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* 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. |
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| calib3d | ||
| core | ||
| cudaarithm | ||
| cudabgsegm | ||
| cudacodec | ||
| cudafeatures2d | ||
| cudafilters | ||
| cudaimgproc | ||
| cudalegacy | ||
| cudaobjdetect | ||
| cudaoptflow | ||
| cudastereo | ||
| cudawarping | ||
| cudev | ||
| dnn | ||
| features2d | ||
| flann | ||
| highgui | ||
| imgcodecs | ||
| imgproc | ||
| java | ||
| js | ||
| ml | ||
| objdetect | ||
| photo | ||
| python | ||
| shape | ||
| stitching | ||
| superres | ||
| ts | ||
| video | ||
| videoio | ||
| videostab | ||
| viz | ||
| world | ||
| CMakeLists.txt | ||