.heatmap *= self.decay_factor # decay factor
self.extract_results(tracks)
self.annotator = Annotator(self.im0, self.count_txt_thickness, None)
if self.count_reg_pts is not None:
# Draw counting region
if self.view_in_counts or self.view_out_counts:
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):
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)
# Count objects
if len(self.count_reg_pts) == 4:
if self.counting_region.contains(Point(track_line[-1])) and track_id not in self.counting_list:
self.counting_list.append(track_id)
if box[0] < self.counting_region.centroid.x:
self.out_counts += 1
else:
self.in_counts += 1
elif len(self.count_reg_pts) == 2:
distance = Point(track_line[-1]).distance(self.counting_region)
if distance < self.line_dist_thresh and track_id not in self.counting_list:
self.counting_list.append(track_id)
if box[0] < self.counting_region.centroid.x:
self.out_counts += 1
else:
self.in_counts += 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
# 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)
incount_label = f"In Count : {self.in_counts}"
outcount_label = f"OutCount : {self.out_counts}"
# Display counts based on user choice
counts_label = None
if not self.view_in_counts and not self.view_out_counts:
counts_label = None
elif not self.view_in_counts:
counts_label = outcount_label
elif not self.view_out_counts:
counts_label = incount_label
else:
counts_label = f"{incount_label} {outcount_label}"
if self.count_reg_pts is not None and counts_label is not None:
self.annotator.count_labels(
counts=counts_label,
count_txt_size=self.count_txt_thickness,
txt_color=self.count_txt_color,
color=self.count_color,
)
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__":
Heatmap()