In this tutorial, we are going to see how to detect faces with OpenCV and Haar cascade then we will use image blurring to only blur the part of the face on the image. Face detection is a computer vision technology that consists of detecting human faces in digital images, videos, and real-time video streams.
In this tutorial, we will see how to create a blink detector/counter using Dlib, Python, and OpenCV. We will first use the face detector from Dlib to detect faces in a video. Then we will use the shape predictor from Dlib to determine the location of the eyes in the face. Finally, we will use
In this tutorial, we are going to see another image processing technique: detect edges and contours in an image. Edge detection is a fundamental task in computer vision. It can be defined as the task of finding boundaries between regions that have different properties, such as brightness or texture. Simply put, edge detection is the process of
In this tutorial, we will use deep learning to build a more robust smile detector than the one we built in the previous tutorial where we used a Haar cascade smile detector. We will use the SMILES dataset to train our convolutional neural network. Once our model is trained, we will follow the same steps as in
You can train a Haar cascade classifier to detect whatever you want and there are different pre-trained Haar cascades to detect faces, cats, number plates, smiles, and more. In this tutorial, we are going to implement a smile detector algorithm using OpenCV and a pre-trained Haar cascade. Smile detention is a subset of facial recognition
In this tutorial, we will use the YOLO object detection algorithm to detect objects in images and videos. Object detection is a computer vision task that involves identifying and localizing objects in an image or video. It is a challenging task that requires both localizing objects in the image and classifying what type of object
In this tutorial, we will see how to detect objects using deep learning and OpenCV. Object detection is the process of locating objects with bounding boxes in an image or a video. It is one of the most important tasks in computer vision, and it has many applications in various fields such as surveillance, people counting, traffic
In this tutorial, we will tackle the Fashion MNIST dataset to train a neural network that will classify images of clothing. The dataset contains 70,000 grayscale images of 28 × 28 pixels each in 10 categories. The Fashion MNIST dataset is a drop-in replacement of the MNIST dataset which gives us a more challenging problem than the MNIST
OpenCV makes the process of drawing shapes on an image straightforward by providing convenient functions. In this tutorial, we will see 5 built-in functions to annotate images: cv2.line, cv2.rectangle, cv2.circle, cv2.ellipse, cv2.putText. The image below will be used throughout this part: Draw a Line with OpenCV and Python Let’s start with drawing lines using the cv2.line function: We start by importing cv2 and
Image blurring is an important preprocessing step in computer vision. It is used to reduce noise and unnecessary detail in an image. In this tutorial, we will cover four blurring techniques: The image below will be used throughout this part: Average Blurring Blurring an image means convolving the image with an MxN matrix called a filter or a