Question pretty much says it all, can the Xavier run OpenCL applications?
Based on this post https://devtalk.nvidia.com/default/topic/1010166/jetson-tx2/does-jetson-tx1-or-tx2-support-opencl/ I’m guessing the answer is no, but I’d prefer official confirmation from NVIDIA personnel.
Hi cdahms123, OpenCL is not supported on Jetson AGX Xavier.
Searching for a way to utilize OpenCL on my Jetson Xavier for MVTec Halcon library I’ve encountered a YouTube instructional video to install OpenCL on Jetson Nano . Alan seems to install some build tools and libraries and then compiles pocl.
Now I’ve followed the instructions and get some interesting output on my Jetson when typing clinfo:
Number of platforms 1
Platform Name Portable Computing Language
Platform Vendor The pocl project
Platform Version OpenCL 1.2 pocl 1.3 Release, LLVM 6.0.0, SLEEF, POCL_DEBUG, FP16
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_icd
Platform Extensions function suffix POCL
Platform Name Portable Computing Language
Number of devices 1
Device Name pthread-0x004
Device Vendor 0x4e
Device Vendor ID 0x13b5
Device Version OpenCL 1.2 pocl HSTR: pthread-aarch64-unknown-linux-gnu-cortex-a57
Driver Version 1.3
Device OpenCL C Version OpenCL C 1.2 pocl
Device Type CPU
Device Profile FULL_PROFILE
Device Available Yes
Compiler Available Yes
Linker Available Yes
Max compute units 8
Max clock frequency 2265MHz
Device Partition (core)
Max number of sub-devices 8
Supported partition types equally, by counts
Max work item dimensions 3
Max work item sizes 4096x4096x4096
Max work group size 4096
Preferred work group size multiple 8
Preferred / native vector sizes
char 16 / 16
short 8 / 8
int 4 / 4
long 2 / 2
half 8 / 8 (cl_khr_fp16)
float 4 / 4
double 2 / 2 (cl_khr_fp64)
Half-precision Floating-point support (cl_khr_fp16)
Denormals No
Infinity and NANs No
Round to nearest No
Round to zero No
Round to infinity No
IEEE754-2008 fused multiply-add No
Support is emulated in software No
Single-precision Floating-point support (core)
Denormals No
Infinity and NANs Yes
Round to nearest Yes
Round to zero No
Round to infinity No
IEEE754-2008 fused multiply-add No
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Address bits 64, Little-Endian
Global memory size 31321673728 (29.17GiB)
Error Correction support No
Max memory allocation 8589934592 (8GiB)
Unified memory for Host and Device Yes
Minimum alignment for any data type 128 bytes
Alignment of base address 1024 bits (128 bytes)
Global Memory cache type Read/Write
Global Memory cache size 2097152 (2MiB)
Global Memory cache line size 64 bytes
Image support Yes
Max number of samplers per kernel 16
Max size for 1D images from buffer 536870912 pixels
Max 1D or 2D image array size 2048 images
Max 2D image size 16384x16384 pixels
Max 3D image size 2048x2048x2048 pixels
Max number of read image args 128
Max number of write image args 128
Local memory type Global
Local memory size 1048576 (1024KiB)
Max number of constant args 8
Max constant buffer size 1048576 (1024KiB)
Max size of kernel argument 1024
Queue properties
Out-of-order execution No
Profiling Yes
Prefer user sync for interop Yes
Profiling timer resolution 1ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels Yes
printf() buffer size 16777216 (16MiB)
Built-in kernels
Device Extensions **cl_khr_byte_addressable_store** cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_3d_image_writ
es cl_khr_fp16 cl_khr_fp64
NULL platform behavior
clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...) Portable Computing Language
clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...) Success [POCL]
clCreateContext(NULL, ...) [default] Success [POCL]
clCreateContextFromType(NULL, CL_DEVICE_TYPE_DEFAULT) Success (1)
Platform Name Portable Computing Language
Device Name pthread-0x004
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU) Success (1)
Platform Name Portable Computing Language
Device Name pthread-0x004
clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL) Success (1)
Platform Name Portable Computing Language
Device Name pthread-0x004
ICD loader properties
ICD loader Name OpenCL ICD Loader
ICD loader Vendor OCL Icd free software
ICD loader Version 2.2.11
ICD loader Profile OpenCL 2.1
As @cdahms123 mentioned in a thread he started on the topic the device should be detected as compute device by Halcon under this condition:
At present, HALCON only supports OpenCL compatible GPUs supporting the OpenCL extension cl_khr_byte_addressable_store and image objects. If you are not sure whether a certain device is supported, please refer to the manufacturer.
cl_khr_byte_addressable_store is detected as seen in the clinfo output above.
Now I wonder why the hbench utility still doesn’t detect the GPU. As I’m on the limit of the device in terms of performance I also need all the acceleration I can get …
Do I understand something wrong about the OpenCL support on Jetson or is this “workaround” through pocl not actually working properly?
@dusty_nv Thanks for your great GitHub repos for Jetson ML BTW. They’ve helped me a lot!
You did not compile PoCL with CUDA support. For this, you have to append the “-DENABLE_CUDA=1” flag when calling cmake before compiling.
Thanks, Jerome. I’m recompiling PoCL as we speak. Will keep you updated.
I can confirm that it can be compiled with CUDA support, but I’m not sure if everything is correct. At least I don’t see image support. Any ideas?
Device Name Xavier
Device Vendor NVIDIA Corporation
Device Vendor ID 0x10de
Device Version OpenCL 1.2 pocl HSTR: CUDA-sm_72
Driver Version 1.6
Device OpenCL C Version OpenCL C 1.2 pocl
Device Type GPU
Device Profile FULL_PROFILE
Device Available Yes
Compiler Available Yes
Linker Available Yes
Max compute units 8
Max clock frequency 1377MHz
Device Partition (core)
Max number of sub-devices 1
Supported partition types None
Supported affinity domains (n/a)
Max work item dimensions 3
Max work item sizes 1024x1024x64
Max work group size 1024
Preferred work group size multiple 32
Preferred / native vector sizes
char 1 / 1
short 1 / 1
int 1 / 1
long 1 / 1
half 0 / 0 (n/a)
float 1 / 1
double 1 / 1 (cl_khr_fp64)
Half-precision Floating-point support (n/a)
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Address bits 64, Little-Endian
Global memory size 33477574656 (31.18GiB)
Error Correction support No
Max memory allocation 8369393664 (7.795GiB)
Unified memory for Host and Device Yes
Minimum alignment for any data type 128 bytes
Alignment of base address 4096 bits (512 bytes)
Global Memory cache type None
Image support No
Local memory type Local
Local memory size 49152 (48KiB)
Max number of constant args 8
Max constant buffer size 65536 (64KiB)
Max size of kernel argument 1024
Queue properties
Out-of-order execution No
Profiling Yes
Prefer user sync for interop Yes
Profiling timer resolution 1ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels No
printf() buffer size 16777216 (16MiB)
Built-in kernels (n/a)
Device Extensions cl_khr_byte_addressable_store cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_int64_base_atomics cl_khr_int64_extended_atomics
Here are clpeak results:
Platform: Portable Computing Language
Device: Xavier
Driver version : 1.6 (Linux ARM64)
Compute units : 8
Clock frequency : 1377 MHz
Global memory bandwidth (GBPS)
float : 84.52
float2 : 107.46
float4 : 106.80
float8 : 107.15
float16 : 105.47
Single-precision compute (GFLOPS)
float : 1355.57
float2 : 1403.25
float4 : 1398.78
float8 : 1394.55
float16 : 1384.85
No half precision support! Skipped
Double-precision compute (GFLOPS)
double : 44.03
double2 : 43.96
double4 : 43.85
double8 : 43.57
double16 : 43.16
Integer compute (GIOPS)
int : 1367.98
int2 : 1400.67
int4 : 1391.98
int8 : 1399.31
int16 : 1398.18
Integer compute Fast 24bit (GIOPS)
int : 1367.96
int2 : 1400.73
int4 : 1392.01
int8 : 1399.45
int16 : 1398.25
Transfer bandwidth (GBPS)
enqueueWriteBuffer : 8.07
enqueueReadBuffer : 8.22
enqueueWriteBuffer non-blocking : 8.29
enqueueReadBuffer non-blocking : 8.28
enqueueMapBuffer(for read) : 23585.76
memcpy from mapped ptr : 8.39
enqueueUnmap(after write) : 13.49
memcpy to mapped ptr : 8.38
Kernel launch latency : -30.71 us
Managed to compile it with llvm-11 the trick is to build it with:
cmake -DCMAKE_BUILD_TYPE=Release -DWITH_LLVM_CONFIG=/usr/lib/llvm-11/bin/llvm-config -DSINGLE_LLVM_LIB=1 -DENABLE_CUDA=ON -DSTATIC_LLVM=ON ..
llvm-11 can be had from the llvm site. It has a download for ARM64 and has support for Carmel CPU of the AGX.
the CPU benchmark isn’t too shabby either:
Platform: Portable Computing Language
Device: pthread-0x004
Driver version : 1.7-pre master-0-g89af801e (Linux ARM64)
Compute units : 8
Clock frequency : 2265 MHz
Global memory bandwidth (GBPS)
float : 14.73
float2 : 23.17
float4 : 21.41
float8 : 18.91
float16 : 16.47
Single-precision compute (GFLOPS)
float : 4.34
float2 : 8.56
float4 : 17.37
float8 : 33.49
float16 : 67.40
Half-precision compute (GFLOPS)
half : 4.36
half2 : 8.76
half4 : 16.92
half8 : 34.29
half16 : 67.78
Double-precision compute (GFLOPS)
double : 4.27
double2 : 8.37
double4 : 17.32
double8 : 34.20
double16 : 59.22
Integer compute (GIOPS)
int : 8.77
int2 : 23.35
int4 : 46.31
int8 : 86.20
int16 : 128.71
Integer compute Fast 24bit (GIOPS)
int : 8.77
int2 : 23.23
int4 : 46.33
int8 : 86.69
int16 : 131.30
Transfer bandwidth (GBPS)
enqueueWriteBuffer : 10.60
enqueueReadBuffer : 10.42
enqueueWriteBuffer non-blocking : 10.61
enqueueReadBuffer non-blocking : 10.60
enqueueMapBuffer(for read) : 6.73
memcpy from mapped ptr : 10.29
enqueueUnmap(after write) : 10.22
memcpy to mapped ptr : 10.22
Kernel launch latency : 68.42 us
Thanks for sharing. I also looked into this yesterday and downloaded the LLVM11 aarch64 tarball after seeing this would support carmel, but unsure how to install it and what it implies for further apt (stiil using LLVM6) update. May you share some install info ?
Also, is the carmel arch auto-detected, so that we don’t need to set LLC_HOST_CPU nor any further detail ? No warning at link ?
The download is from LLVM Download Page
steps:
mkdir tempcd temptar xf ~/Downloads/clang+llvm-11.0.0-aarch64-linux-gnu.tar.xzsudo mkdir -p /usr/lib/llvm-11/sudo mv clang+llvm-11.0.0-aarch64-linux-gnu/* /usr/lib/llvm-11/That’s all for being able to use it to compile pocl.
Still a few things that aren’t quite there yet:
Possibly something the POCL people can pick up in the near future.
does anyone occur segfault on pocl GPU?
Yes, it happens if I use clpeak. CPU part works properly, but segmentation fault occurs when it starts GPU tests. It was indicated to happen if both CPU and GPU devices are available and a simple solution is to disable CPU device as indicated here: [CUDA] pocl on Jetson Tx2 / Segfault · Issue #853 · pocl/pocl · GitHub
Simply add POCL_DEVICES=CUDA before the application that you start.
The POCL library built with LLVM-11 seems to do well. Although i have not tested much more than the clpeak test.
I tried to test some other apps but they don’t seem to work.
https://github.com/mikr/OpenCL-path-tracing-tutorial-3-Part-2
the above is forked from a Windows OpenCL example/tutorial but i get error -44 on the opencl kernel program build.
Maybe someone else here knows how to fix that.
Got back to this topic for some reason, and I may share my findings at that time (Xavier NX, last trial was on Dec 2020).
I just then tried a simple example of Sobel filtering from opencv on 1280x720p30. Using POCL built with llvm11.
This may be a side case, not sure it can be further generalized.
These were excluding first 20 frames from measurement on next 500 frames:
So the big improvement may be on CPU side with carmel CPU support.
Unable to retry now with R32.5.1, only from what I remind.
#include <signal.h>
#include <iostream>
#include <vector>
#include <CL/cl.h>
#include <opencv2/opencv.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/core/cuda.hpp>
#include <opencv2/cudafilters.hpp>
//#include <opencv2/cudaobjdetect.hpp>
//#include <opencv2/cudaimgproc.hpp>
#define IGNORE_FIRST_FRAMES 20
#define LOOP_MEASURE_FRAMES 500
typedef enum {
test_no_opencl_cpu = 0,
test_opencl_cpu,
test_opencl_gpu,
test_cuda,
test_unknown
} test_case_t;
test_case_t tcase = test_opencl_gpu;
static cv::VideoCapture *capPtr = NULL;
void my_handler(int s){
std::cerr<< "Caught signal " << s << std::endl;
if(capPtr) {
capPtr->release();
capPtr = NULL;
}
exit(s);
}
void Process_Sobel_CPU(cv::Mat frameBGRin, cv::Mat frameBGRout) {
cv::Sobel(frameBGRin, frameBGRout, -1, 1, 1, 1, cv::BORDER_DEFAULT);
}
void Process_Sobel_UMat(cv::UMat frameBGRin, cv::UMat frameBGRout) {
cv::Sobel(frameBGRin, frameBGRout, -1, 1, 1, 1, cv::BORDER_DEFAULT);
}
void Process_Sobel_CUDA(cv::cuda::GpuMat frameBGRin, cv::cuda::GpuMat frameBGRout) {
static cv::Ptr < cv::cuda::Filter > cuda_Sobel_filter = cv::cuda::createSobelFilter (CV_8UC3, CV_8UC3, 1, 1, 1, 1, cv::BORDER_DEFAULT);
cuda_Sobel_filter->apply (frameBGRin, frameBGRout);
}
void PrintOclDeviceInfo(cv::ocl::Device dev) {
std::cout << "\tName: " << dev.name() << std::endl;
std::cout << "\tType: " << dev.type() << std::endl;
std::cout << "\tAvailable: " << (dev.available() ? "YES":"NO") << std::endl;
std::cout << "\tOpenCL version: " << dev.OpenCLVersion() << std::endl;
std::cout << "\tVendor: " << dev.vendorName() << std::endl;
std::cout << "\tDriver version: " << dev.driverVersion() << std::endl;
std::cout << "\tVersion: " << dev.version() << std::endl;
//std::cout << "\tExtensions: " << dev.extensions() << std::endl;
std::cout << "\tHost unified memory: " << (dev.hostUnifiedMemory() ? "YES":"NO") << std::endl;
std::cout << "\tCompiler available: " << (dev.compilerAvailable() ? "YES":"NO") << std::endl;
std::cout << "\tLinker available: " << (dev.linkerAvailable() ? "YES":"NO") << std::endl;
}
void ShowAllPlatformsInfo() {
std::vector< cv::ocl::PlatformInfo > platforms_info;
cv::ocl::getPlatfomsInfo(platforms_info);
for (auto platform : platforms_info) {
std::cout << "Platform: " << platform.name() << " Devices: " << platform.deviceNumber() << std::endl;
for (unsigned int devIdx = 0; devIdx < platform.deviceNumber(); ++devIdx) {
std::cout << " Device: " << devIdx << std::endl;
cv::ocl::Device dev;
platform.getDevice(dev, devIdx);
PrintOclDeviceInfo(dev);
}
std::cout << std::endl;
}
}
void DiscoverOpenCLDevices() {
//ShowAllPlatformsInfo();
cv::ocl::Context cpu_contexts;
cpu_contexts.create(cv::ocl::Device::TYPE_CPU);
std::cout << "CPU devices detected:" << cpu_contexts.ndevices() << std::endl;
for(unsigned int devIdx = 0; devIdx < cpu_contexts.ndevices(); ++devIdx) {
cv::ocl::Device dev = cpu_contexts.device(devIdx);
PrintOclDeviceInfo(dev);
}
cv::ocl::Context gpu_contexts;
gpu_contexts.create(cv::ocl::Device::TYPE_GPU);
std::cout << "GPU devices detected:" << gpu_contexts.ndevices() << std::endl;
for(unsigned int devIdx = 0; devIdx < gpu_contexts.ndevices(); ++devIdx) {
cv::ocl::Device dev = gpu_contexts.device(devIdx);
PrintOclDeviceInfo(dev);
}
}
int main (int argc, char **argv)
{
if (argc > 1) {
std::cout << "Trying to interpret code " << argv[1] << std::endl;
unsigned int code = (unsigned int)atoi(argv[1]);
if (code >= (unsigned int) test_unknown) {
std::cerr << "Unknown code " << code << std::endl;
return (-1);
}
tcase = (test_case_t) code;
}
std::cerr << "Main Starting: " << std::endl;
const char *gst =
"nvarguscamerasrc ! video/x-raw(memory:NVMM), width=1920, height=1080, format=NV12, framerate=30/1 ! "
"nvvidconv ! video/x-raw, format=BGRx, width=1280, height=720 ! "
"videoconvert ! video/x-raw, format=BGR ! appsink";
capPtr = new cv::VideoCapture (gst, cv::CAP_GSTREAMER);
if (!capPtr || !capPtr->isOpened()) {
std::cerr << "Failed to open capture. Aborting." << std::endl;
return (-4);
}
switch (tcase) {
case test_no_opencl_cpu:
cv::ocl::setUseOpenCL(false);
cv::namedWindow ("FrameOut", cv::WINDOW_AUTOSIZE);
break;
case test_opencl_cpu:
if (!cv::ocl::haveOpenCL()) {
std::cerr << "No OpenCL support, aborting" << std::endl;
return (-2);
}
DiscoverOpenCLDevices();
putenv((char*)"OPENCV_OPENCL_DEVICE=Portable Computing Language:CPU");
cv::ocl::setUseOpenCL(true);
cv::namedWindow ("FrameOut", cv::WINDOW_AUTOSIZE | cv::WINDOW_OPENGL);
break;
case test_opencl_gpu:
if (!cv::ocl::haveOpenCL()) {
std::cerr << "No OpenCL support, aborting" << std::endl;
return (-3);
}
DiscoverOpenCLDevices();
putenv((char*)"OPENCV_OPENCL_DEVICE=Portable Computing Language:GPU");
cv::ocl::setUseOpenCL(true);
cv::namedWindow("FrameOut", cv::WINDOW_AUTOSIZE | cv::WINDOW_OPENGL);
break;
case test_cuda:
cv::ocl::setUseOpenCL(false);
cv::namedWindow("FrameOut", cv::WINDOW_AUTOSIZE | cv::WINDOW_OPENGL);
break;
default:
std::cerr << "Unknown mode " << (int)tcase << std::endl;
return (-4);
}
cv::Mat frameBGRin (720, 1280, CV_8UC3);
cv::Mat frameBGRout (720, 1280, CV_8UC3);
cv::UMat uframeBGRin (720, 1280, CV_8UC3);
cv::UMat uframeBGRout (720, 1280, CV_8UC3);
cv::cuda::GpuMat dframeBGRin (720, 1280, CV_8UC3);
cv::cuda::GpuMat dframeBGRout (720, 1280, CV_8UC3);
int nbFrames = 0;
for ( ; nbFrames < IGNORE_FIRST_FRAMES; ++nbFrames) {
switch (tcase) {
case test_no_opencl_cpu:
if (!capPtr->read(frameBGRin)) {
std::cerr << "Failed to read frame " << nbFrames << std::endl;
capPtr->release();
return (-5);
}
break;
case test_opencl_cpu:
case test_opencl_gpu:
if (!capPtr->read(uframeBGRin)) {
std::cerr << "Failed to read frame " << nbFrames << std::endl;
capPtr->release();
return (-6);
}
break;
case test_cuda:
if (!capPtr->read(frameBGRin)) {
std::cerr << "Failed to read frame " << nbFrames << std::endl;
capPtr->release();
return (-5);
}
break;
}
}
double startTime = 0.0;
double waitAndReadUsed_time = 0.0;
double processUsed_time = 0.0;
double displayUsed_time = 0.0;
nbFrames=0;
startTime = (double)cv::getTickCount();
for ( ; nbFrames < LOOP_MEASURE_FRAMES; ++nbFrames) {
double loop_start = cv::getTickCount ();
/****************************************
* Wait and read frame
***************************************/
switch (tcase) {
case test_no_opencl_cpu:
if (!capPtr->read(frameBGRin)) {
std::cerr << "Failed to read frame " << nbFrames << std::endl;
capPtr->release();
return (-6);
}
break;
case test_opencl_cpu:
case test_opencl_gpu:
if (!capPtr->read(uframeBGRin)) {
std::cerr << "Failed to read frame " << nbFrames << std::endl;
capPtr->release();
return (-6);
}
break;
case test_cuda:
if (!capPtr->read(frameBGRin)) {
std::cerr << "Failed to read frame " << nbFrames << std::endl;
capPtr->release();
return (-6);
}
dframeBGRin.upload(frameBGRin);
break;
}
double wait_read = (cv::getTickCount () - loop_start) / cv::getTickFrequency ();
waitAndReadUsed_time += wait_read;
/****************************************
* Process frame
***************************************/
double proc_start = cv::getTickCount ();
double proc_time = 0;
switch (tcase) {
case test_no_opencl_cpu:
Process_Sobel_CPU(frameBGRin, frameBGRout);
break;
case test_opencl_cpu:
case test_opencl_gpu:
Process_Sobel_UMat(uframeBGRin, uframeBGRout);
break;
case test_cuda:
Process_Sobel_CUDA(dframeBGRin, dframeBGRout);
break;
}
proc_time = ((cv::getTickCount () - proc_start) / cv::getTickFrequency () );
processUsed_time += proc_time;
/****************************************
* Display frame
***************************************/
double display_start = cv::getTickCount();
//std::stringstream ss;
//ss << "Processing: " << (nbFrames / processUsed_time) << " FPS - Frame: " << nbFrames;
//cv::putText (frameBGR, ss.str (), cv::Point (30, 30), cv::FONT_HERSHEY_SIMPLEX, 1.0, cv::Scalar (255));
//cv::imshow ("FrameOut", frameBGR);
switch (tcase) {
case test_no_opencl_cpu:
//cv::imshow ("FrameOut", frameBGRout);
break;
case test_opencl_cpu:
case test_opencl_gpu:
//cv::imshow ("FrameOut", uframeBGRout);
break;
case test_cuda:
//cv::imshow ("FrameOut", dframeBGRout);
break;
default:
std::cerr << "No display implemented for this case" << std::endl;
break;
}
char c = cv::waitKey (1);
if (c == 27) {
break;
}
double disp_time = (cv::getTickCount() - display_start) / cv::getTickFrequency ();
displayUsed_time += disp_time;
double loop_time = (cv::getTickCount() - loop_start) / cv::getTickFrequency ();
//std::cout << "Frame: " << nbFrames << " Wait & read: " << wait_read << " Process time: " << proc_time << " Display time: " << disp_time << " Loop:" << loop_time << std::endl;
}
std::cout << "Terminated\n";
double totalTime = (cv::getTickCount () - startTime) / cv::getTickFrequency ();
std::cout << "Total Time for " << nbFrames << " frames: " << totalTime << " s. average: " << 1000.0*totalTime/(double)nbFrames << " ms" << std::endl;
std::cout << "Wait & read time: " << waitAndReadUsed_time << " s. average: " << 1000.0*waitAndReadUsed_time/(double)nbFrames << " ms\n";
std::cout << "Process time: " << processUsed_time << " s. average: " << 1000.0*processUsed_time/(double)nbFrames << " ms\n";
std::cout << "Display time: " << displayUsed_time << " s. average: " << 1000.0*displayUsed_time/(double)nbFrames << " ms\n";
capPtr->release();
}