Bokehlicious Selfies in Python (2020) (www.rahulrav.com)

In a recent exploration within the fastai MOOC, an innovative project was initiated to enhance selfies by adding a bokeh effect using deep learning techniques. The process utilizes image segmentation, specifically leveraging the pretrained Mask R-CNN model from the torchvision library, to identify the subject in a selfie. By creating a segmentation mask that distinguishes the person from the background, the project aims to apply a bokeh effect to the background while leaving the subject in sharp focus—enhancing the quality of selfies typically captured with lower-quality front cameras. This development is significant for the AI/ML community as it demonstrates practical applications of deep learning in image processing and photography, an area increasingly influenced by AI. The effective use of convolutional filters to simulate a bokeh effect showcases the potential of combining computer vision techniques with accessible programming tools like Python and OpenCV. The project not only illustrates the power of deep learning in enhancing ordinary images but also serves as a stepping stone for further applications in mobile photography and real-time image enhancement, reinforcing the trend of AI making sophisticated visual effects more accessible to everyday users.