Follow the steps described in the OPENCV_GPU page for Visual Studio 64-bit build.
module-gpu build error: Configuration(null)
Solution - missing vcvars64.bat in Windows SDK amd64 directory. Create that by following the simple instructions here http://www.w7forums.com/vcvarsall-bat-no-64bit-support-vcvars64-bat-missing-t6606.html
Taken by surprised at first because I am able to build 64-bit OpenCV. I suspect it has to do with nvidia compiler (nvcc). It probably open a windows shell to do compilation. And that would not have the 64-bit environment set up without this vcvars64.bat.
Test GPU build by running module-gpu-test suite from VS 2010 Express
See "Implementing tests" section of http://opencv.willowgarage.com/wiki/CodingStyleGuide
Setting up Test Data
Test-Data is required by the gpu-test-suite (and others too). Download a snapshot of the opencv-extra package that is tagged for OpenCV 2.3.0 release from WillowGarage. There is a "Download Zip" link in the source browsing page that makes it convenient.
Set the environment variable OPENCV_TEST_DATA_PATH to point to the testdata directory.
Run the project module-gpu-test
Resulted in 3 types of failures
- My NVidia hardware that has compute-capability of 1.2. 1 case requires 1.3
- Crash in meanShift and meanShiftProc. The stack trace shows that it dies at the point where GpuMat variable is being released.
- Assertion error in NVidia.TestHaarCascadeAppl. (Didn't investigate further).
The other tests run OK.
Learned to use the gtest_ command-line argument - see code comments above ParseGoogleTestFlagsOnlyImpl()
- gtest_list_tests : shows the tests selected to run and quit
- gtest_filter=
: select the tests to run / or not to run by matching a specified pattern against test name. Pattern for negative matching begins with minus sign. - gtest_output=xml[: directory name / file-name ] : output a summary of tests results in XML. Details see ts_gtest.cpp (search for GTEST_DEFINE_string_)
The library implements accelerated versions of other areas of OpenCV - image processing, image filtering, matrix calculations, features-2D and, object detection, camera calibration. The API and data-structures are defined in nested namespace cv::gpu::. The accelerations makes use of both NPP API and CUDA parallelization.
Run a few OpenCV GPU samples that could readily compared with non-GPU ones
- surf_keypoint_matcher vs matcher_simple: speed up from 46 secs to 6 secs with the graffiti image from VGG set.
- mofology vs morphology2 : not very obvious in my quick test. still noticeable when changing the element shape at a Open/Close set at 17-iterations.
- hog_gpu vs peopledetect : speed up from 67 to 17 secs with my 5M-pixel test image.
- cascadeclassifier_nvidia_api vs cascadeclassifier(GPU)vs facedetect (no-nested-cascade) : overall (secs): 5.1 / 4.8 / 4.5; detection-only(secs): 1 secs / 1 / 3.1
No comments:
Post a Comment