How to Crop Computer Vision Model Predictions

Cropping regions where predictions have been made is an essential task in computer vision to reduce noise and facilitate processes like Optical Character Recognition (OCR). This guide outlines how to crop model predictions using the cv2 Python package by calculating the x1, y1, x2, y2 coordinates necessary to define a region of interest (ROI). An example is provided that focuses on cropping a motorcycle license plate from an image. The tutorial explains how to transform x, y, width, and height values into these coordinates, which are then used to isolate the desired image area. Once the ROI is identified, the cv2.imwrite() function is used to save the cropped section as a new file. Additionally, the article discusses setting up a model from Roboflow to obtain predictions, emphasizing the need for a free account and API key to run inference on images, enabling users to test and ensure the model meets their requirements.