This project was completed as part of Computer Vision Nanodegree offered by Udacity. The Project has been reviewed by Udacity and graded to meet sufficient requirements.
The project was developed using python 3.6 and the latest version of PyTorch. Though GPU can be used, was not required.
- opencv-python==3.2.0.6
- matplotlib==2.1.1
- pandas==0.22.0
- numpy==1.12.1
- pillow>=6.2.0
- scipy==1.0.0
- torch>=0.4.0
- torchvision>=0.2.0
- jupyterlab
Installation with pip:
pip install -r requirements.txtThis facial keypoints dataset consists of 5770 color images.
- 3462 of these images are training images,to create a model to predict keypoints.
- 2308 are test images, which will be used to test the accuracy of the model.
Note: All images are extracted from the YouTube Faces Dataset which includes videos of people in YouTube videos.
Step 1: Load and Visualize the Facial Keypoint Data
- Visualizng the dataset gives us an understanding of the data. This includes all the preprocessing and standardizing the train and test image data.
Step 2: Define the CNN Network architecture
- Next step would be to experiment with different model architectures for with different layers such as Convolutional, Fully-Connected, Max-Pooling and Dropouts. This typically is defined in models.py file and can be imported in the pipeline.
Step 3: Train the CNN to predict facial keypoints
- Training a network includes trying out various loss functions, different optimizers, batch sizes and tuning the model with the best hyper-parameters.
Step 4: Dectect Faces in the images using Haar Cascades
- The trained neural network can now detect facial keypoints, from any image that includes faces. So before inferencing we need to use a Face Detector such as Haar Cascades to detect the faces.
Step 5: Predict Facial Keypoints and Complete the pipeline
- Put all the building blocks together and build a complete pipeline from loading data to inferencing the facial keypoints.
Step 6: Try applications of the project, like Fun Face Filters!