Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
89 lines
3.8 KiB
Python
89 lines
3.8 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from typing import Any
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import cv2
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import numpy as np
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from ultralytics.solutions.solutions import BaseSolution, SolutionAnnotator, SolutionResults
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from ultralytics.utils.plotting import colors
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class TrackZone(BaseSolution):
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"""A class to manage region-based object tracking in a video stream.
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This class extends the BaseSolution class and provides functionality for tracking objects within a specific region
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defined by a polygonal area. Objects outside the region are excluded from tracking.
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Attributes:
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region (np.ndarray): The polygonal region for tracking, represented as a convex hull of points.
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line_width (int): Width of the lines used for drawing bounding boxes and region boundaries.
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names (list[str]): List of class names that the model can detect.
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boxes (list[np.ndarray]): Bounding boxes of tracked objects.
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track_ids (list[int]): Unique identifiers for each tracked object.
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clss (list[int]): Class indices of tracked objects.
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Methods:
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process: Process each frame of the video, applying region-based tracking.
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extract_tracks: Extract tracking information from the input frame.
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display_output: Display the processed output.
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Examples:
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>>> tracker = TrackZone()
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>>> frame = cv2.imread("frame.jpg")
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>>> results = tracker.process(frame)
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>>> cv2.imshow("Tracked Frame", results.plot_im)
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"""
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def __init__(self, **kwargs: Any) -> None:
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"""Initialize the TrackZone class for tracking objects within a defined region in video streams.
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Args:
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**kwargs (Any): Additional keyword arguments passed to the parent class.
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"""
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super().__init__(**kwargs)
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default_region = [(75, 75), (565, 75), (565, 285), (75, 285)]
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self.region = cv2.convexHull(np.array(self.region or default_region, dtype=np.int32))
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self.mask = None
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def process(self, im0: np.ndarray) -> SolutionResults:
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"""Process the input frame to track objects within a defined region.
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This method initializes the annotator, creates a mask for the specified region, extracts tracks only from the
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masked area, and updates tracking information. Objects outside the region are ignored.
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Args:
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im0 (np.ndarray): The input image or frame to be processed.
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Returns:
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(SolutionResults): Contains processed image `plot_im` and `total_tracks` (int) representing the total number
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of tracked objects within the defined region.
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Examples:
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>>> tracker = TrackZone()
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>>> frame = cv2.imread("path/to/image.jpg")
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>>> results = tracker.process(frame)
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"""
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annotator = SolutionAnnotator(im0, line_width=self.line_width) # Initialize annotator
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if self.mask is None: # Create a mask for the region
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self.mask = np.zeros_like(im0[:, :, 0])
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cv2.fillPoly(self.mask, [self.region], 255)
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masked_frame = cv2.bitwise_and(im0, im0, mask=self.mask)
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self.extract_tracks(masked_frame)
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# Draw the region boundary
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cv2.polylines(im0, [self.region], isClosed=True, color=(255, 255, 255), thickness=self.line_width * 2)
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# Iterate over boxes, track ids, classes indexes list and draw bounding boxes
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for box, track_id, cls, conf in zip(self.boxes, self.track_ids, self.clss, self.confs):
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annotator.box_label(
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box, label=self.adjust_box_label(cls, conf, track_id=track_id), color=colors(track_id, True)
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)
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plot_im = annotator.result()
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self.display_output(plot_im) # Display output with base class function
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# Return a SolutionResults
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return SolutionResults(plot_im=plot_im, total_tracks=len(self.track_ids))
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