add readme. delete useless files. add demo.

anl13 · Jul 19, 2022 · 28cb282 · 28cb282
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diff --git a/.gitignore b/.gitignore
@@ -1,3 +1,8 @@
+# outputfile 
+*.pth 
+output*/
+*.zip
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -105,4 +110,4 @@ venv.bak/
 /output
 /models
 /log
-*.swp
+*.swp
diff --git a/README.md b/README.md
diff --git a/README_hrnet.md b/README_hrnet.md
@@ -0,0 +1,228 @@
+# Simple Baselines for Human Pose Estimation and Tracking
+
+## News
+- Our new work [High-Resolution Representations for Labeling Pixels and Regions](https://arxiv.org/abs/1904.04514) is available at [HRNet](https://github.com/HRNet). Our HRNet has been applied to a wide range of vision tasks, such as [image classification](https://github.com/HRNet/HRNet-Image-Classification), [objection detection](https://github.com/HRNet/HRNet-Object-Detection), [semantic segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation) and [facial landmark](https://github.com/HRNet/HRNet-Facial-Landmark-Detection).
+- Our new work [Deep High-Resolution Representation Learning for Human Pose Estimation](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch) has already been released at <https://github.com/leoxiaobin/deep-high-resolution-net.pytorch>. The best single HRNet can obtain an **AP of 77.0** on COCO test-dev2017 dataset and **92.3% of [email protected]** on MPII test set. The new repositoty also support the SimpleBaseline method, and you are welcomed to try it.<br>
+- Our entry using this repo has won the winner of [PoseTrack2018 Multi-person Pose Tracking Challenge](https://posetrack.net/workshops/eccv2018/posetrack_eccv_2018_results.html)!<br>
+- Our entry using this repo ranked 2nd place in the [keypoint detection task of COCO 2018](http://cocodataset.org/#keypoints-leaderboard)!
+
+## Introduction
+This is an official pytorch implementation of [*Simple Baselines for Human Pose Estimation and Tracking*](https://arxiv.org/abs/1804.06208). This work provides baseline methods that are surprisingly simple and effective, thus helpful for inspiring and evaluating new ideas for the field. State-of-the-art results are achieved on challenging benchmarks. On COCO keypoints valid dataset, our best **single model** achieves **74.3 of mAP**. You can reproduce our results using this repo. All models are provided for research purpose.    </br>
+
+## Main Results
+### Results on MPII val
+| Arch | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mean | [email protected]|
+|---|---|---|---|---|---|---|---|---|---|
+| 256x256_pose_resnet_50_d256d256d256 | 96.351 | 95.329 | 88.989 | 83.176 | 88.420 | 83.960 | 79.594 | 88.532 | 33.911 |
+| 384x384_pose_resnet_50_d256d256d256 | 96.658 | 95.754 | 89.790 | 84.614 | 88.523 | 84.666 | 79.287 | 89.066 | 38.046 |
+| 256x256_pose_resnet_101_d256d256d256 | 96.862 | 95.873 | 89.518 | 84.376 | 88.437 | 84.486 | 80.703 | 89.131 | 34.020 |
+| 384x384_pose_resnet_101_d256d256d256 | 96.965 | 95.907 | 90.268 | 85.780 | 89.597 | 85.935 | 82.098 | 90.003 | 38.860 |
+| 256x256_pose_resnet_152_d256d256d256 | 97.033 | 95.941 | 90.046 | 84.976 | 89.164 | 85.311 | 81.271 | 89.620 | 35.025 |
+| 384x384_pose_resnet_152_d256d256d256 | 96.794 | 95.618 | 90.080 | 86.225 | 89.700 | 86.862 | 82.853 | 90.200 | 39.433 |
+
+### Note:
+- Flip test is used.
+
+### Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset
+| Arch | AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
+|---|---|---|---|---|---|---|---|---|---|---|
+| 256x192_pose_resnet_50_d256d256d256 | 0.704 | 0.886 | 0.783 | 0.671 | 0.772 | 0.763 | 0.929 | 0.834 | 0.721 | 0.824 |
+| 384x288_pose_resnet_50_d256d256d256 | 0.722 | 0.893 | 0.789 | 0.681 | 0.797 | 0.776 | 0.932 | 0.838 | 0.728 | 0.846 |
+| 256x192_pose_resnet_101_d256d256d256 | 0.714 | 0.893 | 0.793 | 0.681 | 0.781 | 0.771 | 0.934 | 0.840 | 0.730 | 0.832 |
+| 384x288_pose_resnet_101_d256d256d256 | 0.736 | 0.896 | 0.803 | 0.699 | 0.811 | 0.791 | 0.936 | 0.851 | 0.745 | 0.858 |
+| 256x192_pose_resnet_152_d256d256d256 | 0.720 | 0.893 | 0.798 | 0.687 | 0.789 | 0.778 | 0.934 | 0.846 | 0.736 | 0.839 |
+| 384x288_pose_resnet_152_d256d256d256 | 0.743 | 0.896 | 0.811 | 0.705 | 0.816 | 0.797 | 0.937 | 0.858 | 0.751 | 0.863 |
+
+
+### Results on *Caffe-style* ResNet
+| Arch | AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
+|---|---|---|---|---|---|---|---|---|---|---|
+| 256x192_pose_resnet_50_*caffe*_d256d256d256 | 0.704 | 0.914 | 0.782 | 0.677 | 0.744 | 0.735 | 0.921 | 0.805 | 0.704 | 0.783 |
+| 256x192_pose_resnet_101_*caffe*_d256d256d256 | 0.720 | 0.915 | 0.803 | 0.693 | 0.764 | 0.753 | 0.928 | 0.821 | 0.720 | 0.802 |
+| 256x192_pose_resnet_152_*caffe*_d256d256d256 | 0.728 | 0.925 | 0.804 | 0.702 | 0.766 | 0.760 | 0.931 | 0.828 | 0.729 | 0.806 |
+
+
+### Note:
+- Flip test is used.
+- Person detector has person AP of 56.4 on COCO val2017 dataset.
+- Difference between *PyTorch-style* and *Caffe-style* ResNet is the position of stride=2 convolution
+
+## Environment
+The code is developed using python 3.6 on Ubuntu 16.04. NVIDIA GPUs are needed. The code is developed and tested using 4 NVIDIA P100 GPU cards. Other platforms or GPU cards are not fully tested.
+
+## Quick start
+### Installation
+1. Install pytorch >= v0.4.0 following [official instruction](https://pytorch.org/).
+2. Disable cudnn for batch_norm:
+   ```
+   # PYTORCH=/path/to/pytorch
+   # for pytorch v0.4.0
+   sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
+   # for pytorch v0.4.1
+   sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
+   ```
+   Note that instructions like # PYTORCH=/path/to/pytorch indicate that you should pick a path where you'd like to have pytorch installed  and then set an environment variable (PYTORCH in this case) accordingly.
+1. Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.
+2. Install dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+3. Make libs:
+   ```
+   cd ${POSE_ROOT}/lib
+   make
+   ```
+3. Install [COCOAPI](https://github.com/cocodataset/cocoapi):
+   ```
+   # COCOAPI=/path/to/clone/cocoapi
+   git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
+   cd $COCOAPI/PythonAPI
+   # Install into global site-packages
+   make install
+   # Alternatively, if you do not have permissions or prefer
+   # not to install the COCO API into global site-packages
+   python3 setup.py install --user
+   ```
+   Note that instructions like # COCOAPI=/path/to/install/cocoapi indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (COCOAPI in this case) accordingly.
+3. Download pytorch imagenet pretrained models from [pytorch model zoo](https://pytorch.org/docs/stable/model_zoo.html#module-torch.utils.model_zoo) and caffe-style pretrained models from [GoogleDrive](https://drive.google.com/drive/folders/1yJMSFOnmzwhA4YYQS71Uy7X1Kl_xq9fN?usp=sharing). 
+4. Download mpii and coco pretrained models from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW0D5ZE4ArK9wk_fvw) or [GoogleDrive](https://drive.google.com/drive/folders/13_wJ6nC7my1KKouMkQMqyr9r1ZnLnukP?usp=sharing). Please download them under ${POSE_ROOT}/models/pytorch, and make them look like this:
+
+   ```
+   ${POSE_ROOT}
+    `-- models
+        `-- pytorch
+            |-- imagenet
+            |   |-- resnet50-19c8e357.pth
+            |   |-- resnet50-caffe.pth.tar
+            |   |-- resnet101-5d3b4d8f.pth
+            |   |-- resnet101-caffe.pth.tar
+            |   |-- resnet152-b121ed2d.pth
+            |   `-- resnet152-caffe.pth.tar
+            |-- pose_coco
+            |   |-- pose_resnet_101_256x192.pth.tar
+            |   |-- pose_resnet_101_384x288.pth.tar
+            |   |-- pose_resnet_152_256x192.pth.tar
+            |   |-- pose_resnet_152_384x288.pth.tar
+            |   |-- pose_resnet_50_256x192.pth.tar
+            |   `-- pose_resnet_50_384x288.pth.tar
+            `-- pose_mpii
+                |-- pose_resnet_101_256x256.pth.tar
+                |-- pose_resnet_101_384x384.pth.tar
+                |-- pose_resnet_152_256x256.pth.tar
+                |-- pose_resnet_152_384x384.pth.tar
+                |-- pose_resnet_50_256x256.pth.tar
+                `-- pose_resnet_50_384x384.pth.tar
+
+   ```
+
+4. Init output(training model output directory) and log(tensorboard log directory) directory:
+
+   ```
+   mkdir output 
+   mkdir log
+   ```
+
+   Your directory tree should look like this:
+
+   ```
+   ${POSE_ROOT}
+   ├── data
+   ├── experiments
+   ├── lib
+   ├── log
+   ├── models
+   ├── output
+   ├── pose_estimation
+   ├── README.md
+   └── requirements.txt
+   ```
+
+### Data preparation
+**For MPII data**, please download from [MPII Human Pose Dataset](http://human-pose.mpi-inf.mpg.de/). The original annotation files are in matlab format. We have converted them into json format, you also need to download them from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW00SqrairNetmeVu4) or [GoogleDrive](https://drive.google.com/drive/folders/1En_VqmStnsXMdldXA6qpqEyDQulnmS3a?usp=sharing).
+Extract them under {POSE_ROOT}/data, and make them look like this:
+```
+${POSE_ROOT}
+|-- data
+`-- |-- mpii
+    `-- |-- annot
+        |   |-- gt_valid.mat
+        |   |-- test.json
+        |   |-- train.json
+        |   |-- trainval.json
+        |   `-- valid.json
+        `-- images
+            |-- 000001163.jpg
+            |-- 000003072.jpg
+```
+
+**For COCO data**, please download from [COCO download](http://cocodataset.org/#download), 2017 Train/Val is needed for COCO keypoints training and validation. We also provide person detection result of COCO val2017 to reproduce our multi-person pose estimation results. Please download from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blWzzDXoz5BeFl8sWM-) or [GoogleDrive](https://drive.google.com/drive/folders/1fRUDNUDxe9fjqcRZ2bnF_TKMlO0nB_dk?usp=sharing).
+Download and extract them under {POSE_ROOT}/data, and make them look like this:
+```
+${POSE_ROOT}
+|-- data
+`-- |-- coco
+    `-- |-- annotations
+        |   |-- person_keypoints_train2017.json
+        |   `-- person_keypoints_val2017.json
+        |-- person_detection_results
+        |   |-- COCO_val2017_detections_AP_H_56_person.json
+        `-- images
+            |-- train2017
+            |   |-- 000000000009.jpg
+            |   |-- 000000000025.jpg
+            |   |-- 000000000030.jpg
+            |   |-- ... 
+            `-- val2017
+                |-- 000000000139.jpg
+                |-- 000000000285.jpg
+                |-- 000000000632.jpg
+                |-- ... 
+```
+
+### Valid on MPII using pretrained models
+
+```
+python pose_estimation/valid.py \
+    --cfg experiments/mpii/resnet50/256x256_d256x3_adam_lr1e-3.yaml \
+    --flip-test \
+    --model-file models/pytorch/pose_mpii/pose_resnet_50_256x256.pth.tar
+```
+
+### Training on MPII
+
+```
+python pose_estimation/train.py \
+    --cfg experiments/mpii/resnet50/256x256_d256x3_adam_lr1e-3.yaml
+```
+
+### Valid on COCO val2017 using pretrained models
+
+```
+python pose_estimation/valid.py \
+    --cfg experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3.yaml \
+    --flip-test \
+    --model-file models/pytorch/pose_coco/pose_resnet_50_256x192.pth.tar
+```
+
+### Training on COCO train2017
+
+```
+python pose_estimation/train.py \
+    --cfg experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3.yaml
+```
+
+### Other Implementations
+- TensorFlow [[Version1](https://github.com/mks0601/TF-SimpleHumanPose)]
+- PaddlePaddle [[Version1](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/human_pose_estimation)]
+- Gluon [[Version1](https://gluon-cv.mxnet.io/model_zoo/pose.html)]
+
+### Citation
+If you use our code or models in your research, please cite with:
+```
+@inproceedings{xiao2018simple,
+    author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
+    title={Simple Baselines for Human Pose Estimation and Tracking},
+    booktitle = {European Conference on Computer Vision (ECCV)},
+    year = {2018}
+}
+```
diff --git a/coco_to_univ.py b/coco_to_univ.py
@@ -81,10 +81,14 @@ def convert_iccv2019(infile, outfile):
     from IPython import embed; embed() 
 
 if __name__ == "__main__":
-    infile = "data/iccv2019/annotations/animalpose_val.json"
-    outfile = "data/iccv2019_univ/annotations/animalpose_val.json" 
-    convert_iccv2019(infile, outfile) 
+    #infile = "data/iccv2019/annotations/animalpose_val.json"
+    #outfile = "data/iccv2019_univ/annotations/animalpose_val.json" 
+    #convert_iccv2019(infile, outfile) 
 
     # infile = "data/atrw/annotations/keypoints_trainval.json"
     # outfile = "data/atrw_univ/annotations/keypoints_trainval.json"
     # convert_atrw(infile, outfile)
+    infile = "/Data/anliang/dataset/pig20210225/annotations/train_pig_cocostyle.json"
+    outfile = "/Data/anliang/dataset/pig20210225_univ/annotations/train_pig_cocostyle.json"
+    convert_pig(infile, outfile)
+
diff --git a/demo.sh b/demo.sh
diff --git a/demo_BamaPig3D.sh b/demo_BamaPig3D.sh
@@ -0,0 +1,8 @@
+#!/bin/bash
+CUDA_VISIBLE_DEVICES=0 python pose_estimation/demo_BamaPig3D.py \
+    --dataset_folder /media/AnimalData2/MAMMAL/BamaPig3D/ \
+    --cfg experiments/pig/w48_384x384_univ_20210225.yaml \
+    --GPUS "(0,)" \
+    --vis True \
+    --write_json False
+