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Turn your facial expression into an emoji
| Date | Stars |
|---|---|
| 2026-07-24 | 288 |
| 2026-07-25 | 288 |
| 2026-07-28 | 288 |
| 2026-07-30 | 288 |
| 2026-08-06 | 288 |
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# Emojify Turn your facial expression into an emoji. ## Note This project is still under development. Expect errors in recognition of emoji or other errors. ## What I did here 1. Decided what emojis to use. 2. Stored 250 faces for each facial expression in a special way. The special way is that I took only the eyebrows, eyes, nose and mouth of the face. Rest of the face is removed. (Only my face was used) 3. Trained a CNN on these images. 4. As of today there are 11 facial expressions. (Discussed later). ## Outcome Watch it <a href="https://youtu.be/izUO2rl0Ur8">here</a>. ## Requirements 0. Python 3.x 1. <a href="https://tensorflow.org">Tensorflow 1.5</a> 2. <a href="https://keras.io">Keras</a> 3. OpenCV 3.4 4. h5py 5. A good grasp over the above 4 topics along with neural networks. Refer to the internet if you have problems with those. I myself am just a begineer in those. 6. dlib 7. A good CPU (preferably with a GPU). 8. Patience.... A lot of it. 9. Tensorboard (for visualizing training) ## Facial expressions used 0 - Neutral<br> 1 - Smile/Happy<br> 2 - Sad<br> 3 - Wink<br> 4 - Kiss<br> 5 - Surprised<br> 6 - Angry<br> 7 - Monkey face<br> 8 - Wink with tongue out<br> 9 - Scared/Terrified<br> 10 - Disgusted<br> ## How to use this repo This project is done by me and me only. No one else helped me. The model is trained with my face only. So the model might not detect your expressions correctly. Here is what you can do. If you are a newbie to neural networks or machine learning please learn them. This guide contains a lot of technical stuff which you might find hard to understand. ### Create your facial expression dataset 1. Start this file python create_dataset_webcam.py 2. It will ask for label i.e. facial expression id (more about it later), number of pictures you want to take, and starting image number. You can take as many pictures for each expression as you want but make sure you do it using different lighting conditions, facial poisitions etc. Also make sure you take same number of images for each gesture or else you might introduce a bias. I usually keep it to 250. 3. For the starting image number, make sure you check the images in the dataset/ folder. If the file name of the last file is 249.jpg then you should enter 250 i.e. (last image number + 1) 4. The images will be stored in the new_dataset/ folder. ### Retraining with the new_dataset (HARDER way) You will see why this method is a bit hard. #### Load the images of the new_dataset/ 1. Start the load_images.py file python load_images.py 2. Here you will be asked for which dataset folder to use. Enter 'new_dataset/' 3. The images will be stored as pickle file. 4. You will get 6 pickle files viz train_images, train_labels, test_images, test_labels, val_images, and val_labels #### Retrain the model 1. Start the retrain_cnn_keras.py file. python retrain_cnn_keras.py 2. Here you will be asked for the trained model file name, new model file name, learning rate, epochs, and batch number. 3. For the trained model file name enter cnn_model_keras.h5 4. For the new model file name you can enter anything. A warning though, if you keep it blank or enter cnn_model_keras.h5, it will replace the original model when training if the validation accuracy increases from the previous step. 5. What I usually do is I enter cnn_model_keras1.h5 or something like that so that I do not mess it up. 6. For the rest of the hyper parameters, they will depend on how large the new_dataset/ is. 7. If the number of images for each expression is >= 250, I usually keep the default learning rate, 10-20 epochs and a batch size of 100. 8. After the training you will see the accuracy of the model. #### Check the model's accuracy against the dataset/ folder 1. Start the load_images.py file python load_images.py 2. Here you will be asked for which dataset folder to use. Enter 'dataset/' 3. Start the compute_accuracy.py file python compute_accuracy.py 4. You will be
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ea79b66d1eebaabf, topic:computer-vision, topic:opencv
matched fp:ea79b66d1eebaabf, topic:deep-learning