Extraction of Frames from Multiple Videos
We know that computer vision applications are increasing rapidly in real-life problems i.e., traffic monitoring using CCTV cameras, person identification for security purposes, disease detection and classification, and many others.
We know that for any computer vision application development, we will need a dataset, on which we train our model. Sometimes customers will give you a dataset but in many cases, customers ask you to collect or find a dataset in that case, so you will need to find a dataset, but what happens if you search for data and nothing is found? So here real-time data collection will be a good approach to solve this problem, Now in this article, we will make a dataset from videos and from real-time.
Note: We will work on Google Colab, but the same steps can be followed for Jupyter-Notebook users because in Google Colab we will use videos stored in Google Drive, so here we will first need to mount Google Drive while in Jupyter Notebook you will directly set path of video from the system folder. All other steps will be the same for the implementation of the Jupyter-notebook and Google-Colab implementation.
Let us start implementation step by step. If you follow the below steps so at the end you will be able to extract frames from live videos or stored videos.
