import numpy as np
 
 #Use PIL to access image data
 from PIL import Image
 img = Image.open('monalisa.jpg')
 
 #Create array from image data
 M = np.array(img)
 
 #Display array from image data
 display(Image.fromarray(M))

def reduce_image_size_by_n(image, n):
 
    # Get the height and width of the image
    height, width, channels = image.shape
 
    # Reduce the height and width by n
    new_height = height // n
    new_width = width // n
 
    # Create a new array to store the reduced image
    downsampled_image = np.zeros((new_height, new_width, channels), dtype=image.dtype)
 
    # Iterate over each pixel of the reduced image
    for i in range(new_height):
        for j in range(new_width):
 
            # Take every other pixel along each axis to reduce the image
 
            downsampled_image[i, j] = image[n*i, n*j]
 
    return downsampled_image
 
 #Try the function using n = 2
 
 reduced_M = reduce_image_size_by_n(M, 2)
 
 display(reduced_M)

def flip_image(image):
 
  # Takes all rows in image (:) and reverses it the order of columns (::-1)
  flip_image = image[:, ::-1]
  return flip_image
 
 #Try function using reduced image
 display(flip_image(reduced_M))

def rotate_image (image, n):
  # rotate image using rot90, use n to determine number of rotation
  rotated_img = Image.fromarray(np.rot90(image, k=n, axes=(1, 0)))
  return rotated_img
 
 #rotate image twice (n=2)
 display(rotate_image(reduced_M, 2))

def crop_image(image, crop_ratio, zoom_ratio):
 
  #create focused part using crop_ratio and zoom_ratio of choice
   
  top = image.shape[0] // crop_ratio
  bottom = zoom_ratio * image.shape[0] // crop_ratio
  left = image.shape[1] // crop_ratio
  right = zoom_ratio * image.shape[1] // crop_ratio
 
  # Extract the focused part using array slicing
  focused_part = image[top:bottom, left:right]
  return focused_part
 
 display(crop_image(reduced_M, 4, 2))

def RGB_image(image,image_color):
 
  if image_color == 'R':
    #make a copy of image for the color channel
    img_R = image.copy()
    #set other color channel to zero. Here Red is the first channel [0]
    img_R[:, :, (1, 2)] = 0
    return img_R
 
  elif image_color == 'G':
    img_G = image.copy()
    #set other color channel to zero. Here Green is the second channel [1]
    img_G[:, :, (0, 2)] = 0
    return img_G
 
  elif image_color == 'B':
    img_B = image.copy()
    #set other color channel to zero. Here Blue is the third channel [2]
    img_B[:, :, (0, 1)] = 0
    return img_B

M_red = Image.fromarray(RGB_image(reduced_M, 'R'))
 
 display(M_red)

M_green = Image.fromarray(RGB_image(reduced_M, 'G'))
 
 display(M_green)

M_blue = Image.fromarray(RGB_image(reduced_M, 'B'))
 
 display(M_blue)

def apply_sepia(image):
    # Sepia transformation matrix
    sepia_matrix = np.array([[0.393, 0.769, 0.189],
                              [0.349, 0.686, 0.168],
                              [0.272, 0.534, 0.131]])
 
    # Apply the sepia transformation
    sepia_img = image.dot(sepia_matrix.T) # Using matrix multiplication
 
    # Ensure values are within valid range [0, 255]
    sepia_img = np.clip(sepia_img, 0, 255)
 
    return sepia_img.astype(np.uint8)
 
 # Apply sepia effect
 M_sepia = Image.fromarray(apply_sepia(reduced_M))
 
 display(M_sepia)

import numpy as np
 
 def grayscale(image):
    # Convert the RGB image to grayscale using weighted average
    grayscale_img = np.dot(image[..., :3], [0.2989, 0.5870, 0.1140])
 
    # Ensure values are within valid range [0, 255]
    grayscale_img = np.clip(grayscale_img, 0, 255)
 
    # Convert to uint8 data type
    grayscale_img = grayscale_img.astype(np.uint8)
 
    return grayscale_img
 
 # Convert the image to grayscale
 M_gray = grayscale(reduced_M)
 
 display(M_gray)

def pixelate_image(image, block_size):
   
    # Determine the number of blocks in each dimension
    num_blocks_y = image.shape[0] // block_size
    num_blocks_x = image.shape[1] // block_size
 
    # Calculate the average color for each block
    block_means = np.zeros((num_blocks_y, num_blocks_x, 3), dtype=np.uint8)
    for y in range(num_blocks_y):
        for x in range(num_blocks_x):
            block = image[y * block_size: (y + 1) * block_size,
                        x * block_size: (x + 1) * block_size]
            block_mean = np.mean(block, axis=(0, 1))
            block_means[y, x] = block_mean.astype(np.uint8)
 
    # Upsample block means to original image size
    pixelated_image = np.repeat(np.repeat(block_means, block_size, axis=0), block_size, axis=1)
 
    return pixelated_image
 
 
 # Set the block size for pixelation (adjust as needed)
 block_size = 10
 
 # Pixelate the image
 M_pixelated = Image.fromarray(pixelate_image(reduced_M, block_size))
 
 display(M_pixelated)

def binarize_image(image, threshold):
 
  #set pixel value greater than threshold to 255
  binarize_image = ((image > threshold) * 255).astype(np.uint8)
   
  return binarize_image
 
 #set threshold
 threshold = 68
 
 M_binarized = Image.fromarray(binarize_image(reduced_M, threshold))
 
 display(M_binarized)

#import and resize second image
 
 img_2 = np.array(Image.open('Eiffel.jpg').resize(reduced_M.shape[1::-1]))  
 
 def blend_image(image1, image2, , visibility_2 ):
 
  #blend images by multiplying by visibility ratio for each image
 
  blend_image = (image1 * visibility_1 + image2 * visibility_2).astype(np.uint8)
 
  return blend_image
 
 
 
 modified_image = Image.fromarray(blend_image(reduced_M, img_2, 0.7, 0.3))
 
 display(modified_image)