Descargar Label Matrix 8.7 Crackl
Margart Kalvig
The regionprops function measures properties such as area, centroid, and bounding box, for each object (connected component) in an image. regionprops supports both contiguous regions and discontiguous regions.
regionprops finds unique objects in binary images using 8-connected neighborhoods for 2-D images and maximal connectivity for higher dimension images. For more information, see Pixel Connectivity. To find objects using other types of connectivity, use bwconncomp to create the connected components, and then pass the result to regionprops using the CC argument instead.
stats = regionprops(regions,I,properties) also measures pixel value properties for each labeled region in the grayscale image I. The first input argument, regions, must be a valid value for the BW, CC, or L argument.
regionprops sorts the objects in the binary image from left to right based on the top-left extremum of each component. When multiple objects have the same horizontal position, the function then sorts those objects from top to bottom, and again along any higher dimensions. regionprops returns the measured properties, stats, in the same order as the sorted objects.
A numeric array of any dimension. Pixels labeled 0 are the background. Pixels labeled 1 make up one object; pixels labeled 2 make up a second object; and so on. regionprops treats negative-valued pixels as background and rounds down input pixels that are not integers. You can get a numeric label image from labeling functions such as watershed or labelmatrix.
Position and size of the smallest box containing the region, returned as a 1-by-(2*Q) vector, where Q is the image dimensionality. The first Q elements are the coordinates of the minimum corner of the box. The second Q elements are the size of the box along each dimension. For example, a 2-D bounding box with value [5.5 8.5 11 14] indicates that the (x,y) coordinate of the top-left corner of the box is (5.5, 8.5), the horizontal width of the box is 11 pixels, and the vertical height of the box is 14 pixels.
Center of mass of the region, returned as a 1-by-Q vector, where Q is the image dimensionality. The first element of Centroid is the horizontal coordinate (or x-coordinate) of the center of mass. The second element is the vertical coordinate (or y-coordinate). All other elements of Centroid are in order of dimension.
Extrema points in the region, returned as an 8-by-2 matrix. Each row of the matrix contains the x- and y-coordinates of one of the points. The format of the vector is [top-left top-right right-top right-bottom bottom-right bottom-left left-bottom left-top]. For some shapes, multiple extrema points can have identical coordinates.
This figure illustrates the extrema of two different regions. In the region on the left, each extrema point is distinct. For the region on the right, certain extrema points (such as top-left and left-top) are identical.
Angle between the x-axis and the major axis of the ellipse that has the same second-moments as the region, returned as a scalar. The value is in degrees, ranging from -90 degrees to 90 degrees. This figure illustrates the axes and orientation of the ellipse. The left side of the figure shows an image region and its corresponding ellipse. The right side shows the same ellipse with the solid blue lines representing the axes. The red dots are the foci. The orientation is the angle between the horizontal dotted line and the major axis.
Distance around the boundary of the region returned as a scalar. regionprops computes the perimeter by calculating the distance between each adjoining pair of pixels around the border of the region. If the image contains discontiguous regions, regionprops returns unexpected results. This figure illustrates the pixels included in the perimeter calculation for this object.
Measurement values, returned as an array of structures or a table. The number of structures in the array or the number of rows in the table is equal to the number of objects in BW, CC.NumObjects, or max(L(:)). The fields of each structure or the variables in each row denote the properties calculated for each region, as specified by properties. If the input image is a categorical label image L, then stats includes an additional field or variable with the property "LabelName".
1 1 0 1 1 0 2 21 1 0 1 1 0 2 2Elements of L equal to 1 belong to the first region, which is discontiguous and contains two connected components. Elements of L equal to 2 belong to the second region, which is a single connected component.
Starting in R2022a, when a table output format is specified, the regionprops function stores the Image, ConvexImage, and FilledImage property values as cell arrays, regardless of the size of the image objects. In previous releases, if the size of the bounding box of an object was 1-by-1 or 1-by-n, these properties were stored in the output table as a numeric scalar or row vector.
To update your code, access the value of the Image, ConvexImage, and FilledImage properties by using dot notation with curly braces, . For example, use the below code to access the Image property for the first object in the input image BW. In previous releases, curly braces were not required to access values stored as a numeric scalar or row vector.
regionprops now measures the circularity and Feret properties of regions in a binary image. To measure the circularity, minimum Feret properties, or maximum Feret properties, include "Circularity", "MinFeretProperties", or "MaxFeretProperties", respectively, when specifying the properties argument.
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