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- Tensorflow
- Download my repository
- Own Expression dataset(NOTE: You can downlaod expression images from google, or you can record your video make diffrent expression ,and converts into Grayscale images(For more accurate prediction))
- Song dataset
- Download my repository
- Make 'Images' folder in your project ,make subfolder for emotions like Happy,sad,Angry.
- Put
Face_crop.py&haarcascade_frontalface_alt.xmlin every type of image folder,ex : put this program in "happy' image folder and run this program it will detect faces from images and convert it into grayscale and make a new images in same folder. - Make 'Songs' folder make subfolders for emotions and put Songs,Like Happy songs in happy folder.
- After that you have to create model, for that copy code from code.txt file and open CMD in your project folder and paste it & enter
- It will take training aaround 20-25 minutes so keep patience.
- After training it will create two files
retrained_graph.pb&retrained_labels.txt - Now run
music_player_webcam.py(give proper path of songs and Mediaplayer according to your location in code) - If you want to fetch video from your mobile cam than use
music_player_android.py,but you have to install IPWebcam app in your system and replace your server URL with my URL - That's all
- This is just coded version with no GUI, i ggiven it to opensource ,but if you want to purchase a GUI version ,then go to here STORE
- Full GUI (Tkinter)
- Own developed Music player with all basic functionalities
- See here are some screenshots of full GUI version.
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- It will require high processing power(I have 8 GB RAM & 2 GB GC)
- If you think it will recognise expression just like humans,than leave it ,its not possible.
- Download 300 Images for every expression(you can use batch downloader)
- Noisy image can reduce your accuracy so quality of images matter.