OpenCV (Open Source Computer Vision Library) is a powerful open-source library designed for real-time computer vision and image processing. It provides tools for manipulating images, detecting objects, and even working with deep learning models.

Reading and Displaying an Image

Before performing transformations, let's first load and display an image using OpenCV. We have already imported the photo. But in your local computer for simple reading and displaying the photo you should use:

Resizing an Image

Resizing is useful for scaling images up or down while maintaining aspect ratio: cv2.resize(image, (0, 0), fx=0.5, fy=0.5), where fx and fy define the scaling factor for width and height.

You can also specify exact pixel dimensions: cv2.resize(image, (1000, 1200)), where (1000, 1200) is the output shape.

Rotating an Image

To rotate an image by a specific angle, we use cv2.getRotationMatrix2D() and cv2.warpAffine().

• cv2.getRotationMatrix2D(center, angle, scale) defines the rotation matrix.

• cv2.warpAffine(image, matrix, output_size) applies the transformation.

Cropping an Image

Cropping extracts a specific region from an image. It’s done using NumPy slicing. The syntax image[h_start:h_end, w_start:w_end] selects a region of interest.