Class Features

java.lang.Object
org.opencv.features.Features
public class Features extends Object

  • Field Details

    • DrawMatchesFlags_DEFAULT

      public static final int DrawMatchesFlags_DEFAULT
      See Also:

    • DrawMatchesFlags_DRAW_OVER_OUTIMG

      public static final int DrawMatchesFlags_DRAW_OVER_OUTIMG
      See Also:

    • DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS

      public static final int DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS
      See Also:

    • DrawMatchesFlags_DRAW_RICH_KEYPOINTS

      public static final int DrawMatchesFlags_DRAW_RICH_KEYPOINTS
      See Also:

  • Constructor Details

    • Features

      public Features()

  • Method Details

    • goodFeaturesToTrack

      public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, boolean useHarrisDetector, double k)
      Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94
      • Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
      • Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
      • The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
      • The remaining corners are sorted by the quality measure in the descending order.
      • Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
      The function can be used to initialize a point-based tracker of an object. Note: If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
      blockSize - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
      useHarrisDetector - Parameter indicating whether to use a Harris detector (see #cornerHarris) or #cornerMinEigenVal.
      k - Free parameter of the Harris detector. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

    • goodFeaturesToTrack

      public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, boolean useHarrisDetector)
      Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94
      • Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
      • Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
      • The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
      • The remaining corners are sorted by the quality measure in the descending order.
      • Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
      The function can be used to initialize a point-based tracker of an object. Note: If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
      blockSize - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
      useHarrisDetector - Parameter indicating whether to use a Harris detector (see #cornerHarris) or #cornerMinEigenVal. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

    • goodFeaturesToTrack

      public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize)
      Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94
      • Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
      • Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
      • The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
      • The remaining corners are sorted by the quality measure in the descending order.
      • Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
      The function can be used to initialize a point-based tracker of an object. Note: If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
      blockSize - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs . or #cornerMinEigenVal. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

    • goodFeaturesToTrack

      public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask)
      Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94
      • Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
      • Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
      • The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
      • The remaining corners are sorted by the quality measure in the descending order.
      • Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
      The function can be used to initialize a point-based tracker of an object. Note: If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. pixel neighborhood. See cornerEigenValsAndVecs . or #cornerMinEigenVal. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

    • goodFeaturesToTrack

      public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance)
      Determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region, as described in CITE: Shi94
      • Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
      • Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are retained).
      • The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
      • The remaining corners are sorted by the quality measure in the descending order.
      • Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.
      The function can be used to initialize a point-based tracker of an object. Note: If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners. CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. pixel neighborhood. See cornerEigenValsAndVecs . or #cornerMinEigenVal. SEE: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

    • goodFeaturesToTrack

      public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, boolean useHarrisDetector, double k)

    • goodFeaturesToTrack

      public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, boolean useHarrisDetector)

    • goodFeaturesToTrack

      public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize)

    • goodFeaturesToTrackWithQuality

      public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, boolean useHarrisDetector, double k)
      Same as above, but returns also quality measure of the detected corners.
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
      cornersQuality - Output vector of quality measure of the detected corners.
      blockSize - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
      gradientSize - Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
      useHarrisDetector - Parameter indicating whether to use a Harris detector (see #cornerHarris) or #cornerMinEigenVal.
      k - Free parameter of the Harris detector.

    • goodFeaturesToTrackWithQuality

      public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize, boolean useHarrisDetector)
      Same as above, but returns also quality measure of the detected corners.
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
      cornersQuality - Output vector of quality measure of the detected corners.
      blockSize - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
      gradientSize - Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
      useHarrisDetector - Parameter indicating whether to use a Harris detector (see #cornerHarris) or #cornerMinEigenVal.

    • goodFeaturesToTrackWithQuality

      public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize, int gradientSize)
      Same as above, but returns also quality measure of the detected corners.
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
      cornersQuality - Output vector of quality measure of the detected corners.
      blockSize - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
      gradientSize - Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs . or #cornerMinEigenVal.

    • goodFeaturesToTrackWithQuality

      public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality, int blockSize)
      Same as above, but returns also quality measure of the detected corners.
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
      cornersQuality - Output vector of quality measure of the detected corners.
      blockSize - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs . See cornerEigenValsAndVecs . or #cornerMinEigenVal.

    • goodFeaturesToTrackWithQuality

      public static void goodFeaturesToTrackWithQuality(Mat image, Mat corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, Mat cornersQuality)
      Same as above, but returns also quality measure of the detected corners.
      Parameters:
      image - Input 8-bit or floating-point 32-bit, single-channel image.
      corners - Output vector of detected corners.
      maxCorners - Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
      qualityLevel - Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
      minDistance - Minimum possible Euclidean distance between the returned corners.
      mask - Region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
      cornersQuality - Output vector of quality measure of the detected corners. pixel neighborhood. See cornerEigenValsAndVecs . See cornerEigenValsAndVecs . or #cornerMinEigenVal.

    • drawKeypoints

      public static void drawKeypoints(Mat image, MatOfKeyPoint keypoints, Mat outImage, Scalar color, int flags)
      Draws keypoints.
      Parameters:
      image - Source image.
      keypoints - Keypoints from the source image.
      outImage - Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below.
      color - Color of keypoints.
      flags - Flags setting drawing features. Possible flags bit values are defined by DrawMatchesFlags. See details above in drawMatches . Note: For Python API, flags are modified as cv.DRAW_MATCHES_FLAGS_DEFAULT, cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, cv.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS

    • drawKeypoints

      public static void drawKeypoints(Mat image, MatOfKeyPoint keypoints, Mat outImage, Scalar color)
      Draws keypoints.
      Parameters:
      image - Source image.
      keypoints - Keypoints from the source image.
      outImage - Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below.
      color - Color of keypoints. DrawMatchesFlags. See details above in drawMatches . Note: For Python API, flags are modified as cv.DRAW_MATCHES_FLAGS_DEFAULT, cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, cv.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS

    • drawKeypoints

      public static void drawKeypoints(Mat image, MatOfKeyPoint keypoints, Mat outImage)
      Draws keypoints.
      Parameters:
      image - Source image.
      keypoints - Keypoints from the source image.
      outImage - Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below. DrawMatchesFlags. See details above in drawMatches . Note: For Python API, flags are modified as cv.DRAW_MATCHES_FLAGS_DEFAULT, cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, cv.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, MatOfByte matchesMask, int flags)
      Draws the found matches of keypoints from two images.
      Parameters:
      img1 - First source image.
      keypoints1 - Keypoints from the first source image.
      img2 - Second source image.
      keypoints2 - Keypoints from the second source image.
      matches1to2 - Matches from the first image to the second one, which means that keypoints1[i] has a corresponding point in keypoints2[matches[i]] .
      outImg - Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below.
      matchColor - Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) , the color is generated randomly.
      singlePointColor - Color of single keypoints (circles), which means that keypoints do not have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
      matchesMask - Mask determining which matches are drawn. If the mask is empty, all matches are drawn.
      flags - Flags setting drawing features. Possible flags bit values are defined by DrawMatchesFlags. This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). See cv::DrawMatchesFlags.

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, MatOfByte matchesMask)
      Draws the found matches of keypoints from two images.
      Parameters:
      img1 - First source image.
      keypoints1 - Keypoints from the first source image.
      img2 - Second source image.
      keypoints2 - Keypoints from the second source image.
      matches1to2 - Matches from the first image to the second one, which means that keypoints1[i] has a corresponding point in keypoints2[matches[i]] .
      outImg - Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below.
      matchColor - Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) , the color is generated randomly.
      singlePointColor - Color of single keypoints (circles), which means that keypoints do not have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
      matchesMask - Mask determining which matches are drawn. If the mask is empty, all matches are drawn. DrawMatchesFlags. This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). See cv::DrawMatchesFlags.

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor)
      Draws the found matches of keypoints from two images.
      Parameters:
      img1 - First source image.
      keypoints1 - Keypoints from the first source image.
      img2 - Second source image.
      keypoints2 - Keypoints from the second source image.
      matches1to2 - Matches from the first image to the second one, which means that keypoints1[i] has a corresponding point in keypoints2[matches[i]] .
      outImg - Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below.
      matchColor - Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) , the color is generated randomly.
      singlePointColor - Color of single keypoints (circles), which means that keypoints do not have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. drawn. DrawMatchesFlags. This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). See cv::DrawMatchesFlags.

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, Scalar matchColor)
      Draws the found matches of keypoints from two images.
      Parameters:
      img1 - First source image.
      keypoints1 - Keypoints from the first source image.
      img2 - Second source image.
      keypoints2 - Keypoints from the second source image.
      matches1to2 - Matches from the first image to the second one, which means that keypoints1[i] has a corresponding point in keypoints2[matches[i]] .
      outImg - Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below.
      matchColor - Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) , the color is generated randomly. have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. drawn. DrawMatchesFlags. This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). See cv::DrawMatchesFlags.

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg)
      Draws the found matches of keypoints from two images.
      Parameters:
      img1 - First source image.
      keypoints1 - Keypoints from the first source image.
      img2 - Second source image.
      keypoints2 - Keypoints from the second source image.
      matches1to2 - Matches from the first image to the second one, which means that keypoints1[i] has a corresponding point in keypoints2[matches[i]] .
      outImg - Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below. , the color is generated randomly. have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly. drawn. DrawMatchesFlags. This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). See cv::DrawMatchesFlags.

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, int matchesThickness, Scalar matchColor, Scalar singlePointColor, MatOfByte matchesMask, int flags)

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, int matchesThickness, Scalar matchColor, Scalar singlePointColor, MatOfByte matchesMask)

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, int matchesThickness, Scalar matchColor, Scalar singlePointColor)

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, int matchesThickness, Scalar matchColor)

    • drawMatches

      public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, int matchesThickness)

    • drawMatchesKnn

      public static void drawMatchesKnn(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, List<MatOfByte> matchesMask, int flags)

    • drawMatchesKnn

      public static void drawMatchesKnn(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, List<MatOfByte> matchesMask)

    • drawMatchesKnn

      public static void drawMatchesKnn(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor)

    • drawMatchesKnn

      public static void drawMatchesKnn(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg, Scalar matchColor)

    • drawMatchesKnn

      public static void drawMatchesKnn(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg)