In this tutorial, you’ll learn how to create custom cursors that follow your mouse in Unity. This used mostly for 2D games, particularly arcade style games, RPG’s and top down shooting. Custom cursors are easy to implement in Unity, and we will need minimal coding as most of the structure is already set up for us.
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What We’re Making (Cursor Video Demo)
Day 4: Trac3 Video Demo + Custom Crosshair Cursor
Importing
First, make sure you have your custom cursor sprite ready. It must be an image file of some type (.png, .jpeg, etc.) and probably with small dimensions. You can make one in software like inkscape (free, extremely powerful vector image maker) or Aseprite (paid but cheap pixel art tool).
You’ll want to import (Import New Asset > /path/to/your/asset) your custom cursor into Unity, but you should change a few settings when you do this.
Most importantly, you’ll have to change the texture type to Cursor in the inspector with your cursor texture selected.
Additionally, for pixel textures like the one I will be using, set the filter mode to Clamp to disable anti-aliasing (so your texture doesn’t get blurred), and the Format to RGBA 32 bit if you have a colourful image that you don’t want compressed. This will make sure your colours are reproduced accurately, at the cost of size (though, for a cursor sprite, the size shouldn’t be very big).
Reading Unity Documentation (Optional)
A good exercise for the reader would be to try to find out what function we are going to use from the Unity documentation. This is an essential skill for all developers, and Unity’s documentation is quite nice so it shouldn’t be too difficult. Traversing the documentation and finding answers to your own questions is definitely an important skill to have.
Izotope ozone 5 vst download. Let’s develop this skill by finding the function to set a cursor in this exercise.
Start here: https://docs.unity3d.com/ScriptReference/index.html
And see if you can find:
- The class that allows you to set, manipulate and use cursors (Answer)
- The specific function to set custom cursors (Answer)
Did you find it? If so, good job! If not, don’t worry, and practice more - see if you can find how to take in input from the right arrow on a keyboard, or how to set the text field of a UI text object.
The Code
We will be using Unity’s custom cursor function (click to check the docs) and setting the cursor with it.
We need to create a new script (New > C# Script) and call it “setCursor.cs”. Inside, we have two parts.
Cursor Import
This line exposes the cursor texture in the inspector, so we will be able to set it there. We need an image, or Texture2D, and we are calling it crosshair. This is the standard variable declaration in programming - scope - type - name ;
Cursor Setting
We will break this up into two lines of code to make the meaning more explicit. From the documentation, (which I strongly suggest you check out) we know we need these parameters:
SetCursor(Texture2Dtexture, Vector2hotspot, CursorModecursorMode);
We have our texture from the first line - we’re calling it “crosshair”.
The hotspot is where Unity positions the cursor relative to the mouse pointer. The mouse has coordinates on the screen - if your screen was 100px by 100px, and we had the mouse at the centre, its coordinate would be (50px, 50px). Now, Unity will by default draw the upper left corner of the cursor at the mouse position. However, for a centred crosshair, we need to specify the displacement.
We’re using the Vector2 object as it allows us to encode, along with a few other things, coordinates on a 2D screen. We create a new one by this line:
Vector2 variableName = new Vector2(xCoordinate, yCoordinate);
Exercise for the reader - What should the x and y coordinates be if we want the centre of the object, where (0,0) is the upper left corner?
This image should clarify it. The centre of the texture has the coordinates (texWidth/2, texHeight/2), and we set that as our hotspot.
We don’t need to worry about the cursor mode, so we’ll set it to automatic - CursorMode.Auto.
Assembling these features gives us the code below.
Scene Setup
In your scene, add an empty game object (Add > Empty) and call it whatever you want.
I prefer to have all general scripts (those that act on the whole game at all times, like cursor setting, menu loading, saving, stat tracking and a lot of other things) applied to a “Game Manager” empty object, which is a standard practice, and you could try using this as well.
Now, attach the setCursor script to it by dragging and dropping it into the inspector or adding it as a component. A new field will appear where you can specify a Texture2D object, as we have exposed it in the code. It looks like this.
You can drag and drop your cursor texture into this empty field, and then run your game, now with your new custom cursor!
Conclusion
While this tutorial explains how to make custom cursors in Unity, I hope it also provided some insight on using Unity Documentation to your advantage and thinking about vectors in 2D.
Hopefully you managed to get custom cursors working in Unity. If you have any questions about this, please leave a comment and I will see if I or another commenter can assist you.
A while ago I made an example of how to use TensorFlow models in Unity using TensorFlow Sharp plugin: TFClassify-Unity. Image classification worked well enough, but object detection had poor performance. Still, I figured it can be a good starting point for someone who needs this kind of functionality in Unity app.
Unfortunately, Unity stopped supporting TensorFlow and moved to their own inference engine code-named Barracuda. You can still use the example above, but the latest plugin was built with TensorFlow 1.7.1 in mind and anything trained with higher versions might not work at all. The good news is that Barracuda should support ONNX models from the box and you can convert your TensorFlow model to the supported format easy enough. Bad news is that Barracuda is still in preview and there are some caveats.
Differences
With TensorFlow Sharp plugin, my the idea was to take TensorFlow example for Android and make a similar one for Unity using the same models, which is
inception_v1
for image classification and ssd_mobilenet_v1
for object detection. I had successfully tried mobilenet_v1
architecture as well - it's not in the example, but all you need is to replace input/output names and std/mean values.With Barracuda, things are a bit more complicated. There are 3 ways to try certain architecture in Unity: use ONNX model that you already have, try to convert TensorFlow model using TensorFlow to ONNX converter, or to try to convert it to Barracuda format using TensorFlow to Barracuda script provided by Unity (you'll need to clone the whole repo to use this converter, or install it with
pip install mlagents
).None of those things worked for me with inception and ssd-mobilenet models. There were either problems with converting, or Unity wouldn't load the model complaining about not supported tensors, or it would crash or return nonsensical results during inference. Pre-trained inception ONNX seemed like it really wanted to get there, but craping out on the way with either weird errors or weird results (perhaps someone else will have more luck with that one).
But some things did work.
Image classification
MobileNet is a great architecture for mobile inference since, as it goes from its name, it was created exactly for that. It's small, fast and there are different versions that provide a trade-off between size/latency and accuracy. I didn't try latest mobilenet_v3, but v1 and v2 are working great both as ONNX and after tf-barracuda conversion. If you have .onnx model - you're set, but if you got .pb (TensorFlow model in protobuf format), the conversion is easy enough using the tensorflow-to-barracuda converter:
Converter figures out inputs/outputs itself. Those are good to keep around in case you need to modify them in code later. Here I have input name 'input' and output name 'MobilenetV2/Predictions/Reshape_1'. You can also see those in Unity editor when you choose this model for inspection. One thing to note: with mobilenet_v2, the converter and Unity inspector shows wrong input dimensions - it should be [1, 224, 224, 3] instead, but this doesn't seem to matter in practice.
Unity Texture2d To Sprite
Then you can load and run this model using Barracuda as described in the documentation:
The important thing that has to be done with the input image to make inference work is normalization, which means shifting and scaling down pixel values so that they go from [0;255] range to [-1;1]:
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Barracuda actually has a method to create a tensor from Texture2D, but it doesn't accept parameters for scaling and bias. That's weird since it is often a necessary step before running inference on an image. Although be careful trying your own models - some of them might actually have scaling a bias layers as part of the model itself, so be sure to inspect it in Unity before using.
Object detection
There seem to be 2 object detection architectures that are currently used most often: SSD-MobileNet and YOLO. Unfortunately, SSD is not yet supported by Barracuda (as stated in this issue). I had to settle on YOLO v2, but originally YOLO is implemented in DarkNet and to get either Tensorflow or ONNX model you'll need to convert darknet weights to necessary format first.
Fortunately, already converted ONNX models exist, however, full network seemed like way too huge for mobile inference, so I chose Tiny-YOLO v2 model available here (opset version 7 or 8). But if you already have a Tensorflow model, then tensorflow-to-barracuda converter works just as well, in fact I have one in
also works
folder in my repository that you can try.Funny enough, ONNX model already has layers for normalizing image pixels, except that they don't appear to actually do anything because this model doesn't require normalization and works with pixels in [0;255] range just fine.
![Texture2d Texture2d](/uploads/1/1/7/7/117734238/790409232.png)
The biggest pain with YOLO is that its output requires much more interpretation than SSD-Mobilenet. Here is the description of Tiny-YOLO output from ONNX repository:
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'The output is a
(125x13x13)
tensor where 13x13 is the number of grid cells that the image gets divided into. Each grid cell corresponds to 125 channels, made up of the 5 bounding boxes predicted by the grid cell and the 25 data elements that describe each bounding box (5x25=125
).'Yeah. Fortunately, Microsoft has a good tutorial on using ONNX models for object detection with .NET where we can steal o lot of code from, although with some modifications.
Let's not block things
My TensorFlow Sharp example was pretty dumb in terms of parallelism since I simply run inference once a second in the main thread, blocking the camera from playing. There were other examples showing a more reasonable approach like running model in a separate thread (thanks MatthewHallberg).
However, running Barracuda in a separate thread simply didn't work, producing some ugly looking crashes. Judging by documentation Barracuda should be asynchronous by default, scheduling inference on GPU automatically (if available), so you simply call
Execute()
and then query the result sometime later. In reality, there are still caveats.So Barracuda worker class has 3 methods that you can run inference with:
Execute()
, ExecuteAsync()
and an extension method ExecuteAndWaitForCompletion()
. The last one is obvious: it blocks. Don't use it unless you want your app to freeze during the process. Execute()
method works asynchronously, so you should be able to do something like this:.or query output in a different method entirely. However, I've noticed that there is still a slight delay even if you just call
Execute()
and nothing else, causing camera feed to jitter slightly. This might be less noticeable on newer devices, so try before buy.ExecuteAsync()
seems like a very nice option to run inference asynchronously: it returns an enumerator which you can run with StartCoroutine(worker.ExecuteAsync(inputs))
. However, internally this method does yield return null
after each layer, which means executing one layer per frame, which, depending on amount of layers in your model and complexity of operations in them, might just be too often and cause the model execute much slower than it can (as I found out is the case with mobilenet model for image classification). YOLO model does seem to work better with ExecuteAsync()
than other methods, although amount of devices I can test it on is quite limited.Playing around with different methods to run a model, I found another possibility: since
ExecuteAsync()
is an IEnumerator, you can iterate it manually, executing as many layers per frame as you want:That's a bit hacky and totally not platform-independent - so judge yourself, but I found it actually work better for mobilenet image classification model, causing minimum lag.
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Conclusion
Once again, Barracuda is still in early development, a lot of things change often and radically. Like my example being tested with Barracuda 0.4.0-preview version of the plugin, and 0.5.0-preview already breaks it, making object detection produce wrong results (so make sure to install 0.4.0 if you're gonna try the example, and I'll be looking into newer versions later). But I think that an inference engine that works cross-platform without all the hassle with TensorFlow versions, supports ONNX from the box and baked into Unity is a great development.
Unity Texture2d Object
So does Barracuda example give better performance than TensorFlow Sharp one? It's hard to compare, especially with object detection. Different architectures are used, Tiny-YOLO has a lot fewer labels than SSD-Mobilenet, TFSharp example faster with OpenGLES while Barracuda works better with Vulcan, async strategies are different and it's not clear how to get best async results from Barracuda yet. But comparing image classification with MobileNet v1 on my Galaxy S8, I got following inference times running it synchronously: ~400ms for TFSharp with OpenGLES 2/3 vs ~110ms for Barracuda with Vulcan. But what's more important is that Barracuda will likely be developed, supported and improved for years to come, so I fully expect even bigger performance gains in the future.
I hope this article and example will be useful for you and thanks for reading!
Unity Texture2d To Rendertexture
Check out complete code on my github: https://github.com/Syn-McJ/TFClassify-Unity-Barracuda