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import numpy as np |
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from PIL import Image, ImageOps |
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import logging |
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class Image_Processor: |
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def __init__(self): |
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pass |
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def is_image_white_by_percentage(self,image_path, white_threshold): |
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image = image_path.convert('RGB') |
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image_np = np.array(image) |
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white_pixel = np.array([255, 255, 255]) |
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white_pixels_count = np.sum(np.all(image_np == white_pixel, axis=-1)) |
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total_pixels = image_np.shape[0] * image_np.shape[1] |
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white_pixel_percentage = (white_pixels_count / total_pixels) * 100 |
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return white_pixel_percentage > white_threshold |
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def padding_white(self,image, output_size=(336, 336)): |
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if image.mode != 'RGB': |
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image = image.convert('RGB') |
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new_image = ImageOps.pad(image, output_size, method=Image.Resampling.LANCZOS, color=(255, 255, 255)) |
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return new_image |
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def resize_image_with_aspect_ratio(self,img): |
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target_size=336 |
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width, height = img.size |
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original_aspect_ratio = width / height |
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if width > height: |
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new_width = target_size |
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new_height = int(target_size / original_aspect_ratio) |
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else: |
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new_height = target_size |
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new_width = int(target_size * original_aspect_ratio) |
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resized_img = img.resize((new_width, new_height)) |
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return resized_img |
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def get_processed_img(self,image): |
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white_thresh = self.is_image_white_by_percentage(image,50) |
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if white_thresh == True: |
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resized_image = self.resize_image_with_aspect_ratio(image) |
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final_image = self.padding_white(resized_image) |
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logging.info('Resized and Padded Image') |
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else: |
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final_image = image.resize((336,336)) |
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logging.info('Resized Image') |
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final_image = final_image.convert('L') if final_image.mode != 'L' else final_image |
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return final_image |
