Project 1

import cv2  # cv2 is the importing for Opencv
# Opencv is a library of Python bindings designed to solve computer vision problems

import numpy as np # np is an alias for numpy
# general-purpose array-processing package. 
# It provides a high-performance multidimensional array object and tools for working with these arrays


# Method to read an image

def read_image(image_path):
    image = cv2.imread(image_path)
    # cv2.imread(PATH) : method loads an image from the specified file.
    # Parameters: 
    # 1) PATH - A string representing the path of the image to be read.
    return image


# Method to display an image

def display_image(window_name, read_image):
    cv2.imshow(window_name, read_image)
    # cv2.imshow(window_name, image) : method is used to display an image in a window.
    # Parameters:
    # 1) window_name - A string representing the name of the window in which the image is to be displayed; 
    # 2) image - It is the image that is to be displayed.
    cv2.waitKey(0)


# Method to perform Image Rotation

def rotate_image(image, dimensions, angle):
    rotation_matrix = cv2.getRotationMatrix2D(center = dimensions, angle = angle, scale = 1)
    # cv2.getRotationMatrix2D(center, angle, scale) : function is used to make the transformation matrix M which will be used for rotating a image
    # Parameters:
    # 1) center - Center of rotation
    # 2) angle - Angle of Rotation. Angle is positive for anti-clockwise and negative for clockwise.
    # 3) scale - scaling factor which scales the image
    rotated_image = cv2.warpAffine(image, rotation_matrix, (image.shape[1],image.shape[0]))
    return rotated_image


# Method to perform Image Scaling

def scaling_image(image, dimensions, scale):
    scaled_matrix = cv2.getRotationMatrix2D(center = dimensions, angle = 0, scale = scale)
    scaled_image = cv2.warpAffine(image, scaled_matrix, (image.shape[1],image.shape[0]))
    # dst = cv2.warpAffine(src, M, dsize)
    # Parameters:
    # 1) src - input image.
    # 2) M - transformation matrix.
    # 3) dsize - size of the output image.
    # 4) dst - output image that has the size dsize and the same type as src.
    return scaled_image


# Method to perform Image cropping

def crop_image(image, dimensions):
    image_dims = image.shape
    h_s, h_e, w_s, w_e = dimensions
    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])
    # numpy.clip(a,a_min,a_max) : numpy method which clip(limit) the values in an array.
    # Parameters:
    # 1) a - Array containing elements to clip.
    # 2) a_min - Minimum value
    # 3) a_max - Maximum value
    crop_image = image[height_dims[0]:height_dims[1], width_dims[0]:width_dims[1]]
    return crop_image

# To save image

def save_image(image_name, image):
    cv2.imwrite(image_name, image)
    # cv2.imwrite(filename, image) : method is used to save an image to any storage device.
    # Parameters:
    # 1) filename - A string representing the file name. The filename must include image format like .jpg, .png, etc.
    # 2) image - It is the image that is to be saved.

# ************************************************************************************ #
# Main function

# Flow of the code structure
# 1. Before doing "Image Manipulation" we should read the input image from the specified path by using the "read_image()" method.
# 2. Then the image can be displayed by using the "display_image()" method.
# 3. "rotate_image()" method is to perform image rotation by deriving the rotation matrix[cv2.getRotationMatrix2D()] for a 140-degree rotation angle.
# 4. "scaling_image()" method is to perform a scaling operation that reduces the size of the image by 50% of the original size on the rotated image 
# 5. "crop_image()" method is to perform the cropping operation manually which focuses on the subject of the input image on the original image

# NOTE: PFA of the resultant image(in jpg) after transformation operations(Rotation, scaling, Cropping) 
# and jupyter notebook file "Project1.ipynb"


# Reading an image
image = read_image('Challenge1.png')
# To display original image
display_image('Original Image', image)
save_image('C1_Original_image.jpg', image)

# To rotate an image by an angle of 140 degree
image_dims = image.shape
rotate_dim = (image_dims[1]/2, image_dims[0]/2)
rotate_angle = 140
rotated_image = rotate_image(image, rotate_dim, rotate_angle)
display_image('Rotated Image', rotated_image)
save_image('C1_Rotated_image.jpg', rotated_image)

# Performing scaling operation on the rotated image which reduces the size of the image by 50% of the original image
image_dims = image.shape
scale_dim = (image_dims[1]/2, image_dims[0]/2)
scale_value = 0.5
scaled_image = scaling_image(rotated_image, scale_dim, scale_value)
display_image('Scaled Image', scaled_image)
save_image('C1_Scaled_image.jpg', scaled_image)

# Performing cropping operation on the original image which focuses on the subject of the input image
crop_dim = [105, 538, 180, 850]
cropped_image = crop_image(image, crop_dim)
display_image('Cropped Image', cropped_image)
save_image('C1_Cropped_image.jpg', cropped_image)