Millions of people have been infected by the coronavirus disease of 2019 (COVID-19) and lost their lives to it despite various measures to curb the same. To make the situation worse, a traditional observational method of in-person reporting cannot be used because it poses a risk for the observer of catching the infection. Social Distancing and Face Mask compliance, therefore, remain vital measures to curb the spread of COVID-19. We propose an end-to-end solution that can monitor different social distancing and face mask compliance metrics and be deployed efficiently in Python using open-source libraries. It is scalable and enables the users to implement the solution at a large scale, i.e., cover a broader area using multiple live cameras feeds simultaneously. Our solution precisely calculates the distance between two people or objects by mapping the 2-dimensional pixel distances to 3-dimensional actual distances. These attributes make our solution unique, and it can be deployed for usage in various situations and locations such as shopping malls, supermarkets, large workspaces, manufacturing facilities, etc., which can help to dampen the effect of COVID-19 as early as possible.