20 OpenCV-Python Interview Questions and Answers

OpenCV-Python is a library of computer vision algorithms that can be used to detect and track objects, as well as to perform other image processing tasks. As a Python developer, if you’re looking to get into the field of computer vision, then knowing how to use OpenCV-Python is a valuable skill to have.

In this article, we’ll go over some common interview questions that you may be asked about OpenCV-Python, as well as some tips on how to answer them.

OpenCV-Python Interview Questions and Answers

Here are 20 commonly asked OpenCV-Python interview questions and answers to prepare you for your interview:

1. What is OpenCV?

OpenCV is a computer vision library that allows you to perform image processing and computer vision tasks in real-time. It is also open source, meaning that you can use it for free in your own projects.

2. How do you install opencv-python on your system?

You can install opencv-python using pip:

pip install opencv-python

3. What are the main features of OpenCV?

The main features of OpenCV are its ability to process images and videos, perform machine learning and computer vision algorithms, and support various programming languages.

4. Is it possible to perform face detection in real time with OpenCV? If yes, then how?

Yes, it is possible to perform face detection in real time with OpenCV. The way to do this is to use the Haar Cascade classifier, which is a pre-trained machine learning model that can be used to detect faces in images. To use it in real time, you would need to have a webcam connected to your computer and then use the OpenCV library to capture frames from the webcam and feed them into the Haar Cascade classifier. The classifier would then output whether or not a face was detected in each frame.

5. Can you explain what a Cascade Classifier does?

A Cascade Classifier is a machine learning algorithm that is used to detect objects in images. It is often used in applications like face detection or object detection. The algorithm works by training on a set of images that contain the object you want to detect. Once it has been trained, it can then be used to detect the object in new images.

6. Is it possible to create custom classifiers using OpenCV? If yes, then how?

Yes, it is possible to create custom classifiers using OpenCV. This can be done by creating a Haar Cascade classifier. A Haar Cascade classifier is a machine learning based approach that is used to identify objects in images. To create a custom Haar Cascade classifier, you will need to collect a large number of positive and negative images. Positive images are images that contain the object you are trying to detect. Negative images are images that do not contain the object you are trying to detect. Once you have a large dataset of positive and negative images, you can then train a Haar Cascade classifier to learn to detect the object in images.

7. What is the difference between grayscaling and thresholding an image?

Grayscaling is the process of reducing an image to a range of gray tones, usually from a range of 256 colors down to 2. Thresholding is the process of converting an image into a binary image, which is an image composed of only black and white pixels.

8. What’s the best way to detect faces in an RGB color space?

The best way to detect faces in an RGB color space is to use a Viola-Jones object detection algorithm.

9. Can we obtain edges from images using OpenCV? If yes, then how?

Yes, we can obtain edges from images using OpenCV. We can do this by using the Canny edge detector. The Canny edge detector is an algorithm that can be used to detect edges in an image.

10. Is it possible to work with colored images using OpenCV? If yes, then how?

Yes, it is possible to work with colored images using OpenCV. To do so, you would need to convert the image from RGB to HSV using the cvtColor function.

11. What is a histogram used for?

A histogram is used to visualize the distribution of data. It is a graph that shows the frequency of data values.

12. What’s the difference between grayscale, binary, blur, and edge detection? Which one would be best suited for detecting faces?

Grayscale is a type of image where each pixel is a single shade of gray, with no color. Binary images are images where each pixel is either black or white. Blur is a type of image where the pixels are less distinct, making the image appear fuzzy. Edge detection is a type of image where the pixels are very distinct, and the edges of objects are more visible. For detecting faces, edge detection would be the best suited type of image.

13. Is it possible to use OpenCV to read data from videos? If yes, then how?

Yes, it is possible to use OpenCV to read data from videos. You can use the VideoCapture class to open a video file or stream and then use the read() method to read frames from the video.

14. What is correlation coefficient and why is it important?

Correlation coefficient is a statistical measure that calculates the strength of the relationship between two variables. In the context of image processing, it is used to measure the similarity between two images. If the correlation coefficient is high, it means that the two images are very similar. If it is low, it means that the two images are not very similar.

15. Can you explain what blurring/smoothing is?

Blurring is the process of making an image less detailed or less distinct. Smoothing is a method of blurring that is often used to reduce noise in an image.

16. What are some basic operations that can be performed on binary images?

Some basic operations that can be performed on binary images include:
-Thresholding
-Morphological operations
-Edge detection
-Contour detection
-Connected component analysis

17. What are the various types of filters available for convolution?

The types of filters available for convolution include linear filters, non-linear filters, and edge detectors. Linear filters are the most common and include things like low-pass and high-pass filters. Non-linear filters are less common and include things like median filters. Edge detectors are used to find boundaries in images and include things like Sobel and Canny filters.

18. Can you explain what a pyramid is?

A pyramid is a computer vision algorithm that can be used for image processing. It is called a pyramid because it typically involves creating a set of images, each of which is a reduced version of the original image.

19. What’s the difference between erosion and dilation?

Erosion is the process of removing pixels from the boundaries of an image, while dilation is the process of adding pixels to the boundaries of an image.

20. What is the importance of morphology in computer vision?

Morphology is a process that can be used to extract features from images, and it is often used in computer vision applications. Morphology can be used to remove noise from an image, or to find objects in an image.