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DensePose-master

于 2019-06-17 发布 文件大小:9846KB
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代码说明：

  DensePose用深度学习把2D图像坐标映射到3D人体表面上，再加上以每秒多帧的速度处理密集坐标，最后实现动态人物的精确定位和姿态估计。该技术集目标检测、姿态估计、目标部分/实例分割等多种计算机视觉任务于一身的一个综合问题。(DensePost maps 2D image coordinates to 3D human body surface by in-depth learning, and processes dense coordinates at the speed of multiple frames per second. Finally, it realizes precise positioning and attitude estimation of dynamic characters. This technology integrates many kinds of computer vision tasks, such as target detection, attitude estimation, target part/instance segmentation, etc.)

文件列表：

DensePose-master, 0 , 2018-08-28
DensePose-master\.gitignore, 495 , 2018-08-28
DensePose-master\CMakeLists.txt, 2012 , 2018-08-28
DensePose-master\CODE_OF_CONDUCT.md, 286 , 2018-08-28
DensePose-master\CONTRIBUTING.md, 1250 , 2018-08-28
DensePose-master\DensePoseData, 0 , 2018-08-28
DensePose-master\DensePoseData\demo_data, 0 , 2018-08-28
DensePose-master\DensePoseData\demo_data\demo_dp_single_ann.pkl, 980845 , 2018-08-28
DensePose-master\DensePoseData\demo_data\demo_im.jpg, 149246 , 2018-08-28
DensePose-master\DensePoseData\demo_data\synth_UV_example.png, 25997 , 2018-08-28
DensePose-master\DensePoseData\demo_data\texture_atlas_200.png, 1237384 , 2018-08-28
DensePose-master\DensePoseData\demo_data\texture_from_SURREAL.png, 831242 , 2018-08-28
DensePose-master\DensePoseData\get_DensePose_COCO.sh, 346 , 2018-08-28
DensePose-master\DensePoseData\get_densepose_uv.sh, 163 , 2018-08-28
DensePose-master\DensePoseData\get_eval_data.sh, 173 , 2018-08-28
DensePose-master\DensePoseData\infer_out, 0 , 2018-08-28
DensePose-master\DensePoseData\infer_out\demo_im.jpg.pdf, 1056420 , 2018-08-28
DensePose-master\DensePoseData\infer_out\demo_im_INDS.png, 8801 , 2018-08-28
DensePose-master\DensePoseData\infer_out\demo_im_IUV.png, 77229 , 2018-08-28
DensePose-master\GETTING_STARTED.md, 2815 , 2018-08-28
DensePose-master\INSTALL.md, 6712 , 2018-08-28
DensePose-master\LICENSE, 19333 , 2018-08-28
DensePose-master\MODEL_ZOO.md, 3109 , 2018-08-28
DensePose-master\Makefile, 491 , 2018-08-28
DensePose-master\NOTICE, 1344 , 2018-08-28
DensePose-master\PoseTrack, 0 , 2018-08-28
DensePose-master\PoseTrack\DensePose-PoseTrack-Visualize.ipynb, 792870 , 2018-08-28
DensePose-master\PoseTrack\README.md, 3352 , 2018-08-28
DensePose-master\PoseTrack\configs, 0 , 2018-08-28
DensePose-master\PoseTrack\configs\DensePose_ResNet50_FPN_s1x-e2e.yaml, 1976 , 2018-08-28
DensePose-master\PoseTrack\get_DensePose_PoseTrack.sh, 696 , 2018-08-28
DensePose-master\README.md, 3216 , 2018-08-28
DensePose-master\challenge, 0 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose, 0 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\data_format.md, 3303 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\evaluation.md, 4694 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\example_results.json, 181922 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\readme.md, 3797 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\results_format.md, 2153 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\upload.md, 3823 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose, 0 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\data_format.md, 4449 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\evaluation.md, 4641 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\readme.md, 3594 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\results_format.md, 2157 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\upload.md, 3873 , 2018-08-28
DensePose-master\challenge\encode_results_for_competition.py, 3508 , 2018-08-28
DensePose-master\cmake, 0 , 2018-08-28
DensePose-master\cmake\Summary.cmake, 1020 , 2018-08-28
DensePose-master\cmake\legacy, 0 , 2018-08-28
DensePose-master\cmake\legacy\Cuda.cmake, 9531 , 2018-08-28
DensePose-master\cmake\legacy\Dependencies.cmake, 1341 , 2018-08-28
DensePose-master\cmake\legacy\Modules, 0 , 2018-08-28
DensePose-master\cmake\legacy\Modules\FindCuDNN.cmake, 2100 , 2018-08-28
DensePose-master\cmake\legacy\Summary.cmake, 940 , 2018-08-28
DensePose-master\cmake\legacy\Utils.cmake, 10724 , 2018-08-28
DensePose-master\cmake\legacy\legacymake.cmake, 1621 , 2018-08-28
DensePose-master\configs, 0 , 2018-08-28
DensePose-master\configs\DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.yaml, 2681 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN.yaml, 1975 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN_32x8d_s1x-e2e.yaml, 2151 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN_32x8d_s1x.yaml, 2155 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN_s1x-e2e.yaml, 1977 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN_s1x.yaml, 2117 , 2018-08-28
DensePose-master\configs\DensePose_ResNet50_FPN.yaml, 1974 , 2018-08-28
DensePose-master\configs\DensePose_ResNet50_FPN_s1x-e2e.yaml, 1975 , 2018-08-28
DensePose-master\configs\DensePose_ResNet50_FPN_s1x.yaml, 2115 , 2018-08-28
DensePose-master\configs\DensePose_ResNet50_FPN_single_GPU.yaml, 2109 , 2018-08-28
DensePose-master\configs\rpn_densepose_only_R-50-FPN_1x.yaml, 1075 , 2018-08-28
DensePose-master\configs\rpn_densepose_only_X-101-32x8d-FPN_1x.yaml, 1263 , 2018-08-28
DensePose-master\detectron, 0 , 2018-08-28
DensePose-master\detectron\__init__.py, 0 , 2018-08-28
DensePose-master\detectron\core, 0 , 2018-08-28
DensePose-master\detectron\core\__init__.py, 0 , 2018-08-28
DensePose-master\detectron\core\config.py, 47570 , 2018-08-28
DensePose-master\detectron\core\rpn_generator.py, 9280 , 2018-08-28
DensePose-master\detectron\core\test.py, 37386 , 2018-08-28
DensePose-master\detectron\core\test_engine.py, 15303 , 2018-08-28
DensePose-master\detectron\core\test_retinanet.py, 7104 , 2018-08-28
DensePose-master\detectron\datasets, 0 , 2018-08-28
DensePose-master\detectron\datasets\VOCdevkit-matlab-wrapper, 0 , 2018-08-28
DensePose-master\detectron\datasets\VOCdevkit-matlab-wrapper\get_voc_opts.m, 231 , 2018-08-28
DensePose-master\detectron\datasets\VOCdevkit-matlab-wrapper\voc_eval.m, 1332 , 2018-08-28
DensePose-master\detectron\datasets\VOCdevkit-matlab-wrapper\xVOCap.m, 258 , 2018-08-28
DensePose-master\detectron\datasets\__init__.py, 0 , 2018-08-28
DensePose-master\detectron\datasets\cityscapes_json_dataset_evaluator.py, 2960 , 2018-08-28
DensePose-master\detectron\datasets\coco_to_cityscapes_id.py, 2495 , 2018-08-28
DensePose-master\detectron\datasets\data, 0 , 2018-08-28
DensePose-master\detectron\datasets\data\README.md, 3187 , 2018-08-28
DensePose-master\detectron\datasets\dataset_catalog.py, 8164 , 2018-08-28
DensePose-master\detectron\datasets\densepose_cocoeval.py, 39088 , 2018-08-28
DensePose-master\detectron\datasets\dummy_datasets.py, 1899 , 2018-08-28
DensePose-master\detectron\datasets\json_dataset.py, 20722 , 2018-08-28
DensePose-master\detectron\datasets\json_dataset_evaluator.py, 19172 , 2018-08-28
DensePose-master\detectron\datasets\roidb.py, 7497 , 2018-08-28
DensePose-master\detectron\datasets\task_evaluation.py, 14367 , 2018-08-28
DensePose-master\detectron\datasets\voc_dataset_evaluator.py, 6719 , 2018-08-28
DensePose-master\detectron\datasets\voc_eval.py, 7386 , 2018-08-28
DensePose-master\detectron\modeling, 0 , 2018-08-28
DensePose-master\detectron\modeling\FPN.py, 20218 , 2018-08-28

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  • DENTIST-master
    说明：  红外小目标检测算法RIPI，基于红外块图像，张量加权，PCA(infrared target detection algorithm RIPI)
    2020-10-19 10:27:26下载
    积分：1
  • PreProcessor
    this Class used to re-sampling image and convert at as you wont and as u need to use it in your program
    2011-10-07 18:01:55下载
    积分：1
  • hrrpfangzhen
    对高分辨距离像进行仿真的源程序，仿真效果良好(Pairs of high resolution range profile simulation of the source code, simulation, good results)
    2021-04-23 15:18:47下载
    积分：1
  • dynamics-simulation
    这是一个对动力学模型进行仿真的代码，可以得出动力学仿真图像(dynamics simulation )
    2013-05-30 20:32:59下载
    积分：1
  • add_noising
    说明：  my_noise文件是已知信号方差，加入信号噪声，可以加入高斯噪声，淑盐噪声,等其他噪声十分有用 其他两个函数时加入噪声信噪比SNR,但是有一点,这种方法并不准确,不能保证加入噪声信噪比为SNR,实际测试中可以看出,但是可以加入相同的信噪比的噪声(my_noise document is known to signal variance, by adding signal noise, Gaussian noise can be added, suk salt noise, noise, and other very useful function when the other two noise signal to noise ratio SNR, but there is one point, this method is not accurate , can not be guaranteed by adding the noise signal to noise ratio for the SNR, the actual test can be seen, but can join the same noise signal to noise ratio)
    2008-10-06 13:19:56下载
    积分：1
  • entropy_correlation
    说明：  用于计算图像的信息熵和相关性系数，在信息隐藏的图像算法评价中有很好的应用。(It is used to calculate the information entropy and correlation coefficient of image, and has a good application in the evaluation of image algorithm of information hiding.)
    2019-03-03 17:12:10下载
    积分：1
  • HoughChange
    检测图形中的直线，在程序中稍微改动，即可检测多条直线(Detection of graphics in a straight line, a slight change in the procedure, can detect more than a straight line)
    2007-12-09 14:33:41下载
    积分：1
  • Compressed_Sensing_for_MRI
    使用Bregman算法做MRI图像压缩感知(MRI using the Bregman algorithm to do image compression-aware)
    2009-12-15 10:24:14下载
    积分：1
  • resection
    本代码主要关于摄影测量中的单向空间后方交会(resection of photogrammetry)
    2013-11-11 21:49:56下载
    积分：1
  • 代码
    说明：  1、正弦函数 f (t) = K sin(wt +a) 2、矩形脉冲函数 f (t) = u(t) - u(t - t0 ) 3、抽样函数 4、单边指数函数 f (t) = Ke -t 5、已知信号 f1(t) = u(t + 2) - u(t - 2)， f 2 (t) = cos(2pt) ，用 MATLAB 绘制 f1(t) + f 2 (t) 和 f1(t)′ f 2 (t)的波形。(such as : Sine function f (t) = K sin(wt +a) Rectangular pulse function f (t) = u(t)-u(t-t0) Sampling function One-sided exponential function f (t) = Ke -t Known signal f1(t) = u(t + 2)-u(t-2), f 2 (t) = cos(2pt), draw with MATLAB The waveforms of f1(t) + f 2 (t) and f1(t)*f 2 (t))
    2021-03-19 10:39:19下载
    积分：1
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