Add demo/demo.py with more common functions and update demo/README.md

Author: CrystalSixone

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 --writeVideo
+```
+
+#### (2) the input is a video
+```python
+python demo/demo.py --video test.mp4 --showFps --writeVideo
+```
+#### (3) the input is a image
+
+```python
+python demo/demo.py --image test.jpg --showFps --writeImage 
+```
+
+**===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)