YOLOv11区域检测

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 cv2

from ultralytics import solutions

cap = 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 output
    region=region_points,  # pass region points
    model="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.")
        break
    results = 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/license

import cv2
import numpy as np

from ultralytics.solutions.solutions import BaseSolution, SolutionAnnotator, SolutionResults
from ultralytics.utils.plotting import 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.

    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 tracks
        only 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 the
                               total 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 image
        mask = 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 boundary
        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):
            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 SolutionResults
        return SolutionResults(plot_im=plot_im, total_tracks=len(self.track_ids))