In this OpenCV python tutorial, you are going to learn computer vision, how a computer reads an image, applications, image processing, Basic Operations on Images, applications and a lot.
What is an OpenCV:
OpenCV is a python library that is used to solve computer vision-related problems. It supports many programming languages like Java, C++, python etc. and platforms like Windows, macOS and Linux.
OpenCV python is a wrapper for the original OpenCV C++ implementation. It mainly uses Numpy because of all the OpenCV array structures are converted into Numpy arrays and also Numpy arrays into OpenCV arrays.
OpenCV made it easy for all the businesses to utilize and modify the code.
What is Computer Vision:
It is a scientific field that how computers deal with the understanding of videos and Digital Images i.e. how the computers identify and process the digital images like a human.
Humans can understand the images easily but when coms to machines it has to detect the images by using edges, shapes and pixels, colours and classification detections etc.
The best example of Computer vision is identifying your friend’s faces and tagging them in every photo on your Facebook.
How does a computer read an image:
A computer can read an image based on the values between 0 to 255 pixels and an image consists of mainly red, blue and green.
For every colour, a matrix is created and then all these matrices are combined to form total pixel values of the red, blue and green pixel values for each and every colour.
What is the use of OpenCV Python?
OpenCV Python is mainly used for recognizing and detection of faces, analysis of images, videos, editing photos, reading the number plates, recognition of optical character, advanced robotic vision.
Image processing operations, image filtering, colour conversions, shape analysis, motion analysis, building GUI, 3D reconstruction, feature extraction, object detection.
Computational photography, optical flow algorithms, surface matching, Text detection and recognization.
Basic operations with OpenCV Python on Images:
Accessing and Modifying pixel values:
Before accessing and modifying pixel values of any image we have to load the colour image by using cv2.imread()
Accessing a pixel value:
If you want to access a pixel value of any image then you can access the image pixel values by coordinates of rows and columns.
px = img[100,100] print px
If you need to modify any image then you can do the same way as above by modifying the pixel values.
img[100,100]=[155,155,155] print img[100,100] [155,155,155]
Displaying the Image:
If you need to display an image it’s very easy to display by using OpenCV.
Import cv2 Img = cv2.imread(image) cv2.imshow(image;, img) cv2.waitkey(value) cv2.destroyAllWindows()
Before displaying an image we need to import the image by using imread.
To display any image we are using the function imshow by opening a new window.
You need to enter two parameters in the imshow function one is the name of the window and the second parameter is an object to be displayed.
Here waitkey() is used to make the window static until the user presses any key.
Here, time is in milliseconds.
DestroyAllwindows function is used to close all the windows.
Accessing Image Properties:
In order to access image properties, it includes many properties like row, column, channels, number of pixels and many more.
Img.shape is used to access the image properties and returns in rows and columns.
Print img.shape (350, 600, 3)
Here an image data type can be obtained by using img.dtype.
If you want to change the color of any image then you can use cvtcolor from one color to another color.
Face detection using OpenCV Python:
In order to detect faces first, we need an image to detect the face of the person. To give the features of the face we need to create the classifier called cascade.
Next, we need to find the rows and column values of the face by using the Numpy ndarray that indicates the coordinates of the face in a rectangle.
Next, we need to display the image in a rectangle shape.
Edge detection OpenCV Python:
Do you want to know how edge detection works? Edge detection of images mainly corresponds to two types of colours.
Black colour corresponds to (0, 0, 0) pixel values and white colour lines correspond to (255, 255, 255) pixel values.
Every digital image or frame video first breaks into pixels. The White edges of the image are compared with the black edges of the image in case of Edge detection.
We have to note all the white pixel location coordinates and then marking these coordinates on the original video or image we can see the original image with these edges.
What is Alpha blending in OpenCV Python:
A background image is overlayed by a foreground image with transparency is known as Alpha blending. An alpha mask is also known as a transparency mask.
Combining the foreground and background image colors at every pixel of an image using an alpha mask.
Companies using OpenCV python:
Most of the big companies that are using OpenCV are:
- Evergreen team stack
- Inside Scoop
- Sticker Stars
- Open room
- Microsoft etc.
Many popular tools like Jovian, biicode, Amazon kinesis video streams, Applitools etc.
Here are some of the pros of OpenCV:
- OpenCV is an opensource library
- Used for imaging
- Used to identifying face detection
- Computer vision
- Machine Learning
- Realtime image processing
- Great community
- Image augmentation
- Helping in CV problems
Features of OpenCV python:
Here are the features of OpenCV in Python
- OpenCV is optimized with more than 2500 algorithms that also include Machine Learning Algorithms.
- It is mainly written in C++
- An OpenCV is mainly used in research projects and Image detections, track moving objects, to track moving objects etc.
Alternatives for OpenCV:
The alternative tools/libraries for OpenCV are:
- Imagekit etc.
Prerequisite to learn OpenCV:
If you are willing to learn OpenCV it is good to have a piece of basic knowledge of Python programming.