utils/mask_processing.py

import cv2
from matplotlib import pyplot as plt
import PIL.Image as Image
import numpy as np


def crop_for_filling_pre(image: np.array, mask: np.array, crop_size: int = 512):
    # Calculate the aspect ratio of the image
    height, width = image.shape[:2]
    aspect_ratio = float(width) / float(height)

    # If the shorter side is less than 512, resize the image proportionally
    if min(height, width) < crop_size:
        if height < width:
            new_height = crop_size
            new_width = int(new_height * aspect_ratio)
        else:
            new_width = crop_size
            new_height = int(new_width / aspect_ratio)

        image = cv2.resize(image, (new_width, new_height))
        mask = cv2.resize(mask, (new_width, new_height))

    # Find the bounding box of the mask
    x, y, w, h = cv2.boundingRect(mask)

    # Update the height and width of the resized image
    height, width = image.shape[:2]

    # # If the 512x512 square cannot cover the entire mask, resize the image accordingly
    if w > crop_size or h > crop_size:
        # padding to square at first
        if height < width:
            padding = width - height
            image = np.pad(image, ((padding // 2, padding - padding // 2), (0, 0), (0, 0)), 'constant')
            mask = np.pad(mask, ((padding // 2, padding - padding // 2), (0, 0)), 'constant')
        else:
            padding = height - width
            image = np.pad(image, ((0, 0), (padding // 2, padding - padding // 2), (0, 0)), 'constant')
            mask = np.pad(mask, ((0, 0), (padding // 2, padding - padding // 2)), 'constant')

        resize_factor = crop_size / max(w, h)
        image = cv2.resize(image, (0, 0), fx=resize_factor, fy=resize_factor)
        mask = cv2.resize(mask, (0, 0), fx=resize_factor, fy=resize_factor)
        x, y, w, h = cv2.boundingRect(mask)

    # Calculate the crop coordinates
    crop_x = min(max(x + w // 2 - crop_size // 2, 0), width - crop_size)
    crop_y = min(max(y + h // 2 - crop_size // 2, 0), height - crop_size)

    # Crop the image
    cropped_image = image[crop_y:crop_y + crop_size, crop_x:crop_x + crop_size]
    cropped_mask = mask[crop_y:crop_y + crop_size, crop_x:crop_x + crop_size]

    return cropped_image, cropped_mask
    
    
def crop_for_filling_post(
        image: np.array,
        mask: np.array,
        filled_image: np.array, 
        crop_size: int = 512,
        ):
    image_copy = image.copy()
    mask_copy = mask.copy()
    # Calculate the aspect ratio of the image
    height, width = image.shape[:2]
    height_ori, width_ori = height, width
    aspect_ratio = float(width) / float(height)

    # If the shorter side is less than 512, resize the image proportionally
    if min(height, width) < crop_size:
        if height < width:
            new_height = crop_size
            new_width = int(new_height * aspect_ratio)
        else:
            new_width = crop_size
            new_height = int(new_width / aspect_ratio)

        image = cv2.resize(image, (new_width, new_height))
        mask = cv2.resize(mask, (new_width, new_height))

    # Find the bounding box of the mask
    x, y, w, h = cv2.boundingRect(mask)

    # Update the height and width of the resized image
    height, width = image.shape[:2]

    # # If the 512x512 square cannot cover the entire mask, resize the image accordingly
    if w > crop_size or h > crop_size:
        flag_padding = True
        # padding to square at first
        if height < width:
            padding = width - height
            image = np.pad(image, ((padding // 2, padding - padding // 2), (0, 0), (0, 0)), 'constant')
            mask = np.pad(mask, ((padding // 2, padding - padding // 2), (0, 0)), 'constant')
            padding_side = 'h'
        else:
            padding = height - width
            image = np.pad(image, ((0, 0), (padding // 2, padding - padding // 2), (0, 0)), 'constant')
            mask = np.pad(mask, ((0, 0), (padding // 2, padding - padding // 2)), 'constant')
            padding_side = 'w'

        resize_factor = crop_size / max(w, h)
        image = cv2.resize(image, (0, 0), fx=resize_factor, fy=resize_factor)
        mask = cv2.resize(mask, (0, 0), fx=resize_factor, fy=resize_factor)
        x, y, w, h = cv2.boundingRect(mask)
    else:
        flag_padding = False

    # Calculate the crop coordinates
    crop_x = min(max(x + w // 2 - crop_size // 2, 0), width - crop_size)
    crop_y = min(max(y + h // 2 - crop_size // 2, 0), height - crop_size)

    # Fill the image
    image[crop_y:crop_y + crop_size, crop_x:crop_x + crop_size] = filled_image
    if flag_padding:
        image = cv2.resize(image, (0, 0), fx=1/resize_factor, fy=1/resize_factor)
        if padding_side == 'h':
            image = image[padding // 2:padding // 2 + height_ori, :]
        else:
            image = image[:, padding // 2:padding // 2 + width_ori]

    image = cv2.resize(image, (width_ori, height_ori))

    image_copy[mask_copy==255] = image[mask_copy==255]
    return image_copy


if __name__ == '__main__':

    # image = cv2.imread('example/boat.jpg')
    # mask = cv2.imread('example/boat_mask_2.png', cv2.IMREAD_GRAYSCALE)
    image = cv2.imread('./example/groceries.jpg')
    mask = cv2.imread('example/groceries_mask_2.png', cv2.IMREAD_GRAYSCALE)
    # image = cv2.imread('example/bridge.jpg')
    # mask = cv2.imread('example/bridge_mask_2.png', cv2.IMREAD_GRAYSCALE)
    # image = cv2.imread('example/person_umbrella.jpg')
    # mask = cv2.imread('example/person_umbrella_mask_2.png', cv2.IMREAD_GRAYSCALE)
    # image = cv2.imread('example/hippopotamus.jpg')
    # mask = cv2.imread('example/hippopotamus_mask_1.png', cv2.IMREAD_GRAYSCALE)

    cropped_image, cropped_mask = crop_for_filling_pre(image, mask)
    # ^ ------------------------------------------------------------------------------------
    # ^ Please conduct inpainting or filling here on the cropped image with the cropped mask
    # ^ ------------------------------------------------------------------------------------

    # e.g.
    # cropped_image[cropped_mask==255] = 0
    cv2.imwrite('cropped_image.jpg', cropped_image)
    cv2.imwrite('cropped_mask.jpg', cropped_mask)
    print(cropped_image.shape)
    print(cropped_mask.shape)

    image = crop_for_filling_post(image, mask, cropped_image)
    cv2.imwrite('filled_image.jpg', image)
    print(image.shape)