Did you know the computer vision market is projected to hit nearly $50 billion in the next few years? That’s huge! Computer vision is transforming how machines “see” and interpret the world, just as we do.
Computer vision is finding out how to teach computers to see and comprehend pictures. This is enormously important, because it affects everything from self-driving cars to medical diagnoses.
This guide will help you get started with computer vision. You will be taught how to download the tools and software. We can start off on your computer vision trip now!
All About Computer Vision Software and Libraries
Some of the main computer vision tools that you will have to work are needed to get started. These are toolkit and libraries to build your own computer vision projects.
OpenCV: The Industry Standard
OpenCV is an extremely well known computer vision library. It is widely used by individuals and organizations. It is packed with lots of features for processing images and videos.
OpenCV has many versions. Some work better with particular programming languages or computers. So, make sure you choose one appropriate to your requirements. It is a widely used library.
TensorFlow & Keras: Two Powerhouses in Deep Learning
Tensorflow and Keras for Deep Learning in Computer Vision They teach you how to create advanced models that can identify objects in images. It is much more than simple image adjustments.
While I warn you that installation of these steps can be a mess if you watch closely, do not fear! Carefully follow the instructions, and you will be okay. Next, you will have the preparation to start building deep learning applications.
Part 1: PyTorch: The Research Favorite
Researchers tend to be more familiar with PyTorch. It allows you to construct and modify models on the fly. It’s very flexible.
At first glance, PyTorch may look quite tough. But that it pays off in its flexibility for many. Install it correctly. You can do some real heavy stuff.
All information is stored on your system only.
Now, let’s get these tools on your computer. You can see the installation with me.
Download and Install Python
You can learn a super important language, Python, for computer vision. Most tools use Python. Python has diverse applications.
Download Python from its official site Next, create a virtual environment. This isolates and organizes your projects.
OpenCV Installation on Various Operating System
Depending on your system, installing OpenCV varies a little. That could be Windows, macOS, or Linux.
Pip install opencv-python on windows. It’s also the same command on macOS, for the macOS alternative, or use conda. On Linux it is similar, but might require some additional steps. Be certain to follow the instructions.
Install TensorFlow & Keras
We need pip to install TensorFlow and Keras. Use pip install tensorflow. If you want to use your computer graphics card (gpu), you may need to configure additional stuff.
If you have a GPU, it makes things a lot faster. If you have a supported NVIDIA card, follow the NVIDIA instructions. The correct setup saves time.
[Read more:How to Access and Manage Image Datasets for Computer Vision]
Training a computer vision model requires datasets. Accurate models rely on good datasets.
Extract only your Publicly Available Datasets: The Goldmine
There is a lot of free datasets available. ImageNet is massive — millions of images. Since COCO is great for object detection. MNIST is easy for learning the character recognition. This means, CIFAR is used to identify regular objects.
Links to download are on their websites. Adhere to their directions on how you can utilize them.
Part 2: Create Your Own Datasets: Collection and Annotation
However, sometimes you want your own dataset. That means collecting and labeling your own images.
Use tools like LabelImg to annotate bounding boxes around objects in your pictures. If you are organized and have good notes, it can save a lot of time.
Data is any information, a piece of fact that is in any form.
Data augmentation increases the number of your dataset without creating new images. It is about rotating, flipping or cropping of images.
This is facilitated by libraries like Albumentations In most cases, more data is correlated with better models. Get the most out of what you have with this technique.
Get Your Computer Vision Dev Environment Up and Running
That a good environment setup gives you the freedom to focus on your code. It increases the speed to create projects.
Getting an IDE That Suits You — VS Code, PyCharm, etc.
Some of the popular ones are VS Code and PyCharm. They have features that ease the process of coding. Features like code completion.
VS Code or Visual Studio Code is a free and lightweight code editor. PyCharm is much more robust but is paid software. Both can perform well in the computer vision domain.
Using package managers: pip and conda
Libraries are managed by pip and conda. Use them, and log what you use.
Conda is well for managing environments. For simple installs, pip is more straightforward. They both help you keep your projects organized.
Use of GPUs to speed up processing
Getting it all to work with GDI was another thing and had to be done once per application for training a model. Ensure that TensorFlow or PyTorch is configured to use your GPU.
This may take the form of installing the CUDA drivers. It can be fussy, but is worth it for the speed improvement.
How to Fix Some Common Installation Errors
Sometimes, things go wrong. Here are some common issues and how to resolve them.
Dear reader, here is a brief introduction of what we are going to do:
Dependency hell occurs if two libraries try to require different versions of the same thing. Avoid these by using virtual environments.
It is also important to see what version of each of the libraries you are using. Make sure they work together.
Dealing with Import Errors
Import errors indicate that Python cannot locate a library. Make sure you installed it. Second, check that your environment is activated.
Every once in a while, this problem resolves itself by reinstalling the library. When importing, ensure that correctly spelled the library name.
Technology Fails: Resolve GPU(Graphic Processing Unit) Incompatibility Issues
Such problems with GPUs are usually related to drivers or CUDA versions. Ensure to keep your drivers updated. Also, ensure that CUDA version is compatible with what TensorFlow or PyTorch require.
Visit the NVIDIA website to get the correct drivers. This solves a lot of problems with GPUs.
Conclusion
You need time to set up your computer vision on your computer. But it does allow you to create some neat things. Make sure to try new things. There is so much to learn.
So keep learning, keep playing with code, and have a good time. There are plenty of good resources around, for example tutorials and guides. This is just the beginning of your computer vision journey!
