https://github.com/spidy20/emotion_recognition_system
It can recognise your mood using face detection
https://github.com/spidy20/emotion_recognition_system
crop emotion emotion-api emotion-detection emotion-recognition emotions-folder face-emotion-detection face-emotion-detector
Last synced: about 1 month ago
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It can recognise your mood using face detection
- Host: GitHub
- URL: https://github.com/spidy20/emotion_recognition_system
- Owner: Spidy20
- License: mit
- Created: 2018-12-11T05:01:46.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-03-07T04:54:05.000Z (about 6 years ago)
- Last Synced: 2025-03-26T11:44:52.590Z (about 2 months ago)
- Topics: crop, emotion, emotion-api, emotion-detection, emotion-recognition, emotions-folder, face-emotion-detection, face-emotion-detector
- Language: Python
- Homepage:
- Size: 31.4 MB
- Stars: 18
- Watchers: 0
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Emotion😂😥😡😱 Recognition system [](https://github.com/Spidy20/Emotion_recognition_system/blob/master/LICENSE)
### Sourcerer
### Code Requirements
- Tensorflow
Installation code:- `pip install 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))
### What steps you have to follow??
- Download my repository
- Make `Images` folder in your project ,make subfolder for emotions like Happy,sad,Angry.
- Put `Face_crop.py` & `haarcascade_frontalface_alt.xml` in 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.
- 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 `recognition_webcam.py`.
- If you want to fetch video from your mobile cam than use `android_recognition.py`,but you have to install IPWebcam app in your system
and replace your server URL with my URL
- That's all
### How it works? See:)
### Notes
- 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.
## Just follow☝️ me and Star⭐ my repository