Create README.md

README.md

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+## AutoRCCar
+  A scaled down version of self-driving system using a RC car, Raspberry Pi, Arduino and open source software. The system uses a Raspberry Pi with a camera and an ultrasonic sensor as inputs, a processing computer that handles steering, object recognition (stop sign and traffic light) and distance measurement, and an Arduino board for RC car control.
+
+### Dependencies
+* Raspberry Pi: 
+  - Picamera
+* Computer
+  - Numpy
+  - OpenCV
+  - Pygame
+  - PiSerial
+
+### About
+- raspberrt_pi/ 
+  -	***stream_client.py***: stream video frames in jpeg format to the host computer
+  -	***ultrasonic_client.py***: send distance data measured by sensor to the host computer
+- arduino/
+  -	***rc_keyboard_control.ino***: acts as a interface between rc controller and computer and allows user to send command via USB serial interface
+- computer/
+  -	cascade_xml/ 
+    - trained cascade classifiers xml files
+  -	chess_board/ 
+    - images for calibration, captured by pi camera 
+  -	training_data/ 
+    - training image data for neural network in npz format
+  -	testing_data/ 
+    - testing image data for neural network in npz format
+  -	training_images/ 
+    - saved video frames during image training data collection stage (optional)
+  -	mlp_xml/ 
+    - trained neural network parameters in a xml file
+  -	***rc_control_test.py***: drive RC car with keyboard (testing purpose)
+  -	***picam_calibration.py***: pi camera calibration, returns camera matrix
+  -	***collect_training_data.py***: receive streamed video frames and label frames for later training
+  -	***mlp_training.py***: neural network training
+  -	***mlp_predict_test.py***: test trained neural network with testing data
+  -	***rc_driver.py***: a multithread server program receives video frames and sensor data, and allows RC car drives by itself with stop sign, traffic light detection and front collision avoidance capabilities
+
+### How to drive
+1. **Flash Arduino**: Flash *“rc_keyboard_control.ino”* to Arduino and run *“rc_control_test.py”* to drive the rc car with keyboard (testing purpose)
+
+2. **Pi Camera calibration:** Take multiple chess board images using pi camera at various angles and put them into “chess_board” folder, run *“picam_calibration.py”* and it returns the camera matrix, those parameters will be used in *“rc_driver.py”*
+
+3. **Collect training data and testing data:** First run *“collect_training_data.py”* and then run *“stream_client.py”* on raspberry pi. User presses keyboard to drive the RC car, frames are saved only when there is a key press action. When finished driving, press “q” to exit, data is saved as a npz file. 
+
+4. **Neural network training:** Run *“mlp_training.py”*, depend on the parameters chosen, it will take some time to train. After training, parameters are saved in “mlp_xml” folder
+
+5. **Neural network testing:** Run *“mlp_predict_test.py”* to load testing data from “testing_data” folder and trained parameters from the xml file in “mlp_xml” folder
+
+6. **Cascade classifiers training (optional):** trained stop sign and traffic light classifiers are included in the "cascade_xml" folder, if you are interested training your own classifiers, please refer to [OpenCV documentation](http://docs.opencv.org/doc/user_guide/ug_traincascade.html) and [this great tutorial by Thorsten Ball](http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html)
+
+7. **Self-driving in action**: First run *“rc_driver.py”* to start the server on the computer and then run *“stream_client.py”* and *“ultrasonic_client.py”* on raspberry pi.