The most renowned open-source OpenCV software provides access to a broad range of computer vision and image processing techniques. It provides a wide range of tools and techniques that allow programmers to produce cutting-edge applications in several different fields. For instance robots, augmented reality, facial recognition, object detection, and much more. OpenCV has grown in popularity among developers all around the world because of its strong capabilities and approachable interface.
In this blog, we will start with the basics and work our way up to more complicated topics. Regardless of your prior computer vision experience, we will teach you everything you need to know to master OpenCV. Whether you are a beginner making your initial moves or an experienced developer looking to improve. Even if you have completed a Python course, this blog will guide you to OpenCV in Python.
For computer vision and machine learning, there is a free software library called OpenCV. Open Source Computer Vision Library is what OpenCV is also known as. It was designed to speed up the usage of machine perception in consumer goods and to provide a standard infrastructure for computer vision applications. Companies can use and modify the code via OpenCV, a BSD-licensed piece of software. OpenCV is one of the libraries and set-up packages that make our lives easier.
Over 2500 optimised algorithms are available in the library, including a sizable number of both traditional and cutting-edge computer vision and machine learning methods. With OpenCV, it's simple to complete complicated tasks like recognising faces, identifying objects, defining human actions in videos, tracking camera movements, tracking moving objects, extracting 3D object models, creating 3D point clouds from stereo cameras, stitching images together to create an entire scene with a high-resolution image, and many more. You can start learning Python from any of the best python institute to become familiar with it.
The ease of use and user-friendliness of Python comes at the expense of speed, as Python is slower than languages like C or C++. Thus, we add C/C++ support to Python, enabling us to write computationally demanding C/C++ code and produce Python wrappers that may be used as Python modules. As a result, the code runs because it is original C/C++ code (since the C++ code itself is running in the background) and because Python is a more user-friendly programming language than C/C++. The original OpenCV C++ implementation is wrapped in Python by OpenCV-Python. Hence, learning Python will be beneficial for you even if it is a Python online course.
In artificial intelligence (AI) and deep learning (DL) applications, the word computer vision (CV) is used and used frequently. The phrase refers to enabling a computer to perceive the environment in the same way that people do. It is now possible for computers to mimic the human visual system thanks to the field of study known as computer vision. As was stated earlier, it is a subset of artificial intelligence that gathers data from digital photos or videos and analyses it to identify qualities. Images must be acquired, screened, analysed, identified, and information must be extracted throughout the process. Computers can interpret any visual content and take appropriate action thanks to this thorough processing. Computer vision projects describe digital visual content to gather multi-dimensional data. Then, to assist with decision-making, this data is translated into a language that can be understood by computers. The main objective of this branch of AI is to educate computers how to extract information from pixels.
How Does A Computer Interpret A Picture?
How does the human brain process an image? What do you notice and how would you describe what is in the image when you look at the one below?
You may scan the image in search of various shapes and colours, which may aid in your determination that the image is a car. But does a machine see it as similar to humans? No, is the response.
A digital image is made up of pixels, or picture parts, each of which has a discrete, finite amount of numeric representation for its level of intensity. A computer thus perceives an image as the numerical values of these pixels, and to identify a specific image, it must identify the patterns and regularities in this numerical data.
Here is an illustrative depiction of how pixels create an image. The values that represent lighter pixels approach one, while those that represent darker pixels are closer to zero. The numbers between 0 and 1 represent every other colour.
But often you will discover that there are 3 main channels red, green, and blue for every colour image and that each channel's value ranges from 0-255. A grayscale image has only one channel, whose values range from 0-255, although a digital image is created by combining the three fundamental colour channels of red, green, and blue.
Since real-time operation is crucial in today's systems, OpenCV, a sizable open-source library for computer vision, machine learning, and image processing, has taken on a significant role in this area. It can be used to process photos and videos to recognise items, people, and even a person's handwriting. Python can process the OpenCV array structure for analysis when it is coupled with a variety of libraries, such as NumPy. Using vector space, we execute mathematical operations on the features of an image to identify patterns and their various characteristics. PIP and Python need to be already installed on a machine to install OpenCV. If you have learned Python from the best Python course institute, then you won't face any difficulty in working with it.
Follow these methods to determine whether Python is already installed on your system: Enter cmd into the Run dialogue box by pressing the key combination R. Execute the following command now:
python –version
If Python is already installed, a message containing the Python version available will appear.
Go through How to install Python on Windows if it is not already there. and adhere to the guidance given. PIP is a package management tool used to set up and maintain Python software packages and libraries. These files are kept in the Python Package Index (PyPI), a sizable "online repository." Go to the command prompt and enter the following command to see if PIP is already installed on your system:
pip -V
The package manager pip can be used to download and install OpenCV directly. Go to the command line and enter the following command to install OpenCV:
pip install opencv-python:
Now check if OpenCV is installed by running the following commands to perform a version check:
python
>>>import cv2
>>>print(cv2.__version__)
Setting Up OpenCV With An Anaconda Environment
You have come to the correct place if you enjoy using Python for image processing and video analysis. One of the popular languages that may be used to process photos or movies is Python.
OpenCV and Anaconda requirements
Our Learners Also Read: List Some Of The Most In-Demand Python Libraries And Frameworks
Open-source software called Anaconda includes tools like Jupyter and Spyder that are used for big data processing, data analytics, and intensive scientific computing. Python is used with Spyder, a component of Anaconda. Spyder will support
OpenCV for Python. The package management tool conda controls package versioning.
Installing Anaconda is as simple as going to continuum.io/downloads/ and doing so. Install "Python 3.6 Version" for the proper architecture, and make sure. Use the default installation options.
Step 1: Search for Anaconda in your taskbar and choose ANACONDA NAVIGATOR to create an Anaconda environment.
Step 2: At this point, you will see a menu with several choices, including Spyder and the Jupyter Notebook. The Anaconda Environment is this.
Step 3: Choose Spyder as it is Anaconda's Python IDE and the only one that supports the OpenCV library.
Step 1: Open the Anaconda Prompt after installing Anaconda.
Step 2: Type the command, hit Enter, and wait for the entire package to download.
Command
conda install -c menpo opencv
Step 3: To use OpenCV's image processing features in your Python program, import it.
The OpenCV library supports the following file types:
Installing these libraries is a must for using the OpenCV library in Python:
If you have done any best Python course then you might be aware of these libraries.
To install these libraries, you need to run these pip commands in cmd:
To Read And Display A Picture In OpenCV, Follow These Steps:
1. Use imread() to read an image.
2. Use the imshow() function to display an image in a GUI window.
3.To maintain the image window on the screen for the specified number of seconds or until the user closes the GUI window, use the waitkey(0) function.
4. Use the destroyAllWindows() function to remove the image window from memory after it has been displayed.
After reading the photos, we will explore some of the fundamental actions we can perform on them in this part. The procedures we will perform in this example are:
1. Get pixel values and change them
So, there are two essential methods for accessing and changing pixel values in an image. Let's first examine how we can access a specific image pixel value.
import numpy as np
import cv2 as cv
img = cv.imread(r'C:\Users\Renu\car.jpeg')
px = img[100,100]
print( px )
Output:
[157 166 200]
You can now see that we have a list with three values. Since the colour image is stored by OpenCV as a BGR colour image, the first value in the list represents the value of the blue channel for this specific pixel, and the remaining values represent the values for the green and red channels.
Also, as indicated below, we can only access one of the channels.
# accessing only blue pixel
blue = img[100,100,0]
print( blue )
Output:
157
As shown below, all we need to do to change the values is access the pixel and then replace it with the new value.
img[100,100] = [255,255,255]
print( img[100,100] )
Output:
[255 255 255]
You should use the NumPy library as it is optimised for quick array calculations instead of using this sluggish way to access and modify the pixel values. Since they always return a scalar, the Numpy array methods array.item() and array.itemset() are used for retrieving specific pixel values. The following example illustrates how to call array.item() for each value in turn if you want to access all the B, G, and R values:
# accessing RED value
img.item(10,10,2)
>>59
# modifying RED value
img.itemset((10,10,2),100)
img.item(10,10,2)
>>100
2. Get To The Image's Properties
What are the image attributes in this context? The size (total number of pixels in the image), and the number of rows, columns, and channels are crucial details to know. The shape() method, as demonstrated below, can be used to retrieve the latter three:
print( img.shape )
>>(342, 548, 3)
print( img.size )
>>562248
In the returned tuple, three numbers represent the number of rows, columns, and channels, respectively. The tuple returned only includes the number of rows and columns when an image is grayscale.
Img.dtype, which returns the image datatype, is crucial while debugging since improper data type leads to a significant number of mistakes in OpenCV-Python programs.
Region of interest (ROI) in an image
You may find photographs in which you are just concerned with a small portion of the picture. Would you search the entire image if you wanted to find eyeballs in a picture? Probably not, as you might not get reliable results. But, since the eyes are a component of the face and it is preferable to detect the face first, the face is our ROI in this case.
3. Splitting and Merging Image Channels
An image's channels can also be divided, and each channel can then be worked on separately. In addition, you might need to combine them once more. To accomplish the task using Numpy, follow these steps:
b,g,r = cv.split(img)
img = cv.merge((b,g,r))
b = img[:,:,0]
g = img[:,:,1]
r = img[:,:,2]
Let's say you wanted to change all the values in the red channel to zero. It would be done as follows:
#sets all values in a red channel as zero
img[:,:,2] = 0
4. Image Resize Using OpenCV
To meet specific standards, we frequently need to resize the photographs we work with. As it shortens the time required to train a neural network, you tend to perform similar operations in machine le
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