* Add Operator override for multi-channel Mat with literal constant.
* simple test
* Operator overloading channel constraint for primitive types
* fix some test for #13586
* Remove isIntel check from deep learning layers
* Remove fp16->fp32 fallbacks where it's not necessary
* Fix Kernel::run to prevent localsize > globalsize
Support asymmetric padding in pooling layer (#12519)
* Add Inception_V1 support in ONNX
* Add asymmetric padding in OpenCL and Inference engine
* Refactoring
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
* 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
* 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
Remove some assertions
Replace std::ifstream to std::istream
Add test for new importer
Remove constructor to load file
Rename cfgStream and darknetModelStream to ifile
Add error notification to inform pathname to user
Use FileStorage instead of std::istream
Use FileNode instead of FileStorage
Fix typo
dnn: Fix output mismatch when forward dnn model contain [depthwise conv(group=1) + bn + prelu] (#11649)
* this can make sure [depthwise conv(group=1) + bn + prelu] output not shift
* add TEST to show the output mismatch in [DWconv+Prelu]
* fix typo
* change loading image to init cvMat directly
* build runtime model, without loading external model
* remove whitespace
* change way to create a cvmat
* add bias_term, add target output
* fix [dwconv + prelu] value mismatch when no optimizations
* fix Test error when change output channels
* add parametric test
* change num_output to group value
* change conv code and change test back
* Added ResizeBilinear op for tf
Combined ResizeNearestNeighbor and ResizeBilinear layers into Resize (with an interpolation param).
Minor changes to tf_importer and resize layer to save some code lines
Minor changes in init.cpp
Minor changes in tf_importer.cpp
* Replaced implementation of a custom ResizeBilinear layer to all layers
* Use Mat::ptr. Replace interpolation flags
