10,000 Images Processed in Seconds: Here's How

A recent study showcased how automation can drastically reduce the time needed for repetitive image editing tasks. The experiment involved processing 10,000 images, which were frames extracted from a video of a beach. The task was to crop each image to a square aspect ratio centered on the frame and then resize it to 224×224 pixels. This operation, if done manually, would be incredibly time-consuming. However, by using Python and OpenCV, the entire process was automated. The automation not only saved time but also ensured consistency across all images, which is crucial for preparing datasets for machine learning models. This example highlights the efficiency of automation in handling large-scale image processing tasks, demonstrating that what could take hours or days can now be accomplished in mere seconds.

Source: towardsdatascience.com