![]() They will marvel at your design and expect good value from your presentation.Ī photo mosaic is also a great section header slide and will lend design consistency to your deck if you had used the mosaic in the opening slide of your presentation. A photo mosaic captures audience interest like nothing else. It’s THE BEST cover slide you can have for your presentation. Step by Step Process for Creating Photo Mosaic in PowerPoint:įirst of all, let’s be clear about the utility of a photo mosaic. What you will learn to create in this tutorial:ĭownload this Editable Photo Mosaic Template Besides, nothing beats the satisfaction of creating a professional mosaic on your own from scratch! ![]() PowerPoint lets you do everything for free. You’ll find online websites and apps that do this for you and charge a small fee for downloading the mosaic in higher resolution. As you zoom in, those tiny shapes give way to small pictures that speak a thousand words in themselves. From far, you can see one big image forming out of tiny shapes. Tens or hundreds of tiny pictures blend together into one big picture to create a wow effect. One such extraordinary design trick that PowerPoint lets you accomplish is the Photo Mosaic. What makes PowerPoint such a powerhouse is its user-friendliness that allows amateur designers (like me) to create decent and sometimes extraordinary designs that seem as good as those designed by a professional using Photoshop, CorelDraw or other advanced software. In the wrong hands.well, better left unsaid. Link to my full code implementation is attached at the end of this article.In the right hands, PowerPoint is a creative powerhouse. Here is the main part of the code to assign the relevant source image. The selected source image for the corresponding pixel ‘batch’ is resized into a tensor with size k. Assign relevant source image to each pixel ‘batch’ So, if the target image’s size is 300x100, then the zeros tensor’s size is (300x50)x(100x50). I myself decided to initialize the zeros tensor with size 50x50 ( k) bigger than the target image’s size. One way to solve this is by initializing the zeros tensor with a bigger size ( k times bigger). No worries! We as a human are blessed with creativity! Then, we assign each of the pixels with our selected source image that has been resized into a 1x1x3 tensor.īUT it’s not a wise approach! If we resized our source image into that very tiny tensor, then there’s so much loss of information there. We can simply initialize the tensor with size same as the target image. One thing to be highlighted here is the size of the zeros tensor. Notice that when batch_size=1, ‘Pixel Batching’ approach is the same as the brute-force approach.Īfter the list of relevant filenames is generated, now we have to initialize a zeros tensor which will be used as the ‘container’ of our mosaic image. ![]() You can see the below figure to have a better understanding of how this approach works. I named this approach as ‘Pixel Batching’ approach. Rather than finding a relevant source image for each of the pixels, we can just try to find a relevant source image for each ‘batch’ of the pixels. If you realized, this is a very brute-force approach. Then, choose the one with the lowest RMSE value. We can simply measure the RMSE ( Root Mean Squared Error) between the RGB vector of each target image’s pixel with the RGB vector from our database. ![]() How can we know which image is ‘relevant’? Given the average RGB dataset and the target image, the first thing we have to do is generating a list of relevant source image filenames for each of the target image’s pixels. Generate a list of relevant filenames for each pixel ‘batch’ ![]()
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