CV Hands On

  Computer Vision (CV) Hands On Object Detection with YOLO : Use a pre-trained YOLO (You Only Look Once) model to detect multiple objects in a single image or a video stream. Lane Line Detection : Create a program that can detect lane lines in a video feed, a foundational task for self-driving cars. Face Detection and Recognition : Build an app that can detect faces in an image and then recognize them based on a pre-trained face dataset. Image Segmentation : Develop a model that can classify each pixel in an image to belong to a certain class (e.g., car, pedestrian, road). Real-Time Object Counter : Create a program that counts the number of specific objects (e.g., cars, people) in a video. Image Captioning : Combine a CNN with an RNN to generate a natural language description for a given image. Simple Augmented Reality (AR) App : Use a library like OpenCV to overlay a 3D object onto a specific marker in a video feed. Visual Search Engine : Create a system where a user can upload a...

Optical Character Recognition (OCR) Optical Character Recognition (OCR) A guide to converting text from images into machine-encoded text using an OCR engine like Tesseract . Libraries and Tools Used This project uses a combination of image processing and a dedicated OCR engine: pytesseract: A Python wrapper for Google's Tesseract OCR engine. cv2 (OpenCV): For loading and preprocessing the image to improve OCR accuracy. Pillow (PIL): A library for handling image files, often used by `pytesseract`. 1. Image Preprocessing What the code does: It ...

Pose Estimation Pose Estimation A guide to detecting and tracking the body posture of a person in an image or video using a pre-trained model like MediaPipe . Libraries and Tools Used This project uses a specialized framework for computer vision pipelines: mediapipe: Google's framework for building and deploying ML pipelines. It provides a pre-built pose solution. cv2 (OpenCV): For capturing video from a webcam and displaying the results. 1. Initialize the Pose Model What the code does: It imports the necessary libraries and initializes the MediaPipe Pose model. The model is configured to detect ...

Visual Search Engine Visual Search Engine A guide to creating a system where a user can upload an image and find other visually similar images. Libraries and Tools Used This project uses a combination of deep learning and vector search libraries: tensorflow: For using a pre-trained CNN to extract image features. numpy: For handling the numerical representations (embeddings) of images. FAISS: A library for efficient similarity search on dense vectors. 1. Feature Extraction with a CNN What the code does: It loads a pre-trained CNN model (like VGG1...

Simple Augmented Reality (AR) App Simple Augmented Reality (AR) App A guide to overlaying a virtual object onto a specific marker in a video feed using basic Computer Vision . Libraries and Tools Used This project uses key Python libraries for Computer Vision and linear algebra: cv2 (OpenCV): The primary library for video capture and ArUco marker detection. numpy: Essential for handling matrix operations for camera calibration and pose estimation. 1. Camera Calibration and Marker Generation What the code does: It generates a simple ArUco marker and outlines how to use camera calibration parameters. ...

Image Captioning Image Captioning A guide to combining a CNN and an RNN to generate a natural language description for a given image. Libraries and Tools Used This project requires deep learning frameworks and a library for image handling: tensorflow: The deep learning framework for both the CNN and RNN parts of the model. numpy: For numerical operations on image and text data. cv2 (OpenCV): To load and preprocess images. 1. The Encoder: A CNN for Feature Extraction What the code does: It loads a pre-trained CNN model (like InceptionV3), remov...

Real-Time Object Counter Real-Time Object Counter A guide to creating a program that counts the number of objects crossing a virtual line in a video feed. Libraries and Tools Used This project combines object detection and tracking libraries: ultralytics: Provides the pre-trained YOLO model for object detection. cv2 (OpenCV): For video processing and displaying the output. 1. Load Model and Define Counter What the code does: It loads the YOLOv8 model for object detection and defines a simple `Counter` class. This class will keep track of detected objects and ensure they are only counted once. ...

Image Segmentation Image Segmentation A guide to developing a model that can classify each pixel in an image to belong to a specific class. Libraries and Tools Used This project requires a deep learning framework and a library for image handling: tensorflow: The deep learning framework for building the segmentation model. numpy: For efficient numerical operations on image data. matplotlib: To visualize the input image and the generated segmentation mask. 1. The U-Net Model Architecture What the code does: It defines a function to create a simplif...

Face Detection and Recognition Face Detection and Recognition A guide to building an application that can both detect faces in an image and recognize specific individuals. Libraries and Tools Used This project leverages a couple of powerful Python libraries for these tasks: face_recognition: A popular and easy-to-use library that wraps around `dlib` for a simple API. cv2 (OpenCV): For loading and displaying images, and drawing bounding boxes. 1. Load and Encode Known Faces What the code does: It loads images of known individuals, finds their faces, and computes their 128-dimensional face encodings. T...

Lane Line Detection Lane Line Detection A guide to creating a simple pipeline to detect lane lines in a video feed using basic Computer Vision techniques. Libraries and Tools Used This project uses key Python libraries for image and video processing: cv2 (OpenCV): For all image manipulation, including color conversion, edge detection, and drawing. numpy: Essential for numerical operations on image arrays. matplotlib: Useful for visualizing intermediate steps if needed. 1. Preprocessing the Image What the code does: It takes a video frame, conver...

Object Detection with YOLO Object Detection with YOLO A guide to using a pre-trained YOLO (You Only Look Once) model to detect and localize objects in images or video. Libraries and Tools Used This project uses a few key Python libraries for Computer Vision and deep learning: torch: The deep learning framework for loading and running the YOLO model. ultralytics: A library providing a simple API for the latest YOLO models. cv2 (OpenCV): For reading and displaying images or video frames. 1. Load the Pre-trained YOLO Model What the code does: It lo...