Master the Basics of Image Processing with OpenCV and NumPy

Image Processing Basics

Before working with images, we must answer the question: what is an image? An image is a collection of pixels, the smallest units of visual information. For example, an 11 × 11 px image consists of 121 individual pixel squares.

Types of Images

Binary image: Each pixel is either black or white, represented by 0 or 1.

Grayscale image: Pixels have 256 possible intensity levels (0‑255), typically stored with 8 bits per pixel.

RGB image: Each pixel contains three channels—Red, Green, and Blue—each usually 8 bits, so a pixel is represented as ([0…255], [0…255], [0…255]).

Image as a Matrix

Any image can be naturally expressed as a matrix where each element corresponds to a pixel. In programming, a multidimensional array is used to store this matrix. For a 4 × 4 RGB image, the matrix can be visualized as a three‑dimensional array of size 4 × 4 × 3.

The essence of image processing is manipulating this pixel matrix (multidimensional array).

Basic Processing Implementation

The following examples use opencv‑python and NumPy to perform common image operations.

Crop: Slice the matrix to the desired region.

Canvas: Create a background of the target size and paste the image onto it.

Watermark: Combine matrices using cv2.addWeighted for transparent overlay.

Translation: Build a translation matrix and apply cv2.warpAffine .

Rotation: Build a rotation matrix and apply cv2.warpAffine .

Scaling: Use cv2.resize with an appropriate interpolation algorithm.

Resizing Algorithms

OpenCV provides several interpolation methods for resize:

According to the official documentation, INTER_AREA is recommended for down‑sampling, while INTER_CUBIC (slower but higher quality) or INTER_LINEAR (faster with acceptable quality) are preferred for up‑sampling.

Conclusion

The article covered fundamental image processing concepts and demonstrated how to implement common operations such as cropping, canvas creation, watermarking, translation, rotation, and scaling using OpenCV and NumPy in Python.