给非吸公司建设网站广告资源发布平台

TrackZone 使用Ultralytics YOLO11 -Ultralytics YOLO 文档

如何通过Ultralytics YOLO11 在Python 中使用 TrackZone？

只需几行代码，您就可以在特定区域设置对象跟踪，从而轻松将其集成到您的项目中。

import cv2from ultralytics import solutionscap = cv2.VideoCapture("path/to/video.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))# Define region points
region_points = [(150, 150), (1130, 150), (1130, 570), (150, 570)]# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))# Init trackzone (object tracking in zones, not complete frame)
trackzone = solutions.TrackZone(show=True,  # display the outputregion=region_points,  # pass region pointsmodel="yolo11n.pt",
)# Process video
while cap.isOpened():success, im0 = cap.read()if not success:print("Video frame is empty or video processing has been successfully completed.")breakresults = trackzone(im0)video_writer.write(results.plot_im)cap.release()
video_writer.release()
cv2.destroyAllWindows()

效果图：只检测一定范围内的人

部分相关库代码

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseimport cv2
import numpy as npfrom ultralytics.solutions.solutions import BaseSolution, SolutionAnnotator, SolutionResults
from ultralytics.utils.plotting import colorsclass 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 regiondefined by a polygonal area. Objects outside the region are excluded from tracking.Attributes:region (np.ndarray): The polygonal region for tracking, represented as a convex hull of points.line_width (int): Width of the lines used for drawing bounding boxes and region boundaries.names (List[str]): List of class names that the model can detect.boxes (List[np.ndarray]): Bounding boxes of tracked objects.track_ids (List[int]): Unique identifiers for each tracked object.clss (List[int]): Class indices of tracked objects.Methods:process: Processes each frame of the video, applying region-based tracking.extract_tracks: Extracts tracking information from the input frame.display_output: Displays the processed output.Examples:>>> tracker = TrackZone()>>> frame = cv2.imread("frame.jpg")>>> results = tracker.process(frame)>>> cv2.imshow("Tracked Frame", results.plot_im)"""def __init__(self, **kwargs):"""Initialize the TrackZone class for tracking objects within a defined region in video streams.Args:**kwargs (Any): Additional keyword arguments passed to the parent class."""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 process(self, im0):"""Process the input frame to track objects within a defined region.This method initializes the annotator, creates a mask for the specified region, extracts tracksonly from the masked area, and updates tracking information. Objects outside the region are ignored.Args:im0 (np.ndarray): The input image or frame to be processed.Returns:(SolutionResults): Contains processed image `plot_im` and `total_tracks` (int) representing thetotal number of tracked objects within the defined region.Examples:>>> tracker = TrackZone()>>> frame = cv2.imread("path/to/image.jpg")>>> results = tracker.process(frame)"""annotator = SolutionAnnotator(im0, line_width=self.line_width)  # Initialize annotator# Create a mask for the region and extract tracks from the masked imagemask = np.zeros_like(im0[:, :, 0])mask = cv2.fillPoly(mask, [self.region], 255)masked_frame = cv2.bitwise_and(im0, im0, mask=mask)self.extract_tracks(masked_frame)# Draw the region boundarycv2.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 boxesfor box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):annotator.box_label(box, label=f"{self.names[cls]}:{track_id}", color=colors(track_id, True))plot_im = annotator.result()self.display_output(plot_im)  # display output with base class function# Return a SolutionResultsreturn SolutionResults(plot_im=plot_im, total_tracks=len(self.track_ids))