Project 1

import cv2
import numpy as np 
import os 

#READ AND DISPLAY IMG FUNCTION
def read_img (image_path):
    img = cv2.imread(image_path) #reads in bgr fromat 
    return img


def disp_img (img_window,read_img):
    image = cv2.imshow(img_window,read_img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

#CROPPING FUCNTION
def crop_img(image,crp_dimension,):    #function is defined 
    img_shape = image.shape            #shape of input image is obtained
    hs,he,ws,we = crp_dimension        #cropping dimensions for height and width  
    width_dims = [ws,we]               #width dimensions 
    height_dims = [hs,he]              #height dimensions  
    np.clip(height_dims,a_min=0, a_max=img_shape[0])        #both the dimensions are #restricted to the height and width of the image 
    np.clip(width_dims,a_min=0, a_max=img_shape[1])
    crop_image = image[height_dims[0]:height_dims[1],width_dims[0]:width_dims[1]]  
    return crop_image

#ROTATION FUNCTION
def img_rotation(image,rot_angle,req_scale,trans_w,trans_h): #rotations fucntion is #defined 
    img_center = (image.shape[1]/2,image.shape[0]/2)                #image centre is #obtained from where theimage is rotated at the rotation anglr which is taken as #argument in to the function
    rot_matrix = cv2.getRotationMatrix2D(center=img_center ,angle=rot_angle 
                             ,scale=req_scale) #this returns rotation matrix 
    rot_img = cv2.warpAffine(src=image ,M=rot_matrix ,dsize=(trans_w,trans_h))  #here #the warping operation takes place where the roation matrix is kind of multiplied by the #image to ge the desired o/p
    return rot_img

#SCALING OPERATION 
#It is kind of resizing operation ,
#increasing / decreasing the image size = scaling UP/DOWN of the image 
#this can be done either by specifying dimensions or by scale factor which is matrix #which multplied by the image performs scaling
# og width = scaling factor X new width
#the function below also iterates over the different interpolation methods and saves the #o/p by each method into a list which is returned.
def scaling(image ,dimension) :
    width ,height = image.shape[0],image.shape[1]
    s_width ,s_height =dimension 
    sfactor_x = s_width / width
    sfactor_y = s_height / height

    print(f'SCALE FACTOR x ={sfactor_x} \n SCALE FACTOR y ={sfactor_y}\n')

    print(f'SCALE FACTOR x percentage ={sfactor_x*100} \n SCALE FACTOR y = 
                                            {sfactor_y*100}')
    scaled_img_list = []
    methods =[
        cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_CUBIC, cv2.INTER_LANCZOS4
    ]
    for i in range(len(methods)):
        resize_method = methods[i]
        img_resized = cv2.resize(image, None, fx=sfactor_x,fy=sfactor_y 
                ,interpolation=resize_method)
        scaled_img_list.append(img_resized)

    return scaled_img_list


#MAIN EXECUTION 
image = read_img('Challenge1_.png')

disp_img('original image',image)

print(image.shape)

img_center = (image.shape[0]/2,image.shape[1]/2)
print(img_center)


#CROP IMAGE
crop_dimension = [300,400,530,610]
cropped_img = crop_img(image,crop_dimension)
disp_img('cropped_img',cropped_img)


#ROTATION OF IMAGE 
rotation_angle = 30
required_scale = 1
required_width = 0
required_height = 0
rotated_img = img_rotation(image,rotation_angle,required_scale,required_width,required_height)
disp_img('Rotated Image',rotated_img)


#SCALE IMAGE 
dimension = (900,1000)
scale = scaling(image,dimension)
for j in range(len(scale)):
    disp_img(f'Image {j}',scale[j])
    print(scale[j].shape)

ORIGINAL IMAGE

CROPPED IMAGE

ROTATED IMAGE

SCALED UP IMAGE

SCALED DOWN IMAGE

ALL THE OUTPUT FILES ARE UPLOADED ALONGWITH THE JUPYTER NOTEBOOK FOR THE CODE OF SCALING,ROTATION,CROPPING OPERATIONS