Update readme.

README.md

@@ -5,13 +5,37 @@ This repo shows how to estimate human head pose from videos using TensorFlow and
 ![demo](https://github.com/yinguobing/head-pose-estimation/raw/master/demo.gif)
 ![demo](https://github.com/yinguobing/head-pose-estimation/raw/master/demo1.gif)
 
-## Dependence
-- TensorFlow 1.4
-- OpenCV 3.3
-- Python 3
+## Getting Started
+
+These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
+
+### Prerequisites
+
+- TensorFlow 1.4. It seems v1.12 also works.
+- OpenCV 3.3 or higher.
+- Python 3.5
 
 The code is tested under Ubuntu 16.04.
 
+### Installing
+
+This repository comes with a pre-trained model for facial landmark detection. Just git clone than you are good to go.
+
+```bash
+# From the directory where you want to put this project:
+git clone https://github.com/yinguobing/head-pose-estimation.git
+```
+
+### Running
+The entrance file is `estimate_head_pose.py`. This will use your usb camera as the video source for demonstration.
+
+```bash
+# From the project directory, run:
+python3 estimate_head_pose.py
+```
+
+You can change the video source to any video file that OpenCV supports.
+
 ## How it works
 
 There are three major steps:
@@ -22,15 +46,14 @@ There are three major steps:
 
 3. Pose estimation. Once we got the 68 facial landmarks, a mutual PnP algorithms is adopted to calculate the pose.
 
-## Miscellaneous
-- The marks is detected frame by frame, which result in small variance between adjacent frames. This makes the pose unstaible. A Kalman filter is used to solve this problem, you can draw the original pose to observe the difference.
+The marks is detected frame by frame, which result in small variance between adjacent frames. This makes the pose unstable. A Kalman filter is used to solve this problem, you can draw the original pose to observe the difference.
 
-- The 3D model of face comes from OpenFace, you can find the original file [here](https://github.com/TadasBaltrusaitis/OpenFace/blob/master/lib/local/LandmarkDetector/model/pdms/In-the-wild_aligned_PDM_68.txt).
-
-- The build in face detector comes from OpenCV. https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector
 
 ## License
-The code is licensed under the MIT license. However, the pre-trained TensorFlow model file is trained with various public data sets which have their own licenses. Please refer to them before using this code.
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
+
+## Acknowledgments
+The pre-trained TensorFlow model file is trained with various public data sets which have their own licenses. Please refer to them before using this code.
 
 - 300-W: https://ibug.doc.ic.ac.uk/resources/300-W/
 - 300-VW: https://ibug.doc.ic.ac.uk/resources/300-VW/
@@ -39,4 +62,11 @@ The code is licensed under the MIT license. However, the pre-trained TensorFlow
 - AFW: https://www.ics.uci.edu/~xzhu/face/
 - IBUG: https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/
 
-(Currently we are trying to empower embeded devices to be more powerful with deep learning, please get in touch if you are interested and would like to join us.)
+The 3D model of face comes from OpenFace, you can find the original file [here](https://github.com/TadasBaltrusaitis/OpenFace/blob/master/lib/local/LandmarkDetector/model/pdms/In-the-wild_aligned_PDM_68.txt).
+
+The build in face detector comes from OpenCV. https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector
+
+To reproduce the facial landmark detection model, you can refer to this series of posts(in Chinese only).
+
+## Finally
+If you are interested in Deep Learning and happened to be seeking for a job opportunity, feel free to get in touch.