import numpy as np
from PIL import Image, ImageOps
import logging

class Image_Processor:
    def __init__(self):
        pass
    def is_image_white_by_percentage(self,image_path, white_threshold):
        image = image_path.convert('RGB')
        image_np = np.array(image)
        white_pixel = np.array([255, 255, 255])
        white_pixels_count = np.sum(np.all(image_np == white_pixel, axis=-1))
        total_pixels = image_np.shape[0] * image_np.shape[1]
        white_pixel_percentage = (white_pixels_count / total_pixels) * 100
        return white_pixel_percentage > white_threshold

    def padding_white(self,image, output_size=(512, 512)):
        # Ensure image is in RGB mode before padding
        if image.mode != 'RGB':
            image = image.convert('RGB')
        new_image = ImageOps.pad(image, output_size, method=Image.Resampling.LANCZOS, color=(255, 255, 255))
        return new_image

    def resize_image_with_aspect_ratio(self,img):
        target_size=512
        width, height = img.size
        original_aspect_ratio = width / height
        if width > height:
            new_width = target_size
            new_height = int(target_size / original_aspect_ratio)
        else:
            new_height = target_size
            new_width = int(target_size * original_aspect_ratio)
        resized_img = img.resize((new_width, new_height))
        return resized_img
    
    def get_processed_img(self,image):
        white_thresh = self.is_image_white_by_percentage(image,50)
        if white_thresh == True:
            resized_image = self.resize_image_with_aspect_ratio(image)
            final_image = self.padding_white(resized_image)
            logging.info('Resized and Padded Image')
        else:
            #final_image = self.resize_image_with_aspect_ratio(image)
            final_image = image.resize((512,512))
            logging.info('Resized Image')
        
        final_image = final_image.convert('L') if final_image.mode != 'L' else final_image
        return final_image