Top 50 OpenCV Interview Questions and Answers for Beginners

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. It is widely used for image processing, object detection, face recognition and other computer vision applications. Here are the top 50 OpenCV interview questions and answers for beginners to help you prepare for your next job interview:

Basic OpenCV Questions

Q1 What is OpenCV?

OpenCV (Open Source Computer Vision Library) is an open source library for computer vision, machine learning and image processing. It provides a common infrastructure for applications related to computer vision and machine learning while accelerating them exploiting performance optimization for diverse hardware platforms.

Q2 What languages is OpenCV written in?

The OpenCV library is primarily written in C/C++. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android platforms.

Q3 What are some important features of OpenCV?

Some key features of OpenCV are:

• Over 500 algorithms for computer vision tasks
• Supports machine learning tools like neural networks, K-nearest neighbors etc.
• Real-time optimized computer vision algorithms
• Easy to integrate with multiple programming languages (C++, Python, Java etc.)
• Extensive cross-platform support

Q4: What are some areas where OpenCV is used?

OpenCV is commonly used for:

• Object detection
• Image segmentation
• Face detection and recognition
• Motion tracking
• Image stitching
• 3D model reconstruction
• Video tracking
• Image denoising

Q5: How do you read and write images in OpenCV using Python?

We can use the cv2.imread() method to load an image into OpenCV. The image can be read as BGR or grayscale. To write an image to disk, we can use cv2.imwrite().

For example:

img = cv2.imread('image.jpg', cv2.IMREAD_COLOR) cv2.imwrite('result.png', img)

OpenCV Matrix Questions

Q6: What is a Matrix in OpenCV?

The OpenCV Matrix is a specialized data structure optimized for image processing. It is the primary data structure used in OpenCV to represent images as arrays of pixels. The Matrix class contains methods for image processing operations.

Q7: How can you create a Matrix in OpenCV using Python?

We can use the cv2.Mat() method to create a matrix. For example:

# Create 3x3 matrix filled with zeros img = cv2.Mat(3, 3, cv2.CV_8UC1, 0)# Create from existing Numpy arraynparr = np.ones([3,3], np.uint8)img = cv2.Mat(nparr)

Q8: What is the difference between Mat and IplImage structures?

IplImage is the original image structure used in OpenCV C interface. Mat is a new C++ class that replaces IplImage. Key differences are:

• Mat stores matrix data more efficiently
• Mat provides better memory management
• Mat has optimized image processing methods

Q9: How do you convert between Mat objects and Numpy arrays?

We can directly convert between Numpy arrays and Mat using:

# Mat to Numpynparr = img.getNumpy() # Numpy to Matimg = cv2.Mat(nparr)

Q10: What are some common operations on Matrices in OpenCV?

Some common matrix operations in OpenCV are:

• Addition and subtraction using cv2.add() and cv2.subtract()
• Bitwise operations like AND, OR using cv2.bitwise_and(), etc.
• Matrix multiplication using cv2.gemm()
• Transpose and inversion using cv2.transpose(), cv2.invert() etc.
• Splitting and merging channels using cv2.split(), cv2.merge()

Image Processing

Q11: How do you access pixel values and image properties in OpenCV?

We can access individual pixel values using the img[row, col] notation. Image properties like height, width and number of channels can be obtained using img.shape, img.size and img.dtype respectively.

Q12: How can you convert an image from BGR to grayscale in OpenCV?

We can use cv2.cvtColor() and specify cv2.COLOR_BGR2GRAY flag. For example:

img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

Q13: How do you apply blurring and smoothing on images in OpenCV?

OpenCV provides various blurring techniques like averaging, Gaussian, median and bilateral filter. We can apply them using cv2.blur(), cv2.GaussianBlur(), cv2.medianBlur() and cv2.bilateralFilter() methods.

Q14: How can you detect edges in an image using OpenCV?

We can detect edges using the Canny edge detector with the cv2.Canny() function. It takes the image, minimum and maximum thresholds as input. For example:

edges = cv2.Canny(img, 100, 200)

Q15: What are some image thresholding techniques available in OpenCV?

Some common thresholding techniques in OpenCV are:

• Binary threshold using cv2.threshold()
• Adaptive threshold using cv2.adaptiveThreshold()
• Otsu’s binarization using cv2.threshold() with cv2.THRESH_OTSU
• Triangle thresholding using cv2.threshold() with cv2.THRESH_TRIANGLE

Object Detection and Tracking

Q16: How can you perform background subtraction in OpenCV?

Background subtraction separates foreground objects from the background in a video sequence. We can perform it using cv2.createBackgroundSubtractorMOG2(), which uses Gaussian Mixture Models.

For example:

fgmask = bgSub.apply(frame) 

Q17: What are some object detection techniques used in OpenCV?

Some common object detection techniques in OpenCV are:

• Template matching using cv2.matchTemplate()
• Object classifiers using cv2.CascadeClassifier()
• Deep neural network models like YOLO, SSD, Faster R-CNN

Q18: How can you implement motion tracking in OpenCV?

We can track motion using optical flow techniques like Lucas-Kanade, which can find sparse feature correspondences between frames using cv2.calcOpticalFlowPyrLK(). We can also track objects using correlation-based tracking with cv2.TrackerCSRT() class.

Q19: How do you perform face detection in OpenCV?

We can detect faces in images and videos using pre-trained classifiers like cv2.CascadeClassifier('haarcascade_frontalface_default.xml'). The detectMultiScale() method returns bounding box coordinates for detected faces.

Q20: How can you recognize and match images in OpenCV?

We can match image features using techniques like template matching with cv2.matchTemplate(), keypoint matching using SIFT, SURF etc. and compare matching vectors. We can also match and recognize objects using deep learning models like Siamese networks.

Wrapping Up

• Know basics of OpenCV library, its features and applications
• Be thorough with core concepts like Mat object, image I/O, pixel access
• Study essential image processing techniques like thresholding, blurring, edge detection
• Understand object detection and tracking workflows and algorithms
• Practice OpenCV code snippets in Python for image processing and computer vision tasks