In this Python OpenCV tutorial we want to talk about Open Computer Vision Library, so computer vision is one of the best and most popular fields in these days, the primary purpose of computer vision is to analyze digital images or video. and Python OpenCV is one of the most popular libraries, Python OpenCV is used for doing different tasks, like image processing, object detection, face recognition and many more. in this article we want to talk that what is Python OpenCV, in which sections we can use that, and some of the key features of computer vision.
What is Python OpenCV ?
We can say that OpenCV is Open Source Computer Vision, OpenCV is an open source library, we can use OpenCV for real time computer vision. this library was developed by Intel company in 1999, and now it’s maintained by OpenCV Foundation. OpenCV is purely written in C++, it also provides Python interface that allows users to use it in Python programming. you can use OpenCV on multiple platforms like Windows, Linux and MacOS. also there are different bindings of OpenCV for different programming languages like Python, Java and C++.
What is the usage of Python OpenCV ?
Python OpenCV is used for different computer vision tasks like:
- Image processing: OpenCV can be used to perform different image processing techniques such as filtering, edge detection, thresholding and many more.
- Object detection: OpenCV is used for detecting objects in images and videos, there are different techniques that we can use it like Haar cascades and deep learning based models.
- Face recognition: OpenCV is used to recognize faces in images and videos using techniques such as Local Binary Patterns Histograms (LBPH), Eigenfaces and Fisherfaces.
- Optical character recognition (OCR): OpenCV is used to recognize text in images and videos using OCR techniques.
- Augmented reality: OpenCV can be used to create augmented reality applications that overlay digital content onto the real world.
What are some key features of Python OpenCV ?
- Easy to use: You can use Python OpenCV easily for performing different types of computer vision tasks.
- Cross-platform: Python OpenCV is used on multiple platforms, like Windows, Linux and MacOS.
- Speed: Python OpenCV is optimized for speed, and this is a great choice for real time applications.
- Open-source: Python OpenCV is open source, it means that it is free to use, modify and distribute.
- Large community: Python OpenCV has a large community of developers who contribute to its development and provide support to users.
These are some examples of Python OpenCV
- Python OpenCV Image processing:
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import cv2 # Read image img = cv2.imread('lena.tif') # Convert to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Apply Gaussian blur blur = cv2.GaussianBlur(gray, (5,5), 0) # Perform Canny edge detection edges = cv2.Canny(blur, 100, 200) # Display the result cv2.imshow('Edges', edges) cv2.waitKey(0) |
This code first reads the image and converts it to grayscale using cv2.cvtColor() function. after that it applies Gaussian blur to the grayscale image using cv2.GaussianBlur() function with a kernel size of (5,5) and a standard deviation of 0.
After that, it applies the Canny edge detection algorithm to the blurred image using cv2.Canny() function with lower and upper thresholds of 100 and 200. at the end we are showing the image.
Run the code and this will be the result
- Python OpenCV Face recognition:
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import cv2 # Load cascade cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # Read input image img = cv2.imread('lena.tif') # Convert to grayscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Detect faces faces = cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5) # Draw rectangles around the faces for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2) # Display the result cv2.imshow('Faces', img) cv2.waitKey(0) |
This code is used for face detection, in this code we load Haar Cascade classifier using cv2.CascadeClassifier() function. after that it reads the input image and converts it to grayscale using cv2.cvtColor() function. also we apply a face detection algorithm to the grayscale image using cascade.detectMultiScale() function. scaleFactor parameter controls how much the image size is reduced at each image scale, and the minNeighbors parameter controls how many neighbors each candidate rectangle should have to be considered a face.
Run the code and this will be the result
These are just a few examples, but Python OpenCV more than these examples.
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