Before this fix, the code would fail if only standard deviations of
extrinsic parameters are requested. While standard deviations matrix
should be computed if any set of standard deviations is requested. A
variable is added to represent this case.
Support asymmetric padding in pooling layer (#12519)
* Add Inception_V1 support in ONNX
* Add asymmetric padding in OpenCL and Inference engine
* Refactoring
* add new chessboard detector
The chessboar detector is based on the paper.
Accurate Detection and Localization of Checkerboard Corners for
Calibration Alexander Duda, Udo Frese
British Machine Vision Conference, o.A., 2018.
It utilizes point symmetry of checkerboard corners in combination with a
localized Radon transform approximated by box filters to achieve high
performance even on large images. Here, tests have shown that the
ability to localize checkerboard corners is close to the theoretical
limit of 1/100 of a pixel while being considerably less sensitive
to image noise than standard methods.
* chessboard: add reference to bibtex file
* chessboard: add dependency to opencv_flann
* fix: test chesscorners. It is valid to return an empty list
In case no chessboard was detected it should be valid for the detector
to return an empty list.
For simplifcation, it should be allowed to return any number of corners
if they are flagged as not found.
* fix: opencv.bib remove empty lines
* fix: doc findChessboardCorners replace cvSize with cv::Size
* chessboard tests: factor out logic selecting detector
* chessboard: add unit test for findChessboardCorners2
This is includes a new chessboard generator which supports subpix
corners with high accuracy by wrapping an optimal chessboard using
wrapPerspective.
* fix: chessboard unit test - overwrite of default parameter flag of findCirclesGrid
* chessboard: remove trailing whitespace
* chessboard: fix debug drawing
* chessboard: fix some issues during code review
* chessboard: normalize asymmetric chessboard
* chessboard: fix float double warning
* remove trailing whitespace
* chessboards: fix compiler warnings
* chessboards: fix compiler warnings
* checkerboard: some performance improvements
* chessboard: remove NULL macros for language bindinges from internal headers
* chessboard: shorten license terms
* chessboard: remove unused internal method
* chessboard: set helper functions to static
* chessboard: fix normalizePoints1D using unshifted points
* chessboard: remove wrongly copied text
* chessboard: use CV_CheckTypeEQ macro
* chessboard: comment all NaN checks
* chessboard: use consistent color conversion
* chessboard: use CheckChannelEQ macro
* chessboard: assume gray color image for internal methods
* chessboard: use std::swap
* chessboard: use Mat.dataend
* chessboard: fix compiler warnings
* chessboard: replace some checks witch CV_CHECK macro
* chessboard: fix comparison function for partial sort
* chessboard: small cleanup
* chessboard: use short license header
* chessboard: rename findChessboard2 to findChessboardSB
* chessboard: fix type in unit test
Feature/region layer batch mode (#12249)
* Add batch mode for Darknet networks.
Swap variables in test_darknet.
Adapt reorg layer to batch mode.
Adapt region layer.
Add OpenCL implementation.
Remove trailing whitespace.
Bugifx reorg opencl implementation.
Fix bug in OpenCL reorg.
Fix modulo bug.
Fix bug.
Reorg openCL.
Restore reorg layer opencl code.
OpenCl fix.
Work on openCL reorg.
Remove whitespace.
Fix openCL region layer implementation.
Fix bug.
Fix softmax region opencl bug.
Fix opencl bug.
Fix openCL bug.
Update aff_trans.cpp
When the fullAffine parameter is set to false, the estimateRigidTransform function maybe return empty, then the _localAffineEstimate function will be called, but the bug in it will result in incorrect results.
core(libva): support YV12 too
Added to CPU path only.
OpenCL code path still expects NV12 only (according to Intel OpenCL extension)
cmake: allow to specify own libva paths
via CMake:
- `-DVA_LIBRARIES=/opt/intel/mediasdk/lib64/libva.so.2\;/opt/intel/mediasdk/lib64/libva-drm.so.2`
android: NDK17 support
tested with NDK 17b (17.1.4828580)
Enable more deep learning tests using Intel's Inference Engine backend
ts: don't pass NULL for std::string() constructor
openvino: use 2018R3 defines
experimental version++
OpenCV version++
OpenCV 3.4.3
OpenCV version '-openvino'
openvino: use 2018R3 defines
Fixed windows build with InferenceEngine
dnn: fix variance setting bug for PriorBoxLayer
- The size of second channel should be size[2] of output tensor,
- The Scalar should be {variance[0], variance[0], variance[0], variance[0]}
for _variance.size() == 1 case.
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
Fix lifetime of networks which are loaded from Model Optimizer IRs
Adds a small note describing BUILD_opencv_world (#12332)
* Added a mall note describing BUILD_opencv_world cmake option to the Installation in Windows tutorial.
* Made slight changes in BUILD_opencv_world documentation.
* Update windows_install.markdown
improved grammar
Update opengl_interop.cpp
resolves#12307
java: fix LIST_GET macro
fix typo
Added option to fail on missing testdata
Fixed that object_detection.py does not work in python3.
cleanup: IPP Async (IPP_A)
except header file with conversion routines (will be removed in OpenCV 4.0)
imgcodecs: add null pointer check
Include preprocessing nodes to object detection TensorFlow networks (#12211)
* Include preprocessing nodes to object detection TensorFlow networks
* Enable more fusion
* faster_rcnn_resnet50_coco_2018_01_28 test
countNonZero function reworked to use wide universal intrinsics instead of SSE2 intrinsics
resolve#5788
imgcodecs(webp): multiple fixes
- don't reallocate passed 'img' (test fixed - must use IMREAD_UNCHANGED / IMREAD_ANYCOLOR)
- avoid memory DDOS
- avoid reading of whole file during header processing
- avoid data access after allocated buffer during header processing (missing checks)
- use WebPFree() to free allocated buffers (libwebp >= 0.5.0)
- drop unused & undefined `.close()` method
- added checks for channels >= 5 in encoder
ml: fix adjusting K in KNearest (#12358)
dnn(perf): fix and merge Convolution tests
- OpenCL tests didn't run any OpenCL kernels
- use real configuration from existed models (the first 100 cases)
- batch size = 1
dnn(test): use dnnBackendsAndTargets() param generator
Bit-exact resize reworked to use wide intrinsics (#12038)
* Bit-exact resize reworked to use wide intrinsics
* Reworked bit-exact resize row data loading
* Added bit-exact resize row data loaders for SIMD256 and SIMD512
* Fixed type punned pointer dereferencing warning
* Reworked loading of source data for SIMD256 and SIMD512 bit-exact resize
Bit-exact GaussianBlur reworked to use wide intrinsics (#12073)
* Bit-exact GaussianBlur reworked to use wide intrinsics
* Added v_mul_hi universal intrinsic
* Removed custom SSE2 branch from bit-exact GaussianBlur
* Removed loop unrolling for gaussianBlur horizontal smoothing
doc: fix English gramma in tutorial out-of-focus-deblur filter (#12214)
* doc: fix English gramma in tutorial out-of-focus-deblur filter
* Update out_of_focus_deblur_filter.markdown
slightly modified one sentence
doc: add new tutorial motion deblur filter (#12215)
* doc: add new tutorial motion deblur filter
* Update motion_deblur_filter.markdown
a few minor changes
Replace Slice layer to Crop in Faster-RCNN networks from Caffe
js: use generated list of OpenCV headers
- replaces hand-written list
imgcodecs(webp): use safe cast to size_t on Win32
* Put Version status back to -dev.
follow the common codestyle
Exclude some target engines.
Refactor formulas.
Refactor code.
* Remove unused variable.
* Remove inference engine check for yolov2.
* Alter darknet batch tests to test with two different images.
* Add yolov3 second image GT.
* Fix bug.
* Fix bug.
* Add second test.
* Remove comment.
* Add NMS on network level.
* Add helper files to dev.
* syntax fix.
* Fix OD sample.
Fix sample dnn object detection.
Fix NMS boxes bug.
remove trailing whitespace.
Remove debug function.
Change thresholds for opencl tests.
* Adapt score diff and iou diff.
* Alter iouDiffs.
* Add debug messages.
* Adapt iouDiff.
* Fix tests
* Add Squeezenet support in ONNX
* Add AlexNet support in ONNX
* Add Googlenet support in ONNX
* Add CaffeNet and RCNN support in ONNX
* Add VGG16 and VGG16 with batch normalization support in ONNX
* Add RCNN, ZFNet, ResNet18v1 and ResNet50v1 support in ONNX
* Add ResNet101_DUC_HDC
* Add Tiny Yolov2
* Add CNN_MNIST, MobileNetv2 and LResNet100 support in ONNX
* Add ONNX models for emotion recognition
* Add DenseNet121 support in ONNX
* Add Inception v1 support in ONNX
* Refactoring
* Fix tests
* Fix tests
* Skip unstable test
* Modify Reshape operation
* added basic support for CV_16F (the new datatype etc.). CV_USRTYPE1 is now equal to CV_16F, which may break some [rarely used] functionality. We'll see
* fixed just introduced bug in norm; reverted errorneous changes in Torch importer (need to find a better solution)
* addressed some issues found during the PR review
* restored the patch to fix some perf test failures
* may be an typo fix
* remove identical branch,may be paste error
* add parentheses around macro parameter
* simplify if condition
* check malloc fail
* change the condition of branch removed by commit 3041502861
* rewrote Mat::convertTo() and convertScaleAbs() to wide universal intrinsics; added always-available and SIMD-optimized FP16<=>FP32 conversion
* fixed compile warnings
* fix some more compile errors
* slightly relaxed accuracy threshold for int->float conversion (since we now do it using single-precision arithmetics, not double-precision)
* fixed compile errors on iOS, Android and in the baseline C++ version (intrin_cpp.hpp)
* trying to fix ARM-neon builds
* trying to fix ARM-neon builds
* trying to fix ARM-neon builds
* trying to fix ARM-neon builds
* trying to fix the custom AVX2 builder test failures (false alarms)
* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests
* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests
* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests
* seemingly disabled false alarm warning in surf.cpp; increased tolerance thresholds in the tests for SolvePnP and in DNN/ENet
* bgr2gray 8u fixed to be in conformance with IPP code
* coefficients fixed so their sum is 32768
* java test for CascadeDetect fixed: equalizeHist added
* Remove a forward method in dnn::Layer
* Add a test
* Fix tests
* Mark multiple dnn::Layer::finalize methods as deprecated
* Replace back dnn's inputBlobs to vector of pointers
* Remove Layer::forward_fallback from CV_OCL_RUN scopes
fix some errors found by static analyzer. (#12391)
* fix possible divided by zero and by negative values
* only 4 elements are used in these arrays
* fix uninitialized member
* use boolean type for semantic boolean variables
* avoid invalid array index
* to avoid exception and because base64_beg is only used in this block
* use std::atomic<bool> to avoid thread control race condition
Currently the private control enumeration will be stopped when QUERYCTRL
returns -EINVAL only. It is possible however that other errors occur.
One particular case is when the v4l2 device doesn't support any controls
and doesn't implement the QUERYCTRL ioctl. In that case the v4l2
framework returns -ENOTTY. In that case the current control enumeration
will go in an endless loop.
To fix this change the control enumeration stop condition. If any errors
occur, end the control enumeration.
Signed-off-by: Todor Tomov <todor.tomov@linaro.org>
* fix 12218
* Update test_distancetransform.cpp
marked the test as "BIGDATA_TEST" in order to skip it on low-mem platforms
* modify test
* use a smaller image in the test
* fix test code
* Bit-exact resize reworked to use wide intrinsics
* Reworked bit-exact resize row data loading
* Added bit-exact resize row data loaders for SIMD256 and SIMD512
* Fixed type punned pointer dereferencing warning
* Reworked loading of source data for SIMD256 and SIMD512 bit-exact resize
* Add HPX backend for OpenCV implementation
Adds hpx backend for cv::parallel_for_() calls respecting the nstripes chunking parameter. C++ code for the backend is added to modules/core/parallel.cpp. Also, the necessary changes to cmake files are introduced.
Backend can operate in 2 versions (selectable by cmake build option WITH_HPX_STARTSTOP): hpx (runtime always on) and hpx_startstop (start and stop the backend for each cv::parallel_for_() call)
* WIP: Conditionally include hpx_main.hpp to tests in core module
Header hpx_main.hpp is included to both core/perf/perf_main.cpp and core/test/test_main.cpp.
The changes to cmake files for linking hpx library to above mentioned test executalbles are proposed but have issues.
* Add coditional iclusion of hpx_main.hpp to cpp cpu modules
* Remove start/stop version of hpx backend
- The size of second channel should be size[2] of output tensor,
- The Scalar should be {variance[0], variance[0], variance[0], variance[0]}
for _variance.size() == 1 case.
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
When the fullAffine parameter is set to false, the estimateRigidTransform function maybe return empty, then the _localAffineEstimate function will be called, but the bug in it will result in incorrect results.
