Update README.md

README.md

@@ -14,11 +14,11 @@ The code has been tested using python 3.7.1 under 18.10 Ubuntu.
 3. Run `python prepare_facenet.py path/to/facenet.zip` and cd to parent directory.
 
 ## Demo
-1. Cd to the 'src' directory
-2. Run `python main.py demo/avengers.mp4 demo/user_data.csv demo/embeddings.npy`
+1. Cd to the 'src' directory.
+2. Run `python main.py demo/avengers.mp4 demo/user_data.csv demo/embeddings.npy`.
 
 ## Guide
 The program consists of three parts: video file (webcam feed or downloaded video); user metadata and embeddings (both created from the *create_database.py* script). User metadata and embeddings derive from an image folder that contains all of the users to be identified. As a prerequisite you should have both video file and the images folder. Take into account that the image filename of a user will be used as his username.
-1. Cd to the 'src' directory
-2. Run `python create_database.py output/user_data.csv output/embeddings.npy path/to/images/` to create embedddings
-3. Run `python main.py video_file.mp4 output/user_data.csv output/embeddings.npy` for face tracking and verification. If you wish to keep track of time a user appears in the video, run *screentime.py* instead of *main.py*
+1. Cd to the 'src' directory.
+2. Run `python create_database.py output/user_data.csv output/embeddings.npy path/to/images/` to create embedddings.
+3. Run `python main.py video_file.mp4 output/user_data.csv output/embeddings.npy` for face tracking and verification. If you wish to keep track of time a user appears in the video, run *screentime.py* instead of *main.py*.