dlib-19.24交叉编译aarch646.5.0版本移植，人脸聚类_交叉编译dlib,dlib交叉编译资源-CSDN文库

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h：1009个

cmake：19个

iostream：2个

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dlib

人脸聚类

交叉编译

aarch64

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**标题与描述解析** 标题和描述提到了"**dlib**"，这是一个广泛使用的C++库，特别在计算机视觉和机器学习领域。版本号"**19.24**"表明这是dlib的一个特定更新，可能包含了一些修复和新功能。"**交叉编译**"是指在一种架构（如x86）上构建代码，以运行在另一种架构（如**aarch64**，即ARM64架构）上。这里提到的是针对aarch64的6.5.0版本进行的交叉编译。"**人脸聚类**"是dlib库的一个应用，它涉及人脸识别技术，可以自动将不同人脸分组到相似类别中。 **dlib库详解** dlib库由戴维·马库斯（Davis Marcu）开发，是一个开源、无版权的C++工具箱，包含了大量的实用程序，主要用于机器学习和图像处理。dlib包含了一套完整的机器学习算法，如支持向量机（SVM）、决策树、随机森林以及深度学习模型。在图像处理方面，dlib提供了各种图像处理函数，如图像变换、特征检测等。 **交叉编译aarch64** 交叉编译是在一个系统上生成可在另一个系统上运行的代码的过程。在本案例中，开发者可能在x86或x64架构的机器上执行编译，目的是生成能在aarch64（ARM 64位架构）设备，如树莓派4或其他基于ARM的服务器上运行的二进制文件。这通常涉及到配置合适的交叉编译工具链，例如GCC或Clang的特定版本，以及确保所有依赖项都正确设置。 **人脸聚类** dlib的人脸聚类功能是基于其强大的特征提取能力。它使用称为HOG（Histogram of Oriented Gradients）的特征描述符，或者在深度学习模型中使用预训练的面部关键点检测器来定位人脸。然后，通过计算这些人脸特征之间的距离，实现对相似人脸的聚类。这种方法可以用于人脸识别、人脸检测、人脸属性分析等多种应用场景。 **文件名"install"** 这个文件可能是交叉编译和移植过程的安装指南或脚本，包含了编译、配置、链接和安装dlib库到aarch64目标系统的步骤。它可能包括了环境变量的设置、依赖库的安装、编译选项的指定等内容。用户需要按照这个文件的指示进行操作，才能成功在aarch64平台上运行dlib库和人脸聚类功能。 **总结** dlib库的交叉编译aarch64版本移植涉及到多个技术层面，包括理解和配置交叉编译环境，掌握dlib的特性，特别是其人脸聚类功能的应用。文件"install"是这一过程的关键，它提供了一个详细步骤，帮助用户将dlib 19.24版本在aarch64平台上部署并运行。通过深入理解这些概念和技术，开发者可以有效地利用dlib进行高级的计算机视觉和机器学习项目。

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dlib - 19.24 交叉编译aarch64 6.5.0 版本移植，人脸聚类（1047个子文件）

libdlib.a 5.95MB

cassert 45B

cassert 42B

find_blas.cmake 19KB

pybind11Tools.cmake 9KB

FindPythonLibsNew.cmake 8KB

set_compiler_specific_options.cmake 7KB

use_cpp_11.cmake 5KB

dlib.cmake 3KB

FindEigen3.cmake 3KB

FindCatch.cmake 2KB

dlibConfig.cmake 2KB

dlibConfigVersion.cmake 1KB

add_global_compiler_switch.cmake 1KB

find_libjpeg.cmake 1KB

find_libwebp.cmake 1KB

find_libpng.cmake 1KB

dlib-release.cmake 809B

tell_visual_studio_to_use_static_runtime.cmake 739B

check_if_sse4_instructions_executable_on_host.cmake 638B

check_if_avx_instructions_executable_on_host.cmake 629B

check_if_neon_available.cmake 582B

cstring 42B

fstream 42B

fftr_good_data.h 267KB

frontal_face_detector.h 214KB

loss.h 162KB

matrix_utilities.h 156KB

layers.h 149KB

core.h 140KB

png.h 140KB

layers_abstract.h 135KB

optimization_bobyqa.h 135KB

widgets.h 118KB

widgets_abstract.h 115KB

zlib.h 94KB

loss_abstract.h 92KB

pngpriv.h 88KB

pybind11.h 87KB

cast.h 84KB

serialize.h 83KB

core_abstract.h 83KB

interpolation.h 80KB

tensor_tools.h 79KB

base_widgets_abstract.h 75KB

matrix_blas_bindings.h 73KB

random_hashing.h 72KB

matrix.h 70KB

base_widgets.h 68KB

interpolation_abstract.h 66KB

numpy.h 64KB

matrix_utilities_abstract.h 62KB

binary_search_tree_kernel_1.h 60KB

bayes_utils.h 57KB

pixel.h 56KB

statistics.h 56KB

trainer.h 55KB

fhog.h 55KB

spatial_filtering.h 55KB

matrix_la.h 54KB

binary_search_tree_kernel_2.h 53KB

matrix_subexp.h 51KB

pytypes.h 50KB

jpeglib.h 49KB

scan_fhog_pyramid.h 47KB

xml_parser_kernel_1.h 47KB

image_pyramid.h 45KB

statistics_abstract.h 44KB

thread_pool_extension.h 44KB

bsp_abstract.h 42KB

algs.h 42KB

scan_image_pyramid.h 41KB

kcentroid_overloads.h 41KB

matrix_eigenvalue.h 40KB

graph_utils.h 40KB

input.h 40KB

matrix_data_layout.h 39KB

optimization_solve_qp_using_smo.h 38KB

matrix_la_abstract.h 38KB

rvm.h 37KB

segment_image.h 37KB

cblas.h 37KB

sequence_kernel_1.h 37KB

matrix_assign.h 37KB

common.h 37KB

svm.h 37KB

sparse_vector.h 36KB

vector.h 36KB

trainer_abstract.h 36KB

scan_fhog_pyramid_abstract.h 35KB

canvas_drawing.h 35KB

shape_predictor_trainer.h 34KB

image_loader.h 34KB

thread_pool_extension_abstract.h 34KB

visitors.h 33KB

function_abstract.h 33KB

find_max_factor_graph_potts.h 33KB

morphological_operations.h 31KB

bigint_kernel_c.h 31KB

bayes_utils_abstract.h 31KB

共 1047 条

#ifndef FFTR_GOOD_DATA_H #define FFTR_GOOD_DATA_H #include <sstream> inline std::string get_fftr_stringstream() { std::stringstream ss; ss << "gREgHkVPSRDT5z8rk175NS624DOfWDQcPg2MOBWP6zKp5jxHZcD2U1VxamzdjaOSqfLISuO5XxmI"; ss << "wNbs2oCQ05Jnba2XSMe0/fI4D3HwzE2Py5EXfucAyO7xzaN+5OQO/vFwu9K7ocEhrFnCIIrELaOE"; ss << "F3Ocpy9CMf1bs5fhz2LHnfXO/hYboIhIiT7tDmny1OI/diOkdfSvEOBARrBx72j+beMHFoAuH4/L"; ss << "/V76B7hRdlR2O9G6w7NcgpqkAMav7L6NshODMCnm3a+oCMOb9k+x20CupDgjh5GrUk4TS98a+6vF"; ss << "n3jhOLiW08ydsHJN7L15AKc1IFXITHLlHJzReNVI/vlsJ/bfpahh/gJ1ToHm8OVsSQnBX2SBqv9Z"; ss << "IxDea8Gbkjsxo2Ptvewv5MTm7d9K13T8ACoIP8rhyhd/J7d1n7/qb7dwIquGMnd4ugbX2UdXGzhA"; ss << "lQjtEbulmCpTfGvR2owFYhcViTmzh1Q4nMYfT1JFAZ9bWIVMi//QdBKASkhcepD/FS8eCKcNDqzJ"; ss << "4oVpYHvJNauFx2visid1/uiRVfuI3hzOkVFQtxzNywqU/nJ4TgY6cGk+Fc74pXezjNQt653LDEQz"; ss << "0m2Tzq+IjGymYRKU+PqcQbxzO+7A47wg2wRO5bHCxI1DZ9rU45jrxkyzgYrmx6B8R4wXLJlqB2Jc"; ss << "4LmVQXixuu7Y6ria6Dmdq91/P1QW5F/GPirFEhV+1k1FBg3gj+wDdWwVaEnFP/sSWdNfsu8ysBlS"; ss << "sroFquFysayDxtmxu4SA5u17bOhpRvWIkEm8MciQaTYNYvQcgfShlFSBrmdnbGGZyO4vTCjgl5iQ"; 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