This project consists of two computer vision models:

The code in has_sunglasses.py preprocesses images and converts PGM format to PNG format. It separates the training and testing datasets and labels images as having sunglasses (1) or not having sunglasses (0).

The CNN model architecture is as follows:

Convolutional Layer with 32 filters, 3x3 kernel, and ReLU activation Max-Pooling Layer (2x2) Second Convolutional Layer with 32 filters, 3x3 kernel, and ReLU activation Second Max-Pooling Layer (2x2) Flattening Layer Fully Connected Layer with 128 units and ReLU activation Output Layer with 1 unit and Sigmoid activation The CNN is compiled with the Adam optimizer and binary cross-entropy loss. It is trained on the training set and evaluated on the test set.

A test image is loaded, preprocessed, and passed through the trained model to predict whether the person in the image has sunglasses or not.

The code in emotion_rec.py preprocesses images and converts PGM format to PNG format. It organizes the data into directories based on emotions (angry, happy, sad, neutral). The ImageDataGenerator is used for data augmentation.

The CNN model architecture is as follows:

Convolutional Layer with 32 filters, 3x3 kernel, and ReLU activation Max-Pooling Layer (2x2) Second Convolutional Layer with 32 filters, 3x3 kernel, and ReLU activation Second Max-Pooling Layer (2x2) Flattening Layer Fully Connected Layer with 128 units and ReLU activation Output Layer with 4 units (for 4 emotions) and Softmax activation The CNN is compiled with the Adam optimizer and categorical cross-entropy loss. It is trained on the training set and evaluated on the test set.

The code loops through the test images, loads each image, preprocesses it, and passes it through the trained model to predict the emotion of the person in the image (angry, happy, sad, or neutral).