OpenCV Interview Questions and Answers (2025)

1. What is OpenCV?

Answer:
OpenCV (Open Source Computer Vision Library) is an open-source software library that provides a collection of algorithms and tools for computer vision, image processing, and machine learning tasks. It was initially developed by Intel and later open-sourced. OpenCV supports a wide range of functions such as image manipulation, object detection, motion analysis, camera calibration, and more. It can be used in Python, C++, and Java and is widely utilized in industries like robotics, healthcare, and surveillance.

Key Points:

·         Open-source library for computer vision tasks

·         Supports Python, C++, and Java

·         Used in image processing, object detection, and machine learning

·         OpenCV is platform-independent and can run on multiple operating systems

2. What are the basic functionalities of OpenCV?

Answer:
OpenCV offers a comprehensive set of functionalities for computer vision and image processing, including:

·         Image Processing: Manipulating images (e.g., resizing, cropping, blurring, thresholding).

·         Feature Detection and Matching: Detecting key points, edges, and features (e.g., SIFT, SURF, ORB).

·         Object Detection: Detecting objects in images (e.g., face detection, car detection).

·         Machine Learning: Integrating machine learning algorithms like k-means clustering, decision trees, and SVM for vision tasks.

·         Video Processing: Reading, writing, and processing video frames.

·         Camera Calibration: Correcting lens distortion and estimating camera parameters.

·         Tracking: Tracking objects across frames in a video using algorithms like KLT or CAMShift.

Key Points:

·         Image manipulation and feature detection

·         Object detection and machine learning integration

·         Video and camera calibration

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

Answer:
In OpenCV, you can use the cv2.imread() function to read an image and cv2.imshow() to display it.

Example:

import cv2

# Read an image

image = cv2.imread('image.jpg')

# Display the image

cv2.imshow('Image', image)

# Wait for a key event and close the window

cv2.waitKey(0)

cv2.destroyAllWindows()

In this example, cv2.imread() reads the image, and cv2.imshow() displays it. The cv2.waitKey() function is used to wait for a user keypress before closing the window.

Key Points:

·         Use cv2.imread() to read an image

·         Use cv2.imshow() to display an image

·         cv2.waitKey(0) waits for a key event

4. What is the difference between cv2.imread() and cv2.VideoCapture()?

Answer:
The key difference between cv2.imread() and cv2.VideoCapture() is that:

·         cv2.imread() is used to read and load a single image from a file.

·         cv2.VideoCapture() is used to capture video from a file or camera. This function is typically used for real-time video processing.

Example:

·         cv2.imread() loads an image:

image = cv2.imread('image.jpg')

·         cv2.VideoCapture() captures video from a camera or video file:

cap = cv2.VideoCapture(0)  # Capture from the default camera

Key Points:

·         cv2.imread() for static image loading

·         cv2.VideoCapture() for capturing video or real-time footage

5. How do you perform edge detection in OpenCV?

Answer:
Edge detection can be performed using the Canny edge detector, which is available in OpenCV as cv2.Canny(). It detects the boundaries of objects in an image.

Example:

import cv2

# Read the image

image = cv2.imread('image.jpg', 0)  # Read in grayscale

# Apply Canny edge detector

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

# Display the results

cv2.imshow('Edges', edges)

cv2.waitKey(0)

cv2.destroyAllWindows()

In this example, cv2.Canny() detects edges in the grayscale version of the input image.

Key Points:

·         Canny edge detector used for edge detection

·         cv2.Canny() accepts two threshold values

·         Grayscale image is preferred for edge detection

6. How can you perform image resizing in OpenCV?

Answer:
To resize an image in OpenCV, you can use the cv2.resize() function. This function allows you to specify the new dimensions or the scaling factor.

Example:

import cv2

# Read the image

image = cv2.imread('image.jpg')

# Resize the image

resized_image = cv2.resize(image, (200, 300))  # Resize to 200x300 pixels

# Display the resized image

cv2.imshow('Resized Image', resized_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

Alternatively, you can scale the image by a factor:

resized_image = cv2.resize(image, None, fx=0.5, fy=0.5)  # Scale by 50%

Key Points:

·         cv2.resize() resizes images

·         You can specify the new width and height or scale factors (fx, fy)

7. What are contours in OpenCV, and how can you find them?

Answer:
Contours are curves that join continuous points along a boundary that share the same color or intensity. In OpenCV, contours are useful for shape analysis, object detection, and recognition.

To find contours, you can use the cv2.findContours() function. The function returns the contours and a hierarchy of the contours.

Example:

import cv2

# Read the image and convert to grayscale

image = cv2.imread('image.jpg')

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply thresholding

_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# Find contours

contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Draw contours

cv2.drawContours(image, contours, -1, (0, 255, 0), 2)

# Display the image with contours

cv2.imshow('Contours', image)

cv2.waitKey(0)

cv2.destroyAllWindows()

Key Points:

·         cv2.findContours() identifies contours in a binary image

·         Contours can be used for object detection, shape analysis, and more

8. How do you perform face detection in OpenCV?

Answer:
Face detection in OpenCV is commonly performed using the Haar Cascade Classifier, which is trained on a dataset of positive and negative images to recognize faces.

You can use the cv2.CascadeClassifier() method to load the trained classifier and detect faces.

Example:

import cv2

# Load the pre-trained Haar Cascade face detector

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Read the image

image = cv2.imread('image.jpg')

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces

faces = face_cascade.detectMultiScale(gray, 1.3, 5)

# Draw rectangles around detected faces

for (x, y, w, h) in faces:

cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)

# Display the image with detected faces

cv2.imshow('Faces', image)

cv2.waitKey(0)

cv2.destroyAllWindows()

Key Points:

·         cv2.CascadeClassifier() is used for face detection

·         Haar cascade classifiers are pre-trained for various objects, including faces

9. How can you apply a filter to an image in OpenCV?

Answer:
In OpenCV, you can apply various filters to an image using functions like cv2.GaussianBlur() for blurring, cv2.filter2D() for custom filters, or cv2.medianBlur() for median filtering.

Example (Gaussian Blur):

import cv2

# Read the image

image = cv2.imread('image.jpg')

# Apply Gaussian blur

blurred_image = cv2.GaussianBlur(image, (5, 5), 0)

# Display the blurred image

cv2.imshow('Blurred Image', blurred_image)

cv2.waitKey(0)

cv2.destroyAllWindows()

Key Points:

·         cv2.GaussianBlur() applies Gaussian blur

·         Filters like median or custom filters can be applied using cv2.filter2D() or cv2.medianBlur()

10. How do you save an image in OpenCV?

Answer:
To save an image in OpenCV, you can use the cv2.imwrite() function. This function saves an image to a specified path with the desired file extension.

Example:

import

cv2

Read the image

image = cv2.imread('image.jpg')

Save the image

cv2.imwrite('saved_image.jpg', image)

**Key Points:**

- `cv2.imwrite()` saves the image to the specified path

- Ensure the correct file format (e.g., PNG, JPEG)

Top Interview Questions and Answers on OpenCV ( 2025 )

Some common interview questions regarding OpenCV, along with their answers:

 Basic Questions

1. What is OpenCV?

   - Answer: OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning library. It contains more than 2500 optimized algorithms for various tasks such as detecting and recognizing faces, identifying objects, classifying human actions in videos, and tracking camera movements.

2. How do you install OpenCV?

   - Answer: OpenCV can be installed using package managers like `pip` for Python:

  ```bash

  pip install opencv-python

  ```

  For C++, OpenCV can be built from source or an installer can be used depending on the operating system.

3. What are the primary data structures in OpenCV?

   - Answer: The primary data structures in OpenCV are:

  - `cv::Mat`: Represents images as matrices. It contains pixel values and their types.

  - `cv::Point`: Used to represent 2D points.

  - `cv::Rect`: Represents a rectangle with a starting point and size.

 Intermediate Questions

4. How do you read and display an image in OpenCV?

   - Answer:

  You can read an image using `cv::imread()` and display it using `cv::imshow()`. Here’s an example in Python:

  ```python

  import cv2

  # Read the image

  img = cv2.imread('image.jpg')

  # Display the image

     cv2.imshow('Image', img)

  cv2.waitKey(0)

     cv2.destroyAllWindows()

  ```

5. What is the difference between `cv::imread()` and `cv::imwrite()`?

   - Answer: `cv::imread()` is used to load an image from a file into memory (as a matrix), while `cv::imwrite()` is used to save a matrix (image) to a file on disk.

6. How can you convert an image from BGR to GRAY in OpenCV?

   - Answer:

  You can use `cv::cvtColor()` to convert an image from BGR color space to grayscale:

  ```python

  gray_img = cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)

  ```

 Advanced Questions

7. What is image thresholding and how do you apply it in OpenCV?

   - Answer: Image thresholding is a technique to convert a grayscale image to a binary image based on a certain threshold value. You can apply it in OpenCV using `cv2.threshold()` or `cv2.adaptiveThreshold()`:

  ```python

  ret, thresh_img = cv2.threshold(gray_img, 127, 255, cv2.THRESH_BINARY)

  ```

8. Explain how you would detect edges in an image using OpenCV.

   - Answer: You can detect edges using the Canny edge detector:

  ```python

  edges = cv2.Canny(image, threshold1, threshold2)

  ```

  You can also use Sobel or Laplacian filters for edge detection.

9. What are contours and how do you find them using OpenCV?

   - Answer: Contours are curves joining all continuous points along a boundary that have the same intensity. You can find contours using `cv2.findContours()`:

  ```python

  contours, hierarchy = cv2.findContours(thresh_img, cv2.RETR_SIMPLE, cv2.CHAIN_APPROX_SIMPLE)

  ```

 Application Questions

10. How would you use OpenCV to track an object in a video stream?

- Answer: One common approach is to use background subtraction or color-based tracking (e.g., using the HSV color space). An example would be:

   - Convert the video frame to HSV.

   - Define a mask based on the color of the object.

   - Use `cv2.findContours()` to detect the object based on the mask.

11. What image processing technique would you use to resize an image in OpenCV?

- Answer: You can use `cv2.resize()` to resize an image. The function takes the input image and the desired width and height:

   ```python

   resized_img = cv2.resize(img, (width, height))

   ```

These questions cover different aspects of OpenCV, from basic understanding to advanced applications, and should help you prepare for an interview focused on computer vision using OpenCV.

Advance Interview Questions and Answers on OpenCV in (2025)

Some advanced OpenCV interview questions along with detailed answers:

 1. What is the difference between `cv2.imshow()` and `cv2.imshow()`?

Answer:

`cv2.imshow()` is a function used to display an image in a window, while `cv2.imshow()` is a method from OpenCV's C++ API which serves the same purpose. In Python, you typically would use `cv2.imshow()` to show images, while in C++, you would have a similar function. The question might be ambiguous since `cv2.imshow()` appears twice but likely refers to different contexts or implementations.

 2. Explain how Image Pyramid works in OpenCV.

Answer:

Image pyramids are a set of images that are progressively reduced in size, often used in multi-scale image processing tasks. OpenCV provides functions like `cv2.pyrDown()` to downsample (reduce) the image and `cv2.pyrUp()` to upsample (increase) the image.

- Gaussian Pyramid: The process of downsampling an image using Gaussian blurring and then resizing.

- Laplacian Pyramid: Represents the differences between the Gaussian pyramids at successive levels, allowing for better reconstruction and image analysis.

Pyramids are useful for tasks like image blending, stereo imaging, and object detection at different scales.

 3. How can you perform image segmentation using OpenCV?

Answer:

OpenCV provides several methods for image segmentation:

- Thresholding: Simple techniques like binary thresholding, adaptive thresholding, or Otsu's method.

  ```python

  _, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

  ```

- Watershed Algorithm: A powerful segmentation technique useful for separating touching objects.

  ```python

  markers = cv2.watershed(image, markers)

  ```

- GrabCut: A semi-automatic segmentation technique that is useful for foreground extraction.

  ```python

  mask = np.zeros(image.shape[:2],np.uint8)

  cv2.grabCut(image, mask, rect, bgd_model, fgd_model, iterCount=cv2.GC_INIT_WITH_MASK)

  ```

- K-means Clustering: Can be applied to segment an image based on color.

 4. What is the purpose of the `cv2.findContours()` function?

Answer:

`cv2.findContours()` is used to detect contours in a binary image. Contours are curves joining all the continuous points along a boundary that have the same color or intensity. The function returns a list of contours found, which can be used for shape analysis, object detection, and recognition.

The function can return different information depending on the retrieval mode specified:

- `cv2.RETR_EXTERNAL`: retrieves only the extreme outer contours.

- `cv2.RETR_TREE`: retrieves all of the contours and arranges them in a hierarchy.

 5. What are Haar Cascades, and how do they work in OpenCV?

Answer:

Haar Cascades are a machine learning object detection method used to identify objects in an image. They work by using a series of classifiers based on Haar features, which are similar to the concept of edges or textures in images.

The process involves the following steps:

1. Training: A classifier is trained with many positive and negative samples of the object of interest. The classifier learns to distinguish between the target object and the background.

2. Detection: During detection, the image is scanned at multiple scales (image pyramids), and the classifier is applied to different regions of the image to identify potential objects. This process is efficient because of the cascade structure that allows for quick rejection of areas unlikely to contain the object.

OpenCV provides pre-trained Haar Cascade classifiers for common objects such as faces using `cv2.CascadeClassifier()`.

 6. How can you improve the performance of image processing tasks in OpenCV?

Answer:

Improving performance can involve several strategies:

- Use of Multi-threading or Multiprocessing: Leverage Python's multiprocessing library or threads to process images in parallel, especially for batch processing.

- Image Resizing: Process downsampled versions of the images when the full resolution is not required.

- Optimized Libraries: Utilize optimized OpenCV builds (like OpenCV with Intel's TBB or OpenMP for parallel processing) which inherently improve processing speed.

- GPU Acceleration: OpenCV has a module (`cv2.cuda`) that allows for GPU computations to handle more complex operations quickly, utilizing NVIDIA CUDA.

- Efficient Algorithms: Use more efficient algorithms or methods that are designed to reduce computational complexity, such as using simpler color models for color segmentation.

 7. Explain the concept of template matching in OpenCV.

Answer:

Template matching is a technique in OpenCV used for finding a template image within a larger image. The function `cv2.matchTemplate()` takes two images: the source image (where you want to find the template) and the template image, and it slides the template over the source image, comparing the template and patch of the source image.

This can be done using different methods (e.g., `cv2.TM_CCOEFF`, `cv2.TM_CCOEFF_NORMED`, etc.), which measure the similarity. The result of this function is a similarity map, where higher values indicate a better match. You can then find locations with the highest similarity using `cv2.minMaxLoc()` to get the coordinates of the best match.

 8. How would you implement edge detection in OpenCV?

Answer:

Edge detection can be implemented in several ways in OpenCV, with the Canny edge detector being one of the most popular methods. Here’s an example of applying Canny edge detection:

```python

# Load the image

image = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Apply Gaussian Blur to reduce noise

blurred_image = cv2.GaussianBlur(image, (5, 5), 1.5)

# Canny edge detection

edges = cv2.Canny(blurred_image, threshold1=100, threshold2=200)

# Display the result

cv2.imshow('Edges', edges)

cv2.waitKey(0)

cv2.destroyAllWindows()

```

In addition to Canny, OpenCV also includes Sobel and Laplacian operators for edge detection.

 9. Explain the process of image stitching in OpenCV.

Answer:

Image stitching involves seamlessly merging multiple images to create a single panorama. The process generally involves the following steps:

1. Feature Detection: Using algorithms like SIFT, SURF, or ORB to detect keypoints and descriptors in the images.

2. Feature Matching: Matching the keypoints between the images using a matching algorithm like FLANN or BFMatcher.

3. Homography Estimation: Estimating the transformation matrix that aligns the matched features using `cv2.findHomography()`.

4. Image Warping: Warping one image to align with another using `cv2.warpPerspective()`.

5. Blending: Finally, blend the stitched images to smoothen transitions.

OpenCV has a built-in module for stitching images, `cv2.Stitcher`, which simplifies this process.

 10. What considerations would you take into account for computer vision applications in real-time processing?

Answer:

Real-time computer vision applications have high requirements for efficiency and speed. Considerations include:

- FPS Requirement: Determine the frames per second (FPS) necessary for the application and optimize processing to meet or exceed this.

- Algorithm Complexity: Use lightweight algorithms where possible. For example, opt for faster object detection models like YOLO over slower models like R-CNN for tasks requiring high frame rates.

- Hardware Optimization: Leverage specialized hardware like GPUs, or even dedicated AI accelerators (like Google's Edge TPU) for real-time processing.

- Resolution Handling: Use appropriate image resolutions to balance between detail and processing time. Lower resolutions are faster but may lose critical details important for decisions.

- Efficient Data Handling: Streamline data capture and processing pipelines to minimize bottlenecks (such as reading images from disk).

- Buffer Management: Efficiently manage buffers and memory to prevent latency issues arising from memory overload.

 Conclusion

OpenCV is a powerful tool for image processing, and understanding its capabilities and efficient implementation techniques can significantly enhance performance in real-world applications. These advanced questions can help you gauge a candidate's depth of knowledge and practical experience with OpenCV.