25 Common OpenCV Interview Questions You Need to Know

Preparing for an OpenCV interview can be daunting, given the vast array of topics it covers. To help you ace your next interview, we've compiled a list of 25 essential OpenCV questions and answers. Whether you're a beginner or an experienced developer, these questions will give you the edge you need.

What are OpenCV interview questions?

OpenCV interview questions typically focus on assessing a candidate's knowledge of computer vision concepts, image processing techniques, and proficiency with the OpenCV library. These questions may range from basic definitions and functions to more complex algorithm implementations and real-world application scenarios.

Why do interviewers ask OpenCV questions?

The main purpose of OpenCV interview questions is to evaluate a candidate's understanding of computer vision principles and their ability to apply OpenCV tools effectively. Interviewers ask these questions to gauge both theoretical knowledge and practical skills, ensuring the candidate can handle real-world image processing tasks.

25 OpenCV interview questions

1. What is OpenCV and what are its main features?
2. How do you read and display an image using OpenCV?
3. Write a Python code snippet to convert a color image to grayscale using OpenCV.
4. Explain the difference between cv2.imread() and cv2.imreadmulti().
5. How can you resize an image in OpenCV? Provide a code example.
6. Write a function to detect edges in an image using the Canny edge detection method.
7. What is the purpose of the cv2.VideoCapture() function? Provide a code example to capture video from a webcam.
8. How do you draw shapes (like circles and rectangles) on an image using OpenCV? Provide a code example.
9. Explain how to perform image thresholding in OpenCV. Write a code snippet for binary thresholding.
10. What is the purpose of the cv2.findContours() function? Write a code example to find and draw contours on an image.
11. How can you apply Gaussian blur to an image? Provide a code example.
12. Write a function to perform image rotation using OpenCV.
13. Explain the concept of image pyramids in OpenCV. How do you create a Gaussian pyramid?
14. Write a code snippet to perform template matching in OpenCV.
15. How do you perform face detection using OpenCV? Provide a code example using Haar cascades.
16. What are the differences between cv2.SIFT, cv2.SURF, and cv2.ORB? Provide a code example for feature detection using ORB.
17. Write a function to apply a perspective transformation to an image.
18. How can you use OpenCV to track an object in a video? Provide a code example using color tracking.
19. Explain the concept of homography in OpenCV. How do you compute it?
20. Write a code snippet to perform image stitching using OpenCV.
21. How do you save an image to disk using OpenCV? Provide a code example.
22. Explain the use of cv2.split() and cv2.merge() functions. Provide a code example.
23. Write a function to perform histogram equalization on an image.
24. How can you detect and recognize text in images using OpenCV? Provide a brief overview of the process.
25. Explain the concept of optical flow in OpenCV. Write a code example to calculate dense optical flow using the Farneback method.

1. What is OpenCV and what are its main features?

Why you might get asked this: Understanding what OpenCV is and its main features is fundamental for roles involving computer vision and image processing, as it demonstrates your grasp of essential tools and concepts in the field.

How to answer:

• Define OpenCV as an open-source computer vision and machine learning software library.
• Mention its primary features, such as image processing, video capture, and analysis.
• Highlight its cross-platform support and extensive collection of algorithms.

Example answer:

"OpenCV is an open-source computer vision and machine learning software library that provides a comprehensive set of tools for image processing, video capture, and analysis. Its main features include cross-platform support, a vast collection of algorithms, and real-time operation capabilities."

2. How do you read and display an image using OpenCV?

Why you might get asked this: Interviewers often ask how to read and display an image using OpenCV to assess your practical skills in handling basic image processing tasks, which are essential for roles such as computer vision engineers or software developers working with visual data.

How to answer:

• Explain the use of the cv2.imread() function to read an image from a file.
• Mention the cv2.imshow() function to display the image in a window.
• Highlight the importance of using cv2.waitKey() to keep the window open until a key is pressed.

Example answer:

"To read and display an image using OpenCV, you can use the cv2.imread() function to load the image and the cv2.imshow() function to display it. Don't forget to use cv2.waitKey() to keep the window open until a key is pressed."

3. Write a Python code snippet to convert a color image to grayscale using OpenCV.

Why you might get asked this: Interviewers may ask you to write a Python code snippet to convert a color image to grayscale using OpenCV to evaluate your practical coding skills and understanding of basic image processing techniques, which are crucial for roles such as computer vision engineers or software developers working with visual data.

How to answer:

• Mention the use of the cv2.imread() function to read the color image.
• Explain the use of the cv2.cvtColor() function to convert the image to grayscale.
• Highlight the importance of displaying or saving the grayscale image using cv2.imshow() or cv2.imwrite().

Example answer:

"To convert a color image to grayscale using OpenCV, you can first read the image using cv2.imread() and then convert it to grayscale with cv2.cvtColor(). Finally, display or save the grayscale image using cv2.imshow() or cv2.imwrite()."

4. Explain the difference between `cv2.imread()` and `cv2.imreadmulti()`.

Why you might get asked this: Interviewers may ask you to explain the difference between cv2.imread() and cv2.imreadmulti() to assess your understanding of image handling functions in OpenCV, which is crucial for roles that involve processing multiple images, such as a computer vision engineer.

How to answer:

• Define cv2.imread() as a function to read a single image from a file.
• Explain that cv2.imreadmulti() is used to read multiple images from a multi-page file.
• Highlight the typical use cases for each function, such as single image processing versus handling image stacks.

Example answer:

"The cv2.imread() function is used to read a single image from a file, while cv2.imreadmulti() is designed to read multiple images from a multi-page file. Typically, cv2.imread() is used for single image processing, whereas cv2.imreadmulti() is useful for handling image stacks."

5. How can you resize an image in OpenCV? Provide a code example.

Why you might get asked this: Interviewers may ask how to resize an image in OpenCV to evaluate your practical skills in image manipulation, which are essential for roles that involve image processing tasks, such as a computer vision engineer or software developer working with visual data.

How to answer:

• Mention the use of the cv2.resize() function to resize the image.
• Explain the importance of specifying the desired dimensions or scale factor.
• Highlight the need to display or save the resized image using cv2.imshow() or cv2.imwrite().

Example answer:

"To resize an image in OpenCV, you can use the cv2.resize() function, specifying the desired dimensions or scale factor. Here's a code example: resized_image = cv2.resize(image, (width, height))."

6. Write a function to detect edges in an image using the Canny edge detection method.

Why you might get asked this: Interviewers may ask you to write a function to detect edges in an image using the Canny edge detection method to evaluate your practical skills in edge detection and image analysis, which are crucial for roles such as computer vision engineers or software developers working with visual data.

How to answer:

• Mention the use of the cv2.Canny() function to detect edges.
• Explain the importance of specifying the threshold values for edge detection.
• Highlight the need to display or save the edge-detected image using cv2.imshow() or cv2.imwrite().

Example answer:

"To detect edges in an image using the Canny edge detection method, you can use the cv2.Canny() function, specifying the lower and upper threshold values. Here's a code example: edges = cv2.Canny(image, 100, 200)."

7. What is the purpose of the `cv2.VideoCapture()` function? Provide a code example to capture video from a webcam.

Why you might get asked this: Interviewers may ask about the purpose of the cv2.VideoCapture() function and how to capture video from a webcam to assess your practical skills in video processing, which are essential for roles such as computer vision engineers or software developers working with real-time visual data.

How to answer:

• Mention that cv2.VideoCapture() is used to capture video from a camera or video file.
• Explain the importance of checking if the video capture was successful.
• Highlight the need to release the video capture object and close all windows after capturing the video.

Example answer:

"The cv2.VideoCapture() function is used to capture video from a camera or video file. Here's a code example to capture video from a webcam: cap = cv2.VideoCapture(0)."

8. How do you draw shapes (like circles and rectangles) on an image using OpenCV? Provide a code example.

Why you might get asked this: Interviewers may ask how to draw shapes on an image using OpenCV to evaluate your practical skills in image annotation and manipulation, which are essential for roles that involve visual data processing, such as a computer vision engineer or software developer working with graphical applications.

How to answer:

• Mention the use of functions like cv2.circle() and cv2.rectangle() to draw shapes.
• Explain the importance of specifying parameters such as coordinates, color, and thickness.
• Highlight the need to display or save the annotated image using cv2.imshow() or cv2.imwrite().

Example answer:

"To draw shapes on an image using OpenCV, you can use functions like cv2.circle() and cv2.rectangle(). For example, cv2.circle(image, (x, y), radius, color, thickness) draws a circle, and cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness) draws a rectangle."

9. Explain how to perform image thresholding in OpenCV. Write a code snippet for binary thresholding.

Why you might get asked this: Interviewers may ask you to explain how to perform image thresholding in OpenCV and write a code snippet for binary thresholding to evaluate your practical skills in image segmentation, which are crucial for roles that involve processing and analyzing visual data, such as a computer vision engineer.

How to answer:

• Mention the use of the cv2.threshold() function for image thresholding.
• Explain the importance of specifying the threshold value and maximum value.
• Highlight the need to display or save the thresholded image using cv2.imshow() or cv2.imwrite().

Example answer:

"To perform image thresholding in OpenCV, you can use the cv2.threshold() function, specifying the threshold value and maximum value. Here's a code snippet for binary thresholding: _, binary_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)."

10. What is the purpose of the `cv2.findContours()` function? Write a code example to find and draw contours on an image.

Why you might get asked this: Interviewers may ask about the purpose of the cv2.findContours() function and request a code example to find and draw contours on an image to evaluate your practical skills in shape detection and image analysis, which are crucial for roles such as computer vision engineers or software developers working with visual data.

How to answer:

• Mention that cv2.findContours() is used to detect the contours of objects in an image.
• Explain the importance of specifying the retrieval mode and approximation method.
• Highlight the need to draw the detected contours using cv2.drawContours() and display or save the result.

Example answer:

"The cv2.findContours() function is used to detect the contours of objects in an image, which is essential for shape analysis and object detection. Here's a code example to find and draw contours on an image: contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE); cv2.drawContours(image, contours, -1, (0, 255, 0), 2)."

11. How can you apply Gaussian blur to an image? Provide a code example.

Why you might get asked this: Interviewers may ask how to apply Gaussian blur to an image to evaluate your practical skills in image smoothing and noise reduction, which are essential for roles that involve image preprocessing, such as a computer vision engineer or software developer working with visual data.

How to answer:

• Mention the use of the cv2.GaussianBlur() function to apply Gaussian blur.
• Explain the importance of specifying the kernel size and standard deviation.
• Highlight the need to display or save the blurred image using cv2.imshow() or cv2.imwrite().

Example answer:

"To apply Gaussian blur to an image, you can use the cv2.GaussianBlur() function, specifying the kernel size and standard deviation. Here's a code example: blurred_image = cv2.GaussianBlur(image, (5, 5), 0)."

12. Write a function to perform image rotation using OpenCV.

Why you might get asked this: Interviewers may ask you to write a function to perform image rotation using OpenCV to evaluate your practical skills in geometric transformations, which are essential for roles that involve image manipulation, such as a computer vision engineer.

How to answer:

• Mention the use of the cv2.getRotationMatrix2D() function to create the rotation matrix.
• Explain the importance of specifying the center of rotation, angle, and scale.
• Highlight the need to apply the rotation using the cv2.warpAffine() function and display or save the rotated image.

Example answer:

"To perform image rotation using OpenCV, you can use the cv2.getRotationMatrix2D() function to create the rotation matrix and then apply it with cv2.warpAffine(). Here's a code example: rotated_image = cv2.warpAffine(image, cv2.getRotationMatrix2D((cols/2, rows/2), angle, 1), (cols, rows))."

13. Explain the concept of image pyramids in OpenCV. How do you create a Gaussian pyramid?

Why you might get asked this: Interviewers may ask you to explain the concept of image pyramids in OpenCV and how to create a Gaussian pyramid to evaluate your understanding of multi-scale image processing techniques, which are crucial for roles that involve advanced image analysis, such as a computer vision engineer.

How to answer:

• Define image pyramids as a multi-scale representation of an image.
• Explain that Gaussian pyramids are created by repeatedly applying Gaussian blur and downsampling the image.
• Mention the use of the cv2.pyrDown() function to create a Gaussian pyramid in OpenCV.

Example answer:

"Image pyramids are a multi-scale representation of an image, where the image is repeatedly smoothed and subsampled to create a series of progressively smaller images. To create a Gaussian pyramid in OpenCV, you can use the cv2.pyrDown() function to downsample the image after applying Gaussian blur."

14. Write a code snippet to perform template matching in OpenCV.

Why you might get asked this: Interviewers may ask you to write a code snippet to perform template matching in OpenCV to evaluate your practical skills in object detection and image analysis, which are crucial for roles such as computer vision engineers or software developers working with visual data.

How to answer:

• Mention the use of the cv2.matchTemplate() function to perform template matching.
• Explain the importance of specifying the matching method.
• Highlight the need to display the result using cv2.imshow() or save it with cv2.imwrite().

Example answer:

"To perform template matching in OpenCV, you can use the cv2.matchTemplate() function, specifying the matching method. Here's a code snippet: result = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)."

15. How do you perform face detection using OpenCV? Provide a code example using Haar cascades.

Why you might get asked this: Interviewers may ask how to perform face detection using OpenCV and request a code example using Haar cascades to evaluate your practical skills in object detection and image analysis, which are crucial for roles such as computer vision engineers or software developers working with visual data.

How to answer:

• Mention the use of the Haar cascade classifier for face detection.
• Explain the importance of loading the pre-trained Haar cascade XML file.
• Highlight the need to use the detectMultiScale() function to detect faces and draw rectangles around them.

Example answer:

"To perform face detection using OpenCV, you can use the Haar cascade classifier. Here's a code example: face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml'); faces = face_cascade.detectMultiScale(gray, 1.3, 5); for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)."

16. What are the differences between `cv2.SIFT`, `cv2.SURF`, and `cv2.ORB`? Provide a code example for feature detection using ORB.

Why you might get asked this: Interviewers may ask about the differences between cv2.SIFT, cv2.SURF, and cv2.ORB and request a code example for feature detection using ORB to evaluate your understanding of keypoint detection and description techniques, which are crucial for roles such as computer vision engineers or software developers working with image analysis.

How to answer:

• Define cv2.SIFT as a patented algorithm for detecting and describing local features in images.
• Explain that cv2.SURF is a faster alternative to SIFT but also patented.
• Mention that cv2.ORB is an open-source, efficient alternative to SIFT and SURF.

Example answer:

"The main differences between cv2.SIFT, cv2.SURF, and cv2.ORB are their speed, patent status, and efficiency. cv2.ORB is an open-source, efficient alternative to the patented cv2.SIFT and cv2.SURF algorithms."

17. Write a function to apply a perspective transformation to an image.

Why you might get asked this: Interviewers may ask you to write a function to apply a perspective transformation to an image to evaluate your practical skills in geometric transformations, which are crucial for roles that involve advanced image manipulation, such as a computer vision engineer.

How to answer:

• Mention the use of the cv2.getPerspectiveTransform() function to obtain the transformation matrix.
• Explain the importance of specifying the source and destination points for the transformation.
• Highlight the need to apply the transformation using the cv2.warpPerspective() function and display or save the transformed image.

Example answer:

"To apply a perspective transformation to an image, you can use the cv2.getPerspectiveTransform() function to obtain the transformation matrix and then apply it with cv2.warpPerspective(). Here's a code example: transformed_image = cv2.warpPerspective(image, cv2.getPerspectiveTransform(src_points, dst_points), (width, height))."

18. How can you use OpenCV to track an object in a video? Provide a code example using color tracking.

Why you might get asked this: Interviewers may ask how to use OpenCV to track an object in a video using color tracking to evaluate your practical skills in real-time object tracking, which are crucial for roles that involve video analysis and surveillance, such as a computer vision engineer.

How to answer:

• Mention the use of color space conversion to isolate the object's color.
• Explain the importance of creating a mask to filter out the object's color.
• Highlight the need to use contour detection to track the object's position.

Example answer:

"To track an object in a video using OpenCV, you can convert the video frames to the HSV color space and create a mask to isolate the object's color. Here's a code example: hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV); mask = cv2.inRange(hsv, lower_color, upper_color); contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE); for contour in contours: (x, y, w, h) = cv2.boundingRect(contour); cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)."

19. Explain the concept of homography in OpenCV. How do you compute it?

Why you might get asked this: Interviewers may ask you to explain the concept of homography in OpenCV and how to compute it to evaluate your understanding of geometric transformations and their applications, which are crucial for roles that involve advanced image processing, such as a computer vision engineer.

How to answer:

• Define homography as a transformation that maps points from one plane to another.
• Explain that it is computed using corresponding points between two images.
• Mention the use of the cv2.findHomography() function to calculate the homography matrix.

Example answer:

"Homography is a transformation that maps points from one plane to another, often used for image stitching and perspective correction. To compute it, you can use the cv2.findHomography() function with corresponding points from two images."

20. Write a code snippet to perform image stitching using OpenCV.

Why you might get asked this: Interviewers may ask you to write a code snippet to perform image stitching using OpenCV to evaluate your practical skills in combining multiple images into a single panoramic image, which is crucial for roles that involve advanced image processing, such as a computer vision engineer.

How to answer:

• Mention the use of the cv2.Stitcher_create() function to create a stitcher object.
• Explain the importance of passing the list of images to the stitch() method.
• Highlight the need to check the status and display or save the stitched image.

Example answer:

"To perform image stitching using OpenCV, you can use the cv2.Stitcher_create() function to create a stitcher object and then pass the list of images to the stitch() method. Here's a code snippet: stitcher = cv2.Stitcher_create(); status, stitched = stitcher.stitch([image1, image2, image3])."

21. How do you save an image to disk using OpenCV? Provide a code example.

Why you might get asked this: Interviewers may ask how to save an image to disk using OpenCV to evaluate your practical skills in file handling and image processing, which are essential for roles that involve managing visual data, such as a computer vision engineer.

How to answer:

• Mention the use of the cv2.imwrite() function to save the image.
• Explain the importance of specifying the file path and image format.
• Highlight the need to check if the image was saved successfully.

Example answer:

"To save an image to disk using OpenCV, you can use the cv2.imwrite() function, specifying the file path and image format. Here's a code example: cv2.imwrite('output.jpg', image)."

22. Explain the use of `cv2.split()` and `cv2.merge()` functions. Provide a code example.

Why you might get asked this: Interviewers may ask you to explain the use of cv2.split() and cv2.merge() functions and provide a code example to evaluate your practical skills in channel manipulation, which are crucial for roles that involve advanced image processing, such as a computer vision engineer.

How to answer:

• Mention that cv2.split() is used to split an image into its individual color channels.
• Explain that cv2.merge() is used to combine separate color channels back into a single image.
• Highlight the importance of manipulating individual channels for advanced image processing tasks.

Example answer:

"The cv2.split() function is used to split an image into its individual color channels, which allows for separate manipulation of each channel. The cv2.merge() function is then used to combine these channels back into a single image."

Here's a code example: b, g, r = cv2.split(image); merged_image = cv2.merge([b, g, r]).

23. Write a function to perform histogram equalization on an image.

Why you might get asked this: Interviewers may ask you to write a function to perform histogram equalization on an image to evaluate your practical skills in image enhancement techniques, which are crucial for roles that involve improving image contrast, such as a computer vision engineer.

How to answer:

• Mention the use of the cv2.equalizeHist() function to perform histogram equalization.
• Explain the importance of converting the image to grayscale before applying histogram equalization.
• Highlight the need to display or save the equalized image using cv2.imshow() or cv2.imwrite().

Example answer:

"To perform histogram equalization on an image, you can use the cv2.equalizeHist() function after converting the image to grayscale. Here's a code example: equalized_image = cv2.equalizeHist(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY))."

24. How can you detect and recognize text in images using OpenCV? Provide a brief overview of the process.

Why you might get asked this: Interviewers may ask how to detect and recognize text in images using OpenCV to evaluate your practical skills in optical character recognition (OCR), which are crucial for roles that involve extracting textual information from images, such as a computer vision engineer.

How to answer:

• Mention the use of the cv2.imread() function to read the image.
• Explain the importance of preprocessing the image using techniques like thresholding or blurring.
• Highlight the use of Tesseract OCR to detect and recognize text in the preprocessed image.

Example answer:

"To detect and recognize text in images using OpenCV, you can first preprocess the image using techniques like thresholding or blurring. Then, use Tesseract OCR to extract the text from the preprocessed image."

25. Explain the concept of optical flow in OpenCV. Write a code example to calculate dense optical flow using the Farneback method.

Why you might get asked this: Interviewers may ask you to explain the concept of optical flow in OpenCV and write a code example to calculate dense optical flow using the Farneback method to evaluate your understanding of motion detection and analysis, which are crucial for roles that involve video processing and computer vision, such as a computer vision engineer.

How to answer:

• Define optical flow as the pattern of apparent motion of objects in a visual scene.
• Explain that it is used to estimate motion between two consecutive frames.
• Mention the use of the cv2.calcOpticalFlowFarneback() function to calculate dense optical flow.

Example answer:

"Optical flow is the pattern of apparent motion of objects in a visual scene, used to estimate motion between two consecutive frames. To calculate dense optical flow using the Farneback method, you can use the cv2.calcOpticalFlowFarneback() function."

Here's a code example: flow = cv2.calcOpticalFlowFarneback(prev_gray, next_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0).

Tips to prepare for OpenCV questions

• Understand Core Functions: Familiarize yourself with essential OpenCV functions like cv2.imread(), cv2.imshow(), and cv2.imwrite() for basic image handling tasks.
• Practice Image Transformations: Gain hands-on experience with image transformations such as resizing, rotating, and applying filters using functions like cv2.resize(), cv2.getRotationMatrix2D(), and cv2.GaussianBlur().
• Master Edge Detection: Learn to implement edge detection techniques, particularly the Canny edge detection method using cv2.Canny(), and understand how to fine-tune threshold values.
• Explore Object Detection: Get comfortable with object detection methods, including Haar cascades for face detection and contour detection using cv2.findContours() and cv2.drawContours().
• Experiment with Feature Detection: Experiment with keypoint detection and description algorithms like ORB, SIFT, and SURF, and understand their differences and use cases.

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