If you need help configuring your development environment for OpenCV, I highly recommend that you read my pip install OpenCV guide — it will have you up and running in a matter of minutes.
Then join PyImageSearch University today! No installation required. Last week we learned how to perform template matching. The problem with that approach is that it failed when multiple occurrences of the template existed in the input image — template matching would only report one matched template i. Lines import our required Python packages. Applying multi-template matching will result in multiple detections for each object in our input image. We can fix this behavior by applying NMS to suppress weak, overlapping bounding boxes.
Lines 20 and 21 load our image and template from disk. Lines 25 and 26 display our image and template to our screen. The next step is to perform template matching, just like we did last week :. Lines 29 and 30 convert our input images to grayscale while Lines 34 and 35 perform template matching. If we are looking to detect just one instance of our template, we could simply call cv2. However, since we want to detect multiple objects, we need to filter our result matrix and find all x, y -coordinates that have a score greater than our --threshold :.
Line 40 uses np. Line 42 then displays the total number of matched locations before applying NMS. From there, we loop over all the matched x, y -coordinates and draw their bounding boxes on our screen Lines If we ended our implementation here, we would have a problem — a call to np. It could very well be the case that multiple locations refer to the same object. Line 55 starts by initializing our list of bounding box rects. We then loop over all our x, y -coordinates, compute their respective bounding boxes, and then update the rects list.
Applying non-maxima suppression on Line 63 suppresses overlapping bounding boxes with lower scores, essentially collapsing multiple overlapping detections into a single detection. This is the football image we are going to use for the matching purpose.
Code Implementation of Template Matching Importing the libraries. Converting the images into grayscale. References Opencv2. Demo image. Source image and Template image. Google colab for codes. Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.
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Join Discord Community. Telegram Channel Discover special offers, top stories, upcoming events, and more. Join Telegram. Subscribe to our newsletter. Get the latest updates from AIM. So in this problem, the OpenVC template matching techniques are used. For template matching task, there is an accuracy factor, this factor is known as threshold. As an example, we can say that we can easily create face recognizing scheme using this template matching solution.
But in situations where you know the rotation, scale, and viewing angle are constant, template matching can work wonders. To learn how to perform template matching with OpenCV, just keep reading. Looking for the source code to this post? Template matching can be seen as a very basic form of object detection. To find the template in the source image, we slide the template from left-to-right and top-to-bottom across the source:. For the full derivation of the correlation coefficient, including all other template matching methods OpenCV supports, refer to the OpenCV documentation.
For each location T over I , the computed result metric is stored in our result matrix R. Each x, y -coordinate in the source image that also has a valid width and height for the template image contains an entry in the result matrix R :.
Here, we can visualize our result matrix R overlaid on the original image. Notice how R is not the same size as the original template. This is because the entire template must fit inside the source image for the correlation to be computed. Bright locations of the result matrix R indicate the best matches, where dark regions indicate there is very little correlation between the source and template images.
While template matching is extremely simple and computationally efficient to apply, there are many limitations. If there are any object scale variations, rotation, or viewing angle, template matching will likely fail. Even small, minor deviations in appearance can dramatically affect template matching results and render it effectively useless. We can apply template matching using OpenCV and the cv2. Here, you can see that we are providing the cv2.
The output result from cv2. The mask must have the same spatial dimensions and data type as the template. For regions of the image you want to be searched, be sure the mask has a corresponding value of If you need help configuring your development environment for OpenCV, I highly recommend that you read my pip install OpenCV guide — it will have you up and running in a matter of minutes. Then join PyImageSearch University today! No installation required.
On Lines 2 and 3, we import our required Python packages. We only need argparse for command line argument parsing and cv2 for our OpenCV bindings. We start by loading our image and template , then displaying them on our screen.
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