add README for app_tutorials

Author: hanjr

docs/images/3d_human_pose_result.jpg

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+# Quick Start
+TensorLayer Implementation of [YOLOv4: Optimal Speed and Accuracy of Object Detection][1]
+TensorLayer Implementation of [Optimizing Network Structure for 3D Human Pose Estimation][2](ICCV2019)
+
+## YOLOv4
+
+Yolov4 was trained on COCO 2017 Dataset in this demo.
+
+### Data
+
+Download yolov4.weights file [yolov4_model.npz][3], Password: `idsz`, and put yolov4.weights under the folder `./examples/app_tutorials/model/`. Your directory structure should look like this:
+
+```
+${root}/examples
+    └── app_tutorials
+            └── model
+                ├── yolov4_model.npz
+                ├── coco.names
+                └── yolov4_weights_congfig.txt
+
+```
+
+
+You can put an image or a video under the folder `./examples/app_tutorials/data/`,like:
+```
+${root}/examples
+    └──app_tutorials
+            └──data
+                └── *.jpg/*.png/*.mp4/..  
+```
+### demo
+
+1. Image
+   Modify `image_path` in `./examples/app_tutorials/tutorial_object_detection_yolov4_image.py` according to your demand, then
+   ```bash
+python tutorial_object_detection_yolov4_image.py
+   ```
+2. Video
+    Modify `video_path` in `./examples/app_tutorials/tutorial_object_detection_yolov4_video.py` according to your demand, then
+
+```bash
+   python tutorial_object_detection_yolov4_video.py
+```
+3. Output
+   -Image
+   
+   <p align="center"><img src="../../docs/images/yolov4_image_result.png" width="640"\></p>
+   -Video
+   
+   <p align="center"><img src="../../docs/images/yolov4_video_result.gif" width="640"\></p>
+
+
+
+## 3D Human Pose Estimation
+
+### Data
+
+Download 3D Human Pose Estimation model weights [lcn_model.npz][4], Password:`ec07`,and put it under the folder `./examples/app_tutorials/model/`, Your directory structure should look like this:
+
+```
+${root}/examples
+    └── app_tutorials
+            └── model
+                ├── lcn_model.npz
+                └── pose_weights_config.txt
+```
+Download finetuned Stacked Hourglass detections and preprocessed H3.6M data([H36M.rar][5],Password:`kw9i`), then uncompress and put them under the folder `./examples/app_tutorials/data/`, like:
+```
+${root}/examples
+    └──app_tutorials
+            └──data
+                ├── h36m_sh_dt_ft.pkl
+                ├── h36m_test.pkl
+                └── h36m_train.pkl
+```
+Each sample is a list with the length of 34 in three `.pkl` files. The list represents `[x,y]` of 17 human pose points:
+<p align="center"><img src="../../docs/images/human_pose_points.jpg" width="300"\></p>
+
+If you would like to know how to prepare the H3.6M data, please have a look at the [pose_lcn][6].
+
+### Demo
+
+For a quick demo, simply run
+
+```bash
+python tutorial_human_3dpose_estimation_LCN.py
+```
+This will produce a visualization similar to this:
+<p align="center"><img src="../../docs/images/3d_human_pose_result.jpg" width="1500"\></p>
+
+This demo maps 2D poses to 3D space. Each 3D space result list represents `[x,y,z]` of 17 human pose points.
+
+# Acknowledgement
+
+Yolov4 is bulit on https://github.com/AlexeyAB/darknet and https://github.com/hunglc007/tensorflow-yolov4-tflite.
+3D Human Pose Estimation is bulit on https://github.com/rujiewu/pose_lcn and https://github.com/una-dinosauria/3d-pose-baseline.
+We would like to thank the authors for publishing their code.
+
+
+[1]:https://arxiv.org/abs/2004.10934
+[2]:https://openaccess.thecvf.com/content_ICCV_2019/papers/Ci_Optimizing_Network_Structure_for_3D_Human_Pose_Estimation_ICCV_2019_paper.pdf
+[3]:https://pan.baidu.com/s/1MC1dmEwpxsdgHO1MZ8fYRQ
+[4]:https://pan.baidu.com/s/1HBHWsAfyAlNaavw0iyUmUQ
+[5]:https://pan.baidu.com/s/1nA96AgMsvs1sFqkTs7Dfaw
+[6]:https://github.com/rujiewu/pose_lcn