Image Template matching methods with Python using OpenCV

Background

Template matching is a computer vision technique for finding areas of an image that are similar to a patch (template). A patch is a small image with certain features. The goal of template matching is to find the patch/template in an image. To find it, the user has to give two input images: Source Image (S) — The image to find the template in, and Template Image (T) — The image that is to be found in the source image.

• It is usually done by comparing the similarity between the template and different regions in the larger image using a similarity metric, such as cross-correlation or mean-squared error. The location of the region with the highest similarity score is considered the best match.
• It is often used in image processing applications, such as object recognition, tracking, and image alignment. The main advantage of this method is its simplicity and speed, making it suitable for real-time applications. However, it can also be sensitive to variations in illumination, rotation, and scale, and may not always provide the most accurate results in these cases.

The threshold depends on the accuracy with which we want to detect the template in the source image. For instance, if we are applying face recognition and we want to detect the eyes of a person, we can…