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Reference for ultralytics/solutions/object_blurrer.py

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ultralytics.solutions.object_blurrer.ObjectBlurrer 

ObjectBlurrer(**kwargs)

Bases: BaseSolution

A class to manage the blurring of detected objects in a real-time video stream.

This class extends the BaseSolution class and provides functionality for blurring objects based on detected bounding boxes. The blurred areas are updated directly in the input image, allowing for privacy preservation or other effects.

Attributes:

Name Type Description
blur_ratio int

The intensity of the blur effect applied to detected objects (higher values create more blur).
iou float

Intersection over Union threshold for object detection.
conf float

Confidence threshold for object detection.

Methods:

Name Description
process

Apply a blurring effect to detected objects in the input image.
extract_tracks

Extract tracking information from detected objects.
display_output

Display the processed output image.

Examples:

>>> blurrer = ObjectBlurrer()
>>> frame = cv2.imread("frame.jpg")
>>> processed_results = blurrer.process(frame)
>>> print(f"Total blurred objects: {processed_results.total_tracks}")

Parameters:

Name Type Description Default
**kwargs Any

Keyword arguments passed to the parent class and for configuration. blur_ratio (float): Intensity of the blur effect (0.1-1.0, default=0.5).

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Source code in ultralytics/solutions/object_blurrer.py 
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def __init__(self, **kwargs):
    """
    Initialize the ObjectBlurrer class for applying a blur effect to objects detected in video streams or images.

    Args:
        **kwargs (Any): Keyword arguments passed to the parent class and for configuration.
            blur_ratio (float): Intensity of the blur effect (0.1-1.0, default=0.5).
    """
    super().__init__(**kwargs)
    blur_ratio = self.CFG["blur_ratio"]
    if blur_ratio < 0.1:
        LOGGER.warning("blur ratio cannot be less than 0.1, updating it to default value 0.5")
        blur_ratio = 0.5
    self.blur_ratio = int(blur_ratio * 100)

process 

process(im0)

Apply a blurring effect to detected objects in the input image.

This method extracts tracking information, applies blur to regions corresponding to detected objects, and annotates the image with bounding boxes.

Parameters:

Name Type Description Default
im0 ndarray

The input image containing detected objects.

 required

Returns:

Type Description
SolutionResults

Object containing the processed image and number of tracked objects. - plot_im (numpy.ndarray): The annotated output image with blurred objects. - total_tracks (int): The total number of tracked objects in the frame.

Examples:

>>> blurrer = ObjectBlurrer()
>>> frame = cv2.imread("image.jpg")
>>> results = blurrer.process(frame)
>>> print(f"Blurred {results.total_tracks} objects")
Source code in ultralytics/solutions/object_blurrer.py 
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def process(self, im0):
    """
    Apply a blurring effect to detected objects in the input image.

    This method extracts tracking information, applies blur to regions corresponding to detected objects,
    and annotates the image with bounding boxes.

    Args:
        im0 (numpy.ndarray): The input image containing detected objects.

    Returns:
        (SolutionResults): Object containing the processed image and number of tracked objects.
            - plot_im (numpy.ndarray): The annotated output image with blurred objects.
            - total_tracks (int): The total number of tracked objects in the frame.

    Examples:
        >>> blurrer = ObjectBlurrer()
        >>> frame = cv2.imread("image.jpg")
        >>> results = blurrer.process(frame)
        >>> print(f"Blurred {results.total_tracks} objects")
    """
    self.extract_tracks(im0)  # Extract tracks
    annotator = SolutionAnnotator(im0, self.line_width)

    # Iterate over bounding boxes and classes
    for box, cls, conf in zip(self.boxes, self.clss, self.confs):
        # Crop and blur the detected object
        blur_obj = cv2.blur(
            im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])],
            (self.blur_ratio, self.blur_ratio),
        )
        # Update the blurred area in the original image
        im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] = blur_obj
        annotator.box_label(
            box, label=self.adjust_box_label(cls, conf), color=colors(cls, True)
        )  # Annotate bounding box

    plot_im = annotator.result()
    self.display_output(plot_im)  # Display the output using the base class function

    # Return a SolutionResults
    return SolutionResults(plot_im=plot_im, total_tracks=len(self.track_ids))





📅 Created 3 months ago ✏️ Updated 3 months ago
RizwanMunawar