PoseforEveryting的github代码

MP-100 is built upon the 2D pose datasets, including [COCO](http://cocodataset.org/), [300W](https://ibug.doc.ic.ac.uk/resources/300-W/), [AFLW](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/), [OneHand10K](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html), [DeepFashion](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html), [AP-10K](https://github.com/AlexTheBad/AP-10K), [MacaquePose](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html), [Vinegar Fly](https://github.com/jgraving/DeepPoseKit-Data), [Desert Locust](https://github.com/jgraving/DeepPoseKit-Data), [CUB-200](http://www.vision.caltech.edu/datasets/cub_200_2011/), [CarFusion](http://www.cs.cmu.edu/~ILIM/projects/IMgit/CarFusion/cvpr2018/index.html), [AnimalWeb](https://fdmaproject.wordpress.com/author/fdmaproject/), [Keypoint-5](https://github.com/jiajunwu/3dinn). In order to use MP-100, please download images from the original datasets first, then reorganize the data and use our provided [annotation files](https://drive.google.com/drive/folders/1pzC5uEgi4AW9RO9_T1J-0xSKF12mdj1_?usp=sharing) for training and testing. After preparing images and annotations, the project should look like this: ```text Pose-for-Everything ├── assets ├── configs ├── mp100 ├── pomnet ├── tools `── data │── mp100 │-- annotations │ │-- mp100_split1_train.json │ |-- mp100_split1_val.json │ |-- mp100_split1_test-dev-2017.json │ │-- ... │-- human_face │-- human_hand │-- sling_dress │-- human_body │ │-- 000000000009.jpg │ │-- 000000000025.jpg │ │-- 000000000030.jpg │ │-- ... │-- antelope_body │-- ... ``` MP-100 includes 100 categories and the images of different categories are contained in different folders individually. Specifically, - **human_body** is collected from [COCO](http://cocodataset.org/). Here is an example that soft link can be established from the downloaded images to propare the data for each category. ```shell ln -s ${COCO_PATH} data/mp100/human_body ``` - **human_face** is collected from [300W](https://ibug.doc.ic.ac.uk/resources/300-W/). - **amur_tiger_body** is collected from [AFLW](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/). - **human_hand** is collected from [OneHand10K](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html). - 13 categories including **long_sleeved_dress**, **long_sleeved_outwear**, **long_sleeved_shirt**, **shorts**, **short_sleeved_dress**, **short_sleeved_outwear**, **short_sleeved_shirt**, **skirt**, **sling**, **sling_dress**, **trousers**, **vest**, and **vest_dress** are collected from [DeepFashion](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html). For simplicity, all the categories can be linked to the complete downloaded dataset. ```shell ln -s ${DEEPFASHION_DATA} data/mp100/long_sleeved_dress ln -s ${DEEPFASHION_DATA} data/mp100/long_sleeved_outwear ln -s ${DEEPFASHION_DATA} data/mp100/long_sleeved_shirt ... ``` - 34 categories including **antelope_body**, **beaver_body**, **bison_body**, **bobcat_body**, **cat_body**, **cheetah_body**, **cow_body**, **deer_body**, **dog_body**, **elephant_body**, **fox_body**, **giraffe_body**, **gorilla_body**, **hamster_body**, **hippo_body**, **horse_body**, **leopard_body**, **lion_body**, **otter_body**, **panda_body**, **panther_body**, **pig_body**, **polar_bear_body**, **rabbit_body**, **raccoon_body**, **rat_body**, **rhino_body**, **sheep_body**, **skunk_body**, **spider_monkey_body**, **squirrel_body**, **weasel_body**, **wolf_body**, and **zebra_body** are collected from [AP-10K](https://github.com/AlexTheBad/AP-10K). - **macaque_body** is collected from [MacaquePose](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html). - **fly_body** is collected from [Vinegar Fly](https://github.com/jgraving/DeepPoseKit-Data). - **locust_body** is collected from [Desert Locust](https://github.com/jgraving/DeepPoseKit-Data). - 8 categories are collected from [CUB-200](http://www.vision.caltech.edu/datasets/cub_200_2011/). In detail, **grebe_body** is the combination of 050.Eared_Grebe, 051.Horned_Grebe, 052.Pied_billed_Grebe, and 053.Western_Grebe in CUB-200. **gull_body** is the combination of 059.California_Gull, 060.Glaucous_winged_Gull, 061.Heermann_Gull, 062.Herring_Gull, 063.Ivory_Gull, 064.Ring_billed_Gull, 065.Slaty_backed_Gull, and 066.Western_Gull in CUB-200. **kingfisher_body** is the combination of 079.Belted_Kingfisher, 080.Green_Kingfisher, 081.Pied_Kingfisher, 082.Ringed_Kingfisher, and 083.White_breasted_Kingfisher in CUB-200. **sparrow_body** is the combination of 113.Baird_Sparrow, 114.Black_throated_Sparrow, 115.Brewer_Sparrow, 116.Chipping_Sparrow, 117.Clay_colored_Sparrow, 118.House_Sparrow, 119.Field_Sparrow, 120.Fox_Sparrow, 121.Grasshopper_Sparrow, 122.Harris_Sparrow, 123.Henslow_Sparrow, 124.Le_Conte_Sparrow, 125.Lincoln_Sparrow, 126.Nelson_Sharp_tailed_Sparrow, 127.Savannah_Sparrow, 128.Seaside_Sparrow, 129.Song_Sparrow, 130.Tree_Sparrow, 131.Vesper_Sparrow, 132.White_crowned_Sparrow, and 133.White_throated_Sparrow in CUB-200. **tern_body** is the combination of 141.Artic_Tern, 142.Black_Tern, 143.Caspian_Tern, 144.Common_Tern, 145.Elegant_Tern, 146.Forsters_Tern, and 147.Least_Tern in CUB-200. **warbler_body** is the combination of 158.Bay_breasted_Warbler, 159.Black_and_white_Warbler, 160.Black_throated_Blue_Warbler, 161.Blue_winged_Warbler, 162.Canada_Warbler, 163.Cape_May_Warbler, 164.Cerulean_Warbler, 165.Chestnut_sided_Warbler, 166.Golden_winged_Warbler, 167.Hooded_Warbler, 168.Kentucky_Warbler, 169.Magnolia_Warbler, 170.Mourning_Warbler, 171.Myrtle_Warbler, 172.Nashville_Warbler, 173.Orange_crowned_Warbler, 174.Palm_Warbler, 175.Pine_Warbler, 176.Prairie_Warbler, 177.Prothonotary_Warbler, 178.Swainson_Warbler, 179.Tennessee_Warbler, 180.Wilson_Warbler, 181.Worm_eating_Warbler, and 182.Yellow_Warbler in CUB-200. **woodpecker_body** is the combination of 187.American_Three_toed_Woodpecker, 188.Pileated_Woodpecker, 189.Red_bellied_Woodpecker, 190.Red_cockaded_Woodpecker, 191.Red_headed_Woodpecker, and 192.Downy_Woodpecker in CUB-200. **wren_body** is the combination of 193.Bewick_Wren, 194.Cactus_Wren, 195.Carolina_Wren, 196.House_Wren, 197.Marsh_Wren, 198.Rock_Wren, and 199.Winter_Wren in CUB-200. As the images of the category come from multiple sources, we can copy or move all the needed images to the new folder. For example, ```shell mkdir grebe_body # copy images to the new folder cp ${CUB-200_ROOT}/*_Grebe/* data/mp100/grebe_body or # move images to the new folder mv ${CUB-200_ROOT}/*_Grebe/* data/mp100/grebe_body ``` - 3 categories including **bus**, **car**, and **suv** are collected from [CarFusion](http://www.cs.cmu.edu/~ILIM/projects/IM/CarFusion/cvpr2018/index.html). We clean the data and select the samples manually. Also, we rename the images to image_id.jpg using [rename_carfusion_image.py](rename_carfusion_image.py). *image_id* is the ID of each image in COCO format obtained by the [official tools](https://github.com/dineshreddy91/carfusion_to_coco). First, we can use the code provided by [official tools](https://github.com/dineshreddy91/carfusion_to_coco) to convert the annotations to COCO format. Then, we run [rename_carfusion_image.py](rename_carfusion_image.py) to rename the images. ```shell python mp100/rename_carfusion_ima