星月川水

CMake Error: CUDA_nppi_LIBRARY (ADVANCED)

使用Cmake编译opencv源码遇到如下错误

CMake Error: The following variables are used in this project, but they are set to NOTFOUND.

Please set them or make sure they are set and tested correctly in the CMake files:

CUDA_nppi_LIBRARY (ADVANCED)

linked by target "opencv_cudev" in directory D:/Cproject/opencv/opencv/sources/modules/cudev

linked by target "opencv_cudev" in directory D:/Cproject/opencv/opencv/sources/modules/cudev

linked by target "opencv_test_cudev" in directory D:/Cproject/opencv/opencv/sources/modules/cudev/test

linked by target "opencv_core" in directory D:/Cproject/opencv/opencv/sources/modules/core

linked by target "opencv_core" in directory D:/Cproject/opencv/opencv/sources/modules/core

linked by target "opencv_test_core" in directory D:/Cproject/opencv/opencv/sources/modules/core

linked by target "opencv_perf_core" in directory D:/Cproject/opencv/opencv/sources/modules/core

linked by target "opencv_test_cudaarithm" in directory D:/Cproject/opencv/opencv/sources/modules/cudaarithm

linked by target "opencv_cudaarithm" in directory D:/Cproject/opencv/opencv/sources/modules/cudaarithm

linked by target "opencv_cudaarithm" in directory D:/Cproject/opencv/opencv/sources/modules/cudaarithm

linked by target "opencv_perf_cudaarithm" in directory D:/Cproject/opencv/opencv/sources/modules/cudaarithm

linked by target "opencv_flann" in directory D:/Cproject/opencv/opencv/sources/modules/flann

linked by target "opencv_flann" in directory D:/Cproject/opencv/opencv/sources/modules/flann

linked by target "opencv_test_flann" in directory D:/Cproject/opencv/opencv/sources/modules/flann

linked by target "opencv_imgproc" in directory D:/Cproject/opencv/opencv/sources/modules/imgproc

linked by target "opencv_imgproc" in directory D:/Cproject/opencv/opencv/sources/modules/imgproc

linked by target "opencv_test_imgproc" in directory D:/Cproject/opencv/opencv/sources/modules/imgproc

linked by target "opencv_perf_imgproc" in directory D:/Cproject/opencv/opencv/sources/modules/imgproc

linked by target "opencv_test_ml" in directory D:/Cproject/opencv/opencv/sources/modules/ml

linked by target "opencv_ml" in directory D:/Cproject/opencv/opencv/sources/modules/ml

linked by target "opencv_ml" in directory D:/Cproject/opencv/opencv/sources/modules/ml

linked by target "opencv_test_video" in directory D:/Cproject/opencv/opencv/sources/modules/video

linked by target "opencv_video" in directory D:/Cproject/opencv/opencv/sources/modules/video

linked by target "opencv_video" in directory D:/Cproject/opencv/opencv/sources/modules/video

linked by target "opencv_perf_video" in directory D:/Cproject/opencv/opencv/sources/modules/video

linked by target "opencv_cudabgsegm" in directory D:/Cproject/opencv/opencv/sources/modules/cudabgsegm

linked by target "opencv_cudabgsegm" in directory D:/Cproject/opencv/opencv/sources/modules/cudabgsegm

linked by target "opencv_test_cudabgsegm" in directory D:/Cproject/opencv/opencv/sources/modules/cudabgsegm

linked by target "opencv_perf_cudabgsegm" in directory D:/Cproject/opencv/opencv/sources/modules/cudabgsegm

linked by target "opencv_cudafilters" in directory D:/Cproject/opencv/opencv/sources/modules/cudafilters

linked by target "opencv_cudafilters" in directory D:/Cproject/opencv/opencv/sources/modules/cudafilters

linked by target "opencv_test_cudafilters" in directory D:/Cproject/opencv/opencv/sources/modules/cudafilters

linked by target "opencv_perf_cudafilters" in directory D:/Cproject/opencv/opencv/sources/modules/cudafilters

linked by target "opencv_cudaimgproc" in directory D:/Cproject/opencv/opencv/sources/modules/cudaimgproc

linked by target "opencv_cudaimgproc" in directory D:/Cproject/opencv/opencv/sources/modules/cudaimgproc

linked by target "opencv_test_cudaimgproc" in directory D:/Cproject/opencv/opencv/sources/modules/cudaimgproc

linked by target "opencv_perf_cudaimgproc" in directory D:/Cproject/opencv/opencv/sources/modules/cudaimgproc

linked by target "opencv_perf_cudawarping" in directory D:/Cproject/opencv/opencv/sources/modules/cudawarping

linked by target "opencv_cudawarping" in directory D:/Cproject/opencv/opencv/sources/modules/cudawarping

linked by target "opencv_cudawarping" in directory D:/Cproject/opencv/opencv/sources/modules/cudawarping

linked by target "opencv_test_cudawarping" in directory D:/Cproject/opencv/opencv/sources/modules/cudawarping

linked by target "opencv_imgcodecs" in directory D:/Cproject/opencv/opencv/sources/modules/imgcodecs

linked by target "opencv_imgcodecs" in directory D:/Cproject/opencv/opencv/sources/modules/imgcodecs

linked by target "opencv_test_imgcodecs" in directory D:/Cproject/opencv/opencv/sources/modules/imgcodecs

linked by target "opencv_perf_imgcodecs" in directory D:/Cproject/opencv/opencv/sources/modules/imgcodecs

linked by target "opencv_perf_photo" in directory D:/Cproject/opencv/opencv/sources/modules/photo

linked by target "opencv_photo" in directory D:/Cproject/opencv/opencv/sources/modules/photo

linked by target "opencv_photo" in directory D:/Cproject/opencv/opencv/sources/modules/photo

linked by target "opencv_test_photo" in directory D:/Cproject/opencv/opencv/sources/modules/photo

linked by target "opencv_test_shape" in directory D:/Cproject/opencv/opencv/sources/modules/shape

linked by target "opencv_shape" in directory D:/Cproject/opencv/opencv/sources/modules/shape

linked by target "opencv_shape" in directory D:/Cproject/opencv/opencv/sources/modules/shape

linked by target "opencv_test_videoio" in directory D:/Cproject/opencv/opencv/sources/modules/videoio

linked by target "opencv_videoio" in directory D:/Cproject/opencv/opencv/sources/modules/videoio

linked by target "opencv_videoio" in directory D:/Cproject/opencv/opencv/sources/modules/videoio

linked by target "opencv_perf_videoio" in directory D:/Cproject/opencv/opencv/sources/modules/videoio

linked by target "opencv_cudacodec" in directory D:/Cproject/opencv/opencv/sources/modules/cudacodec

linked by target "opencv_cudacodec" in directory D:/Cproject/opencv/opencv/sources/modules/cudacodec

linked by target "opencv_perf_cudacodec" in directory D:/Cproject/opencv/opencv/sources/modules/cudacodec

linked by target "opencv_test_cudacodec" in directory D:/Cproject/opencv/opencv/sources/modules/cudacodec

linked by target "opencv_highgui" in directory D:/Cproject/opencv/opencv/sources/modules/highgui

linked by target "opencv_highgui" in directory D:/Cproject/opencv/opencv/sources/modules/highgui

linked by target "opencv_test_highgui" in directory D:/Cproject/opencv/opencv/sources/modules/highgui

linked by target "opencv_perf_objdetect" in directory D:/Cproject/opencv/opencv/sources/modules/objdetect

linked by target "opencv_objdetect" in directory D:/Cproject/opencv/opencv/sources/modules/objdetect

linked by target "opencv_objdetect" in directory D:/Cproject/opencv/opencv/sources/modules/objdetect

linked by target "opencv_test_objdetect" in directory D:/Cproject/opencv/opencv/sources/modules/objdetect

linked by target "opencv_ts" in directory D:/Cproject/opencv/opencv/sources/modules/ts

linked by target "opencv_ts" in directory D:/Cproject/opencv/opencv/sources/modules/ts

linked by target "opencv_test_features2d" in directory D:/Cproject/opencv/opencv/sources/modules/features2d

linked by target "opencv_features2d" in directory D:/Cproject/opencv/opencv/sources/modules/features2d

linked by target "opencv_features2d" in directory D:/Cproject/opencv/opencv/sources/modules/features2d

linked by target "opencv_perf_features2d" in directory D:/Cproject/opencv/opencv/sources/modules/features2d

linked by target "opencv_calib3d" in directory D:/Cproject/opencv/opencv/sources/modules/calib3d

linked by target "opencv_calib3d" in directory D:/Cproject/opencv/opencv/sources/modules/calib3d

linked by target "opencv_test_calib3d" in directory D:/Cproject/opencv/opencv/sources/modules/calib3d

linked by target "opencv_perf_calib3d" in directory D:/Cproject/opencv/opencv/sources/modules/calib3d

linked by target "opencv_cudafeatures2d" in directory D:/Cproject/opencv/opencv/sources/modules/cudafeatures2d

linked by target "opencv_cudafeatures2d" in directory D:/Cproject/opencv/opencv/sources/modules/cudafeatures2d

linked by target "opencv_test_cudafeatures2d" in directory D:/Cproject/opencv/opencv/sources/modules/cudafeatures2d

linked by target "opencv_perf_cudafeatures2d" in directory D:/Cproject/opencv/opencv/sources/modules/cudafeatures2d

linked by target "opencv_cudalegacy" in directory D:/Cproject/opencv/opencv/sources/modules/cudalegacy

linked by target "opencv_cudalegacy" in directory D:/Cproject/opencv/opencv/sources/modules/cudalegacy

linked by target "opencv_test_cudalegacy" in directory D:/Cproject/opencv/opencv/sources/modules/cudalegacy

linked by target "opencv_perf_cudalegacy" in directory D:/Cproject/opencv/opencv/sources/modules/cudalegacy

linked by target "opencv_cudaobjdetect" in directory D:/Cproject/opencv/opencv/sources/modules/cudaobjdetect

linked by target "opencv_cudaobjdetect" in directory D:/Cproject/opencv/opencv/sources/modules/cudaobjdetect

linked by target "opencv_perf_cudaobjdetect" in directory D:/Cproject/opencv/opencv/sources/modules/cudaobjdetect

linked by target "opencv_test_cudaobjdetect" in directory D:/Cproject/opencv/opencv/sources/modules/cudaobjdetect

linked by target "opencv_cudaoptflow" in directory D:/Cproject/opencv/opencv/sources/modules/cudaoptflow

linked by target "opencv_cudaoptflow" in directory D:/Cproject/opencv/opencv/sources/modules/cudaoptflow

linked by target "opencv_test_cudaoptflow" in directory D:/Cproject/opencv/opencv/sources/modules/cudaoptflow

linked by target "opencv_perf_cudaoptflow" in directory D:/Cproject/opencv/opencv/sources/modules/cudaoptflow

linked by target "opencv_perf_cudastereo" in directory D:/Cproject/opencv/opencv/sources/modules/cudastereo

linked by target "opencv_cudastereo" in directory D:/Cproject/opencv/opencv/sources/modules/cudastereo

linked by target "opencv_cudastereo" in directory D:/Cproject/opencv/opencv/sources/modules/cudastereo

linked by target "opencv_test_cudastereo" in directory D:/Cproject/opencv/opencv/sources/modules/cudastereo

linked by target "opencv_test_stitching" in directory D:/Cproject/opencv/opencv/sources/modules/stitching

linked by target "opencv_stitching" in directory D:/Cproject/opencv/opencv/sources/modules/stitching

linked by target "opencv_stitching" in directory D:/Cproject/opencv/opencv/sources/modules/stitching

linked by target "opencv_perf_stitching" in directory D:/Cproject/opencv/opencv/sources/modules/stitching

linked by target "opencv_superres" in directory D:/Cproject/opencv/opencv/sources/modules/superres

linked by target "opencv_superres" in directory D:/Cproject/opencv/opencv/sources/modules/superres

linked by target "opencv_test_superres" in directory D:/Cproject/opencv/opencv/sources/modules/superres

linked by target "opencv_perf_superres" in directory D:/Cproject/opencv/opencv/sources/modules/superres

linked by target "opencv_videostab" in directory D:/Cproject/opencv/opencv/sources/modules/videostab

linked by target "opencv_videostab" in directory D:/Cproject/opencv/opencv/sources/modules/videostab

linked by target "opencv_traincascade" in directory D:/Cproject/opencv/opencv/sources/apps/traincascade

linked by target "opencv_createsamples" in directory D:/Cproject/opencv/opencv/sources/apps/createsamples

linked by target "opencv_annotation" in directory D:/Cproject/opencv/opencv/sources/apps/annotation

linked by target "opencv_visualisation" in directory D:/Cproject/opencv/opencv/sources/apps/visualisation

linked by target "opencv_version" in directory D:/Cproject/opencv/opencv/sources/apps/version

Please set them or make sure they are set and tested correctly in the CMake files:

CUDA_nppi_LIBRARY (ADVANCED)

linked by target "opencv_cudev" in directory D:/Cproject/opencv/opencv/sources/modules/cudev

linked by target "opencv_cudev" in directory D:/Cproject/opencv/opencv/sources/modules/cudev

linked by target "opencv_test_cudev" in directory D:/Cproject/opencv/opencv/sources/modules/cudev/test

linked by target "opencv_core" in directory D:/Cproject/opencv/opencv/sources/modules/core

linked by target "opencv_core" in directory D:/Cproject/opencv/opencv/sources/modules/core

linked by target "opencv_test_core" in directory D:/Cproject/opencv/opencv/sources/modules/core

linked by target "opencv_perf_core" in directory D:/Cproject/opencv/opencv/sources/modules/core

linked by target "opencv_test_cudaarithm" in directory D:/Cproject/opencv/opencv/sources/modules/cudaarithm

linked by target "opencv_cudaarithm" in directory D:/Cproject/opencv/opencv/sources/modules/cudaarithm

linked by target "opencv_cudaarithm" in directory D:/Cproject/opencv/opencv/sources/modules/cudaarithm

linked by target "opencv_perf_cudaarithm" in directory D:/Cproject/opencv/opencv/sources/modules/cudaarithm

linked by target "opencv_flann" in directory D:/Cproject/opencv/opencv/sources/modules/flann

linked by target "opencv_flann" in directory D:/Cproject/opencv/opencv/sources/modules/flann

linked by target "opencv_test_flann" in directory D:/Cproject/opencv/opencv/sources/modules/flann

linked by target "opencv_imgproc" in directory D:/Cproject/opencv/opencv/sources/modules/imgproc

linked by target "opencv_imgproc" in directory D:/Cproject/opencv/opencv/sources/modules/imgproc

linked by target "opencv_test_imgproc" in directory D:/Cproject/opencv/opencv/sources/modules/imgproc

linked by target "opencv_perf_imgproc" in directory D:/Cproject/opencv/opencv/sources/modules/imgproc

linked by target "opencv_test_ml" in directory D:/Cproject/opencv/opencv/sources/modules/ml

linked by target "opencv_ml" in directory D:/Cproject/opencv/opencv/sources/modules/ml

linked by target "opencv_ml" in directory D:/Cproject/opencv/opencv/sources/modules/ml

linked by target "opencv_test_video" in directory D:/Cproject/opencv/opencv/sources/modules/video

linked by target "opencv_video" in directory D:/Cproject/opencv/opencv/sources/modules/video

linked by target "opencv_video" in directory D:/Cproject/opencv/opencv/sources/modules/video

linked by target "opencv_perf_video" in directory D:/Cproject/opencv/opencv/sources/modules/video

linked by target "opencv_cudabgsegm" in directory D:/Cproject/opencv/opencv/sources/modules/cudabgsegm

linked by target "opencv_cudabgsegm" in directory D:/Cproject/opencv/opencv/sources/modules/cudabgsegm

linked by target "opencv_test_cudabgsegm" in directory D:/Cproject/opencv/opencv/sources/modules/cudabgsegm

linked by target "opencv_perf_cudabgsegm" in directory D:/Cproject/opencv/opencv/sources/modules/cudabgsegm

linked by target "opencv_cudafilters" in directory D:/Cproject/opencv/opencv/sources/modules/cudafilters

linked by target "opencv_cudafilters" in directory D:/Cproject/opencv/opencv/sources/modules/cudafilters

linked by target "opencv_test_cudafilters" in directory D:/Cproject/opencv/opencv/sources/modules/cudafilters

linked by target "opencv_perf_cudafilters" in directory D:/Cproject/opencv/opencv/sources/modules/cudafilters

linked by target "opencv_cudaimgproc" in directory D:/Cproject/opencv/opencv/sources/modules/cudaimgproc

linked by target "opencv_cudaimgproc" in directory D:/Cproject/opencv/opencv/sources/modules/cudaimgproc

linked by target "opencv_test_cudaimgproc" in directory D:/Cproject/opencv/opencv/sources/modules/cudaimgproc

linked by target "opencv_perf_cudaimgproc" in directory D:/Cproject/opencv/opencv/sources/modules/cudaimgproc

linked by target "opencv_perf_cudawarping" in directory D:/Cproject/opencv/opencv/sources/modules/cudawarping

linked by target "opencv_cudawarping" in directory D:/Cproject/opencv/opencv/sources/modules/cudawarping

linked by target "opencv_cudawarping" in directory D:/Cproject/opencv/opencv/sources/modules/cudawarping

linked by target "opencv_test_cudawarping" in directory D:/Cproject/opencv/opencv/sources/modules/cudawarping

linked by target "opencv_imgcodecs" in directory D:/Cproject/opencv/opencv/sources/modules/imgcodecs

linked by target "opencv_imgcodecs" in directory D:/Cproject/opencv/opencv/sources/modules/imgcodecs

linked by target "opencv_test_imgcodecs" in directory D:/Cproject/opencv/opencv/sources/modules/imgcodecs

linked by target "opencv_perf_imgcodecs" in directory D:/Cproject/opencv/opencv/sources/modules/imgcodecs

linked by target "opencv_perf_photo" in directory D:/Cproject/opencv/opencv/sources/modules/photo

linked by target "opencv_photo" in directory D:/Cproject/opencv/opencv/sources/modules/photo

linked by target "opencv_photo" in directory D:/Cproject/opencv/opencv/sources/modules/photo

linked by target "opencv_test_photo" in directory D:/Cproject/opencv/opencv/sources/modules/photo

linked by target "opencv_test_shape" in directory D:/Cproject/opencv/opencv/sources/modules/shape

linked by target "opencv_shape" in directory D:/Cproject/opencv/opencv/sources/modules/shape

linked by target "opencv_shape" in directory D:/Cproject/opencv/opencv/sources/modules/shape

linked by target "opencv_test_videoio" in directory D:/Cproject/opencv/opencv/sources/modules/videoio

linked by target "opencv_videoio" in directory D:/Cproject/opencv/opencv/sources/modules/videoio

linked by target "opencv_videoio" in directory D:/Cproject/opencv/opencv/sources/modules/videoio

linked by target "opencv_perf_videoio" in directory D:/Cproject/opencv/opencv/sources/modules/videoio

linked by target "opencv_cudacodec" in directory D:/Cproject/opencv/opencv/sources/modules/cudacodec

linked by target "opencv_cudacodec" in directory D:/Cproject/opencv/opencv/sources/modules/cudacodec

linked by target "opencv_perf_cudacodec" in directory D:/Cproject/opencv/opencv/sources/modules/cudacodec

linked by target "opencv_test_cudacodec" in directory D:/Cproject/opencv/opencv/sources/modules/cudacodec

linked by target "opencv_highgui" in directory D:/Cproject/opencv/opencv/sources/modules/highgui

linked by target "opencv_highgui" in directory D:/Cproject/opencv/opencv/sources/modules/highgui

linked by target "opencv_test_highgui" in directory D:/Cproject/opencv/opencv/sources/modules/highgui

linked by target "opencv_perf_objdetect" in directory D:/Cproject/opencv/opencv/sources/modules/objdetect

linked by target "opencv_objdetect" in directory D:/Cproject/opencv/opencv/sources/modules/objdetect

linked by target "opencv_objdetect" in directory D:/Cproject/opencv/opencv/sources/modules/objdetect

linked by target "opencv_test_objdetect" in directory D:/Cproject/opencv/opencv/sources/modules/objdetect

linked by target "opencv_ts" in directory D:/Cproject/opencv/opencv/sources/modules/ts

linked by target "opencv_ts" in directory D:/Cproject/opencv/opencv/sources/modules/ts

linked by target "opencv_test_features2d" in directory D:/Cproject/opencv/opencv/sources/modules/features2d

linked by target "opencv_features2d" in directory D:/Cproject/opencv/opencv/sources/modules/features2d

linked by target "opencv_features2d" in directory D:/Cproject/opencv/opencv/sources/modules/features2d

linked by target "opencv_perf_features2d" in directory D:/Cproject/opencv/opencv/sources/modules/features2d

linked by target "opencv_calib3d" in directory D:/Cproject/opencv/opencv/sources/modules/calib3d

linked by target "opencv_calib3d" in directory D:/Cproject/opencv/opencv/sources/modules/calib3d

linked by target "opencv_test_calib3d" in directory D:/Cproject/opencv/opencv/sources/modules/calib3d

linked by target "opencv_perf_calib3d" in directory D:/Cproject/opencv/opencv/sources/modules/calib3d

linked by target "opencv_cudafeatures2d" in directory D:/Cproject/opencv/opencv/sources/modules/cudafeatures2d

linked by target "opencv_cudafeatures2d" in directory D:/Cproject/opencv/opencv/sources/modules/cudafeatures2d

linked by target "opencv_test_cudafeatures2d" in directory D:/Cproject/opencv/opencv/sources/modules/cudafeatures2d

linked by target "opencv_perf_cudafeatures2d" in directory D:/Cproject/opencv/opencv/sources/modules/cudafeatures2d

linked by target "opencv_cudalegacy" in directory D:/Cproject/opencv/opencv/sources/modules/cudalegacy

linked by target "opencv_cudalegacy" in directory D:/Cproject/opencv/opencv/sources/modules/cudalegacy

linked by target "opencv_test_cudalegacy" in directory D:/Cproject/opencv/opencv/sources/modules/cudalegacy

linked by target "opencv_perf_cudalegacy" in directory D:/Cproject/opencv/opencv/sources/modules/cudalegacy

linked by target "opencv_cudaobjdetect" in directory D:/Cproject/opencv/opencv/sources/modules/cudaobjdetect

linked by target "opencv_cudaobjdetect" in directory D:/Cproject/opencv/opencv/sources/modules/cudaobjdetect

linked by target "opencv_perf_cudaobjdetect" in directory D:/Cproject/opencv/opencv/sources/modules/cudaobjdetect

linked by target "opencv_test_cudaobjdetect" in directory D:/Cproject/opencv/opencv/sources/modules/cudaobjdetect

linked by target "opencv_cudaoptflow" in directory D:/Cproject/opencv/opencv/sources/modules/cudaoptflow

linked by target "opencv_cudaoptflow" in directory D:/Cproject/opencv/opencv/sources/modules/cudaoptflow

linked by target "opencv_test_cudaoptflow" in directory D:/Cproject/opencv/opencv/sources/modules/cudaoptflow

linked by target "opencv_perf_cudaoptflow" in directory D:/Cproject/opencv/opencv/sources/modules/cudaoptflow

linked by target "opencv_perf_cudastereo" in directory D:/Cproject/opencv/opencv/sources/modules/cudastereo

linked by target "opencv_cudastereo" in directory D:/Cproject/opencv/opencv/sources/modules/cudastereo

linked by target "opencv_cudastereo" in directory D:/Cproject/opencv/opencv/sources/modules/cudastereo

linked by target "opencv_test_cudastereo" in directory D:/Cproject/opencv/opencv/sources/modules/cudastereo

linked by target "opencv_test_stitching" in directory D:/Cproject/opencv/opencv/sources/modules/stitching

linked by target "opencv_stitching" in directory D:/Cproject/opencv/opencv/sources/modules/stitching

linked by target "opencv_stitching" in directory D:/Cproject/opencv/opencv/sources/modules/stitching

linked by target "opencv_perf_stitching" in directory D:/Cproject/opencv/opencv/sources/modules/stitching

linked by target "opencv_superres" in directory D:/Cproject/opencv/opencv/sources/modules/superres

linked by target "opencv_superres" in directory D:/Cproject/opencv/opencv/sources/modules/superres

linked by target "opencv_test_superres" in directory D:/Cproject/opencv/opencv/sources/modules/superres

linked by target "opencv_perf_superres" in directory D:/Cproject/opencv/opencv/sources/modules/superres

linked by target "opencv_videostab" in directory D:/Cproject/opencv/opencv/sources/modules/videostab

linked by target "opencv_videostab" in directory D:/Cproject/opencv/opencv/sources/modules/videostab

linked by target "opencv_traincascade" in directory D:/Cproject/opencv/opencv/sources/apps/traincascade

linked by target "opencv_createsamples" in directory D:/Cproject/opencv/opencv/sources/apps/createsamples

linked by target "opencv_annotation" in directory D:/Cproject/opencv/opencv/sources/apps/annotation

linked by target "opencv_visualisation" in directory D:/Cproject/opencv/opencv/sources/apps/visualisation

linked by target "opencv_version" in directory D:/Cproject/opencv/opencv/sources/apps/version

原因解析：

cuda9不再支持2.0架构

解决方案如下：

1）.找到FindCUDA.cmake文件

找到行

find_cuda_helper_libs(nppi)

改为


  find_cuda_helper_libs(nppial)
  find_cuda_helper_libs(nppicc)
  find_cuda_helper_libs(nppicom)
  find_cuda_helper_libs(nppidei)
  find_cuda_helper_libs(nppif)
  find_cuda_helper_libs(nppig)
  find_cuda_helper_libs(nppim)
  find_cuda_helper_libs(nppist)
  find_cuda_helper_libs(nppisu)
  find_cuda_helper_libs(nppitc)

2）.找到行

set(CUDA_npp_LIBRARY "${CUDA_nppc_LIBRARY};${CUDA_nppi_LIBRARY};${CUDA_npps_LIBRARY}")

改为

set(CUDA_npp_LIBRARY "${CUDA_nppc_LIBRARY};${CUDA_nppial_LIBRARY};${CUDA_nppicc_LIBRARY};${CUDA_nppicom_LIBRARY};${CUDA_nppidei_LIBRARY};${CUDA_nppif_LIBRARY};${CUDA_nppig_LIBRARY};${CUDA_nppim_LIBRARY};${CUDA_nppist_LIBRARY};${CUDA_nppisu_LIBRARY};${CUDA_nppitc_LIBRARY};${CUDA_npps_LIBRARY}")

3）.找到行

unset(CUDA_nppi_LIBRARY CACHE)

改为


unset(CUDA_nppial_LIBRARY CACHE)
unset(CUDA_nppicc_LIBRARY CACHE)
unset(CUDA_nppicom_LIBRARY CACHE)
unset(CUDA_nppidei_LIBRARY CACHE)
unset(CUDA_nppif_LIBRARY CACHE)
unset(CUDA_nppig_LIBRARY CACHE)
unset(CUDA_nppim_LIBRARY CACHE)
unset(CUDA_nppist_LIBRARY CACHE)
unset(CUDA_nppisu_LIBRARY CACHE)
unset(CUDA_nppitc_LIBRARY CACHE)

4）.找到文件OpenCVDetectCUDA.cmake

修改以下几行


 ...
  set(__cuda_arch_ptx "")
  if(CUDA_GENERATION STREQUAL "Fermi")
    set(__cuda_arch_bin "2.0")
  elseif(CUDA_GENERATION STREQUAL "Kepler")
    set(__cuda_arch_bin "3.0 3.5 3.7")
  ...

改为


  ...
  set(__cuda_arch_ptx "")
  if(CUDA_GENERATION STREQUAL "Kepler")
    set(__cuda_arch_bin "3.0 3.5 3.7")
  elseif(CUDA_GENERATION STREQUAL "Maxwell")
    set(__cuda_arch_bin "5.0 5.2")
  ...

5）.cuda9中有一个单独的halffloat(cuda_fp16.h)头文件,也应该被包括在opencv的目录里

将头文件cuda_fp16.h添加至 opencv\modules\cudev\include\opencv2\cudev\common.hpp

即在common.hpp中添加

#include <cuda_fp16.h>

重新生成即可

李开复：AI 2.0 时代的机遇 AGI大模型与大数据研究院 DeepSeek R1 &大数据AI人工智能 java python javascript kotlin golang 架构人工智能
人工智能，深度学习，Transformer，大模型，通用人工智能，AI2.0，应用场景，未来趋势1.背景介绍人工智能（AI）技术近年来发展迅速，从语音识别、图像识别到自然语言处理等领域取得了突破性进展。其中，深度学习作为人工智能的核心技术之一，推动了AI技术的飞速发展。然而，深度学习模型的训练成本高、数据依赖性强、可解释性差等问题仍然制约着AI技术的进一步发展。李开复先生在《AI2.0时代的机遇》
C#学习笔记（3）：调用YOLOv8 playerofIE c#学习笔记 YOLO python
最近做的项目需要C#编写上位机程序，同时也要使用yolo进行深度学习检测。使用pythonnet调用写好的py文件，C#代码如下:Runtime.PythonDLL="python310.dll";PythonEngine.Initialize();using(Py.GIL()){dynamicsys=Py.Import("sys");dynamictorch=Py.Import("torch")
YOLOv8 的简介及C#中如何简单应用YOLOv8 码上有潜 YOLOv8 YOLO
YOLOv8是YOLO（YouOnlyLookOnce）系列中的最新版本，是一种用于目标检测和图像分割的深度学习模型。YOLO模型以其快速和准确的目标检测性能而著称，广泛应用于实时应用程序中。主要特点高效性：YOLOv8在保持高检测速度的同时，进一步提高了检测精度。端到端训练：可以直接从图像输入端到分类结果输出，简化了训练和部署过程。改进的架构：包括更深的网络结构、更复杂的特征提取方法以及更高效的
llama.cpp 和 LLM（大语言模型）这个懒人 llama 语言模型人工智能
llama.cpp和LLM（大语言模型）的介绍，以及两者的关联与区别：1.LLM（LargeLanguageModel，大语言模型）定义：LLM是基于深度学习技术（如Transformer架构）构建的超大参数量的自然语言处理模型。它通过海量文本数据训练，能够生成连贯、语义丰富的文本，完成问答、创作、推理等任务。特点：参数规模大：如GPT-3（1750亿参数）、Llama-65B（650亿参数）等。
构建未来智能：在Mojo模型中自定义模型架构的艺术 2401_85761003 mojo 架构
构建未来智能：在Mojo模型中自定义模型架构的艺术在深度学习的世界里，模型架构的设计往往决定了算法的性能和适用性。Mojo模型，作为一个假想中的高级机器学习框架，允许用户实现自定义的模型架构来解决特定的问题。本文将深入探讨如何在Mojo模型中实现自定义模型架构，并提供详细的步骤和代码示例，以帮助读者掌握这一强大的技术。自定义模型架构的重要性灵活性自定义模型架构提供了设计适合特定问题需求的模型的灵活
大模型与自然语言理解（NLU）：差异与联系技术流 Gavin AIoT python 语言模型 ai
近年来，人工智能领域取得了显著进展，尤其是在自然语言处理（NLP）方面。大模型和自然语言理解（NLU）作为NLP的两个重要分支，常常被提及，但它们之间存在着本质区别。1.定义与目标大模型:通常指拥有庞大参数规模（数十亿甚至数千亿）的深度学习模型，例如GPT-3、LaMDA等。它们通过海量文本数据进行训练，旨在学习语言的统计规律，并能够生成流畅、连贯的文本。NLU:是NLP的一个子领域，专注于让机器
PyTorch 生态概览：为什么选择动态计算图框架？小诸葛IT课堂 pytorch 人工智能 python
一、PyTorch的核心价值PyTorch作为深度学习框架的后起之秀，通过动态计算图技术革新了传统的静态图模式。其核心优势体现在：动态灵活性：代码即模型，支持即时调试Python原生支持：无缝衔接Python生态高效的GPU加速：通过CUDA实现透明的硬件加速活跃的社区生态：GitHub贡献者超1.8万人，日均更新100+次二、动态计算图VS静态计算图对比#动态计算图示例（PyTorch）impo
学习pytorch 阿什么名字不会重复呢学习 pytorch 人工智能
学习PyTorch是一个很好的选择，尤其是如果你对深度学习和机器学习感兴趣。以下是一个详细的学习计划，可以帮助你系统地掌握PyTorch的基本概念和应用。学习计划概览学习周期：8周（每周约4-5小时）目标：掌握PyTorch基础，能够实现简单的深度学习模型。第1周：基础知识目标：了解深度学习的基础知识，掌握Python和NumPy基础。任务：学习Python基础（数据类型、控制流、函数、类）。资源
AI驱动的代码重构与优化技术 AI天才研究院 DeepSeek R1 &大数据AI人工智能大模型 ChatGPT java python javascript kotlin golang 架构人工智能大厂程序员硅基计算碳基计算认知计算生物计算深度学习神经网络大数据 AIGC AGI LLM 系统架构设计软件哲学 Agent 程序员实现财富自由
AI驱动的代码重构与优化技术概述什么是AI驱动的代码重构与优化？AI驱动的代码重构与优化技术，是指利用人工智能，特别是机器学习和深度学习的算法，对软件代码进行自动分析和改进的技术。这种技术能够通过学习大量的代码样本，识别出代码中的模式、问题和改进点，从而自动完成代码的重构和优化。重构的定义重构（Refactoring）是改进代码内部结构而不改变外部行为的过程。其目的通常是为了提高代码的可读性、可维
神经网络模型压缩&实例教程—非结构化剪枝程序先锋《python深度学习》笔记神经网络剪枝深度学习
目录1.导包&定义一个简单的网络2.获取网络需要剪枝的模块3.模块剪枝（核心）3.1随机剪枝weight3.2L1范数剪枝bias4.总结最先进的深度学习技术依赖于难以部署的过度参数化模型。相反，已知生物神经网络使用高效的稀疏连接。为了在不牺牲准确性的情况下减少内存、电池和硬件消耗，通过减少模型中的参数数量来确定压缩模型的最佳技术是很重要的。这反过来又允许您在设备上部署轻量级模型，并通过设备上的私
深度学习模型压缩：非结构化剪枝与结构化剪枝的定义与对比从零开始学习人工智能深度学习剪枝人工智能
****在深度学习中，模型压缩是优化模型性能、降低存储和计算成本的重要技术之一。其中，剪枝（Pruning）是最常用的方法之一。根据剪枝的粒度和目标，剪枝可以分为非结构化剪枝（UnstructuredPruning）和结构化剪枝（StructuredPruning）。本文将详细介绍这两种剪枝方法的定义，并通过对比帮助读者更好地理解它们的差异。1.非结构化剪枝（UnstructuredPruning
从剪枝到知识蒸馏：深度学习模型压缩与加速的多重策略一键难忘剪枝深度学习算法知识蒸馏
本文收录于专栏：精通AI实战千例专栏合集https://blog.csdn.net/weixin_52908342/category_11863492.html从基础到实践，深入学习。无论你是初学者还是经验丰富的老手，对于本专栏案例和项目实践都有参考学习意义。每一个案例都附带关键代码，详细讲解供大家学习，希望可以帮到大家。正在不断更新中文章目录从剪枝到知识蒸馏：深度学习模型压缩与加速的多重策略1.
PyTorch 模型剪枝实例教程一、非结构化剪枝小风_ 模型压缩与加速 pytorch pytorch 深度学习人工智能
目录1.导包&定义一个简单的网络2.获取网络需要剪枝的模块3.模块剪枝（核心）4.总结目前大部分最先进的（SOTA）深度学习技术虽然效果好，但由于其模型参数量和计算量过高，难以用于实际部署。而众所周知，生物神经网络使用高效的稀疏连接（生物大脑神经网络balabala啥的都是稀疏连接的），考虑到这一点，为了减少内存、容量和硬件消耗，同时又不牺牲模型预测的精度，在设备上部署轻量级模型，并通过私有的设备
优化深度学习模型：PyTorch中的模型剪枝技术详解代码之光_1980 深度学习 pytorch 剪枝
标题：优化深度学习模型：PyTorch中的模型剪枝技术详解在深度学习领域，模型剪枝是一种提高模型效率和性能的技术。通过剪枝，我们可以去除模型中的冗余权重，从而减少模型的复杂度和提高运算速度，同时保持或甚至提升模型的准确率。本文将详细介绍如何在PyTorch框架中实现模型剪枝，并提供相应的代码示例。1.模型剪枝的基本概念模型剪枝主要分为两种类型：结构化剪枝和非结构化剪枝。结构化剪枝通常指的是剪除整个
Dropout: 一种减少神经网络过拟合的技术冰蓝蓝自然语言处理神经网络人工智能深度学习
在深度学习中，过拟合是一个常见的问题，尤其是在模型复杂度较高或训练数据较少的情况下。过拟合意味着模型在训练数据上表现得很好，但在未见过的数据上表现不佳，即泛化能力差。为了解决这个问题，研究者们提出了多种正则化技术，其中之一就是Dropout。什么是Dropout？Dropout是一种正则化技术，由Hinton和他的学生在2012年提出。它通过在训练过程中随机“丢弃”（即暂时移除）网络中的一些神经元
模型保存与加载：PyTorch中的实践指南冰蓝蓝自然语言处理 pytorch 人工智能 python
在深度学习项目中，模型的保存和加载是一个至关重要的步骤。它不仅有助于在训练过程中保存进度，还可以在训练完成后部署模型。PyTorch提供了灵活的方式来保存和加载模型，本文将详细介绍这些方法。模型保存在PyTorch中，有两种主要的模型保存方法：保存整个模型和仅保存模型参数。保存整个模型保存整个模型意味着保存模型的结构和参数。这种方法简单直接，但文件体积较大，且依赖于模型的具体实现。importto
深度学习中的注意力机制：解锁智能模型的新视角冰蓝蓝深度学习深度学习人工智能
在人工智能的快速发展中，深度学习模型已经成为了处理复杂数据和任务的主力军。然而，随着数据量的激增和任务的复杂化，传统的深度学习模型面临着效率和性能的双重挑战。在这样的背景下，注意力机制（AttentionMechanism）应运而生，它不仅提升了模型的处理能力，还为深度学习领域带来了新的研究视角。什么是注意力机制？注意力机制是一种受人类视觉注意力启发的技术，它允许模型在处理大量信息时，能够动态地聚
torch.unsqueeze：灵活调整张量维度的利器冰蓝蓝 transformer transformer 深度学习
在深度学习框架PyTorch中，张量（Tensor）是最基本的数据结构，它类似于NumPy中的数组，但可以在GPU上运行。在日常的深度学习编程中，我们经常需要调整张量的维度以适应不同的操作和层。torch.unsqueeze函数就是PyTorch提供的一个非常有用的工具，用于在指定位置增加张量的维度。本文将详细介绍torch.unsqueeze的用法和一些实际应用场景。什么是torch.unsqu
如何使用MATLAB进行高效的GPU加速深度学习模型训练？百态老人 matlab 深度学习开发语言
要使用MATLAB进行高效的GPU加速深度学习模型训练，可以遵循以下步骤和策略：选择合适的GPU硬件：首先，确保您的计算机配备有支持CUDA的NVIDIAGPU，并且其计算能力至少为3.0或以上。可以通过gpuDevice命令检查GPU是否具备加速功能。安装必要的工具箱：确保安装了MATLAB的DeepLearningToolbox和ParallelComputingToolbox，这些工具箱提供
Matlab GPU加速技术算法工程师y matlab 开发语言
1.GPU加速简介（1）为什么使用GPU加速？CPU擅长处理逻辑复杂的串行任务，而GPU拥有数千个流处理器，专为并行计算设计。对于大规模矩阵运算、深度学习训练或科学计算等任务，GPU加速可将计算速度提升数十至数百倍。（2）Matlab的GPU支持功能依赖：需安装ParallelComputingToolbox（并行计算工具箱）。硬件要求：支持CUDA的NVIDIAGPU（如Tesla、GeForc
pytorch中的DataLoader 朋也透william pytorch 人工智能 python
在PyTorch中，DataLoader是一个工具类，用于高效地加载数据并准备数据输入到模型中。它支持数据的批量加载、随机打乱、并行加载和迭代操作，是训练深度学习模型的关键组件之一。1.基本功能DataLoader的主要职责是从数据集中提取样本，并根据设置返回一个批次的数据。它与Dataset类结合使用：Dataset：定义数据集的来源、结构以及如何获取单个数据样本。DataLoader：负责从D
机器学习中输入输出Tokens的概念详解爱吃土豆的程序员机器学习基础机器学习人工智能 Tokens
随着深度学习技术的快速发展，大语言模型（LargeLanguageModels,LLMs）已经成为自然语言处理（NLP）领域的一个热点研究方向。这些模型不仅能够生成高质量的文本，还能在多种任务中展现出卓越的表现，比如机器翻译、问答系统、文本摘要等。在大语言模型的工作流程中，Tokens的概念扮演着至关重要的角色。本文将详细介绍大语言模型如何使用Tokens，以及如何计算Tokens的数量。什么是T
【深度学习基础】第二十四课：softmax函数的导数 x-jeff 深度学习基础深度学习人工智能
【深度学习基础】系列博客为学习Coursera上吴恩达深度学习课程所做的课程笔记。1.softmax函数softmax函数详解。2.softmax函数的导数假设神经网络输出层的激活函数为softmax函数，用以解决多分类问题。在反向传播时，就需要计算softmax函数的导数，这也就是本文着重介绍的内容。我们只需关注输出层即可，其余层和之前介绍的二分类模型一样，不再赘述。我们先考虑只有一个样本的情况
AI如何创作音乐及其案例 alankuo 人工智能
AI创作音乐主要有以下几种方式：基于深度学习的生成模型深度神经网络：通过大量的音乐数据训练，让AI学习音乐的结构、旋律、和声、节奏等特征。如Transformer架构，其注意力机制可捕捉跨小节的旋律关联性，能生成具有长期依赖性的音乐序列。生成对抗网络（GAN）：包含生成器和判别器，生成器负责生成音乐样本，判别器判断生成的音乐是否真实。两者相互对抗、不断优化，使生成器生成更逼真的音乐。变分自编码器（
【sklearn 01】人工智能概述 @金色海岸人工智能 sklearn python
一、人工智能，机器学习，深度学习人工智能指由人类制造出的具有智能的机器。这是一个非常大的范围，长远目标是让机器实现人工智能，但目前我们仍处在非常初始的阶段，甚至不能称为智能机器学习是指通过数据训练出能完成一定功能的模型，是实现人工智能的手段之一，也是目前最主流的人工智能实现方法深度学习则是机器学习的分支，超过8层的神经网络模型就叫深度学习，深度即层数。深度学习目前在语音、图像等领域取得很好的效果
21-梯度累积原理与实现机器人图像处理深度学习算法与模型人工智能深度学习 YOLO
一、基本概念在深度学习训练的时候，数据的batchsize大小受到GPU内存限制，batchsize大小会影响模型最终的准确性和训练过程的性能。在GPU内存不变的情况下，模型越来越大，那么这就意味着数据的batchsize智能缩小，这个时候，梯度累积（GradientAccumulation）可以作为一种简单的解决方案来解决这个问题。二、Batchsize的作用训练数据的Batchsize大小对训
MV-EB435i立体相机对垃圾分类开哥kg pytorch 深度学习卷积神经网络分类人工智能
最近在v社区发了一篇文章，懒得转移过来了链接：V社区-机器视觉技术交流社区-MV-EB435i立体相机对垃圾分类我觉得这篇文章对于刚入门深度学习想看点项目学点代码的刚刚好，因为我也是新手，嘻嘻嘻！希望这篇文章对大家有所帮助，如有错误请大家指正。
AI人工智能中的概率论与统计学原理与Python实战：Python实现概率模型 AI天才研究院 AI实战 AI大模型企业级应用开发实战大数据人工智能语言模型 AI LLM Java Python 架构设计 Agent RPA
1.背景介绍随着人工智能技术的不断发展，概率论与统计学在人工智能领域的应用越来越广泛。概率论与统计学是人工智能中的基础知识之一，它们在机器学习、深度学习、自然语言处理等领域都有着重要的作用。本文将介绍概率论与统计学的核心概念、算法原理、具体操作步骤以及Python实现方法，并通过具体代码实例进行详细解释。2.核心概念与联系2.1概率论与统计学的区别概率论是一门数学学科，它研究随机事件发生的可能性。
如何使用 Python 实现生成对抗网络 NoABug python 生成对抗网络 tensorflow
如何使用Python实现生成对抗网络生成对抗网络（GenerativeAdversarialNetwork，GAN）是一种能够生成高质量、逼真图像的深度学习模型。GAN模型由两个神经网络组成：一个生成器和一个判别器。生成器的任务是以噪声为输入，生成看似真实的图像；而判别器则需要根据输入的图像，判断该图像是真实的还是由生成器生成的。下面我们将通过Python代码来实现一个简单的GAN模型。首先，我们
GAN模型的Python应用——生成对抗网络代码编织匠人 python 生成对抗网络开发语言
GAN模型的Python应用——生成对抗网络生成对抗网络（GenerativeAdversarialNetwork，GAN）是深度学习中的一种重要模型，已经被广泛应用于图像、文本生成等领域。GAN模型由两个神经网络组成：生成器（Generator）和判别器（Discriminator）。生成器用于生成假样本，判别器用于评估真实性。两个神经网络相互博弈，通过一次次迭代训练，最终生成器可以生成足以骗过
插入表主键冲突做更新 a-john
有以下场景：用户下了一个订单，订单内的内容较多，且来自多表，首次下单的时候，内容可能会不全（部分内容不是必须，出现有些表根本就没有没有该订单的值）。在以后更改订单时，有些内容会更改，有些内容会新增。问题：如果在sql语句中执行update操作，在没有数据的表中会出错。如果在逻辑代码中先做查询，查询结果有做更新，没有做插入，这样会将代码复杂化。解决： mysql中提供了一个sql语
Android xml资源文件中@、@android:type、@*、？、@+含义和区别 Cb123456 @+@?@*
一.@代表引用资源 1.引用自定义资源。格式：@[package:]type/name android：text="@string/hello" 2.引用系统资源。格式：@android:type/name android:textColor="@android:color/opaque_red"
数据结构的基本介绍天子之骄数据结构散列表树、图线性结构价格标签
数据结构的基本介绍数据结构就是数据的组织形式，用一种提前设计好的框架去存取数据，以便更方便，高效的对数据进行增删查改。正确选择合适的数据结构，对软件程序的高效执行的影响作用不亚于算法的设计。此外，在计算机系统中数据结构的作用也是非同小可。例如常常在编程语言中听到的栈，堆等，就是经典的数据结构。经典的数据结构大致如下：一：线性数据结构 (1)：列表 a
通过二维码开放平台的API快速生成二维码一炮送你回车库 api
现在很多网站都有通过扫二维码用手机连接的功能，联图网(http://www.liantu.com/pingtai/)的二维码开放平台开放了一个生成二维码图片的Api,挺方便使用的。闲着无聊，写了个前台快速生成二维码的方法。 html代码如下:(二维码将生成在这div下) ? 1 &nbs
ImageIO读取一张图片改变大小 3213213333332132 java IO image BufferedImage
package com.demo; import java.awt.image.BufferedImage; import java.io.File; import java.io.IOException; import javax.imageio.ImageIO; /** * @Description 读取一张图片改变大小 * @author FuJianyon
myeclipse集成svn（一针见血） 7454103 eclipse SVN MyEclipse
&n
装箱与拆箱----autoboxing和unboxing darkranger J2SE
4.2　自动装箱和拆箱基本数据(Primitive)类型的自动装箱(autoboxing)、拆箱(unboxing)是自J2SE 5.0开始提供的功能。虽然为您打包基本数据类型提供了方便，但提供方便的同时表示隐藏了细节，建议在能够区分基本数据类型与对象的差别时再使用。 4.2.1　autoboxing和unboxing 在Java中，所有要处理的东西几乎都是对象(Object)
ajax传统的方式制作ajax aijuans Ajax
//这是前台的代码 <%@ page language="java" import="java.util.*" pageEncoding="UTF-8"%> <% String path = request.getContextPath(); String basePath = request.getScheme()+
只用jre的eclipse是怎么编译java源文件的？ avords java eclipse jdk tomcat
eclipse只需要jre就可以运行开发java程序了，也能自动编译java源代码，但是jre不是java的运行环境么，难道jre中也带有编译工具？还是eclipse自己实现的？谁能给解释一下呢问题补充：假设系统中没有安装jdk or jre，只在eclipse的目录中有一个jre，那么eclipse会采用该jre，问题是eclipse照样可以编译java源文件，为什么呢？ &nb
前端模块化 bee1314 模块化
背景：前端JavaScript模块化，其实已经不是什么新鲜事了。但是很多的项目还没有真正的使用起来，还处于刀耕火种的野蛮生长阶段。 JavaScript一直缺乏有效的包管理机制，造成了大量的全局变量，大量的方法冲突。我们多么渴望有天能像Java（import），Python (import)，Ruby(require)那样写代码。在没有包管理机制的年代，我们是怎么避免所
处理百万级以上的数据处理 bijian1013 oracle sql 数据库大数据查询
一.处理百万级以上的数据提高查询速度的方法： 1.应尽量避免在 where 子句中使用!=或<>操作符，否则将引擎放弃使用索引而进行全表扫描。 2.对查询进行优化，应尽量避免全表扫描，首先应考虑在 where 及 o
mac 卸载 java 1.7 或更高版本征客丶 java OS
卸载 java 1.7 或更高 sudo rm -rf /Library/Internet\ Plug-Ins/JavaAppletPlugin.plugin 成功执行此命令后，还可以执行 java 与 javac 命令 sudo rm -rf /Library/PreferencePanes/JavaControlPanel.prefPane 成功执行此命令后，还可以执行 java
【Spark六十一】Spark Streaming结合Flume、Kafka进行日志分析 bit1129 Stream
第一步，Flume和Kakfa对接，Flume抓取日志，写到Kafka中第二部，Spark Streaming读取Kafka中的数据，进行实时分析本文首先使用Kakfa自带的消息处理（脚本）来获取消息，走通Flume和Kafka的对接 1. Flume配置 1. 下载Flume和Kafka集成的插件，下载地址：https://github.com/beyondj2ee/f
Erlang vs TNSDL bookjovi erlang
TNSDL是Nokia内部用于开发电信交换软件的私有语言，是在SDL语言的基础上加以修改而成，TNSDL需翻译成C语言得以编译执行，TNSDL语言中实现了异步并行的特点，当然要完整实现异步并行还需要运行时动态库的支持，异步并行类似于Erlang的process（轻量级进程），TNSDL中则称之为hand，Erlang是基于vm(beam)开发，
非常希望有一个预防疲劳的java软件, 预防过劳死和眼睛疲劳,大家一起努力搞一个 ljy325 企业应用
　非常希望有一个预防疲劳的java软件，我看新闻和网站，国防科技大学的科学家累死了，太疲劳，老是加班，不休息，经常吃药，吃药根本就没用，根本原因是疲劳过度。我以前做java,那会公司垃圾，老想赶快学习到东西跳槽离开，搞得超负荷，不明理。深圳做软件开发经常累死人，总有不明理的人，有个软件提醒限制很好，可以挽救很多人的生命。相关新闻：（1）IT行业成五大疾病重灾区：过劳死平均37.9岁
读《研磨设计模式》-代码笔记-原型模式 bylijinnan java 设计模式
声明：本文只为方便我个人查阅和理解，详细的分析以及源代码请移步原作者的博客http://chjavach.iteye.com/ /** * Effective Java 建议使用copy constructor or copy factory来代替clone()方法： * 1.public Product copy(Product p){} * 2.publi
配置管理---svn工具之权限配置 chenyu19891124 SVN
今天花了大半天的功夫，终于弄懂svn权限配置。下面是今天收获的战绩。安装完svn后就是在svn中建立版本库，比如我本地的是版本库路径是C:\Repositories\pepos。pepos是我的版本库。在pepos的目录结构 pepos component webapps 在conf里面的auth里赋予的权限配置为 [groups]
浅谈程序员的数学修养 comsci 设计模式编程算法面试招聘
浅谈程序员的数学修养
批量执行 bulk collect与forall用法 daizj oracle sql bulk collect forall
BULK COLLECT 子句会批量检索结果，即一次性将结果集绑定到一个集合变量中，并从SQL引擎发送到PL/SQL引擎。通常可以在SELECT INTO、 FETCH INTO以及RETURNING INTO子句中使用BULK COLLECT。本文将逐一描述BULK COLLECT在这几种情形下的用法。有关FORALL语句的用法请参考：批量SQL之 F
Linux下使用rsync最快速删除海量文件的方法 dongwei_6688 OS
1、先安装rsync：yum install rsync 2、建立一个空的文件夹：mkdir /tmp/test 3、用rsync删除目标目录：rsync --delete-before -a -H -v --progress --stats /tmp/test/ log/这样我们要删除的log目录就会被清空了，删除的速度会非常快。rsync实际上用的是替换原理，处理数十万个文件也是秒删。
Yii CModel中rules验证规格 dcj3sjt126com rules yii validate
Yii cValidator主要用法分析： yii验证rulesit 分类： Yii yii的rules验证 cValidator主要属性 attributes ,builtInValidators,enableClientValidation,message,on,safe,skipOnError
基于vagrant的redis主从实验 dcj3sjt126com vagrant
平台: Mac 工具: Vagrant 系统: Centos6.5 实验目的: Redis主从实现思路制作一个基于sentos6.5, 已经安装好reids的box, 添加一个脚本配置从机, 然后作为后面主机从机的基础box 制作sentos6.5+redis的box mkdir vagrant_redis cd vagrant_
Memcached(二)、Centos安装Memcached服务器 frank1234 centos memcached
一、安装gcc rpm和yum安装memcached服务器连接没有找到，所以我使用的是make的方式安装，由于make依赖于gcc，所以要先安装gcc 开始安装，命令如下，[color=red][b]顺序一定不能出错[/b][/color]：建议可以先切换到root用户，不然可能会遇到权限问题：su root 输入密码...... rpm -ivh kernel-head
Remove Duplicates from Sorted List hcx2013 remove
Given a sorted linked list, delete all duplicates such that each element appear only once. For example,Given 1->1->2, return 1->2.Given 1->1->2->3->3, return&
Spring4新特性——JSR310日期时间API的支持 jinnianshilongnian spring4
Spring4新特性——泛型限定式依赖注入 Spring4新特性——核心容器的其他改进 Spring4新特性——Web开发的增强 Spring4新特性——集成Bean Validation 1.1(JSR-349)到SpringMVC Spring4新特性——Groovy Bean定义DSL Spring4新特性——更好的Java泛型操作API Spring4新
浅谈enum与单例设计模式 247687009 java 单例
在JDK1.5之前的单例实现方式有两种(懒汉式和饿汉式并无设计上的区别故看做一种)，两者同是私有构造器，导出静态成员变量，以便调用者访问。第一种 package singleton; public class Singleton { //导出全局成员 public final static Singleton INSTANCE = new S
使用switch条件语句需要注意的几点 openwrt c break switch
1. 当满足条件的case中没有break，程序将依次执行其后的每种条件（包括default）直到遇到break跳出 int main() { int n = 1; switch(n) { case 1: printf("--1--\n"); default: printf("defa
配置Spring Mybatis JUnit测试环境的应用上下文 schnell18 spring mybatis JUnit
Spring-test模块中的应用上下文和web及spring boot的有很大差异。主要试下来差异有：单元测试的app context不支持从外部properties文件注入属性 @Value注解不能解析带通配符的路径字符串解决第一个问题可以配置一个PropertyPlaceholderConfigurer的bean。第二个问题的具体实例是：
Java 定时任务总结一 tuoni java spring timer quartz timertask
Java定时任务总结一.从技术上分类大概分为以下三种方式： 1.Java自带的java.util.Timer类，这个类允许你调度一个java.util.TimerTask任务; 说明： java.util.Timer定时器，实际上是个线程，定时执行TimerTask类 &
一种防止用户生成内容站点出现商业广告以及非法有害等垃圾信息的方法 yangshangchuan rank 相似度计算文本相似度词袋模型余弦相似度
本文描述了一种在ITEYE博客频道上面出现的新型的商业广告形式及其应对方法，对于其他的用户生成内容站点类型也具有同样的适用性。最近在ITEYE博客频道上面出现了一种新型的商业广告形式，方法如下： 1、注册多个账号（一般10个以上）。 2、从多个账号中选择一个账号，发表1-2篇博文

CMake Error: CUDA_nppi_LIBRARY (ADVANCED)

你可能感兴趣的:(深度学习)