Here is a selection of 30 commonly asked OpenCV interview questions along with concise answers:
1. What is OpenCV?
Ans. OpenCV (Open Source Computer Vision) is an open-source computer vision and machine learning library that provides various algorithms and tools for image and video analysis.
2. What are the key features of OpenCV?
Ans. OpenCV offers features such as image and video processing, object detection and tracking, facial recognition, camera calibration, and machine learning algorithms for computer vision tasks.
3. Which programming languages are supported by OpenCV?
Ans. OpenCV supports multiple programming languages, including C++, Python, Java, and MATLAB. It provides bindings for these languages to use the library’s functionality.
4. How can images be read and displayed using OpenCV?
Ans. Images can be read using the cv2.imread() function and displayed using the cv2.imshow() function in OpenCV.
5. What are the color spaces supported by OpenCV?
Ans. OpenCV supports various color spaces, including RGB, grayscale, HSV, LAB, and YUV. It provides functions to convert between different color spaces.
6. How can image smoothing and blurring be achieved in OpenCV?
Ans. Image smoothing and blurring can be achieved using functions such as cv2.blur(), cv2.GaussianBlur(), and cv2.medianBlur() in OpenCV.
7. How can image edges be detected using OpenCV?
Ans. Image edges can be detected using functions such as cv2.Canny() and cv2.Sobel() in OpenCV, which implement edge detection algorithms.
8. What is the role of a kernel in image processing using OpenCV?
Ans. A kernel is a small matrix that is applied to an image for various operations, such as blurring, sharpening, or edge detection, in OpenCV.
9. How does OpenCV perform object detection?
Ans. OpenCV provides various techniques for object detection, including Haar cascades, HOG (Histogram of Oriented Gradients), and deep learning-based approaches like YOLO and SSD.
10. What is the purpose of image thresholding in OpenCV?
Ans. Image thresholding is used to convert grayscale images to binary images by assigning different pixel values based on a specified threshold. OpenCV provides functions like cv2.threshold() for thresholding.
11. How can image contours be detected using OpenCV?
Ans. Image contours can be detected using functions such as cv2.findContours() and cv2.drawContours() in OpenCV, which identify and draw the outlines of objects in an image.
12. How can image key points and descriptors be extracted using OpenCV?
Ans. OpenCV provides functions like cv2.SIFT(), cv2.SURF(), and cv2.ORB() for extracting key points and descriptors from images, which are useful for tasks like feature matching and object recognition.
13. How can face detection be performed using OpenCV?
Ans. OpenCV provides pre-trained Haar cascades for face detection. By using the cv2.CascadeClassifier class, faces can be detected in images or video streams.
14. How can image perspective transformation be performed using OpenCV?
Ans. Image perspective transformation can be achieved using the cv2.warpPerspective() function in OpenCV, which applies a linear transformation to warp the image.
15. How does OpenCV handle video processing?
Ans. OpenCV provides functions to read, write, and process videos. Video frames can be accessed, processed, and displayed using OpenCV functions.
16. Difference between a top hat and a black hat?
Ans. Top Hat, also called the white hat morphological operation, is the difference between the original input image and the image after the application of the opening morphological operation. While the Black Hat morphological operation defines the difference between the image with the application of closing morphological operation and the original input image.
17. What are the different interpolation methods available in cv2.resize function?
Ans. The different interpolation methods available in cv2.resize function are:
- cv2.INTER_AREA: Resamples using pixel area relation. It is usually used to shrink images.
- cv2.INTER_CUBIC: It is the Bicubic interpolation method
- cv2.INTER_LINEAR: Used for zooming. It is the Bilinear interpolation method and it is the default interpolation function in OpenCV.
18. What is the importance of gray scaling in image processing?
Ans. Importance of Gray scaling
Reducing the dimension of the images: RGB images have three color channels and constitute a three-dimensional matrix, while in grayscale images, there is no additional parameter for color channels and are only single-dimensional.
Due to the dimension reduction, the information provided to each pixel is comparatively less.
Reduces the complexity of the model: When there is less information provided to each pixel of the image, the input nodes for the neural network will also be considerably less. Hence, this reduces the complexity of a deep learning model.
Difficulty in visualization in color images: Much more information is extracted for some images through gray scaling which might not be possible if the same algorithms or processes are applied to a color image. Features required for extraction become much more visible.
19. What is a clip line in OpenCV? Implement a program to create a clip line using the cv2.clipLine() function.
Ans. The cv2.clipLine() function returns the segment (defined by the pt1 and pt2 output points) inside the rectangle (the function clips the segment against the defined rectangle). The value for retval is False, if the pt1 and pt2 points are outside the rectangle. If some of the points given by pt1 or pt2 lie inside the rectangle, the value for retval is True.
20. What are mouse events in OpenCV?
Ans. Mouse callback functions are created in OpenCV which carry out specific tasks when a certain mouse event occurs. Mouse events are actions of the mouse like left click, double click etc. A callback function returns the coordinates of the mouse events.
21. Define the Scharr operator in OpenCV?
Ans. The Scharr operator is used as a method to identify and highlight gradient edges or features of an image using the 1st derivative. It is commonly used to identify gradients along the x-axis (dx = 1, dy = 0) and y-axis (dx = 0, dy = 1).
The performance of the Scharr operator is quite similar to the Sobel operator.
The Scharr operator is an enhancement of the difference between the Sobel operator, and the two are the same as the principle of the edge of the image.
The Scharr operator increases the difference between the pixel value by amplifying the weight coefficient.
22. What is Cascade Classifier in OpenCV?
Ans. A cascade classifier is a machine-learning approach where the positive and negative images are used to train a cascade function. The cascade classifier, as the name suggests, is used to classify or detect objects in images. The algorithm requires a large amount of training data images with the object to be detected and images without the object to be detected.
23. Define Adaptive Histogram Equalization (ALE).
Ans. The adaptive histogram equalization method makes use of an adaptive function to compute numerous image histograms, each equivalent and referring to different regions of the image. By combining these computed histograms, adaptive
Histogram equalization improves the contrasts of the image by spreading the intensity value of each pixel. Adaptive histogram equalization works better than standard function to improve contrasts of local regions and enhance the edges.
24. What is the application of the Sobel operator in OpenCV?
Ans. The Sobel operator sometimes called the Sobel–Feldman operator or Sobel filter is used in image processing and computer vision, particularly within edge detection algorithms where it emphasizes the edges. The Sobel Operator is a discrete differentiation operator. The operator is used to determine the approximation of the gradient of an image intensity function.
25. What are machine learning algorithms available in OpenCV?
- Normal Bayes Classifier
- K-Nearest Neighbors
- Support Vector Machines
- Decision Trees
- Gradient Boosted Trees
- Random Trees
- Extremely randomized trees
26. How many types of image filters in OpenCV?
- Gaussian Filtering
- Median Filtering
- Bilateral Filtering
27. How many types of video filters in OpenCV ?
- Color Conversion
- Morphology Gradients Canny
28. How can we Increase the Quality of an Image?
Ans. The image enhancement process is purely dependent on the application where we need to use the enhanced image and the type of degradation in the image. Few solutions from my basic knowledge
– If the image contains so many noises, apply filtering operations such as mean/median etc.
– If the image is looking too dark or too bright apply the histogram equalization process to improve the contrast.
– If the image is blurred apply sharpening operations in the image.
29. What Is Image Transform?
Ans. An image can be expanded in terms of a discrete set of basis arrays called basis images. Hence, these basis images can be generated by unitary matrices. An NxN image can be viewed as an N^2×1 vector. It provides a set of coordinates or basis vectors for vector space.
30. What Do You Mean By Zooming Of Digital Images?
Ans. Zooming may be viewed as oversampling. Zooming involves the creation of new pixel locations and the assignment of gray levels to those new locations.