Pierre Chatelier
60b806f9b8
Merge pull request #22947 from chacha21:hasNonZero
...
Added cv::hasNonZero() #22947
`cv::hasNonZero()` is semantically equivalent to (`cv::countNonZero()>0`) but stops parsing the image when a non-zero value is found, for a performance gain
- [X] I agree to contribute to the project under Apache 2 License.
- [X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This pull request might be refused, but I submit it to know if further work is needed or if I just stop working on it.
The idea is only a performance gain vs `countNonZero()>0` at the cost of more code.
Reasons why it might be refused :
- this is just more code
- the execution time is "unfair"/"unpredictable" since it depends on the position of the first non-zero value
- the user must be aware that default search is from first row/col to last row/col and has no way to customize that, even if his use case lets him know where a non zero could be found
- the PR in its current state is using, for the ocl implementation, a mere `countNonZero()>0` ; there is not much sense in trying to break early the ocl kernel call when non-zero is encountered. So the ocl implementation does not bring any improvement.
- there is no IPP function that can help (`countNonZero()` is based in `ippCountInRange`)
- the PR in its current state might be slower than a call to `countNonZero()>0` in some cases (see "challenges" below)
Reasons why it might be accepted :
- the performance gain is huge on average, if we consider that "on average" means "non zero in the middle of the image"
- the "missing" IPP implementation is replaced by an "Open-CV universal intrinsics" implementation
- the PR in its current state is almost always faster than a call to `countNonZero()>0`, is only slightly slower in the worst cases, and not even for all matrices
**Challenges**
The worst case is either an all-zero matrix, or a non-zero at the very last position. In such a case, the `hasNonZero()` implementation will parse the whole matrix like `countNonZero()` would do. But we expect the performance to be the same in this case. And `ippCountInRange` is hard to beat !
There is also the case of very small matrices (<=32x32...) in 8b, where the SIMD can be hard to feed.
For all cases but the worse, my custom `hasNonZero()` performs better than `ippCountInRange()`
For the worst case, my custom `hasNonZero()` performs better than `ippCountInRange()` *except for large matrices of type CV_32S or CV_64F* (but surprisingly, not CV_32F).
The difference is small, but it exists (and I don't understand why).
For very small CV_8U matrices `ippCountInRange()` seems unbeatable.
Here is the code that I use to check timings
```
//test cv::hasNonZero() vs (cv::countNonZero()>0) for different matrices sizes, types, strides...
{
cv::setRNGSeed(1234);
const std::vector<cv::Size> sizes = {{32, 32}, {64, 64}, {128, 128}, {320, 240}, {512, 512}, {640, 480}, {1024, 768}, {2048, 2048}, {1031, 1000}};
const std::vector<int> types = {CV_8U, CV_16U, CV_32S, CV_32F, CV_64F};
const size_t iterations = 1000;
for(const cv::Size& size : sizes)
{
for(const int type : types)
{
for(int c = 0 ; c<2 ; ++c)
{
const bool continuous = !c;
for(int i = 0 ; i<4 ; ++i)
{
cv::Mat m = continuous ? cv::Mat::zeros(size, type) : cv::Mat(cv::Mat::zeros(cv::Size(2*size.width, size.height), type), cv::Rect(cv::Point(0, 0), size));
const bool nz = (i <= 2);
const unsigned int nzOffsetRange = 10;
const unsigned int nzOffset = cv::randu<unsigned int>()%nzOffsetRange;
const cv::Point pos =
(i == 0) ? cv::Point(nzOffset, 0) :
(i == 1) ? cv::Point(size.width/2-nzOffsetRange/2+nzOffset, size.height/2) :
(i == 2) ? cv::Point(size.width-1-nzOffset, size.height-1) :
cv::Point(0, 0);
std::cout << "============================================================" << std::endl;
std::cout << "size:" << size << " type:" << type << " continuous = " << (continuous ? "true" : "false") << " iterations:" << iterations << " nz=" << (nz ? "true" : "false");
std::cout << " pos=" << ((i == 0) ? "begin" : (i == 1) ? "middle" : (i == 2) ? "end" : "none");
std::cout << std::endl;
cv::Mat mask = cv::Mat::zeros(size, CV_8UC1);
mask.at<unsigned char>(pos) = 0xFF;
m.setTo(cv::Scalar::all(0));
m.setTo(cv::Scalar::all(nz ? 1 : 0), mask);
std::vector<bool> results;
std::vector<double> timings;
{
bool res = false;
auto ref = cv::getTickCount();
for(size_t k = 0 ; k<iterations ; ++k)
res = cv::hasNonZero(m);
auto now = cv::getTickCount();
const bool error = (res != nz);
if (error)
printf("!!ERROR!!\r\n");
results.push_back(res);
timings.push_back(1000.*(now-ref)/cv::getTickFrequency());
}
{
bool res = false;
auto ref = cv::getTickCount();
for(size_t k = 0 ; k<iterations ; ++k)
res = (cv::countNonZero(m)>0);
auto now = cv::getTickCount();
const bool error = (res != nz);
if (error)
printf("!!ERROR!!\r\n");
results.push_back(res);
timings.push_back(1000.*(now-ref)/cv::getTickFrequency());
}
const size_t bestTimingIndex = (std::min_element(timings.begin(), timings.end())-timings.begin());
if ((bestTimingIndex != 0) || (std::find_if_not(results.begin(), results.end(), [&](bool r) {return (r == nz);}) != results.end()))
{
std::cout << "cv::hasNonZero\t\t=>" << results[0] << ((results[0] != nz) ? " ERROR" : "") << " perf:" << timings[0] << "ms => " << (iterations/timings[0]*1000) << " im/s" << ((bestTimingIndex == 0) ? " * " : "") << std::endl;
std::cout << "cv::countNonZero\t=>" << results[1] << ((results[1] != nz) ? " ERROR" : "") << " perf:" << timings[1] << "ms => " << (iterations/timings[1]*1000) << " im/s" << ((bestTimingIndex == 1) ? " * " : "") << std::endl;
}
}
}
}
}
}
```
Here is a report of this benchmark (it only reports timings when `cv::countNonZero()` is faster)
My CPU is an Intel Core I7 4790 @ 3.60Ghz
```
============================================================
size:[32 x 32] type:0 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[32 x 32] type:0 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[32 x 32] type:0 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[32 x 32] type:0 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[32 x 32] type:0 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[32 x 32] type:0 continuous = false iterations:1000 nz=true pos=middle
cv::hasNonZero =>1 perf:0.353764ms => 2.82674e+06 im/s
cv::countNonZero =>1 perf:0.282044ms => 3.54555e+06 im/s *
============================================================
size:[32 x 32] type:0 continuous = false iterations:1000 nz=true pos=end
cv::hasNonZero =>1 perf:0.610478ms => 1.63806e+06 im/s
cv::countNonZero =>1 perf:0.283182ms => 3.5313e+06 im/s *
============================================================
size:[32 x 32] type:0 continuous = false iterations:1000 nz=false pos=none
cv::hasNonZero =>0 perf:0.630115ms => 1.58701e+06 im/s
cv::countNonZero =>0 perf:0.282044ms => 3.54555e+06 im/s *
============================================================
size:[32 x 32] type:2 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[32 x 32] type:2 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[32 x 32] type:2 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[32 x 32] type:2 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[32 x 32] type:2 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[32 x 32] type:2 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[32 x 32] type:2 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[32 x 32] type:2 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[32 x 32] type:4 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[32 x 32] type:4 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[32 x 32] type:4 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[32 x 32] type:4 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[32 x 32] type:4 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[32 x 32] type:4 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[32 x 32] type:4 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[32 x 32] type:4 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[32 x 32] type:5 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[32 x 32] type:5 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[32 x 32] type:5 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[32 x 32] type:5 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[32 x 32] type:5 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[32 x 32] type:5 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[32 x 32] type:5 continuous = false iterations:1000 nz=true pos=end
cv::hasNonZero =>1 perf:0.607347ms => 1.64651e+06 im/s
cv::countNonZero =>1 perf:0.467037ms => 2.14116e+06 im/s *
============================================================
size:[32 x 32] type:5 continuous = false iterations:1000 nz=false pos=none
cv::hasNonZero =>0 perf:0.618162ms => 1.6177e+06 im/s
cv::countNonZero =>0 perf:0.468175ms => 2.13595e+06 im/s *
============================================================
size:[32 x 32] type:6 continuous = true iterations:1000 nz=true pos=begin
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size:[32 x 32] type:6 continuous = true iterations:1000 nz=true pos=middle
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size:[32 x 32] type:6 continuous = true iterations:1000 nz=true pos=end
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size:[32 x 32] type:6 continuous = true iterations:1000 nz=false pos=none
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size:[32 x 32] type:6 continuous = false iterations:1000 nz=true pos=begin
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size:[32 x 32] type:6 continuous = false iterations:1000 nz=true pos=middle
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size:[64 x 64] type:2 continuous = true iterations:1000 nz=false pos=none
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size:[128 x 128] type:5 continuous = false iterations:1000 nz=false pos=none
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size:[128 x 128] type:6 continuous = true iterations:1000 nz=true pos=middle
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size:[128 x 128] type:6 continuous = true iterations:1000 nz=true pos=end
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size:[128 x 128] type:6 continuous = true iterations:1000 nz=false pos=none
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size:[128 x 128] type:6 continuous = false iterations:1000 nz=true pos=begin
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size:[128 x 128] type:6 continuous = false iterations:1000 nz=true pos=middle
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size:[128 x 128] type:6 continuous = false iterations:1000 nz=true pos=end
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size:[128 x 128] type:6 continuous = false iterations:1000 nz=false pos=none
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size:[320 x 240] type:0 continuous = true iterations:1000 nz=true pos=begin
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size:[320 x 240] type:0 continuous = true iterations:1000 nz=true pos=middle
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size:[320 x 240] type:0 continuous = true iterations:1000 nz=false pos=none
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size:[320 x 240] type:0 continuous = false iterations:1000 nz=true pos=begin
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size:[320 x 240] type:0 continuous = false iterations:1000 nz=true pos=middle
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size:[320 x 240] type:0 continuous = false iterations:1000 nz=true pos=end
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size:[320 x 240] type:0 continuous = false iterations:1000 nz=false pos=none
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size:[320 x 240] type:2 continuous = true iterations:1000 nz=true pos=begin
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size:[320 x 240] type:2 continuous = true iterations:1000 nz=true pos=middle
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size:[320 x 240] type:2 continuous = true iterations:1000 nz=true pos=end
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size:[320 x 240] type:2 continuous = true iterations:1000 nz=false pos=none
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size:[320 x 240] type:2 continuous = false iterations:1000 nz=true pos=begin
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size:[320 x 240] type:2 continuous = false iterations:1000 nz=true pos=middle
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size:[320 x 240] type:2 continuous = false iterations:1000 nz=true pos=end
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size:[320 x 240] type:2 continuous = false iterations:1000 nz=false pos=none
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size:[320 x 240] type:4 continuous = true iterations:1000 nz=true pos=begin
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size:[320 x 240] type:4 continuous = true iterations:1000 nz=true pos=end
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size:[320 x 240] type:4 continuous = true iterations:1000 nz=false pos=none
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size:[320 x 240] type:4 continuous = false iterations:1000 nz=true pos=begin
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size:[320 x 240] type:4 continuous = false iterations:1000 nz=true pos=end
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size:[320 x 240] type:4 continuous = false iterations:1000 nz=false pos=none
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size:[640 x 480] type:0 continuous = false iterations:1000 nz=true pos=end
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size:[640 x 480] type:2 continuous = true iterations:1000 nz=false pos=none
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size:[640 x 480] type:2 continuous = false iterations:1000 nz=false pos=none
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size:[640 x 480] type:4 continuous = true iterations:1000 nz=true pos=middle
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size:[640 x 480] type:4 continuous = true iterations:1000 nz=true pos=end
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size:[640 x 480] type:4 continuous = true iterations:1000 nz=false pos=none
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size:[640 x 480] type:4 continuous = false iterations:1000 nz=true pos=begin
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size:[640 x 480] type:4 continuous = false iterations:1000 nz=true pos=middle
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size:[640 x 480] type:4 continuous = false iterations:1000 nz=true pos=end
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size:[640 x 480] type:4 continuous = false iterations:1000 nz=false pos=none
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size:[640 x 480] type:5 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[640 x 480] type:5 continuous = true iterations:1000 nz=true pos=middle
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size:[640 x 480] type:5 continuous = true iterations:1000 nz=true pos=end
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size:[640 x 480] type:5 continuous = true iterations:1000 nz=false pos=none
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size:[640 x 480] type:5 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[640 x 480] type:5 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[640 x 480] type:5 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[640 x 480] type:5 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[640 x 480] type:6 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[640 x 480] type:6 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[640 x 480] type:6 continuous = true iterations:1000 nz=true pos=end
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size:[640 x 480] type:6 continuous = true iterations:1000 nz=false pos=none
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size:[640 x 480] type:6 continuous = false iterations:1000 nz=true pos=begin
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size:[640 x 480] type:6 continuous = false iterations:1000 nz=true pos=middle
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size:[640 x 480] type:6 continuous = false iterations:1000 nz=true pos=end
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size:[640 x 480] type:6 continuous = false iterations:1000 nz=false pos=none
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size:[1024 x 768] type:0 continuous = true iterations:1000 nz=true pos=begin
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size:[1024 x 768] type:0 continuous = true iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:0 continuous = true iterations:1000 nz=true pos=end
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size:[1024 x 768] type:0 continuous = true iterations:1000 nz=false pos=none
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size:[1024 x 768] type:0 continuous = false iterations:1000 nz=true pos=begin
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size:[1024 x 768] type:0 continuous = false iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:0 continuous = false iterations:1000 nz=true pos=end
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size:[1024 x 768] type:0 continuous = false iterations:1000 nz=false pos=none
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size:[1024 x 768] type:2 continuous = true iterations:1000 nz=true pos=begin
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size:[1024 x 768] type:2 continuous = true iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:2 continuous = true iterations:1000 nz=true pos=end
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size:[1024 x 768] type:2 continuous = true iterations:1000 nz=false pos=none
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size:[1024 x 768] type:2 continuous = false iterations:1000 nz=true pos=begin
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size:[1024 x 768] type:2 continuous = false iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:2 continuous = false iterations:1000 nz=true pos=end
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size:[1024 x 768] type:2 continuous = false iterations:1000 nz=false pos=none
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size:[1024 x 768] type:4 continuous = true iterations:1000 nz=true pos=begin
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size:[1024 x 768] type:4 continuous = true iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:4 continuous = true iterations:1000 nz=true pos=end
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size:[1024 x 768] type:4 continuous = true iterations:1000 nz=false pos=none
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size:[1024 x 768] type:4 continuous = false iterations:1000 nz=true pos=begin
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size:[1024 x 768] type:4 continuous = false iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:4 continuous = false iterations:1000 nz=true pos=end
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size:[1024 x 768] type:4 continuous = false iterations:1000 nz=false pos=none
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size:[1024 x 768] type:5 continuous = true iterations:1000 nz=true pos=begin
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size:[1024 x 768] type:5 continuous = true iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:5 continuous = true iterations:1000 nz=true pos=end
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size:[1024 x 768] type:5 continuous = true iterations:1000 nz=false pos=none
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size:[1024 x 768] type:5 continuous = false iterations:1000 nz=true pos=begin
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size:[1024 x 768] type:5 continuous = false iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:5 continuous = false iterations:1000 nz=true pos=end
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size:[1024 x 768] type:5 continuous = false iterations:1000 nz=false pos=none
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size:[1024 x 768] type:6 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[1024 x 768] type:6 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[1024 x 768] type:6 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[1024 x 768] type:6 continuous = true iterations:1000 nz=false pos=none
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size:[1024 x 768] type:6 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[1024 x 768] type:6 continuous = false iterations:1000 nz=true pos=middle
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size:[1024 x 768] type:6 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[1024 x 768] type:6 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[2048 x 2048] type:0 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:0 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:0 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[2048 x 2048] type:0 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[2048 x 2048] type:0 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:0 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:0 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[2048 x 2048] type:0 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[2048 x 2048] type:2 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:2 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:2 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[2048 x 2048] type:2 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[2048 x 2048] type:2 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:2 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:2 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[2048 x 2048] type:2 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[2048 x 2048] type:4 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:4 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:4 continuous = true iterations:1000 nz=true pos=end
cv::hasNonZero =>1 perf:895.381ms => 1116.84 im/s
cv::countNonZero =>1 perf:882.569ms => 1133.06 im/s *
============================================================
size:[2048 x 2048] type:4 continuous = true iterations:1000 nz=false pos=none
cv::hasNonZero =>0 perf:899.53ms => 1111.69 im/s
cv::countNonZero =>0 perf:870.894ms => 1148.24 im/s *
============================================================
size:[2048 x 2048] type:4 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:4 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:4 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[2048 x 2048] type:4 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[2048 x 2048] type:5 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:5 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:5 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[2048 x 2048] type:5 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[2048 x 2048] type:5 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:5 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:5 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[2048 x 2048] type:5 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[2048 x 2048] type:6 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:6 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:6 continuous = true iterations:1000 nz=true pos=end
cv::hasNonZero =>1 perf:2018.92ms => 495.313 im/s
cv::countNonZero =>1 perf:1966.37ms => 508.552 im/s *
============================================================
size:[2048 x 2048] type:6 continuous = true iterations:1000 nz=false pos=none
cv::hasNonZero =>0 perf:2005.87ms => 498.537 im/s
cv::countNonZero =>0 perf:1992.78ms => 501.812 im/s *
============================================================
size:[2048 x 2048] type:6 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[2048 x 2048] type:6 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[2048 x 2048] type:6 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[2048 x 2048] type:6 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:0 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:0 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:0 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:0 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:0 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:0 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:0 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:0 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:2 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:2 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:2 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:2 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:2 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:2 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:2 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:2 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:4 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:4 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:4 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:4 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:4 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:4 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:4 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:4 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:5 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:5 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:5 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:5 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:5 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:5 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:5 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:5 continuous = false iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:6 continuous = true iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:6 continuous = true iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:6 continuous = true iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:6 continuous = true iterations:1000 nz=false pos=none
============================================================
size:[1031 x 1000] type:6 continuous = false iterations:1000 nz=true pos=begin
============================================================
size:[1031 x 1000] type:6 continuous = false iterations:1000 nz=true pos=middle
============================================================
size:[1031 x 1000] type:6 continuous = false iterations:1000 nz=true pos=end
============================================================
size:[1031 x 1000] type:6 continuous = false iterations:1000 nz=false pos=none
done
```
2023-06-09 13:37:20 +03:00
Zihao Mu
eec8a20c33
Merge pull request #23763 from zihaomu:add_runtime_check
...
DNN: fix bug for X86 Winograd #23763
Address https://github.com/opencv/opencv/issues/23760
The patch aims to add a runtime check for X86 platform without AVX(2).
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2023-06-09 09:18:12 +03:00
Alexander Smorkalov
5d913f4d72
Merge pull request #21959 from cpoerschke:4.x-intelligent-scissors-optimisation
...
imgproc: optimise local cost computation in IntelligentScissorsMB::buildMap
2023-06-08 16:45:04 +03:00
Alexander Smorkalov
6d2cbc4055
Merge pull request #23761 from LaurentBerger:typeblobfromimages
...
checktype in blobFromImages and blobFromImagesWithParams
2023-06-08 09:59:01 +03:00
Alexander Smorkalov
6d0add4285
Merge pull request #23762 from TolyaTalamanov:at/gapi-ov-backend-hotfix
...
G-API: OpenVINO Backend Hotfix
2023-06-08 09:50:34 +03:00
Christine Poerschke
f597838685
imgproc: optimise local cost computation in IntelligentScissorsMB::buildMap
2023-06-07 22:06:52 +01:00
TolyaTalamanov
af95395fe7
Fix ifdef condition
2023-06-07 15:42:54 +01:00
unknown
5f8e43da85
checktype in blobFromImages and blobFromImagesWithParams
2023-06-07 16:15:58 +02:00
Abduragim Shtanchaev
6b53fe8f7b
Merge pull request #23746 from Abdurrahheem:ash/graph_simplifier
...
Assertion Fix in Split Layer #23746
### Pull Request Readiness Checklist
This PR fixes issue mentioned in [#23663 ](https://github.com/opencv/opencv/issues/23663 )
Merge with https://github.com/opencv/opencv_extra/pull/1067
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-06-07 16:01:42 +03:00
Christine Poerschke
d3e7968927
Merge pull request #23688 from cpoerschke:4.x-pr-21959-prep
...
imgproc: add contour values check to IntelligentScissorsMB tests
Preparation for the #21959 changes as per @asmorkalov's https://github.com/opencv/opencv/pull/21959#issuecomment-1560511500 suggestion.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [X] I agree to contribute to the project under Apache 2 License.
- [X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [X] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2023-06-07 11:32:17 +03:00
Alexander Smorkalov
b9ce87e8e2
Merge pull request #23750 from mshabunin:fix-bgr2hls-access
...
imgproc/cvtColor: fixed invalid read in BGR2HLS
2023-06-06 11:34:08 +03:00
Alexander Smorkalov
af03e000c7
Merge pull request #23732 from vekkuli:vekkuli-patch-create-featherblender
...
Fix missuse of try_gpu in stitching/FeatherBlender
2023-06-06 10:00:36 +03:00
Alexander Smorkalov
635bbfe92b
Merge pull request #23748 from AleksandrPanov:fix_cornerRefinementMethod_binding
...
fix Java bindings for cornerRefinementMethod binding
2023-06-06 09:59:13 +03:00
Maksim Shabunin
adab462e42
imgproc/cvtColor: fixed invalid read in BGR2HLS
2023-06-05 23:25:44 +03:00
Alex
b5ac7ef2f2
fix cornerRefinementMethod binding
2023-06-05 11:04:11 +03:00
Jaakko Rantala
385003e9fe
Update blenders.cpp
...
Removed passing try_gpu parameter to FeatherBlender constructor because it only has sharpness parameter.
2023-06-02 16:46:05 +03:00
Alexander Panov
9fa014edcd
Merge pull request #23264 from AleksandrPanov:add_detect_qr_with_aruco
...
Add detect qr with aruco #23264
Using Aruco to detect finder patterns to search QR codes.
TODO (in next PR):
- add single QR detect (update `detect()` and `detectAndDecode()`)
- need reduce full enumeration of finder patterns
- need add finder pattern info to `decode` step
- need to merge the pipeline of the old and new algorithm
[Current results:](https://docs.google.com/spreadsheets/d/1ufKyR-Zs-IGXwvqPgftssmTlceVjiQX364sbrjr2QU8/edit#gid=1192415584 )
+20% total detect, +8% total decode in OpenCV [QR benchmark](https://github.com/opencv/opencv_benchmarks/tree/develop/python_benchmarks/qr_codes )
78.4% detect, 58.7% decode vs 58.5 detect, 50.5% decode in default
[main.py.txt](https://github.com/opencv/opencv/files/10762369/main.py.txt )
add new info to [google docs](https://docs.google.com/spreadsheets/d/1ufKyR-Zs-IGXwvqPgftssmTlceVjiQX364sbrjr2QU8/edit?usp=sharing )
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2023-06-02 16:18:24 +03:00
Anatoliy Talamanov
5330112f05
Merge pull request #23595 from TolyaTalamanov:at/implement-openvino-backend
...
[G-API] Implement OpenVINO 2.0 backend #23595
### Pull Request Readiness Checklist
Implemented basic functionality for `OpenVINO` 2.0 G-API backend.
#### Overview
- [x] Implement `Infer` kernel with some of essential configurable parameters + IR/Blob models format support.
- [ ] Implement the rest of kernels: `InferList`, `InferROI`, `Infer2` + other configurable params (e.g reshape)
- [x] Asyncrhonous execution support
- [ ] Remote context support
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-06-02 14:31:03 +03:00
Alexander Smorkalov
36d6ca529d
Merge pull request #23721 from asmorkalov:as/openjpeg_2.5_warning_fix
...
Fixed OpenJPEG 2000 compiler warning for some GCC versions (#23710 )
2023-06-02 09:40:45 +03:00
Alexander Smorkalov
2104d61d4a
Merge pull request #23668 from TolyaTalamanov:at/fix-resize-applying-logic-ie-backend
...
WIP: [G-API] IE Backend: Update the condition for applying the resize preprocessing
2023-06-01 13:55:07 +03:00
Alexander Smorkalov
fe7c5e3f64
Merge pull request #23722 from asmorkalov:as/python_classifiers_update
...
Python package classifiers sync with OpenCV-Python repo
2023-06-01 13:53:36 +03:00
Alexander Smorkalov
0787c31f41
Python package classifiers sync with OpenCV-Python repo.
2023-06-01 10:49:27 +03:00
Alexander Smorkalov
2f805ff7ad
Fixed OpenJPEG 2000 compiler warning for some GCC versions ( #23710 ).
2023-06-01 10:21:43 +03:00
Anna Khakimova
6d3dd24622
Merge pull request #21797 from anna-khakimova:ak/merge3_extend_supported_types
...
GAPI Fluid SIMD:Add support of new several types for the Merge3
- Support of the new several types was added.
- Fixes for the Split/Merge and ConvertTo issues.
2023-05-31 14:59:39 +03:00
Dmitry Matveev
fc5d412ba7
Merge pull request #23597 from dmatveev:dm/gapi_onnx_py_integration
...
G-API: Integration branch for ONNX & Python-related changes #23597
# Changes overview
## 1. Expose ONNX backend's Normalization and Mean-value parameters in Python
* Since Python G-API bindings rely on `Generic` infer to express Inference, the `Generic` specialization of `onnx::Params` was extended with new methods to control normalization (`/255`) and mean-value; these methods were exposed in the Python bindings
* Found some questionable parts in the existing API which I'd like to review/discuss (see comments)
UPD:
1. Thanks to @TolyaTalamanov normalization inconsistencies have been identified with `squeezenet1.0-9` ONNX model itself; tests using these model were updated to DISABLE normalization and NOT using mean/value.
2. Questionable parts were removed and tests still pass.
### Details (taken from @TolyaTalamanov's comment):
`squeezenet1.0.*onnx` - doesn't require scaling to [0,1] and mean/std because the weights of the first convolution already scaled. ONNX documentation is broken. So the correct approach to use this models is:
1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44 but without normalization step:
```
# DON'T DO IT:
# mean_vec = np.array([0.485, 0.456, 0.406])
# stddev_vec = np.array([0.229, 0.224, 0.225])
# norm_img_data = np.zeros(img_data.shape).astype('float32')
# for i in range(img_data.shape[0]):
# norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]
# # add batch channel
# norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32')
# return norm_img_data
# INSTEAD
return img_data.reshape(1, 3, 224, 224)
```
2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters:
```
net = cv.gapi.onnx.params('squeezenet', model_filename)
net.cfgNormalize('data_0', False)
```
**Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution.
---
`squeezenet1.1.*onnx` - requires scaling to [0,1] and mean/std - onnx documentation is correct.
1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44
2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters:
```
net = cv.gapi.onnx.params('squeezenet', model_filename)
net.cfgNormalize('data_0', True) // default
net.cfgMeanStd('data_0', [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
```
**Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution.
## 2. Expose Fluid & kernel package-related functionality in Python
* `cv::gapi::combine()`
* `cv::GKernelPackage::size()` (mainly for testing purposes)
* `cv::gapi::imgproc::fluid::kernels()`
Added a test for the above.
## 3. Fixed issues with Python stateful kernel handling
Fixed error message when `outMeta()` of custom python operation fails.
## 4. Fixed various issues in Python tests
1. `test_gapi_streaming.py` - fixed behavior of Desync test to avoid sporadic issues
2. `test_gapi_infer_onnx.py` - fixed model lookup (it was still using the ONNX Zoo layout but was NOT using the proper env var we use to point to one).
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-05-30 17:52:17 +03:00
Pierre Chatelier
93d490213f
Merge pull request #23690 from chacha21:rotatedRectangleIntersection_precision
...
better accuracy for _rotatedRectangleIntersection() (proposal for #23546 ) #23690
_rotatedRectangleIntersection() can be (statically) customized to use double instead of float for better accuracy
this is a proposal for experimentation around #23546
for better accuracy, _rotatedRectangleIntersection() could use double. It will still return cv::Point2f list for backward compatibility, but the inner computations are controlled by a typedef
- [X] I agree to contribute to the project under Apache 2 License.
- [X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [X] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-05-30 17:46:39 +03:00
Alexander Smorkalov
c55aee1e79
Merge pull request #23707 from Orange-OpenSource:ONNX_Squeeze_negative_index_for_the_axes_attribute
...
Addition of normalize_axis to ONNXImporter::parseSqueeze
2023-05-30 16:33:25 +03:00
Olivier Hotel
0442c6fa81
Addition of normalize_axis to ONNXImporter::parseSqueeze to support negative values for the axes attribut.
...
Negative values are part of the ONNX optset>=11.
Signed-off-by: Olivier Hotel <olivier.hotel@orange.com>
2023-05-30 10:21:27 +02:00
Alexander Smorkalov
1457320702
Merge pull request #23703 from Abdurrahheem:ash/graph_simplifier
...
Added missing index for skipping subgraph simplification
2023-05-30 10:31:00 +03:00
Abduragim Shtanchaev
ecd2e8ff47
added index that check all inputs of nodes that
...
match
2023-05-29 14:48:42 +03:00
Alexander Smorkalov
02397ef851
Merge pull request #23567 from seanm:UBSan-overflow
...
Reformulated some pointer arithmetic to avoid (unsigned) overflow
2023-05-29 12:19:34 +03:00
Alexander Smorkalov
f1076541d1
Merge pull request #23514 from tappytaps:mac-catalyst-fix
...
updated mac catalyst target to be compatible with latest xCode / macos
2023-05-29 12:18:16 +03:00
Christine Poerschke
b5e9eb742c
Merge pull request #23698 from cpoerschke:4.x-pr-21959-perf
...
imgproc: add basic IntelligentScissorsMB performance test #23698
Adding basic performance test that can be used before and after the #21959 changes etc. as per @asmorkalov's https://github.com/opencv/opencv/pull/21959#issuecomment-1565240926 comment.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [X] I agree to contribute to the project under Apache 2 License.
- [X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [X] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2023-05-29 11:02:59 +03:00
Alexander Smorkalov
6ad77b2319
Merge pull request #23682 from asmorkalov:as/openjpeg_2.5.0
...
3rdparty: OpenJPEG update 2.4.0 => 2.5.0
2023-05-29 08:56:57 +03:00
Alexander Smorkalov
67e0ed93a9
Merge pull request #23695 from triple-Mu:triplemu/fix-python-binding
...
Update setup.py
2023-05-27 18:29:33 +03:00
triple Mu
1bffe170e1
Update setup.py
...
Fix error:
UnboundLocalError: local variable 'typing_stub_files' referenced before assignment
2023-05-27 17:23:32 +08:00
Alexander Smorkalov
7b998c30e7
Merge pull request #23694 from dkurt:update_matchTemplateMask
...
Update matchTemplate with mask
2023-05-27 09:42:55 +03:00
Sean McBride
2083fdc9c0
Fixed UBSan warning about undefined pointer arithmetic overflow
...
Pointer arithmetic overflow is always undefined, whether signed or unsigned.
It warned here:
`Addition of unsigned offset to 0x00017fd31b97 overflowed to 0x00017fd30c97`
Convert the offset to a signed number, so that we can offset either forward or backwards.
In my own use of OpenCV at least, this is the only case of pointer arithmetic overflow.
2023-05-26 15:54:52 -04:00
Dmitry Kurtaev
380caa1a87
Merge pull request #23691 from dkurt:pycv_float16_fixes
...
Import and export np.float16 in Python #23691
### Pull Request Readiness Checklist
* Also, fixes `cv::norm` with `NORM_INF` and `CV_16F`
resolves https://github.com/opencv/opencv/issues/23687
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2023-05-26 18:56:21 +03:00
Alexander Smorkalov
900f17d563
Merge pull request #23677 from asmorkalov:as/objc_naming_backport
...
ObjC naming backport from 5.x
2023-05-26 18:54:34 +03:00
Dmitry Kurtaev
c97942cf78
Fix mask thresholding
2023-05-26 18:51:33 +03:00
captain-n3m0
6157db6462
Fixed matchTemplate function. #23585
2023-05-26 18:51:01 +03:00
Duong Dac
a9424868a1
Merge pull request #20370 from ddacw:stub-gen-next
...
Python typing stub generation #20370
Add stub generation to `gen2.py`, addressing #14590 .
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV
- [x] The PR is proposed to proper branch
- [x] There is reference to original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2023-05-26 18:25:46 +03:00
Alexander Smorkalov
a63636171e
Re-integrated OpenJPEG 2.5.0 to OpenCV.
2023-05-26 14:14:30 +03:00
Alexander Smorkalov
cf0ba039c3
Merge pull request #23625 from zihaomu:improve_conv
...
DNN: Remove unnecessary flags for convolution
2023-05-26 12:59:36 +03:00
Alexander Smorkalov
c75d0b8ca3
Merge pull request #23684 from LaurentBerger:I23683
...
solve Issue I23683
2023-05-26 12:01:50 +03:00
Alexander Smorkalov
a2b679bc26
Merge pull request #23686 from LaurentBerger:I23685
...
solve Issue 23685
2023-05-26 10:53:29 +03:00
unknown
45dcd156b7
solve Issue 23685
2023-05-25 21:34:51 +02:00
unknown
e6282b8ace
solve Issue I23683
2023-05-25 19:42:01 +02:00
anders-nylander
87331ca1a0
Merge pull request #21736 from anders-nylander:fix_android_sdk_tools_dependency
...
Android: don't require deprecated tools #21736
Checking for these deprecated is no longer necessary, and infact broken on fresh Android SDK installs. Remove the check.
resolves #21735
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2023-05-25 18:29:14 +03:00
