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Test create custom layer in python * check is contiguos * Add custom layer test * Fix test * Remove assert * Move assert to pyopencv dnn * remove assert * Add unregister * Fix python2 * proto to bytearray * Fix data type
- don't override current application thread names - set name for own threads only
…_ipp_bug * imgproc(IPP): wrong result from threshold(THRESH_TOZERO) * imgproc(IPP): disable IPP code to pass THRESH_TOZERO test
* resize: HResizeLinear reduce duplicate work There appears to be a 2x unroll of the HResizeLinear against k, however the k value is only incremented by 1 during the unroll. This results in k - 1 duplicate passes when k > 1. Likewise, the final pass may not respect the work done by the vector loop. Start it with the offset returned by the vector op if implemented. Note, no vector ops are implemented today. The performance is most noticable on a linear downscale. A set of performance tests are added to characterize this. The performance improvement is 10-50% depending on the scaling. * imgproc: vectorize HResizeLinear Performance is mostly gated by the gather operations for x inputs. Likewise, provide a 2x unroll against k, this reduces the number of alpha gathers by 1/2 for larger k. While not a 4x improvement, it still performs substantially better under P9 for a 1.4x improvement. P8 baseline is 1.05-1.10x due to reduced VSX instruction set. For float types, this results in a more modest 1.2x improvement. * Update U8 processing for non-bitexact linear resize * core: hal: vsx: improve v_load_expand_q With a little help, we can do this quickly without gprs on all VSX enabled targets. * resize: Fix cn == 3 step per feedback Per feedback, ensure we don't overrun. This was caught via the failure observed in Test_TensorFlow.inception_accuracy.
* Add eps error checking for approxPolyDP to allow sensible values only for epsilon value of Douglas-Peucker algorithm. * Review changes for PR
-c is required to avoid linking (and the associated missing "main" message) when linker flags like "-Wl,-z,relro" are passed to GCC
…nt_src_channels dnn(eltwise): fix handling of different number of channels * dnn(test): reproducer for Eltwise layer issue from PR16063 * dnn(eltwise): rework support for inputs with different channels * dnn(eltwise): get rid of finalize(), variableChannels * dnn(eltwise): update input sorting by number of channels - do not swap inputs if number of channels are same after truncation * dnn(test): skip "shortcut" with batch size 2 on MYRIAD targets
* imgproc: Prevent 1B overrun of 8C3 SIMD optimization The fourth value read via v_load_q is essentially ignored, but can cause trouble if it happens to cross page boundaries. The final few iterations may attempt to read the most extreme elements of S, which will read 1B beyond the array in most aligment cases. Dynamically compute the stop. This could be hoised from the loop, but will require a more extensive change. Likewise, cleanup the iteration increment statements to make it more obvious they do channel count (3) elements per pass. This should resolve opencv#16137 * imgproc(resize): extra check
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#15257 from pmur:resize
#15988 from l-bat:custom_layer
#16070 from dkurt:backport_15611
#16071 from alalek:update_version_3.4.9-pre
#16076 from l-bat:prior_ngraph
#16079 from alalek:imgproc_color_clarify_error_message
#16085 from alalek:imgproc_threshold_to_zero_ipp_bug
#16088 from alalek:dnn_eltwise_layer_different_src_channels
#16089 from dkurt:dnn_ie_fix_fpga
#16093 from alalek:core_itt_thread_name_16072
#16094 from saskatchewancatch:issue-16053
#16098 from alalek:dnn_clarify_error_getMemoryShapes
#16101 from dkurt:dnn_ie_ngraph_detection_output
#16102 from asmorkalov:as/xperience_c
#16106 from dkurt:dnn_ie_ngraph_weights_fusion
#16107 from dkurt:dnn_ie_ngraph_v1_conv
#16109 from pixelb:gcc-9-pch
#16117 from mshabunin:fix-hist-args-34
#16120 from alalek:python3.8
#16121 from shimat:fix_voronoi_typo
#16123 from alalek:opencv_include_port_file
#16124 from alalek:issue_13354
#16125 from alalek:core_safe_xadd
#16138 from pmur:reg_16137
Previous "Merge 3.4": #16068