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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import cv2
import numpy as np
from ultralytics.solutions.solutions import BaseSolution
from ultralytics.utils.plotting import Annotator, colors
class TrackZone(BaseSolution):
"""
A class to manage region-based object tracking in a video stream.
This class extends the BaseSolution class and provides functionality for tracking objects within a specific region
defined by a polygonal area. Objects outside the region are excluded from tracking. It supports dynamic initialization
of the region, allowing either a default region or a user-specified polygon.
Attributes:
region (ndarray): The polygonal region for tracking, represented as a convex hull.
Methods:
trackzone: Processes each frame of the video, applying region-based tracking.
Examples:
>>> tracker = TrackZone()
>>> frame = cv2.imread("frame.jpg")
>>> processed_frame = tracker.trackzone(frame)
>>> cv2.imshow("Tracked Frame", processed_frame)
"""
def __init__(self, **kwargs):
"""Initializes the TrackZone class for tracking objects within a defined region in video streams."""
super().__init__(**kwargs)
default_region = [(150, 150), (1130, 150), (1130, 570), (150, 570)]
self.region = cv2.convexHull(np.array(self.region or default_region, dtype=np.int32))
def trackzone(self, im0):
"""
Processes the input frame to track objects within a defined region.
This method initializes the annotator, creates a mask for the specified region, extracts tracks
only from the masked area, and updates tracking information. Objects outside the region are ignored.
Args:
im0 (numpy.ndarray): The input image or frame to be processed.
Returns:
(numpy.ndarray): The processed image with tracking id and bounding boxes annotations.
Examples:
>>> tracker = TrackZone()
>>> frame = cv2.imread("path/to/image.jpg")
>>> tracker.trackzone(frame)
"""
self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator
# Create a mask for the region and extract tracks from the masked image
masked_frame = cv2.bitwise_and(im0, im0, mask=cv2.fillPoly(np.zeros_like(im0[:, :, 0]), [self.region], 255))
self.extract_tracks(masked_frame)
cv2.polylines(im0, [self.region], isClosed=True, color=(255, 255, 255), thickness=self.line_width * 2)
# Iterate over boxes, track ids, classes indexes list and draw bounding boxes
for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
self.annotator.box_label(box, label=f"{self.names[cls]}:{track_id}", color=colors(track_id, True))
self.display_output(im0) # display output with base class function
return im0 # return output image for more usage
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