What is Python?
Python is a programming/scripting language that can be used on many different computers and operating systems, including Windows, Unix, Macintosh, etc.
Edge and Contour Detection with OpenCV and Python
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
Morphological Operations with OpenCV and Python
In this article we are going to see how to use the following morphological operations: Morphological operations are some transformations applied to grayscale or binary images. Morphological operations apply a structuring element to add or remove some pixels from the boundaries of objects in an input image. A structuring element is a matrix that defines the neighborhood
Smile Detection with Python, OpenCV, and Deep Learning
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
Smile Detection with Python, OpenCV, and Haar Cascade
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
Object Detection with YOLO using PyTorch
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
Object Detection with Python, Deep Learning, and OpenCV
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
Image Classification with Python, TensorFlow and Deep Learning
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
Image Annotation with OpenCV and Python
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 Filtering and Blurring with OpenCV and Python
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
Image Pyramids in OpenCV with Python
In this article, we will learn about image pyramids and how to implement them in Python using OpenCV. What are Image Pyramids? Image pyramids are a commonly used technique in computer vision and image processing that involves creating a multi-scale representation of an image. This representation consists of a series of progressively downsampled images, where