''' 

since we are going to use opencv and numpy libraries to perform the 

required operations import the opencv and numpy libraries

'''

import cv2

import numpy as np

'''

This function reads the original image on which any operation need to be performed

image to be read is patssed to this function 

 '''

def read_image(image_path):

    image = cv2.imread(image_path)

    return image

" As name indicates this functions saves the image"

def save_image(image_name,image):

    cv2.imwrite(image_name,image) # save image 

'''

Perform image rotation of the loaded image

rotate_image(image,dimensions,angle,scale)

image - image to be rotated

dimensions - to find the centre of image

angle - angle by which the image to be rotated

scale - scaling factor for image ( for this program we are considering it as 1)

'''

def rotate_image(image,dimensions,angle,scale):

    height_img = dimensions[0]

    width_img = dimensions[1]

    centre_height = height_img/2

    centre_width = width_img/2

    rotation_matrix = cv2.getRotationMatrix2D((width_img/2,height_img/2),angle,scale)

    rotated_image = cv2.warpAffine(image,rotation_matrix,(width_img,height_img))

    return rotated_image

''' This function scales up/ down the image by resizing and 

returns resized image. This function uses resize function of open CV 

it takes imageto resized,dimension to which image to be resized and interpolation method

'''

def resize_image(image,dimensions,method):

    resize_method = method

    #resized_image = cv2.resize(image,dimensions)

    resized_image = cv2.resize(image,dimensions,interpolation = resize_method)

    return resized_image

'''

This function crops the image to desired cropping size and

returns cropped image

crop_image(<image to be croped>,<cropping dimensions>)

'''

def crop_image(image,dimensions):

    #image_dims = image.shape

    h_s,h_e,w_s,w_e = dimensions # extract individual components croping dimension 

    width_dims = [int(w_s),int(w_e)] 

    height_dims = [int(h_s),int(h_e)]

    np.clip(height_dims,a_min = 0,a_max = image.shape[0]) # restrict cropping height with image height

    np.clip(width_dims,a_min = 0,a_max = image.shape[1]) # restrict cropping width with image width

    crop_image = image[height_dims[0]:height_dims[1],width_dims[0]:width_dims[1]] # perform cropping operation

    return crop_image

'''

The script in this cell performs rotation operation on orginal image

No need to mention the full path if image is placed in same folder where we have python code

rotate the image by 140 degree

'''

test_image = read_image('Challenge1.png') # read the image

rotation_angle = 140 # 140 degree angle of rotation

scale = 1 # maintain the original size of image

# extract the dimension of image as this would be required to find the centre to rotate the image

dimensions = (test_image.shape[0],test_image.shape[1]) 

​rotated_image = rotate_image(test_image,dimensions,rotation_angle,scale)

save_image("rotated_image1.png",rotated_image) # save the rotated image 

''' 

The script in this cell performs scaling operation on rotated image

load the rotated image to perform scaling operation'''

rotated_image = read_image("rotated_image.png") # load the rotated image

​method = cv2.INTER_AREA # use INTER_AREA method for resizing(scaling) the image

# reduced_dimensions = (reduced_width,reduced_height)

reduced_dimensions = (rotated_image.shape[1]//2,rotated_image.shape[0]//2) # reduce the dimension of rotated image by 50%

resized_image = resize_image(rotated_image,reduced_dimensions,method) # resize the image

save_image("scaled_image.png",resized_image) # save the resized image 

The script in this cell performs cropping operation on original image

This program crops the image by ~30% in width and ~30% in height

'''

image = read_image('Challenge1.png')

# crop the original image in height and width to focus on the suject of image

crop_dimension = (image.shape[0]*0.17,image.shape[0]*0.89,image.shape[1]*0.18,image.shape[1]*0.86)

#crop_dimension = (h_s,h_e,w_s,w_e)

cropped_image = crop_image(image,crop_dimension) # crop_image(<image to be croped>,<cropping dimensions>)

save_image("cropped_image.png",cropped_image)