This paper aims to measure the size and dimensions of objects in an image using OpenCV, a popular computer vision library in Python. The paper involves reading an image, preprocessing it using various image processing techniques such as thresholding, contour detection, and edge detection, and then calculating the size, dimensions, and other relevant features of the object. This paper discusses the techniques involved in proposed technique which include use of convolutional neural networks for object detection and use OpenCV library to perform image processing techniques which will allow us to measure objects using contours drawn over the object. This paper proposes Yolo V8 for the semantic segmentation of objects which allows use of deep neural networks to have proper detection of objects. The “Object measurement using OpenCV” provides a tool for accurately measuring the size and dimensions of objects in an image, with a wide range of applications in various fields. Modern semantic image segmentation systems built on top of convolutional neural networks (CNNs) have reached accuracy levels that were hard to imagine hence we would like to implement the segmentation system for object recognition and object detection. For the measurement part OpenCV allows to get the contour area and contour measurements when used with reference objects for which we know area and measurements can allow us to calculate using simple algebraic expressions.