Introduction to OpenCV with Python

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In this tutorial, we’ll cover OpenCV installation on Mac, Windows, and Linux, image operations, image arithmetics, image smoothing, and geometric transformations using OpenCV. OpenCV introduces a new set of tutorials which will guide you through various functions available in OpenCV-Python. This guide is mainly focused on OpenCV 3.x version (although most of the tutorials will also work with OpenCV 2.x). OpenCV supports a wide variety of programming languages such as C++, Python, Java, etc., and is available on different platforms including Windows, Linux, OS X, Android, and iOS.

If you have used https://forexhero.info/lab, you may be familiar with these image operations. An image is just an array of pixel values without any other meaningful data explicit to the computer. As new modules are added to OpenCV-Python, this tutorial will have to be expanded. If you are familiar with a particular algorithm and can write up a tutorial including basic theory of the algorithm and code showing example usage, please do so.

  • OpenCV and matplotlib integrate so cleanly because an OpenCV image is, in fact, just a multi-dimensional NumPy array containing the pixel values, and matplotlib can work with that.
  • By using it, one can process images and videos to identify objects, faces, or even handwriting of a human.
  • Proficiency with Numpy is a must in order to write optimized code using OpenCV-Python.
  • This course does not require any prior Machine Learning or Computer Vision experience.

For example, cars can be facilitated with computer vision, that can identify different objects around the road, such as traffic lights, pedestrians, traffic signs, and so on, and acts accordingly. PenCV also termed as ‘Open Source Computer Vision Library’ is an open-source library that includes several hundreds of computer vision algorithms. The resulting image is automatically saved in the current working directory. It was a choice made for historical reasons and now we have to live with it. The reason the early developers at OpenCV chose BGR color format is that back then BGR color format was popular among camera manufacturers and software providers, which is not true nowadays. Some people have doubts as to why OpenCV uses the BGR color format instead of RGB.

Image Thresholding in OpenCV

OpenCV has great people and community involved as users, developers and researchers, the number is more than 47 thousand and estimated number of downloads exceeding 7 million. The library contains more than 2500 optimized algorithms, which has excellent accuracy in performance and speed. Let’s try out the pre-trained classifier for Russian car plates, haarcascade_russian_plate_number.xml. If you want an image to test, you can use this image of a Lada Vesta by Sergey Rodovnichenko. Let it be known that this article was just the tip of the iceberg, and OpenCV has a lot more to offer.

Reading this should enable you to dive deeper and learn about other advanced features that OpenCV has to offer. Now that we have installed OpenCV on our workstations, let’s get our hands dirty with some of the functionalities that OpenCV offers. The following video is an example of Canny Edge Detection using OpenCV cv2.Canny() function at the left, side by side with a Deep Neural Network Inference done with OpenCV and OpenVINO, at the right.


IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world. Please checksamples– for the preferred style (small code size, cross-platform). There isno needto provide any project files or makefiles, if it is a short single-file sample, we could build it and run in a minute.

In this module, we will learn the basics of image processing with Python libraries OpenCV and Pillow. This module gives a short introduction to OpenCV and demonstrates its object detection functionality. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being an Apache 2 licensed product, OpenCV makes it easy for businesses to utilize and modify the code. Linear algebra functions and most of the machine learning algorithms work with floating-point arrays only. # python# artificial intelligence# machine learning# tensorflowMost resources start with pristine datasets, start at importing and finish at validation.


Actually you may become an official reviewer in the OpenCV project one day. At first try to troubleshoot the problem usingdocumentationandtutorials. It is the most commonly used, popular, and well-documented Computer Vision library. It is open-source, which means that one does not require a license to utilize the software. An image may be defined as a two-dimensional function f, where x and y are spatial coordinates, and the amplitude of fat any pair of coordinates is called the intensity or grey level of the image at that point. It’s the basic introduction to OpenCV we can continue the Applications and all the things in our upcoming articles.

The warpAffine function call uses the matrix we calculated from the previous method to rotate the image according to our specifications. The sum of the weights given to the addWeighted function should be equal to 1.0. You can also give a scalar value at the end, which would be added to all the pixel values of the resultant image. Saving an image is a very commonly used feature, as we may need to update our image and save the changes to the file system for later use.


In this tutorial, we are going to learn how to use OpenCV library in Python. Computer vision, often abbreviated as CV, is an interdisciplinary scientific field that is concerned with the development of techniques to help computers analyze and understand the content of a single image or a video. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding, and it is concerned with the theory behind artificial systems that extract information from images. No specialized hardware or software is required to complete this course.

In this line of code, we import all methods, operations, and functions that are offered by the Computer Vision library. OpenCV supports a variety of image formats, and when it fails to parse an image, the result of imread will be None. Note that if the image file isn’t found, no error will be raised — the result will be None as well. To conclude it all, let’s reiterate over some important points that we discussed in this article. OpenCV is a library available in multiple languages and is mostly used in conjunction with NumPy, SciPy and Matplotlib, as we saw in some of the examples above, as well. Some of its functions are the same as in Matlab, and it also supports vectorized operations, hence increasing computational efficiency.

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We make use of the imshow() opencv introduction to display the image that has been loaded into memory, onto the digital display . We need to understand that the imshow() is a very powerful OpenCV method because it creates a display for us- it will return a GUI Window to us, which contains our image that has been loaded into memory. From the above original image, lots of pieces of information that are present in the original image can be obtained. Like in the above image there are two faces available and the person in the images wearing a bracelet, watch, etc so by the help of OpenCV we can get all these types of information from the original image. Color space conversion functions support 8-bit unsigned, 16-bit unsigned, and 32-bit floating-point types. The face detection algorithm only works with 8-bit grayscale or color images.

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It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux,Androidand Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featuredCUDAandOpenCLinterfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.

OpenCV — which stands for Open source Computer Vision — is a popular, open-source, computer library originally developed by Intel in 1999, being actively used by the industry and academy. The library is cross-platform and free for use under the BSD license. Cross referencing OpenCV from other Doxygen projects – This document outlines how to create cross references to the OpenCV documentation from other Doxygen projects. As part of the Google Summer of Code 2013 program under the guidance of Alexander Mordvintsev. Prior knowledge of Python and Numpy is recommended as they won’t be covered in this guide. Proficiency with Numpy is a must in order to write optimized code using OpenCV-Python.

Building Your First Convolutional Neural Network With Keras

You will perform all labs and projects in a cloud environment and work with Python in Jupyter Notebooks, OpenCV, and IBM Watson Visual Recognition. You will require a modern web browser (i.e. recent versions of Chrome or Firefox). IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements.

OpenCV-Python makes use of Numpy, which is a highly optimized library for numerical operations with a MATLAB-style syntax. All the OpenCV array structures are converted to and from Numpy arrays. This also makes it easier to integrate with other libraries that use Numpy such as SciPy and Matplotlib. Let’s start with the simple task of reading an image using OpenCV. Contribute to the OpenCV library by providing coder time or by being part of development decisions.

  • Hence, in our example, since we would like to obtain our image in GRAYSCALE format, we have used a value of 0, which corresponds to the predefined flag for IMREAD_GRAYSCALE.
  • First, let us load our image in GRAYSCALE colour mode, and explore from there.
  • Color space conversion functions support 8-bit unsigned, 16-bit unsigned, and 32-bit floating-point types.
  • It enables the programmer to express ideas in fewer lines of code without reducing readability.
  • It is open-source, which means that one does not require a license to utilize the software.

The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding18 million. The library is used extensively in companies, research groups and by governmental bodies. To load an image into our system RAM , we are required to make use of the imread() method provided by OpenCV.

Here you can read tutorials about how to set up your computer to work with the OpenCV library. Additionally you can find very basic sample source code to introduce you to the world of the OpenCV. Image smoothing is a very helpful feature, which is mostly performed before the images are passed on to a machine learning model. It is mostly done to remove noise/high-frequency elements from images by passing the image through a low-pass filter. There are many filters, including box filter , median filter, mode filter, Gaussian filter, and many more; however, to understand image smoothing and how to do it using OpenCV, we will only cover the box filter.

Arithmetic operations on images refer to adding, subtracting, multiplying, or dividing multiple images to generate a new image which is an arithmetic combination of the input images. Image arithmetics has a lot of applications, like adding a watermark to an image, creating a blended combination of two images, applying different types of image filters, etc. The function we’ll use for reading/loading an image is cv2.imread(), which has two variations. First one is IMREAD_GRAYSCALE, which as the name suggests, converts the image to grayscale before reading it.

Zero is the predefined flag that will specify to the GUI system, to display the window for an infinite duration of time- to be precise- waitKey will wait infinitely for terminating the image window. Termination is prompted when the user presses any character or directional key on the keyboard. We will now how to process an image with the OpenCV package in Python Programming Language. You may navigate to, and download the image at this link, or you may save the image found below.


You will learn how to visualize OpenCV matrices and images within Visual Studio 2012. When the input data has a correct format and belongs to the specified value range, but the algorithm cannot succeed for some reason , it returns a special error code . I hope this article helps you in learning the fundamentals needed to get started with OpenCV using Python. In the getRotationMatrix2D function, 180 specifies the degree by which the image should be rotated, 1 is the scaling factor, the function call would return the rotation matrix in the matrix variable. The list of possible transformations is a long one, including scaling, affine, rotation, translation, etc. We will only cover two of them using OpenCV to get a general idea; however, OpenCV provides supporting functions for a wide range of them.

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You will learn about different components such as Layers and different types of activation functions such as ReLU. You also get to know the different CNN Architecture such as ResNet and LenNet. In this module, we will discuss the rapidly developing field of image processing. In addition to being the first step in Computer Vision, it has broad applications ranging anywhere from making your smartphone’s image look crystal clear to helping doctors cure diseases.