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)
90 lines
3.9 KiB
Python
90 lines
3.9 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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from ultralytics.solutions.solutions import BaseSolution, SolutionAnnotator, SolutionResults
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from ultralytics.utils import LOGGER
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from ultralytics.utils.plotting import colors
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class ObjectBlurrer(BaseSolution):
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"""A class to manage the blurring of detected objects in a real-time video stream.
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This class extends the BaseSolution class and provides functionality for blurring objects based on detected bounding
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boxes. The blurred areas are updated directly in the input image, allowing for privacy preservation or other effects.
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Attributes:
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blur_ratio (int): The intensity of the blur effect applied to detected objects (higher values create more blur).
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iou (float): Intersection over Union threshold for object detection.
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conf (float): Confidence threshold for object detection.
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Methods:
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process: Apply a blurring effect to detected objects in the input image.
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extract_tracks: Extract tracking information from detected objects.
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display_output: Display the processed output image.
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Examples:
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>>> blurrer = ObjectBlurrer()
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>>> frame = cv2.imread("frame.jpg")
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>>> processed_results = blurrer.process(frame)
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>>> print(f"Total blurred objects: {processed_results.total_tracks}")
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"""
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def __init__(self, **kwargs: Any) -> None:
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"""Initialize the ObjectBlurrer class for applying a blur effect to objects detected in video streams or images.
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Args:
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**kwargs (Any): Keyword arguments passed to the parent class and for configuration including:
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- blur_ratio (float): Intensity of the blur effect (0.1-1.0, default=0.5).
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"""
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super().__init__(**kwargs)
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blur_ratio = self.CFG["blur_ratio"]
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if blur_ratio < 0.1:
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LOGGER.warning("blur ratio cannot be less than 0.1, updating it to default value 0.5")
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blur_ratio = 0.5
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self.blur_ratio = int(blur_ratio * 100)
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def process(self, im0) -> SolutionResults:
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"""Apply a blurring effect to detected objects in the input image.
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This method extracts tracking information, applies blur to regions corresponding to detected objects, and
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annotates the image with bounding boxes.
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Args:
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im0 (np.ndarray): The input image containing detected objects.
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Returns:
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(SolutionResults): Object containing the processed image and number of tracked objects.
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- plot_im (np.ndarray): The annotated output image with blurred objects.
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- total_tracks (int): The total number of tracked objects in the frame.
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Examples:
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>>> blurrer = ObjectBlurrer()
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>>> frame = cv2.imread("image.jpg")
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>>> results = blurrer.process(frame)
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>>> print(f"Blurred {results.total_tracks} objects")
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"""
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self.extract_tracks(im0) # Extract tracks
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annotator = SolutionAnnotator(im0, self.line_width)
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# Iterate over bounding boxes and classes
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for box, cls, conf in zip(self.boxes, self.clss, self.confs):
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# Crop and blur the detected object
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blur_obj = cv2.blur(
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im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])],
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(self.blur_ratio, self.blur_ratio),
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)
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# Update the blurred area in the original image
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im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] = blur_obj
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annotator.box_label(
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box, label=self.adjust_box_label(cls, conf), color=colors(cls, True)
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) # Annotate bounding box
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plot_im = annotator.result()
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self.display_output(plot_im) # Display the output using the 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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