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DensePose-master

于 2019-06-17 发布 文件大小:9846KB
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  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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  • 00.simulation
    空间光通信中的信标光探测器非均匀性校正，使用matlab进行仿真，得到探测器非均匀性校正前后的光斑位置探测情况(Nonuniformity correction of beacon photodetectors in space optical communication)
    2017-12-17 20:33:13下载
    积分：1
  • New_Method_for_License_Plate_Recognition
    a.牌照定位，定位图片中的牌照位置； b.牌照字符分割，把牌照中的字符分割出来； c.牌照字符识别，把分割好的字符进行识别，最终组成牌照号码。 (a. License Plate Location, location location of the picture of the license b. Character Segmentation license to license the characters split up c. license character recognition, to split a good character to identify the final composition of license number. )
    2009-09-20 18:54:46下载
    积分：1
  • mytest1
    说明：  以中心图像为模板，将其他八张图映射到模板中，然后粘贴到对应位置完成拼接(Take the center image as the template, map the other eight images to the template, and paste them to the corresponding position to complete the splicing)
    2020-04-09 18:59:02下载
    积分：1
  • cseg
    MATLAB基于贝叶斯的RGB的彩色图像分割(MATLAB Based on Bayesian Color Image Segmentation in RGB)
    2009-10-19 14:54:19下载
    积分：1
  • picqiehuan
    多个图片定时轮流切换，切换时可以改变样式(Photos regular rotation than switch can be changed when switching style)
    2009-04-08 12:36:39下载
    积分：1
  • fuzzyedge
    此程序实现了模糊增强实现图像的边缘检测，利用模糊方法结合边缘检测实现边缘点的提取。(Realize this procedure to enhance implementation of fuzzy image edge detection, the use of fuzzy edge detection methods achieve the extraction of edge points.)
    2009-02-25 17:26:50下载
    积分：1
  • movingtargetdetectionandtracking
    在道路交通管理中，为了获得车辆的运动数据，早期经常采用的是感应线圈等硬件测量的方法。而如果采用摄像头拍摄的道路视频，再用计算机软件处理的方法，则可以极大的增加方便性和灵活性。本文运动目标检测与跟踪研究如何让计算机从视频图像序列中获得物体运动数据。(Traffic management in order to obtain motion data of the vehicle, is a method often used early induction coil measurement hardware. If using a video camera to shoot on the road, and then deal with methods of computer software, you can greatly increase the convenience and flexibility. This paper studies the moving target detection and tracking moving objects how to get data from a computer video image sequence.)
    2014-03-12 16:32:21下载
    积分：1
  • imagetool
    1. 将真彩位图转化成各色或黑白位图，进行二值化(支持多种抖动方法)处理后的位图，效果和速度极佳。 2. 将真彩或黑白位图进行 90度 , 180 度 ,270 度 高速度旋转。 (1. The true color bitmap into a colored or black and white bitmap to binary (supports multiple dithering methods) treated bitmap, effects and excellent speed. 2. The true color or black and white bitmap to 90 degrees, 180 degrees, 270-degree high-speed rotation.)
    2010-03-08 13:24:20下载
    积分：1
  • Matlab code
    图像二值化和电润湿像素单元开口率的计算，用于验证电润湿动力学模型是否正确(The calculation of the image two value and the rate of aperture of the electrowetting pixel unit is used to verify whether the electrowetting kinetic model is correct)
    2021-04-21 10:18:50下载
    积分：1
  • upfile_2.4.3
    多图片上交过程,程序的主要功能就是图片上传。可以一次上传多个文件。速度也相当的快哦。 (hand in the picture)
    2013-05-31 08:35:19下载
    积分：1
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