Project 1

Necessary calculations for the scaling operations which fixes the scaling percentage.


scale_factor = 0.5
scaling_matrix = np.array([[scale_factor, 0, 0], [0, scale_factor, 0]], dtype='float32')

import cv2
import numpy as np

def rotate_and_scale_image(image,angle):
    # Get the dimensions of the image
    rows, cols = image.shape[:2] # first two elements of the tuple will be assigned to rows and columns

    # Calculate the rotation matrix along the centre
    rotation_matrix = cv2.getRotationMatrix2D((cols/2,rows/2), angle, 1)

    # Rotate the image along centre
    rotated_image = cv2.warpAffine(image, rotation_matrix, (cols, rows))

#     display = cv2.imshow('Rotated_image',rotated_image)
#     cv2.waitKey(0)
    return rotated_image

def scale_image(image,scale):
    rows, cols = image.shape[:2]
    
    
    #Calculate the scaling matrix
    scaling_matrix = np.array([[scale, 0, 0], [0, scale, 0]], dtype='float32')

    # Scale the image
    result = cv2.warpAffine(image, scaling_matrix, (int(cols * scale), int(rows * scale)))
    return result


def crop_image(image,dimentions):
    image_dims = image.shape
    h_s , h_e ,w_s , w_e = dimentions
    width_dims = [w_s , w_e]
    height_dims = [h_s , h_e]
    np.clip(height_dims, a_min = 0, a_max = image_dims[0])
    np.clip(width_dims, a_min = 0, a_max = image_dims[1])
    crop_image = image[height_dims[0]:height_dims[1],width_dims[0]:width_dims[1]]
    return crop_image

image = cv2.imread('car.jpeg')
rotated = rotate_and_scale_image(image,int(140))
cv2.imwrite("Rotated Image.jpg", rotated )

image_1 = cv2.imread('Rotated Image.jpg')
scaled = scale_image(image_1,int(0.5))
cv2.imwrite("Scaled Image.jpg",scaled)