Libfreenect wrong depth map - opencv

I've been using OpenNI+PrimeSense+NiTE with OpenCV on my project to segment objects according to their distances. However I meant to deploy it in a NVIDIA Jetson TX1 board and it couldn't manage to compile OpenNI+PrimeSense+NiTE with OpenCV on it.
I endded up with libfreenect. However the depth map provided by libfreenect is very, very wrong. I'll share some examples.
Here is the working depth map of OpenNI:
OpenNI Depth Map
The libfreenect wrong depth map is here: Libfreenect Depth Map
I based my libfreenect code on the default C++ wrapper at OpenKinect website.
Can someone help me here? Thank you so much.

Well, for those who are working with libfreenect on ARM or AARCH64 architectures (mainly Jetson TX1) because OpenNI and SensorKinect are problematic to build, I made some adjustments on OpenNI and SensorKinect sources to run with Aarch64 and avoid having to use libfreenect.
Links: OpenNI for TX1 and SensorKinect for TX1

It looks like different mappings of the depth data.
You can try to put the libfreenect data into a cv::Mat and scale that:
const float scaleFactor = 0.05f;
depth.convertTo(depthMat8UC1, CV_8UC1, scaleFactor);
imshow("depth gray",depthMat8UC1);
You can also checkout this article and on building OpenNI2 on a Jetson TK1.
Once you have OpenNI setup and working, you should be able to compile OpenCV from source enabling WITH_OPENNI with cmake. After that you should be able to grab the depth data directly in OpenCV:
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
const float scaleFactor = 0.05f;
int main(){
cout << "opening device(s)" << endl;
VideoCapture sensor;
sensor.open(CV_CAP_OPENNI);
if( !sensor.isOpened() ){
cout << "Can not open capture object 1." << endl;
return -1;
}
for(;;){
Mat depth,depthScaled;
if( !sensor.grab() ){
cout << "Sensor1 can not grab images." << endl;
return -1;
}else if( sensor.retrieve( depth, CV_CAP_OPENNI_DEPTH_MAP ) ) {
depth.convertTo(depthScaled, CV_8UC1, scaleFactor);
imshow("depth",depth);
imshow("depth scaled",depthScaled);
}
if( waitKey( 30 ) == 27 ) break;
}
}

Related

<< operator is crashing program with exit code 0 at some random step of printing

I am working with opencv 3.4 and CLion 2017.3. I have built opencv with mingw and cmake, and i can use the library in my code without problem. Howewher, when i try to run this test code in main.cpp that just prints the matrix, I get a crash at some step of execution of << operator:
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
using namespace cv;
using namespace std;
Mat img(7,6, CV_8UC3, Scalar(126,0,255));
cout << img << endl << endl << endl;
cout << "end!";
If i run the code more than once i get these results:
If i add dimentions up to some big number, the code crashes on some ~30 line or earlier
EDIT: Apparently, the problem is not related to opencv, i get this output when printing numbers from 1 to 1000 in a loop:
After googling anything console-related about CLion i found other corrupted console output problems.
The proposed solution to them was to tweak the run.processes.with.pty setting in the idea.properties file. see this YouTrack answer for details.
That solved my issue, too.

gpu::cvtColor(input_gpu, output_gpu, CV_BGR2GRAy)

I am trying to learn how to use the GPU programs in OpenCV. I have built everything with CUDA and if I run
cout << " Number of devices " << cv::gpu::getCudaEnabledDeviceCount() << endl;
I get the answer 1 device so at least something seems to work. However, I try the following peace of code, it just prints out the message and then nothing happens. It gets stuck on
cv::gpu::cvtColor(input_gpu, output_gpu, CV_BGR2GRAY);
Here is the code
#include<iostream>
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/gpu/gpu.hpp>
#include <opencv2/opencv.hpp>
using std::cout;
using std::endl;
int main(void){
cv::Mat input = cv::imread("image.jpg");
if (input.empty()){
cout << "Image Not Found" << endl;
return -1;
}
cv::Mat output;
// Declare the input and output GpuMat
cv::gpu::GpuMat input_gpu;
cv::gpu::GpuMat output_gpu;
cout << "Number of devices: " << cv::gpu::getCudaEnabledDeviceCount() << endl;
// Copy the input cv::Mat to device.
// Device memory will be allocated automatically according to the parameters of input image
input_gpu.upload(input);
// Convert the input image to grayScale on GPU
cv::gpu::cvtColor(input_gpu, output_gpu, CV_BGR2GRAY);
//// Copy the result from GPU back to host
output_gpu.download(output);
cv::imshow("Input", input);
cv::imshow("Output", output);
cv::waitKey(0);
return 0;
}
I just found this issue and it seems to be a problem with the Maxwell architecture, but that post is over a year old. Has anybody else experienced the same problem? I am using windows 7, Visual Studio 2013 and an Nvidia Geforce GTX 770.
/ Erik
Ok, I do not really know what the problem was, but in CMake, I changed the CUDA_GENERATION to Kepler, which is the micro architecture of my GPU, then I recompiled it, and now the code works as it should.
Interestingly, there were only Fermi and Kepler to choose, so I do not know if one will get problem with Maxwell.
/ Erik

Using OpenCV to recognise similar (not completely identical) simple images?

Say I have a very simple image or shape such as this stick man drawing:
I also have a library of other simple images which I want to compare the first image to and determine the closest match:
Notice that the two stick men are not completely identical but are reasonably similar.
I want to be able to compare the first image to each image in my library until a reasonably close match is found. If necessary, my image library could contain numerous variations of the same image in order to help decide which type of image I have. For example:
My question is whether this is something that OpenCV would be capable of? Has it been done before, and if so, can you point me in the direction of some examples? Many thanks for your help.
Edit: Through my searches I have found many examples of people who are comparing images, or even people that are comparing images which have been stretched or skewed such as this: Checking images for similarity with OpenCV . Unfortunately as you can see, my images are not just translated (Rotated/Skewed/Stretched) versions of one another - They actually different images although they are very similar.
You should be able to do it using feature template match function of OpenCV. You can use matchTemplate function to look for the feature and then, minMaxLoc to find its location. Check out the tutorial on OpenCV web site for matchTemplate.
seems you need feature points detections and matching. Check these docs from OpenCV:
http://docs.opencv.org/doc/tutorials/features2d/feature_detection/feature_detection.html
http://docs.opencv.org/doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.html
For your particular type of images, you might get good results by using moments/HuMoments for the connected components (which you can find with findContours).
since there is a rotation involved, I dont think template matching would work well. You probably need to use Feature point detection such as SIFT or SURF.
EDIT: This won't work with rotation. Same for matchTemplate. I am yet to try the findContours + moments as in bjoernz answer which sounds promising.
Failed Solution:
I tried using ShapeContextDistanceExtractor(1) available in OpenCV 3.0 along with findContours on your sample images to get good results. The sample images were cropped to same size as original image(128*200). You can could as well use resize in OpenCV.
Code below compares images in images folder with 1.png as the base image.
#include "opencv2/shape.hpp"
#include "opencv2/opencv.hpp"
#include <iostream>
#include <string>
using namespace std;
using namespace cv;
const int MAX_SHAPES = 7;
vector<Point> findContours( const Mat& compareToImg )
{
vector<vector<Point> > contour2D;
findContours(compareToImg, contour2D, RETR_LIST, CHAIN_APPROX_NONE);
//converting 2d vector contours to 1D vector for comparison
vector <Point> contour1D;
for (size_t border=0; border < contour2D.size(); border++) {
for (size_t p=0; p < contour2D[border].size(); p++) {
contour1D.push_back( contour2D[border][p] );
}
}
//limiting contours size to reduce distance comparison time
contour1D.resize( 300 );
return contour1D;
}
int main()
{
string path = "./images/";
cv::Ptr <cv::ShapeContextDistanceExtractor> distanceExtractor = cv::createShapeContextDistanceExtractor();
//base image
Mat baseImage= imread( path + "1.png", IMREAD_GRAYSCALE);
vector<Point> baseImageContours= findContours( baseImage );
for ( int idx = 2; idx <= MAX_SHAPES; ++idx ) {
stringstream imgName;
imgName << path << idx << ".png";
Mat compareToImg=imread( imgName.str(), IMREAD_GRAYSCALE ) ;
vector<Point> contii = findContours( compareToImg );
float distance = distanceExtractor->computeDistance( baseImageContours, contii );
std::cout<<" distance to " << idx << " : " << distance << std::endl;
}
return 0;
}
Result
distance to 2 : 89.7951
distance to 3 : 14.6793
distance to 4 : 6.0063
distance to 5 : 4.79834
distance to 6 : 0.0963184
distance to 7 : 0.00212693
Do three things: 1. Forget about image comparison since you really comparing stroke symbols. 2. Download and play wth a Gesture Search app from google store; 3. Realize that for good performance you cannot recognize your strokes without using timestamp information about stroke drawing. Otherwice we would have a successful handwriting recognition. Then you can research Android stroke reco library to write your code properly.

CUDA not running in OpenCV even after successful build

I am trying to build OpenCV 2.4.10 on a Win 8.1 machine with CUDA 6.5. I have other third part libraries as well and they have installed successfully. I ram a simple GPU based program and I got this error No GPU found or the library was compiled without GPU support. I also ran the sample exe files like performance_gpu.exe that were built during the installation and I got the same error. I also had WITH_CUDA flag checked. Following are the flags (related to CUDA) that were set during the CMAKE build.
WITH_CUDA : Checked
WITH_CUBLAS : Checked
WITH_CUFFT : Checked
CUDA_ARCH_BIN : 1.1 1.2 1.3 2.0 2.1(2.0) 3.0 3.5
CUDA_ARCH_PTX : 3.0
CUDA_FAST_MATH : Checked
CUDA_GENERATION : Auto
CUDA_HOST_COMPILER : $(VCInstallDir)bin
CUDA_SPERABLE_COMPILATION : Unchecked
CUDA_TOOLKIT_ROOT_DIR : C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v6.5
Another thing is that in some posts I have read that along with CUDA the built takes a lot of time. My build takes ~ 3 Hrs where maximum time is taken up during the compilation of .cu files. I have not got any errors as far as I know during the compilation of those files.
In some posts I have seen that people talk about a directory names gpu inside the build directory but I don't see any in mine!
I am using Visual Studio 2013.
What could be the issue? Please help!
UPDATE:
I again tried to build opencv and this time before starting the build I added the bin, lib and include directories of CUDA. After the build in E:\opencv\build\bin\Release I ran gpu_perf4au.exe and I got this output
[----------]
[ INFO ] Implementation variant: cuda.
[----------]
[----------]
[ GPU INFO ] Run test suite on GeForce GTX 860M GPU.
[----------]
Time compensation is 0
OpenCV version: 2.4.10
OpenCV VCS version: unknown
Build type: release
Parallel framework: tbb
CPU features: sse sse2 sse3 ssse3 sse4.1 sse4.2 avx avx2
[----------]
[ GPU INFO ] Run on OS Windows x64.
[----------]
*** CUDA Device Query (Runtime API) version (CUDART static linking) ***
Device count: 1
Device 0: "GeForce GTX 860M"
CUDA Driver Version / Runtime Version 6.50 / 6.50
CUDA Capability Major/Minor version number: 5.0
Total amount of global memory: 2048 MBytes (2147483648 bytes)
GPU Clock Speed: 1.02 GHz
Max Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536,65536), 3
D=(4096,4096,4096)
Max Layered Texture Size (dim) x layers 1D=(16384) x 2048, 2D=(16384,16
384) x 2048
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per block: 1024
Maximum sizes of each dimension of a block: 1024 x 1024 x 64
Maximum sizes of each dimension of a grid: 2147483647 x 65535 x 65535
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Concurrent kernel execution: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support enabled: No
Device is using TCC driver mode: No
Device supports Unified Addressing (UVA): Yes
Device PCI Bus ID / PCI location ID: 1 / 0
Compute Mode:
Default (multiple host threads can use ::cudaSetDevice() with device simul
taneously)
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.50, CUDA Runtime Ver
sion = 6.50, NumDevs = 1
I thought that every thing was fine but after running this program where I had included all opencv and CUDA directories in its property files,
#include <cv.h>
#include <highgui.h>
#include <iostream>
#include <opencv2\opencv.hpp>
#include <opencv2\gpu\gpu.hpp>
using namespace std;
using namespace cv;
char key;
Mat thresholder (Mat input) {
gpu::GpuMat dst, src;
src.upload(input);
gpu::threshold(src, dst, 128.0, 255.0, CV_THRESH_BINARY);
Mat result_host(dst);
return result_host;
}
int main(int argc, char* argv[]) {
cvNamedWindow("Camera_Output", 1);
CvCapture* capture = cvCaptureFromCAM(CV_CAP_ANY);
while (1){
IplImage* frame = cvQueryFrame(capture);
IplImage* gray_frame = cvCreateImage(cvGetSize(frame), IPL_DEPTH_8U, 1);
cvCvtColor(frame, gray_frame, CV_RGB2GRAY);
Mat temp(gray_frame);
Mat thres_temp;
thres_temp = thresholder(temp);
//cvShowImage("Camera_Output", frame); //Show image frames on created window
imshow("Camera_Output", thres_temp);
key = cvWaitKey(10);
if (char(key) == 27){
break; //If you hit ESC key loop will break.
}
}
cvReleaseCapture(&capture);
cvDestroyWindow("Camera_Output");
return 0;
}
I got the error:
OpenCV Error: No GPU support (The library is compiled without CUDA support) in E
mptyFuncTable::mallocPitch, file C:\builds\2_4_PackSlave-win64-vc12-shared\openc
v\modules\dynamicuda\include\opencv2/dynamicuda/dynamicuda.hpp, line 126
Thanks to #BeRecursive for giving me a lead to solve my issue. The CMAKE build log has three unavailable opencv modules namely androidcamera, dynamicuda and viz. I could not find any information on dynamicuda i.e. the module whose unavailability might have caused the error that I mentioned in the question. Instead I searched for viz module and checked how is it installed.
After going through some blogs and forums I found out that viz module has not been included in the pre-built versions of OpenCV. It was recommended to build from source version 2.4.9. I thought to give it a try and I installed it with VS 2013 and CMAKE 3.0.1 but there were many build failures and warnings. Upon further search I found that CMAKE versions 3.0.x aren't recommended for building OpenCV as they are producing many warnings.
At last I decided to switch to VS 2010 and CMAKE 2.8.12.2 and after building the source I got no error and luckily the after adding all executables, libraries and DLLs in the PATH, when I ran my program that I have mentioned above I got no errors but it is running very slowly! So I ran this program:
#include <cv.h>
#include <highgui.h>
#include <iostream>
#include <opencv2\opencv.hpp>
#include <opencv2\core\core.hpp>
#include <opencv2\gpu\gpu.hpp>
#include <opencv2\highgui\highgui.hpp>
using namespace std;
using namespace cv;
Mat thresholder(Mat input) {
cout << "Beginning thresholding using GPU" << endl;
gpu::GpuMat dst, src;
src.upload(input);
cout << "upload done ..." << endl;
gpu::threshold(src, dst, 128.0, 255.0, CV_THRESH_BINARY);
Mat result_host(dst);
cout << "Thresolding complete!" << endl;
return result_host;
}
int main(int argc, char** argv) {
Mat image, gray_image;
image = imread("desert.jpg", CV_LOAD_IMAGE_COLOR); // Read the file
if (!image.data) {
cout << "Could not open or find the image" << endl;
return -1;
}
cout << "Orignal image loaded ..." << endl;
cvtColor(image, gray_image, CV_BGR2GRAY);
cout << "Original image converted to Grayscale" << endl;
Mat thres_image;
thres_image = thresholder(gray_image);
namedWindow("Original Image", WINDOW_AUTOSIZE);// Create a window for display.
namedWindow("Gray Image", WINDOW_AUTOSIZE);
namedWindow("GPU Threshed Image", WINDOW_AUTOSIZE);
imshow("Original Image", image);
imshow("Gray Image", gray_image);
imshow("GPU Threshed Image", thres_image);
waitKey(0);
return 0;
}
Later I even tested the build on VS 2013 and it also worked.
The GPU based programs are slow due to reasons mentioned here.
So three important things I want to point out:
BUILD from source only
Use a little older version of CMAKE
Prefer VS 2010 for building the binaries.
NOTE:
This might sound weird but all my first BUILDS failed due to some linker error. So, I don't know whether this is work around or not but try to build opencv_gpu before anything and all other modules one by one after that and then build ALL_BUILDS and INSTALL projects.
When you build this way in DEBUG mode you might get an error iff you are building opencv with Python support i.e. "python27_d.lib" otherwise all projects will be built successfully.
WEB SOURCES:
Following are web sources that helped me in solving my problem:
http://answers.opencv.org/question/32502/opencv-249-viz-module-not-there/
http://home.eps.hw.ac.uk/~cgb7/opencv/opencv_tutorial.pdf
http://perso.uclouvain.be/allan.barrea/opencv/opencv.html
http://eavise.wikispaces.com/Building+OpenCV+yourself+on+Windows+7+x64+with+OpenCV+2.4.5+and+CUDA+5.0
https://devtalk.nvidia.com/default/topic/767647/how-do-i-enable-cuda-when-installing-opencv-/
So that is a run time error, being thrown by OpenCV. If you take a look at your CMake log fro your previous question, you can see that one of the Unavailable packages was dynamiccuda, which appears to be what that error is complaining about.
However, I don't have a lot of experience with Windows OpenCV so that could be a red herring. My gut feeling says that you don't have all the libraries correctly on the path. Have you made sure that you have the CUDA lib/include/bin on the PATH? Have you made sure that you have your OpenCV build lib/include directory on the path. Windows has a very simple linking order that essentially just includes the current directory, anything on the PATH and the main Windows directories. So, I would try making sure everything was correctly on the PATH/that you have copied all the correct libraries into the folder.
A note: this is different from a compiling/linking error because it is at RUNTIME. So setting the compiler paths will not help with runtime linking errors.

low-pass filter in opencv

I would like to know how I can do a low-pass filter in opencv on an IplImage.
For example "boxcar" or something similar.
I've googled it but i can't find a clear solution.
If anyone could give me an example or point me in the right direction on how to implement this in opencv or javacv I would be grateful.
Thx in advance.
Here is an example using the C API and IplImage:
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/highgui/highgui_c.h"
int main()
{
IplImage* img = cvLoadImage("input.jpg", 1);
IplImage* dst=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,3);
cvSmooth(img, dst, CV_BLUR);
cvSaveImage("filtered.jpg",dst);
}
For information about what parameters of the cvSmooth function you can have a look at the cvSmooth Documentation.
If you want to use a custom filter mask you can use the function cvFilter2D:
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/highgui/highgui_c.h"
int main()
{
IplImage* img = cvLoadImage("input.jpg", 1);
IplImage* dst=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,3);
double a[9]={ 1.0/9.0,1.0/9.0,1.0/9.0,
1.0/9.0,1.0/9.0,1.0/9.0,
1.0/9.0,1.0/9.0,1.0/9.0};
CvMat k;
cvInitMatHeader( &k, 3, 3, CV_64FC1, a );
cvFilter2D( img ,dst, &k,cvPoint(-1,-1));
cvSaveImage("filtered.jpg",dst);
}
These examples use OpenCV 2.3.1.
The openCV filtering documentation is a little confusing because the functions try and efficently cover every possible filtering technique.
There is a tutorial on using your own filter kernels which covers box filters

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