Update readme

README.md

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 # LiveChess2FEN
 
-Automatic digitization of live chess games to FEN notation by means of computer vision.
+LiveChess2FEN is a fully functional framework that automatically digitizes
+the configuration of a chessboard. It is optimized for execution on a
+Nvidia Jetson Nano, following the edge computing paradigm.
+
+![](docs/complete_method.png)
+
+## Setup
+
+1. Install Python 3.5 or later and the following dependencies:
+    - NumPy
+    - OpenCV4
+    - Matplotlib
+    - scikit-learn
+    - pillow
+    - pyclipper
+    - tqdm
+
+2. Depending on the inference engine install the following dependencies:
+    - Keras with tensorflow backend
+    - Onnxruntime
+    - TensorRT
+
+3. Create a `selected_models` folder in the project root.
+4. Download the prediction models from the 
+ [releases](https://github.com/davidmallasen/LiveChess2FEN/releases)
+ and save them to the `selected_models` folder.
+5. Create a `predictions/input_board` folder and a `predictions/pieces`
+ folder in the project root.
+6. Download the contents of `TestImages.zip->FullDetection` from the
+[releases](https://github.com/davidmallasen/LiveChess2FEN/releases) into
+ the `predictions/input_board` folder. You should have 5 test images and a
+ boards.fen file.
+7. Edit `lc2fen_predict.py` and set the `ACTIVATE_*`, `MODEL_PATH_*`,
+ `IMG_SIZE_*` and `PRE_INPUT_*` constants.
+8. Run the `lc2fen_predict.py` script.
+
 
 ## License