README.md

@@ -1,5 +1,7 @@
 # Deep High-Resolution Representation Learning for Human Pose Estimation (CVPR 2019)
 ## News
+- [2021/04/12] Welcome to check out our recent work on bottom-up pose estimation (CVPR 2021) [HRNet-DEKR](https://github.com/HRNet/DEKR)!
+- [2020/07/05] [A very nice blog](https://towardsdatascience.com/overview-of-human-pose-estimation-neural-networks-hrnet-higherhrnet-architectures-and-faq-1954b2f8b249) from Towards Data Science introducing HRNet and HigherHRNet for human pose estimation.
 - [2020/03/13] A longer version is accepted by TPAMI: [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/pdf/1908.07919.pdf). It includes more HRNet applications, and the codes are available: [semantic segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation),  [objection detection](https://github.com/HRNet/HRNet-Object-Detection),  [facial landmark detection](https://github.com/HRNet/HRNet-Facial-Landmark-Detection), and [image classification](https://github.com/HRNet/HRNet-Image-Classification).
 - [2020/02/01] We have added demo code for HRNet. Thanks [Alex Simes](https://github.com/alex9311). 
 - Visualization code for showing the pose estimation results. Thanks Depu!
@@ -240,6 +242,12 @@ python visualization/plot_coco.py \
 ### Other applications
 Many other dense prediction tasks, such as segmentation, face alignment and object detection, etc. have been benefited by HRNet. More information can be found at [High-Resolution Networks](https://github.com/HRNet).
 
+### Other implementation
+[mmpose](https://github.com/open-mmlab/mmpose) </br>
+[ModelScope (中文）](https://modelscope.cn/models/damo/cv_hrnetv2w32_body-2d-keypoints_image/summary)</br>
+[timm](https://huggingface.co/docs/timm/main/en/models/hrnet)
+
+
 ### Citation
 If you use our code or models in your research, please cite with:
 ```

demo/README.md

@@ -5,12 +5,13 @@ Inferencing the deep-high-resolution-net.pytoch without using Docker.
 ## Prep
 1. Download the researchers' pretrained pose estimator from [google drive](https://drive.google.com/drive/folders/1hOTihvbyIxsm5ygDpbUuJ7O_tzv4oXjC?usp=sharing) to this directory under `models/`
 2. Put the video file you'd like to infer on in this directory under `videos`
-3. build the docker container in this directory with `./build-docker.sh` (this can take time because it involves compiling opencv)
-4. update the `inference-config.yaml` file to reflect the number of GPUs you have available
+3. (OPTIONAL) build the docker container in this directory with `./build-docker.sh` (this can take time because it involves compiling opencv)
+4. update the `inference-config.yaml` file to reflect the number of GPUs you have available and which trained model you want to use.
 
 ## Running the Model
+### 1. Running on the video
 ```
-python inference.py --cfg inference-config.yaml \
+python demo/inference.py --cfg demo/inference-config.yaml \
     --videoFile ../../multi_people.mp4 \
     --writeBoxFrames \
     --outputDir output \
@@ -23,9 +24,9 @@ Even with usage of GPU (GTX1080 in my case), the person detection will take near
  take nearly **0.07 sec**. In total. inference time per frame will be **0.13 sec**, nearly 10fps. So if you prefer a real-time (fps >= 20) 
  pose estimation then you should try other approach.
 
-## Result
+**===Result===**
 
-Some output image is as:
+Some output images are as:
 
 ![1 person](inference_1.jpg)
 Fig: 1 person inference
@@ -34,4 +35,41 @@ Fig: 1 person inference
 Fig: 3 person inference
 
 ![3 person](inference_5.jpg)
-Fig: 3 person inference
+Fig: 3 person inference
+
+### 2. Demo with more common functions
+Remember to update` TEST.MODEL_FILE` in `demo/inference-config.yaml `according to your model path.
+
+`demo.py` provides the following functions:
+
+- use `--webcam` when the input is a real-time camera.
+- use `--video [video-path]`  when the input is a video.
+- use `--image [image-path]` when the input is an image.
+- use `--write` to save the image, camera or video result.
+- use `--showFps` to show the fps (this fps includes the detection part).
+- draw connections between joints.
+
+#### (1) the input is a real-time carema
+```python
+python demo/demo.py --webcam --showFps --write
+```
+
+#### (2) the input is a video
+```python
+python demo/demo.py --video test.mp4 --showFps --write
+```
+#### (3) the input is a image
+
+```python
+python demo/demo.py --image test.jpg --showFps --write
+```
+
+**===Result===**
+
+![show_fps](inference_6.jpg)
+
+Fig: show fps
+
+![multi-people](inference_7.jpg)
+
+Fig: multi-people

demo/_init_paths.py

@@ -0,0 +1,27 @@
+# ------------------------------------------------------------------------------
+# pose.pytorch
+# Copyright (c) 2018-present Microsoft
+# Licensed under The Apache-2.0 License [see LICENSE for details]
+# Written by Bin Xiao (Bin.Xiao@microsoft.com)
+# ------------------------------------------------------------------------------
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os.path as osp
+import sys
+
+
+def add_path(path):
+    if path not in sys.path:
+        sys.path.insert(0, path)
+
+
+this_dir = osp.dirname(__file__)
+
+lib_path = osp.join(this_dir, '..', 'lib')
+add_path(lib_path)
+
+mm_path = osp.join(this_dir, '..', 'lib/poseeval/py-motmetrics')
+add_path(mm_path)