Imports and Functions

Latest Page Update: 23-09-2026

Geometric transformations on images

The first topic is how to apply geometric transformations on images.

Let us start by defining a utility function, that can show two images side-by-side:

def show_comparison(original, transformed, transformed_name):
    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True,
                                   sharey=True)
    ax1.imshow(original)
    ax1.set_title('Original')
    ax1.axis('off')
    ax2.imshow(transformed)
    ax2.set_title(transformed_name)
    ax2.axis('off')
    io.show()

also import some useful functions:

import matplotlib.pyplot as plt
import math
from skimage.transform import rotate
from skimage.transform import EuclideanTransform
from skimage.transform import SimilarityTransform
from skimage.transform import warp
from skimage.transform import swirl

Exercise script for transformation on video

For use in exercises on Video Transformations:

import time
import cv2
from skimage.transform import swirl
from skimage.transform import rotate
import math


def show_in_moved_window(win_name, img, x, y):
    """
    Show an image in a window, where the position of the window can be given
    """
    cv2.namedWindow(win_name)
    cv2.moveWindow(win_name, x, y)
    cv2.imshow(win_name, img)


def process_rgb_image(img, counter):
    """
    Simple processing of a color (RGB) image
    """
    return rotate(img, counter)


def capture_from_camera_and_show_images():
    print("Starting image capture")

    print("Opening connection to camera")
    url = 0
    use_droid_cam = False
    if use_droid_cam:
        url = "http://192.168.1.120:4747/video"
    cap = cv2.VideoCapture(url)
    if not cap.isOpened():
        print("Cannot open camera")
        exit()

    print("Starting camera loop")
    # To keep track of frames per second using a high-performance counter
    old_time = time.perf_counter()
    fps = 0
    stop = False
    counter = 0
    while not stop:
        ret, new_frame = cap.read()
        if not ret:
            print("Can't receive frame. Exiting ...")
            break

        # Change from OpenCV BGR to scikit image RGB
        new_image = new_frame[:, :, ::-1]
        proc_time_start = time.perf_counter()
        proc_img = process_rgb_image(new_image, counter)
        proc_time = time.perf_counter() - proc_time_start
        # convert back to OpenCV BGR to show it
        proc_img = proc_img[:, :, ::-1]

        counter = counter + 1

        # update FPS - but do it slowly to avoid fast changing number
        new_time = time.perf_counter()
        time_dif = new_time - old_time
        old_time = new_time
        fps = fps * 0.95 + 0.05 * 1 / time_dif

        # Put the FPS on the new_frame
        str_out = f"fps: {int(fps)} proc.time: {int(proc_time * 1000)} ms"
        font = cv2.FONT_HERSHEY_COMPLEX
        cv2.putText(new_frame, str_out, (100, 100), font, 1, 255, 1)

        # Display the resulting frame
        show_in_moved_window('Input', new_frame, 0, 10)
        show_in_moved_window('Processed image', proc_img, 1200, 10)

        if cv2.waitKey(1) == ord('q'):
            stop = True

    print("Stopping image loop")
    cap.release()
    cv2.destroyAllWindows()


if __name__ == '__main__':
    capture_from_camera_and_show_images()