深度图 - Android 中的立体图像与 OpenCV
Depth map - stereo image in Android with OpenCV
请问,如何使用左右相机和JAVACV/OPENCV库中的两张图像创建深度图(视差图)?
我在 Android 中使用 OpenCV(+JavaCV 包装器)。我在 Internet 上找到了这些代码 - 这是正确的代码吗?如何校准相机?
private Mat createDisparityMap(Mat rectLeft, Mat rectRight){
// Converts the images to a proper type for stereoMatching
Mat left = new Mat();
Mat right = new Mat();
Imgproc.cvtColor(rectLeft, left, Imgproc.COLOR_BGR2GRAY);
Imgproc.cvtColor(rectRight, right, Imgproc.COLOR_BGR2GRAY);
// Create a new image using the size and type of the left image
Mat disparity = new Mat(left.size(), left.type());
int numDisparity = (int)(left.size().width/8);
opencv_calib3d.StereoSGBM stereoAlgo = new opencv_calib3d.StereoSGBM(
0, // min DIsparities
numDisparity, // numDisparities
11, // SADWindowSize
2*11*11, // 8*number_of_image_channels*SADWindowSize*SADWindowSize // p1
5*11*11, // 8*number_of_image_channels*SADWindowSize*SADWindowSize // p2
-1, // disp12MaxDiff
63, // prefilterCap
10, // uniqueness ratio
0, // sreckleWindowSize
32, // spreckle Range
false); // full DP
// create the DisparityMap - SLOW: O(Width*height*numDisparity)
stereoAlgo.compute(left, right, disparity);
Core.normalize(disparity, disparity, 0, 256, Core.NORM_MINMAX);
return disparity;
}
试试这个(与 android OpenCV SDK 更兼容):
private Mat createDisparityMap(Mat rectLeft, Mat rectRight){
// Converts the images to a proper type for stereoMatching
Mat left = new Mat();
Mat right = new Mat();
Imgproc.cvtColor(rectLeft, left, Imgproc.COLOR_BGR2GRAY);
Imgproc.cvtColor(rectRight, right, Imgproc.COLOR_BGR2GRAY);
// Create a new image using the size and type of the left image
Mat disparity = new Mat(left.size(), left.type());
int numDisparity = (int)(left.size().width/8);
StereoSGBM stereoAlgo = StereoSGBM.create(
0, // min DIsparities
numDisparity, // numDisparities
11, // SADWindowSize
2*11*11, // 8*number_of_image_channels*SADWindowSize*SADWindowSize // p1
5*11*11, // 8*number_of_image_channels*SADWindowSize*SADWindowSize // p2
-1, // disp12MaxDiff
63, // prefilterCap
10, // uniqueness ratio
0, // sreckleWindowSize
32, // spreckle Range
0); // full DP
// create the DisparityMap - SLOW: O(Width*height*numDisparity)
stereoAlgo.compute(left, right, disparity);
Core.normalize(disparity, disparity, 0, 256, Core.NORM_MINMAX);
return disparity;
}
请问,如何使用左右相机和JAVACV/OPENCV库中的两张图像创建深度图(视差图)?
我在 Android 中使用 OpenCV(+JavaCV 包装器)。我在 Internet 上找到了这些代码 - 这是正确的代码吗?如何校准相机?
private Mat createDisparityMap(Mat rectLeft, Mat rectRight){
// Converts the images to a proper type for stereoMatching
Mat left = new Mat();
Mat right = new Mat();
Imgproc.cvtColor(rectLeft, left, Imgproc.COLOR_BGR2GRAY);
Imgproc.cvtColor(rectRight, right, Imgproc.COLOR_BGR2GRAY);
// Create a new image using the size and type of the left image
Mat disparity = new Mat(left.size(), left.type());
int numDisparity = (int)(left.size().width/8);
opencv_calib3d.StereoSGBM stereoAlgo = new opencv_calib3d.StereoSGBM(
0, // min DIsparities
numDisparity, // numDisparities
11, // SADWindowSize
2*11*11, // 8*number_of_image_channels*SADWindowSize*SADWindowSize // p1
5*11*11, // 8*number_of_image_channels*SADWindowSize*SADWindowSize // p2
-1, // disp12MaxDiff
63, // prefilterCap
10, // uniqueness ratio
0, // sreckleWindowSize
32, // spreckle Range
false); // full DP
// create the DisparityMap - SLOW: O(Width*height*numDisparity)
stereoAlgo.compute(left, right, disparity);
Core.normalize(disparity, disparity, 0, 256, Core.NORM_MINMAX);
return disparity;
}
试试这个(与 android OpenCV SDK 更兼容):
private Mat createDisparityMap(Mat rectLeft, Mat rectRight){
// Converts the images to a proper type for stereoMatching
Mat left = new Mat();
Mat right = new Mat();
Imgproc.cvtColor(rectLeft, left, Imgproc.COLOR_BGR2GRAY);
Imgproc.cvtColor(rectRight, right, Imgproc.COLOR_BGR2GRAY);
// Create a new image using the size and type of the left image
Mat disparity = new Mat(left.size(), left.type());
int numDisparity = (int)(left.size().width/8);
StereoSGBM stereoAlgo = StereoSGBM.create(
0, // min DIsparities
numDisparity, // numDisparities
11, // SADWindowSize
2*11*11, // 8*number_of_image_channels*SADWindowSize*SADWindowSize // p1
5*11*11, // 8*number_of_image_channels*SADWindowSize*SADWindowSize // p2
-1, // disp12MaxDiff
63, // prefilterCap
10, // uniqueness ratio
0, // sreckleWindowSize
32, // spreckle Range
0); // full DP
// create the DisparityMap - SLOW: O(Width*height*numDisparity)
stereoAlgo.compute(left, right, disparity);
Core.normalize(disparity, disparity, 0, 256, Core.NORM_MINMAX);
return disparity;
}