If you’re a programmer or novice who wants to learn about OpenCV and its features, you should consider getting familiar with it in conjunction with Python. The library is extensively used both in academic and commercial contexts. Intel created OpenCV and released it under the BSD license in June 2000. The library is based on the C++ programming language and has APIs for a variety of programming languages. OpenCV includes a range of pre-defined functions for image analysis.
Python programming language can create a number of applications, including image recognition. OpenCV is a powerful machine-learning library that is used for image processing. This software allows you to detect objects in images, track their movements, and more. You’ll be able to implement a number of interesting projects with OpenCV. You can even develop a mobile phone app that can recognize faces! But remember that learning OpenCV can be challenging.
One example of an OpenCV project uses a camera to count the number of people in a room, street, or building. This library is useful for all kinds of computer vision projects, and the Python version is best for beginners as well as experts. This article will assist you in getting to grips with using OpenCV regardless of whether you’re a novice or an experienced one. You can find out how to utilize OpenCV in conjunction with Python by downloading its source code and installing it into the Python program.
Another example of an application using OpenCV is an automated attendance system. It utilizes computer vision to identify students by matching their faces to a database. Once the database is built, your application can mark the students who match the database as being present. In the case of a video, you can also create an OpenCV application using Python. It can even detect faces without requiring the video’s capture to be close.
Introduction to OpenCV for Python
If you want to learn computer vision, you first need to understand openCV for Python. CV stands for computer vision. Computer vision is a subfield of artificial intelligence. It is used to train and interfere with digital images.
OpenCV for Python is an open-sourced library. It is used for Computer vision in machine learning, artificial intelligence, and face recognition. Now CV is the field of computer which helps get the knowledge and understand the content of digital images such as photos and videos.
OpenCV full from is open sourced computer vision library. It was first launched by intel in 1999 in the language of C/C++ but is written in Python in computer vision. l launched the first alpha version of open cv in 2000 at an IEEE Conference on Computer Vision and Pattern Recognition. Then in 2001 and 2005, the beta versions were found and established the 0.1 version in 2006. With crucial changes launched, the second version in 2009. The changes were in the c++ interface. Their goal was to make the better implementation, is easy and safe. Nowadays, there is a version released every six months by an independent Russian team.
OpenCV Object Detection
While we start discussing the topic of OpenCV object detection, first, we need to understand what object detection is. Object detection is a new-age computer technology linked with deep learning. Image processing and computer vision to identify the objects in the image file. The object detection techniques’ technologies transact with the object’s instances in the image or video.
While using the OpenCV library, we can efficiently images and videos that can identify human handwriting, face and objects in the file. Now we will learn how to detect objects using OpenCV. The first thing to do is to import the image into OpenCVthe library in the python program. We will start performing the function in the image file. We have to install the libraries in the system and the matplotlib library. The OpenCV library should be installed on your device so that we can import it into the python program and start object detection in the OpenCV object detection.
Here are some of the steps for object detection –
- Open the image
It will give an image to you, and you have to open it and create an environment. Then we have to import the OpenCV and matplotlib libraries. Define the properties of the image using the cv functions.
- Object detection
Now we have to detect the presence of the object in the image. We have to use a statement in the function to check the object’s presence in the image. If w detects the thing, we will highlight the image. After noticing the image, we will process and display the image.
What OpenCV for Python is used for?
With human eyes, we see and visualize images differently. When a machine sees a snapshot, it converts them into numbers. The pixel plays a role here. It helps the image to covert into numbers. The OpenCV library has almost 2500 algorithms. With the help of OpenCV for Python, the user can resize images, process images and videos, and get helpful information. There are two common ways to detect images.
Grayscale – grayscale images are in two colors- Black and white. The more vigorous intensity measures, the measurement of the contrast belongs to black and the weaker to white. The pixel, the level of darkness, identifies the value of the image.
RGB- reg green and blue combined to make a new color. The computer returns the value from every pixel and inserts them for the result.
Object Detection using Python
Object detection is a vast domain in computer vision. The technology helps to detect the image, video, or track images. Object detection is also called object recognition. There are two objectives of object detection using Python
- Detect the image – whether the image exists or not
- Filter the object – a filter that object which has the attention
There are many OpenCV libraries in Python. In this section, we will talk about the imageAI library of the python programming language and its use. We will detect the image. Python attempts to build libraries to permit the developer and programmers to computer vision capabilities with the assistance of an uncomplicated coding script. imageAI is one of those implementations of Python, which is near the state-of-the-art deep learning algorithm.
To utilize the imageAI, we need to download some dependencies for which s should install Python in the system. Here are some of the steps for object detection using python ImageAI-
Step 1: For the first step to proceed, we need some of the necessary folders.
- Object recognition folder – main folder
- Models – backlog the pre-trained model
- Input – have the image for the object detection
- Output – will have the detected object.
Step 2 – we will use the text editor to write the python script and new file recognizer.py
Step 3 – we will start importing the image from the image library.
Step 4 – the imageAI library and the object detection class have been imported. Now we will create an instance of object detection.
Step 5: Specify the input and output image path from the model.
Step 6 – now, we can call for several other functions from the class
Step 7- We will perform different functions to detect the image by using the model’s folder, input, and output.
OpenCV Applications
There are a lot of functions that an OpenCV can perform. The OpenCV applications are –
- Processing of the image
The image is processed, read, written, and shown. You can also generate new images by making a few changes in shape or color or gain helpful something from the image given. It also rotates the image in some instances where it is required.
- Face detection
The face could be detected using a web camera or live stream any local storage video or image.
- Face recognition
It is detected by videos using OpenCV by drawing bounding boxes after that machine learning algorithm is used to recognize faces.
- Open detection
We have already talked about this application. The object is detected in the images using OpenCV for Python.
- Contours
Contour is a curve that joins all the continuous lines and has the same color and intensity. It is challenging to find contour. It is like finding white lines on black background. OpenCV. OpenCV has two functions: first, to find the contour, and second, to draw the contours. To see contours, use the findContours() process; to draw one, use drawcounter().
Conclusion
Cv is a part of artificial intelligence. OpenCV, a python library, can help the user to read, object detect, display images from local storage, and download the image using OpenCV for Python. Could read the image in color format or grayscale. Using the OpenCV application, there could be edge detection as well. Opencv for Python is more functional these days.openCV for Python is not easy to learn, but various methods can simplify it.
