How do I get started on computer vision?

To get started with computer vision, begin by learning foundational concepts and tools. Computer vision focuses on enabling machines to interpret visual data, such as images or videos. Start with Python, the most common language for prototyping, and libraries like OpenCV for image processing or Pillow for basic image manipulation. For example, use OpenCV to load an image, convert it to grayscale, or detect edges using the Canny algorithm. Familiarize yourself with key mathematical concepts like linear algebra (matrix operations for image transformations) and basic calculus (gradients for edge detection). Understanding how images are represented as arrays of pixels (e.g., RGB channels) is critical. Platforms like Coursera or free tutorials on YouTube can provide structured introductions to these topics.

Next, experiment with practical projects using machine learning frameworks. Pre-trained models like ResNet or MobileNet (available in TensorFlow or PyTorch) allow you to perform tasks like image classification without building models from scratch. For instance, use TensorFlow’s Keras API to load a pre-trained model and classify images of cats versus dogs. Explore datasets like MNIST (handwritten digits) or CIFAR-10 (object recognition) to practice training simple models. Start with convolutional neural networks (CNNs), which are fundamental for tasks like object detection. Implement a basic CNN using PyTorch to recognize shapes in images, focusing on layers like convolution, pooling, and fully connected layers. Tools like Jupyter Notebook help iterate quickly, and platforms like Kaggle offer datasets and code examples to learn from.

Finally, dive into advanced topics and real-world applications. Once comfortable with basics, explore object detection (YOLO or Faster R-CNN), segmentation (U-Net), or pose estimation using libraries like Detectron2. For example, use OpenCV to capture live video from a webcam and apply face detection with Haar cascades. Experiment with real-time applications, such as tracking moving objects in video streams. Learn about data preprocessing techniques like normalization or augmentation (e.g., rotating or flipping images to improve model robustness). Explore 3D computer vision using depth sensors like LiDAR or stereo cameras, and libraries like Open3D for point cloud processing. Join communities like GitHub or Stack Overflow to troubleshoot issues and review open-source projects to see how others structure their code.