# Ultralytics YOLO 🚀, AGPL-3.0 license
from collections import defaultdict
import cv2
from ultralytics.utils.checks import check_imshow, check_requirements
from ultralytics.utils.plotting import Annotator, colors
check_requirements("shapely>=2.0.0")
from shapely.geometry import LineString, Point, Polygon
class ObjectCounter:
"""A class to manage the counting of objects in a real-time video stream based on their tracks."""
def __init__(self):
"""Initializes the Counter with default values for various tracking and counting parameters."""
# Mouse events
self.is_drawing = False
self.selected_point = None
# Region & Line Information
self.reg_pts = [(20, 400), (1260, 400)]
self.line_dist_thresh = 15
self.counting_region = None
self.region_color = (255, 0, 255)
self.region_thickness = 5
# Image and annotation Information
self.im0 = None
self.tf = None
self.view_img = False
self.view_in_counts = True
self.view_out_counts = True
self.names = None # Classes names
self.annotator = None # Annotator
self.window_name = "Ultralytics YOLOv8 Object Counter"
# Object counting Information
self.in_counts = 0
self.out_counts = 0
self.counting_dict = {}
self.count_txt_thickness = 0
self.count_txt_color = (0, 0, 0)
self.count_color = (255, 255, 255)
# Tracks info
self.track_history = defaultdict(list)
self.track_thickness = 2
self.draw_tracks = False
self.track_color = (0, 255, 0)
# Check if environment support imshow
self.env_check = check_imshow(warn=True)
def set_args(
self,
classes_names,
reg_pts,
count_reg_color=(255, 0, 255),
line_thickness=2,
track_thickness=2,
view_img=False,
view_in_counts=True,
view_out_counts=True,
draw_tracks=False,
count_txt_thickness=2,
count_txt_color=(0, 0, 0),
count_color=(255, 255, 255),
track_color=(0, 255, 0),
region_thickness=5,
line_dist_thresh=15,
):
"""
Configures the Counter's image, bounding box line thickness, and counting region points.
Args:
line_thickness (int): Line thickness for bounding boxes.
view_img (bool): Flag to control whether to display the video stream.
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.
reg_pts (list): Initial list of points defining the counting region.
classes_names (dict): Classes names
track_thickness (int): Track thickness
draw_tracks (Bool): draw tracks
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
track_color (RGB color): color for tracks
region_thickness (int): Object counting Region thickness
line_dist_thresh (int): Euclidean Distance threshold for line counter
"""
self.tf = line_thickness
self.view_img = view_img
self.view_in_counts = view_in_counts
self.view_out_counts = view_out_counts
self.track_thickness = track_thickness
self.draw_tracks = draw_tracks
# Region and line selection
if len(reg_pts) == 2:
print("Line Counter Initiated.")
self.reg_pts = reg_pts
self.counting_region = LineString(self.reg_pts)
elif len(reg_pts) >= 3:
print("Region Counter Initiated.")
self.reg_pts = reg_pts
self.counting_region = Polygon(self.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.reg_pts)
self.names = classes_names
self.track_color = track_color
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.line_dist_thresh = line_dist_thresh
def mouse_event_for_region(self, event, x, y, flags, params):
"""
This function is designed to move region with mouse events in a real-time video stream.
Args:
event (int): The type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN, etc.).
x (int): The x-coordinate of the mouse pointer.
y (int): The y-coordinate of the mouse pointer.
flags (int): Any flags associated with the event (e.g., cv2.EVENT_FLAG_CTRLKEY,
cv2.EVENT_FLAG_SHIFTKEY, etc.).
params (dict): Additional parameters you may want to pass to the function.
"""
if event == cv2.EVENT_LBUTTONDOWN:
for i, point in enumerate(self.reg_pts):
if (
isinstance(point, (tuple, list))
and len(point) >= 2
and (abs(x - point[0]) < 10 and abs(y - point[1]) < 10)
):
self.selected_point = i
self.is_drawing = True
break
elif event == cv2.EVENT_MOUSEMOVE:
if self.is_drawing and self.selected_point is not None:
self.reg_pts[self.selected_point] = (x, y)
self.counting_region = Polygon(self.reg_pts)
elif event == cv2.EVENT_LBUTTONUP:
self.is_drawing = False
self.selected_point = None
def extract_and_process_tracks(self, tracks):
"""Extracts and processes tracks for object counting in a video stream."""
# Annotator Init and region drawing
self.annotator = Annotator(self.im0, self.tf, self.names)
if tracks[0].boxes.id is not None:
boxes = tracks[0].boxes.xyxy.cpu()
clss = tracks[0].boxes.cls.cpu().tolist()
track_ids = tracks[0].boxes.id.int().cpu().tolist()
# Extract tracks
for box, track_id, cls in zip(boxes, track_ids, clss):
# Draw bounding box
self.annotator.box_label(box, label=f"{track_id}:{self.names[cls]}", color=colors(int(track_id), True))
# Draw Tracks
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)
# Draw track trails
if self.draw_tracks:
self.annotator.draw_centroid_and_tracks(
track_line, color=self.track_color, track_thickness=self.track_thickness
)
prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
centroid = Point((box[:2] + box[2:]) / 2)
# Count objects
if len(self.reg_pts) >= 3: # any polygon
is_inside = self.counting_region.contains(centroid)
current_position = "in" if is_inside else "out"
if prev_position is not None:
if self.counting_dict[track_id] != current_position and is_inside:
self.in_counts += 1
self.counting_dict[track_id] = "in"
elif self.counting_dict[track_id] != current_position and not is_inside:
self.out_counts += 1
self.counting_dict[track_id] = "out"
else:
self.counting_dict[track_id] = current_position
else:
self.counting_dict[track_id] = current_position
elif len(self.reg_pts) == 2:
if prev_position is not None:
is_inside = (box[0] - prev_position[0]) * (
self.counting_region.centroid.x - prev_position[0]
) > 0
current_position = "in" if is_inside else "out"
if self.counting_dict[track_id] != current_position and is_inside:
self.in_counts += 1
self.counting_dict[track_id] = "in"
elif self.counting_dict[track_id] != current_position and not is_inside:
self.out_counts += 1
self.counting_dict[track_id] = "out"
else:
self.counting_dict[track_id] = current_position
else:
self.counting_dict[track_id] = None
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 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,
)
def display_frames(self):
"""Display frame."""
if self.env_check:
self.annotator.draw_region(reg_pts=self.reg_pts, color=self.region_color, thickness=self.region_thickness)
cv2.namedWindow(self.window_name)
if len(self.reg_pts) == 4: # only add mouse event If user drawn region
cv2.setMouseCallback(self.window_name, self.mouse_event_for_region, {"region_points": self.reg_pts})
cv2.imshow(self.window_name, self.im0)
# Break Window
if cv2.waitKey(1) & 0xFF == ord("q"):
return
def start_counting(self, im0, tracks):
"""
Main function to start the object counting process.
Args:
im0 (ndarray): Current frame from the video stream.
tracks (list): List of tracks obtained from the object tracking process.
"""
self.im0 = im0 # store image
self.extract_and_process_tracks(tracks) # draw region even if no objects
if self.view_img:
self.display_frames()
return self.im0
if __name__ == "__main__":
ObjectCounter()