[{"_1":2,"_155":-5,"_156":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_134":135},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":-5,"_85":86,"_125":-5,"_126":11,"_127":17,"_128":11,"_129":-5,"_130":11,"_131":-5,"_132":133},"canonicalUrl","https://hub.docker.com/r/garyfeng/caffe_rtpose/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","garyfeng","reponame","caffe_rtpose","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":58,"_60":61,"_62":-5,"_63":64,"_65":66,"_67":11,"_68":29,"_69":70,"_75":17,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2017-04-19T01:34:58.258044Z","description","based on alantrrs' docker image, with opencv_contrib support","is_private","last_updated","2017-04-19T02:42:56.82899Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",359,"repository_type","star_count",1,"status","status_description","active","source","collaborator_count",0,"full_description","Realtime Multiperson Pose Estimation\n====================================\nC++ code repo for the ECCV 2016 demo, \"Realtime Multiperson Pose Estimation\", Zhe Cao, Shih-En Wei, Tomas Simon, Yaser Sheikh. Thanks Ginés Hidalgo Martínez for restructuring the code. \n\nThe [full project repo](https://github.com/ZheC/Multi-Person-Pose-Estimation) includes matlab and python version, and training code.\n\nThis project is licensed under the terms of the GPL v3 license [![License](https://img.shields.io/aur/license/yaourt.svg)](LICENSE).\n\n## Quick Start\n1. Required: CUDA & cuDNN installed on your machine.\n2. If you have installed OpenCV 2.4 in your system, go to step 3. If you are using OpenCV 3, uncomment the line `# OPENCV_VERSION := 3` on the file `Makefile.config.Ubuntu14.example` (for Ubuntu 14) and/or `Makefile.config.Ubuntu16.example` (for Ubuntu 15 or 16). In addition, OpenCV 3 does not incorporate the `opencv_contrib` module by default. Assuming you have manually installed it and you need to use it, append `opencv_contrib` at the end of the line `LIBRARIES += opencv_core opencv_highgui opencv_imgproc` in the `Makefile` file.\n3. Build `caffe` & `rtpose.bin` + download the required caffe models (script tested on Ubuntu 14.04 & 16.04, it uses all the available cores in your machine):**\n```\nchmod u+x install_caffe_and_cpm.sh\n./install_caffe_and_cpm.sh\n```\n\n## Running on a video:\n```\n./build/examples/rtpose/rtpose.bin --video video_file.mp4\n```\n\n## Running on your webcam:\n```\n./build/examples/rtpose/rtpose.bin\n```\n\n## Important options:\n`--help` <--- It displays all the available options.\n\n`--video input.mp4` <--- Input video. If omitted, will use webcam.\n\n`--camera #` <--- Choose webcam number (default: 0).\n\n`--image_dir path_to_images/` <--- Run on all jpg, png, or bmp images in `path_to_images/`. If omitted, will use webcam.\n\n`--write_frames path/` <--- Render images with this prefix: path/frame%06d.jpg\n\n`--write_json path/` <--- Output JSON file with joints with this prefix: path/frame%06d.json\n\n`--no_frame_drops` <--- Don't drop frames. Important for making offline results.\n\n`--no_display` <--- Don't open a display window. Useful if there's no X server.\n\n`--num_gpu 4` <--- Parallelize over this number of GPUs. Default is 1.\n\n`--num_scales 3 --scale_gap 0.15` <--- Use 3 scales, 1, (1-0.15), (1-0.15*2). Default is one scale=1.\n\n(HD)\n`--net_resolution 656x368 --resolution 1280x720` (These are the default values.)\n\n(VGA)\n`--net_resolution 496x368 --resolution 640x480`\n\n`--logtostderr` <--- Log messages to standard error.\n\n## Example:\nRun on a video `vid.mp4`, render image frames as `output/frame%06d.jpg` and output JSON files as `output/frame%06d.json`, using 3 scales (1.00, 0.85, and 0.70), parallelized over 2 GPUs:\n```\n./build/examples/rtpose/rtpose.bin --video vid.mp4 --num_gpu 2 --no_frame_drops --write_frames output/ --write_json output/ --num_scales 3 --scale_gap 0.15\n```\n\n## Output format:\nEach JSON file has a `bodies` array of objects, where each object has an array `joints` containing the joint locations and detection confidence formatted as `x1,y1,c1,x2,y2,c2,...`, where `c` is the confidence in [0,1].\n\n```\n{\n\"version\":0.1,\n\"bodies\":[\n{\"joints\":[1114.15,160.396,0.846207,...]},\n{\"joints\":[...]},\n]\n}\n```\n\nwhere the joint order of the COCO parts is: (see src/rtpose/modelDescriptorFactory.cpp )\n```\n\tpart2name {\n\t\t{0, \"Nose\"},\n\t\t{1, \"Neck\"},\n\t\t{2, \"RShoulder\"},\n\t\t{3, \"RElbow\"},\n\t\t{4, \"RWrist\"},\n\t\t{5, \"LShoulder\"},\n\t\t{6, \"LElbow\"},\n\t\t{7, \"LWrist\"},\n\t\t{8, \"RHip\"},\n\t\t{9, \"RKnee\"},\n\t\t{10, \"RAnkle\"},\n\t\t{11, \"LHip\"},\n\t\t{12, \"LKnee\"},\n\t\t{13, \"LAnkle\"},\n\t\t{14, \"REye\"},\n\t\t{15, \"LEye\"},\n\t\t{16, \"REar\"},\n\t\t{17, \"LEar\"},\n\t\t{18, \"Bkg\"},\n\t}\n```\n\n## Custom Caffe:\nWe modified and added several Caffe files in `include/caffe` and `src/caffe`. In case you want to use your own Caffe distribution, these are the files we added and modified:\n\n1. Added folders in `include/caffe` and `src/caffe`: `include/caffe/cpm` and `src/caffe/cpm`.\n2. Modified files in `include/caffe` (search for `// CPM extra code:` to find the modified code): `data_transformer.hpp`.\n3. Modified files in `src/caffe` (search for `// CPM extra code:` to find the modified code): `data_transformer.cpp`, `proto/caffe.proto` and `util/blocking_queue.cpp`.\n4. Replaced files: `README.md`.\n5. Added files: `install_caffe_and_cpm.sh`, `Makefile.config.Ubuntu14.example` (extracted from `Makefile.config.example`) and `Makefile.config.Ubuntu16.example` (extracted from `Makefile.config.example`).\n6. Other added folders: `model/`, `examples/rtpose`, `/include/rtpose` and `/src/rtpose`.\n7. Other modified files: `Makefile`.\n8. Optional - deleted Caffe files and folders (only to save space): `Makefile.config.example`, `data/`, `examples/` (do not delete `examples/rtpose`) and `models/`.\n\n\n## Custom Caffe layers:\nWe created a few Caffe layers (located in `include/caffe/cpm/layers` and `src/caffe/cpm/layers`):\n\n1. ImResizeLayer: Only used for testing (backward pass not implemented). This layer performs 2-D resize over the 4-D data. I.e., given a 4-D input of size (`num` x `channels` x `height_input` x `width_input`), the layer returns a 4-D output of size (`num` x `channels` x `height_output` x `width_output`). It is independently applied to each dimension of `num` and `channels`. Its parameters are:\n\t1. `factor`: Scaling factor with respect to the input width and height. `factor` is the alternative to the pair of variables [`target_spatial_width`, `target_spatial_height`]. If `factor != 0`, the latter are ignored.\n\t2. `scale_gap` and `start_scale`: These parameters are related and used for doing scale search in testing mode. If `start_scale = 1` (default), the CNN input patch size is the net resolution (set with `--net_resolution`). `scale_gap` is used to calculate the scale difference between scales. This parameters are related with the flag `--num_scales`. For instance, using `--start_scale 1 --num_scales 3 --scale_gap 0.1` means using 3 scales: 1, 1-0.1, 1-2*0.1, hence the different patch sizes correspond to the net resolution multiplied by these scales values.\n\t3. `target_spatial_height`: Alternative to `factor`. It sets the output height. Ignored if `factor != 0`.\n\t4. `target_spatial_width`: Alternative to `factor`. It sets the output width. Ignored if `factor != 0`.\n2. NmsLayer: Only used for testing (backward pass not implemented). This layer performs 3-D Non-Maximum Suppression over the 4-D data. I.e., given a 4-D input of size (`num` x `channels` x `height` x `width`), it returns a 4-D output of size (`num` x `num_parts` x `max_peaks+1` x `3`). It is independently applied to each dimension of `num`. The seconds dimension corresponds to the number of limbs (`num_parts`). The third dimension indicates the maximum number of peaks to be analyzed (`max_peaks+1`). Finally, the last one corresponds to the `x`, `y` and `score` values (`3`). Its parameters are:\n\t1. `max_peaks`: The number of peaks to be considered. The last `total_peaks` - `max_peaks` peaks are discarded.\n\t2. `num_parts`: The number of limbs to detect (e.g. 15 for MPI and 18 for COCO).\n\t3. `threshold`: Any input value smaller than this threshold is set to 0.\n\n\n## Citation\nPlease cite the paper in your publications if it helps your research:\n\n\n\n @article{cao2016realtime,\n\t title={Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},\n\t author={Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},\n\t journal={arXiv preprint arXiv:1611.08050},\n\t year={2016}\n\t }\n\n @inproceedings{wei2016cpm,\n author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},\n booktitle = {CVPR},\n title = {Convolutional pose machines},\n year = {2016}\n }\n","has_starred","hub_user","immutable_tags_settings",{"_71":11,"_72":73},"enabled","rules",[74],".*","is_automated","permissions",{"_78":17,"_79":11,"_80":11},"read","write","admin","user","storage_size",5553584815,"orgData","recentTags",[87],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":96,"_46":114,"_115":90,"_116":29,"_117":52,"_53":118,"_119":120,"_121":105,"_122":114,"_123":61,"_124":17},"content_type","creator",496106,"full_size",5553562565,"id",10100052,"images",[97],{"_98":99,"_100":101,"_102":103,"_104":105,"_106":107,"_108":109,"_110":103,"_111":-5,"_112":92,"_59":61,"_113":-5},"architecture","amd64","digest","sha256:736bedb321d9cb6c004908557e41201e32d243a6f5ec2dda84b2f5997bafb210","features","","last_pulled","2026-09-23T03:45:10.205966198Z","last_pushed","2017-04-19T02:42:57Z","os","linux","os_features","os_version","size","variant","2017-04-19T02:42:56.279537Z","last_updater","last_updater_username","media_type","latest","repository",1418899,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":136,"_143":144,"_132":153,"_154":11},{"_36":137,"_38":138,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":139,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":58,"_60":61,"_62":-5,"_63":64,"_65":66,"_67":11,"_68":29,"_69":140,"_75":17,"_76":142,"_81":29,"_82":83},[],[40],[52],{"_71":11,"_72":141},[74],{"_78":17,"_79":11,"_80":11},"tags",{"_145":58,"_146":-5,"_147":-5,"_148":149},"count","next","previous","results",[150],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":151,"_46":114,"_115":90,"_116":29,"_117":52,"_53":118,"_119":120,"_121":105,"_122":114,"_123":61,"_124":17},[152],{"_98":99,"_100":101,"_102":103,"_104":105,"_106":107,"_108":109,"_110":103,"_111":-5,"_112":92,"_59":61,"_113":-5},[],"canDeleteTags","actionData","errors"]

garyfeng/caffe_rtpose

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