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# Ultralytics YOLO πŸš€, AGPL-3.0 license
from collections import defaultdict
import logging
import cv2
import numpy as np
from ultralytics.utils.checks import check_imshow, check_requirements
from ultralytics.utils.plotting import Annotator, colors
from shapely.geometry import LineString, Point, Polygon
# create logger
logger = logging.getLogger(__name__).addHandler(logging.NullHandler())
# need shapely>=2.0.0
check_requirements("shapely>=2.0.0")
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
# Object counting Information
self.in_counts = 0
self.out_counts = 0
self.out_counts_prev = self.out_counts
self.in_counts_prev = self.in_counts
self.counting_list = []
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.draw_boxes = False # added by steve.yin @ 3/1/2024
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,
draw_boxes=False, # added by steve.yin @ 3/1/2024
draw_reg_pts=True, # added by steve.yin @ 3/1/2024
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 display the video stream.
view_in_counts (bool): Flag to control display the incounts.
view_out_counts (bool): Flag to control display the outcounts.
reg_pts (list): Initial list of points for the counting region.
classes_names (dict): Classes names
track_thickness (int): Track thickness
draw_tracks (Bool): draw tracks
draw_boxes (Bool): draw boxes
draw_reg_pts (Bool): draw reg_pts
count_txt_thickness (int): Text thickness 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 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
self.draw_boxes = draw_boxes # added by steve.yin @ 3/1/2024
self.draw_reg_pts = draw_reg_pts # added by steve.yin @ 3/1/2024
# Region and line selection
if len(reg_pts) == 2:
logging.info("Line Counter Initiated.")
self.reg_pts = reg_pts
self.counting_region = LineString(self.reg_pts)
u = np.array([self.reg_pts[0][0], self.reg_pts[0][1]])
v = np.array([self.reg_pts[1][0], self.reg_pts[1][1]])
elif len(reg_pts) == 4:
logging.info("Region Counter Initiated.")
self.reg_pts = reg_pts
self.counting_region = Polygon(self.reg_pts)
u = np.array([
(self.reg_pts[0][0] + self.reg_pts[1][0]) / 2,
(self.reg_pts[0][1] + self.reg_pts[1][1]) / 2,
])
v = np.array([
(self.reg_pts[2][0] + self.reg_pts[3][0]) / 2,
(self.reg_pts[2][1] + self.reg_pts[3][1]) / 2,
])
else:
logging.warning(
"Invalid Region points, which can only be 2 or 4. " +
"Using Line Counter Instead!"
)
self.counting_region = LineString(self.reg_pts)
u = np.array(self.counting_region.coords[0])
v = np.array(
self.counting_region.coords[len(self.counting_region.coords)-1]
)
# get line orientation, rotate ccw 90degrees, get line normal vector
n = v - u
nvec = np.array([-n[1], n[0]])
# print(f"v: {v}, u: {u}, n: {n}, nvec0: {nvec}")
self.counting_region_nvec = nvec / (np.linalg.norm(nvec) + 1e-6)
# print(f"nvec: {self.counting_region_nvec}")
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 passing 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.
"""
boxes = tracks[0].boxes.xyxy.cpu()
clss = tracks[0].boxes.cls.cpu().tolist()
track_ids = tracks[0].boxes.id.int().cpu().tolist()
# Annotator Init and region drawing
self.annotator = Annotator(self.im0, self.tf, self.names)
# self.annotator.draw_region(
# reg_pts=self.reg_pts,
# color=self.region_color,
# thickness=self.region_thickness
# )
# Extract tracks
for box, track_id, cls in zip(boxes, track_ids, clss):
# Draw bounding box [modified by steve.yin @ 3/1/2024]
if self.draw_reg_pts:
self.annotator.draw_region(
reg_pts=self.reg_pts,
color=self.region_color,
thickness=self.region_thickness
)
if self.draw_boxes:
self.annotator.box_label(
box=box,
label=f"{track_id}:{self.names[cls]}",
color=colors(int(cls), 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=track_line,
color=self.track_color,
track_thickness=self.track_thickness
)
prev_position = self.track_history[track_id][0] \
if len(self.track_history[track_id]) > 1 else None
# Count objects
if len(self.reg_pts) == 4:
if (
prev_position is not None
and self.counting_region.contains(Point(track_line[-1]))
and track_id not in self.counting_list
):
self.counting_list.append(track_id)
obj_track_vec = np.array([
track_line[-1][0] - prev_position[0],
track_line[-1][1] - prev_position[1]
])
if np.sign(
np.dot(obj_track_vec, self.counting_region_nvec)
) < 0:
self.out_counts += 1
else:
self.in_counts += 1
elif len(self.reg_pts) == 2:
if prev_position is not None:
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)
obj_track_vec = np.array([
track_line[-1][0] - prev_position[0],
track_line[-1][1] - prev_position[1]
])
logging.info(f"obj_track_vec: {obj_track_vec}")
if np.sign(
np.dot(obj_track_vec, self.counting_region_nvec)
) < 0:
self.out_counts += 1
else:
self.in_counts += 1
self.outcounts_updated()
self.incounts_updated()
self.out_counts_prev = self.out_counts
self.in_counts_prev = self.in_counts
incount_label = f"In: {self.in_counts}"
outcount_label = f"Out: {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:
cv2.namedWindow("Ultralytics YOLOv8 Object Counter")
# only add mouse event If user drawn region
if len(self.reg_pts) == 4:
cv2.setMouseCallback(
"Ultralytics YOLOv8 Object Counter",
self.mouse_event_for_region,
{"region_points": self.reg_pts}
)
cv2.imshow("Ultralytics YOLOv8 Object Counter", 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
if tracks[0].boxes.id is None:
if self.view_img:
self.display_frames()
return im0
self.extract_and_process_tracks(tracks)
if self.view_img:
self.display_frames()
return self.im0
def incounts_updated(self):
if self.in_counts_prev < self.in_counts:
yield f"{self.in_counts}"
def outcounts_updated(self):
if self.out_counts_prev < self.out_counts:
yield f"{self.out_counts}"
if __name__ == "__main__":
ObjectCounter()