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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 |