OpenCV Interview Questions and Answers

What is OpenCV?
  • OpenCV (Open Source Computer Vision Library) is an open-source software library that contains over 2,500 optimized algorithms for a wide range of computer vision tasks, such as image processing, object detection, machine learning, etc.
What is the difference between OpenCV and PIL?
  • OpenCV is a complete computer vision library, while PIL (Python Imaging Library) is a Python-specific library for image processing. OpenCV supports a wide range of vision-based operations, while PIL is limited to basic operations.
What are the advantages of using OpenCV?
  • OpenCV is open-source and free to use. It provides various image processing functionalities, including real-time computer vision, image transformations, feature extraction, and machine learning algorithms.
What are the main applications of OpenCV?
  • OpenCV is used in applications such as face detection, object tracking, image recognition, augmented reality, robotics, and medical image analysis.
How does OpenCV handle image files?
  • OpenCV uses the `imread()` function to read image files and store them as matrices. The `imshow()` function is used to display images, and `imwrite()` is used to save images to a file.
What is the `cv2.imread()` function used for?
  • The `cv2.imread()` function is used to read an image from a file and load it into memory. It returns an image as a NumPy array.
What are the different color formats supported by OpenCV?
  • OpenCV supports several color formats, including:
    • RGB (Red, Green, Blue)
    • GRAY (Grayscale)
    • HSV (Hue, Saturation, Value)
    • LAB (Lightness, A, B channels)
    • YUV (Luminance, Chrominance)
What is the use of the `cv2.cvtColor()` function in OpenCV?
  • The `cv2.cvtColor()` function is used to convert an image from one color space to another, such as converting an image from BGR to RGB, or from RGB to Grayscale.
What are contours in OpenCV?
  • Contours are the boundaries of objects in an image. OpenCV provides functions like `cv2.findContours()` and `cv2.drawContours()` to detect and draw contours in images.
How does image thresholding work in OpenCV?
  • Thresholding is used to create a binary image by turning all pixel values above a certain threshold to one value (typically 255), and all pixel values below the threshold to another value (typically 0). OpenCV provides `cv2.threshold()` to perform this operation.
What is the difference between `cv2.findContours()` and `cv2.drawContours()`?
  • `cv2.findContours()` is used to find the contours in a binary image, whereas `cv2.drawContours()` is used to draw the contours on the image.
What is Gaussian blur in OpenCV?
  • Gaussian blur is a filter used to smooth an image and reduce noise. In OpenCV, it can be applied using the `cv2.GaussianBlur()` function.
What is the purpose of `cv2.dilate()` and `cv2.erode()`?
  • `cv2.dilate()` expands the white regions in a binary image, while `cv2.erode()` shrinks the white regions. Both operations are often used in morphological transformations to remove noise or isolate features.
What is a histogram, and how can it be calculated using OpenCV?
  • A histogram represents the distribution of pixel intensities in an image. In OpenCV, histograms can be calculated using `cv2.calcHist()` function and visualized using `cv2.histCalc()`.
What are some of the commonly used image processing techniques in OpenCV?
  • Common image processing techniques include:
    • Grayscale conversion
    • Thresholding
    • Edge detection (Canny, Sobel)
    • Morphological transformations
    • Gaussian blurring
    • Histogram equalization
    • Image scaling and resizing
What is the difference between `cv2.imshow()` and `cv2.waitKey()`?
  • `cv2.imshow()` is used to display an image, while `cv2.waitKey()` is used to wait for a specific key event to occur. Without `cv2.waitKey()`, the image window will not respond to user input.
What are feature detection and feature matching in OpenCV?
  • Feature detection is used to detect specific points or regions in an image (such as edges, corners, or objects). Feature matching is used to find corresponding points between images, which is useful in object recognition and image stitching.
What is the purpose of `cv2.SIFT()`?
  • SIFT (Scale-Invariant Feature Transform) is an algorithm used to detect and describe local features in images. It is invariant to scale, rotation, and affine transformations.
What is the `cv2.CascadeClassifier()` function used for?
  • The `cv2.CascadeClassifier()` function is used for object detection, such as face detection, by applying a pre-trained classifier to an image.
What is the Canny edge detection algorithm in OpenCV?
  • The Canny edge detection algorithm is used to detect edges in an image by applying various techniques like gradient calculation, non-maximum suppression, and edge tracing by hysteresis.
What is the difference between `cv2.HoughCircles()` and `cv2.HoughLines()`?
  • `cv2.HoughCircles()` is used to detect circles in an image, whereas `cv2.HoughLines()` is used to detect lines.
What are the different types of blurring techniques in OpenCV?
  • Common blurring techniques in OpenCV include:
    • Averaging
    • Gaussian blur
    • Median blur
    • Bilateral filter
How can we perform object tracking in OpenCV?
  • Object tracking can be performed using various algorithms such as:
    • Meanshift
    • KLT (Kanade-Lucas-Tomasi)
    • HOG (Histogram of Oriented Gradients)
    • Deep learning-based trackers (e.g., GOTURN, DeepSORT)
What is the purpose of `cv2.merge()` and `cv2.split()` in OpenCV?
  • `cv2.split()` is used to split an image into its individual channels (e.g., R, G, B), while `cv2.merge()` is used to merge the individual channels into a single image.
What is the significance of the `cv2.remap()` function?
  • The `cv2.remap()` function is used to map the pixel values of an image according to a specified transformation, such as changing the perspective or applying non-linear distortions.
What is image segmentation in OpenCV?
  • Image segmentation is the process of partitioning an image into different regions based on pixel characteristics. This is commonly used in object detection, face recognition, and medical image analysis.
What is the use of `cv2.findContours()` in OpenCV?
  • The `cv2.findContours()` function is used to detect contours in an image, which are useful in shape analysis and object detection.
How do you read and display video in OpenCV?
  • You can use `cv2.VideoCapture()` to read a video, and `cv2.imshow()` to display each frame of the video. Use `cv2.waitKey()` to pause for a short period between frames.
What is the purpose of `cv2.getPerspectiveTransform()`?
  • The `cv2.getPerspectiveTransform()` function is used to compute the perspective transformation matrix, which allows you to apply perspective changes to an image (e.g., rotating or skewing).
What is Optical Flow in OpenCV?
  • Optical flow is a method used to track the movement of objects between consecutive frames of a video. OpenCV provides functions like `cv2.calcOpticalFlowFarneback()` and `cv2.calcOpticalFlowPyrLK()` for optical flow computation.
What is the purpose of the `cv2.VideoWriter()` function?
  • The `cv2.VideoWriter()` function is used to save a video file by writing frames captured from a video stream or generated through processing.
How can we resize an image in OpenCV?
  • We can resize an image using the `cv2.resize()` function. This function allows you to specify the desired width and height or scale factor.
What is the `cv2.filter2D()` function used for?
  • The `cv2.filter2D()` function is used to apply a custom kernel to an image, which is useful for tasks such as image smoothing or sharpening.
How can we perform edge detection using OpenCV?
  • Edge detection can be performed using techniques like the Canny edge detector (`cv2.Canny()`), Sobel operator (`cv2.Sobel()`), or Laplacian operator (`cv2.Laplacian()`).
What is `cv2.findContours()` used for?
  • It is used to find the boundaries of objects in an image (contours), which is useful for object detection, shape analysis, and segmentation.