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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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  • image-processing
    冈萨雷斯数字图像处理MATLAB版，是我们学习图像处理必备的书籍(Gonzales digital image processing MATLAB version, we study the image processing necessary books)
    2016-05-04 11:45:37下载
    积分：1
  • 图像清晰度评价指标Matlab
    图像清晰度评价函数说明 1、熵: 表示图像所包含的平均信息量的多少,嫡值越大则所含信息量越多。 文件名：entropy.m 结果：EN 2、交叉嫡：反映两幅图像的差异,交叉嫡越小,则融合图像和原图像的差别越小。 文件名：cross_entropy.m 结果：平均交叉嫡MCE，均方根交叉嫡RCE 3、峰值信噪比: PSNR越高,说明融合效果与质量越好。 文件名：psnr.m 结果：PSNR 4、Qabf: 评价边缘或梯度质量，越大边缘越明显 文件名：Qabf.m 结果：Qabf（QAB/F) 5、平均梯度（Average Gradient）:也称为清晰度，反映了图像中的微小细节反差与纹理变化特征，同时也反映了图像的清晰度，越大越好。 文件名：avg_gradient 调用：outval = avg_gradient(img) 6、结构相似性指数： SSIM指原图像和融合图像的相似程度,值越大越相似 文件名：ssim.m 结果：SSIM 7、互信息：MI(mutual information) 8、NMI: Normalized mutual information(Image sharpness evaluation function)
    2018-09-30 09:09:39下载
    积分：1
  • 安卓版微信图片视频管理助手
    全网唯一的一款能够快速完全浏览+整理微信图片视 联合开发网 - pudn.com
    2020-06-24 05:20:02下载
    积分：1
  • 箱型图
    说明：  一个画箱型图的代码，按照里面的数据格式将数据添加进去就能画箱型图，体量小，但很实用(A drawing box diagram code, according to the data format will be able to add data to draw box diagram, small volume, but very practical)
    2021-02-08 09:44:14下载
    积分：1
  • peizhun
    基于matlab的图像配准原代码，适合各种格式的图像(Matlab-based image registration of the original code, a variety of formats suitable for image)
    2007-07-12 21:18:10下载
    积分：1
  • wavelet transform program
    说明：  内容：基于MATLAB的小波变换程序 具体功能：实现四级信号的小波分解和重构(Content: wavelet transform based on Matlab: four level signial wavelet decomposition and reconstruction are implemented.)
    2020-11-02 17:09:54下载
    积分：1
  • point_cloud_reduction
    自己开发的一种点云降噪方法，可以去除物体主体之外的部分的点云数据（物体点云数据为三维扫描仪获得）(A point cloud noise reduction method developed by ourselves can remove part of the cloud data outside the main body of the object (object cloud data is obtained for a three-dimensional scanner))
    2020-11-30 11:09:28下载
    积分：1
  • circle
    质心定位算法，代码在matlab2013b下调试通过，希望对下载君有所帮助。(Centroid positioning algorithm, the code in matlab2013b debugging through, and I hope to download Jun help.)
    2017-08-26 15:43:22下载
    积分：1
  • sobel算子
    应用opencv进行图像处理，改变基sobel算子操作，有利于开展下面的图像处理(Applying OpenCV to image processing and changing the operation of base Sobel operator is beneficial to the following image processing)
    2019-03-04 21:40:18下载
    积分：1
  • lsb水印算法
    说明：  lsb数字水印算法，SB算法利用了数字图像处理中位平面的原理,即改变图像的最低位的信息,对图像信息产生的影响非常小,人眼的视觉感知系统往往不能察觉。以一幅256灰度的图像为例, 256 灰度共需要8 个位来表示,但其中每一个位的作用是不一样的,越高位对图像的影响越大,反之越低的位影响越小,甚至不能感知。(Digital watermarking algorithm based on lsb)
    2020-02-10 11:44:44下载
    积分：1
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