Object Detection with Open CV and Python
In this article, I am going to discuss Object Detection with Open CV and Python. Please read our previous article where we discussed Computer Vision Basics.
Object Detection with Open CV and Python
Computer Vision is an intriguing topic that enables machines to see and analyze visual information in the same way that humans do. OpenCV (Open-Source Computer Vision) is a sophisticated library that gives developers a variety of tools and methods for doing computer vision tasks. In this tutorial, we’ll look at the fundamentals of OpenCV with Python and how it can be used to analyze and edit pictures and movies.
How to Begin with OpenCV
To get started with OpenCV, you must have Python installed on your PC. Pip, the Python package manager, may be used to install OpenCV by running the command pip install opencv-python. Once installed, use the following command to import the OpenCV module into your Python script:
Working with Images
OpenCV makes it easy to load, display, and manipulate images. Let’s start by loading an image from the disk:
import cv2 # Load an image from file image = cv2.imread('image.jpg') # Display the image cv2.imshow('Image', image) cv2.waitKey(0) cv2.destroyAllWindows()
The imread function is used in the above code snippet to read the image file from disk and save it in the image variable. The image is then shown in a window using imshow. The waitKey(0) method is used to wait for a key press, whereas the destroyAllWindows function is used to dismiss all active windows.
JPEG, PNG, and BMP are among the image formats supported by OpenCV. Images may be manipulated in a variety of ways, including scaling, cropping, rotating, and adding various filters. OpenCV also includes fundamental image processing operations such as picture thresholding, edge detection, and color space conversions.
Working with Videos
OpenCV can also handle video files and live video streams from webcams. Let’s see how we can capture video from a webcam:
import cv2 # Open a video capture object capture = cv2.VideoCapture(0) while True: # Read a frame from the video ret, frame = capture.read() # Display the frame cv2.imshow('Video', frame) # Exit the loop if 'q' is pressed if cv2.waitKey(1) & 0xFF == ord('q'): break # Release the video capture object and close the window capture.release() cv2.destroyAllWindows()
In the code above, we create a VideoCapture object by passing the index of the webcam (0 for the default webcam). We then continuously read frames from the video using the read function and display them in a window using imshow. The video stream continues until the user presses the ‘q’ key.
Techniques for Advanced Computer Vision
OpenCV offers a diverse set of advanced computer vision algorithms that extend beyond simple image and video processing. Here are a couple of such examples:
- Object Detection: For object detection tasks, OpenCV provides pre-trained models such as Haar cascades and deep learning-based models such as YOLO (You Only Look Once). These models are capable of detecting and recognizing objects in photos and movies.
- Face Recognition: OpenCV has face detection and recognition capabilities, allowing you to create apps that can identify and verify people based on their facial traits.
- Image Segmentation: OpenCV has a number of image segmentation techniques that may divide pictures into relevant sections. This is very beneficial for picture interpretation and object tracking.
OpenCV is a flexible and powerful package that enables a plethora of computer vision applications. Python as the programming language makes it considerably easier for developers to leverage its advantages. So, go ahead and investigate OpenCV to unleash the potential of computer vision in your Python projects!
In the next article, I am going to discuss Introduction to Object Detection. Here, in this article, I try to explain Object Detection with Open CV and Python. I hope you enjoy this Object Detection with Open CV and Python article. Please post your feedback, suggestions, and questions about this article.