Package org.opencv.ximgproc
Class ScanSegment
java.lang.Object
org.opencv.core.Algorithm
org.opencv.ximgproc.ScanSegment
Class implementing the F-DBSCAN (Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm) superpixels
algorithm by Loke SC, et al. CITE: loke2021accelerated for original paper.
The algorithm uses a parallelised DBSCAN cluster search that is resistant to noise, competitive in segmentation quality, and faster than
existing superpixel segmentation methods. When tested on the Berkeley Segmentation Dataset, the average processing speed is 175 frames/s
with a Boundary Recall of 0.797 and an Achievable Segmentation Accuracy of 0.944. The computational complexity is quadratic O(n2) and
more suited to smaller images, but can still process a 2MP colour image faster than the SEEDS algorithm in OpenCV. The output is deterministic
when the number of processing threads is fixed, and requires the source image to be in Lab colour format.
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Field Summary
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic ScanSegment
__fromPtr__
(long addr) protected void
finalize()
void
getLabelContourMask
(Mat image) Returns the mask of the superpixel segmentation stored in the ScanSegment object.void
getLabelContourMask
(Mat image, boolean thick_line) Returns the mask of the superpixel segmentation stored in the ScanSegment object.void
Returns the segmentation labeling of the image.int
Returns the actual superpixel segmentation from the last image processed using iterate.void
Calculates the superpixel segmentation on a given image with the initialized parameters in the ScanSegment object.Methods inherited from class org.opencv.core.Algorithm
clear, empty, getDefaultName, getNativeObjAddr, save
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Constructor Details
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ScanSegment
protected ScanSegment(long addr)
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Method Details
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__fromPtr__
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getNumberOfSuperpixels
public int getNumberOfSuperpixels()Returns the actual superpixel segmentation from the last image processed using iterate. Returns zero if no image has been processed.- Returns:
- automatically generated
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iterate
Calculates the superpixel segmentation on a given image with the initialized parameters in the ScanSegment object. This function can be called again for other images without the need of initializing the algorithm with createScanSegment(). This save the computational cost of allocating memory for all the structures of the algorithm.- Parameters:
img
- Input image. Supported format: CV_8UC3. Image size must match with the initialized image size with the function createScanSegment(). It MUST be in Lab color space.
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getLabels
Returns the segmentation labeling of the image. Each label represents a superpixel, and each pixel is assigned to one superpixel label.- Parameters:
labels_out
- Return: A CV_32UC1 integer array containing the labels of the superpixel segmentation. The labels are in the range [0, getNumberOfSuperpixels()].
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getLabelContourMask
Returns the mask of the superpixel segmentation stored in the ScanSegment object. The function return the boundaries of the superpixel segmentation.- Parameters:
image
- Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.thick_line
- If false, the border is only one pixel wide, otherwise all pixels at the border are masked.
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getLabelContourMask
Returns the mask of the superpixel segmentation stored in the ScanSegment object. The function return the boundaries of the superpixel segmentation.- Parameters:
image
- Return: CV_8UC1 image mask where -1 indicates that the pixel is a superpixel border, and 0 otherwise.
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finalize
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