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import torch |
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import numpy as np |
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from PIL import Image |
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from typing import Tuple, Optional, Union |
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import torchvision.transforms as transforms |
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class ImageProcessorManager: |
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"""Image validation, preprocessing and format standardization""" |
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def __init__(self): |
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self.supported_formats = ['JPEG', 'PNG', 'WEBP', 'JPG'] |
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self.min_resolution = (224, 224) |
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self.clip_transform = transforms.Compose([ |
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transforms.Resize((336, 336), interpolation=transforms.InterpolationMode.BICUBIC), |
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transforms.ToTensor(), |
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transforms.Normalize( |
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mean=[0.48145466, 0.4578275, 0.40821073], |
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std=[0.26862954, 0.26130258, 0.27577711] |
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) |
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]) |
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def load_image(self, file_path: Union[str, Image.Image]) -> Image.Image: |
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"""Load and validate image""" |
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if isinstance(file_path, Image.Image): |
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image = file_path |
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else: |
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try: |
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image = Image.open(file_path) |
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except Exception as e: |
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raise ValueError(f"Failed to load image: {e}") |
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if image.mode != 'RGB': |
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image = image.convert('RGB') |
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if image.size[0] < self.min_resolution[0] or image.size[1] < self.min_resolution[1]: |
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raise ValueError(f"Image resolution too low, minimum required: {self.min_resolution}") |
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return image |
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def preprocess_for_yolo(self, image: Image.Image) -> np.ndarray: |
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"""Preprocess image for YOLO (keep original format)""" |
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return np.array(image) |
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def preprocess_for_clip(self, image: Image.Image) -> torch.Tensor: |
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"""Preprocess image for CLIP (336x336, ImageNet normalization)""" |
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return self.clip_transform(image) |
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def preprocess_for_qwen(self, image: Image.Image) -> Image.Image: |
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"""Preprocess image for Qwen2.5-VL (dynamic resolution)""" |
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return image |
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def resize_with_aspect_ratio(self, image: Image.Image, max_size: int = 1024) -> Image.Image: |
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"""Resize image while maintaining aspect ratio""" |
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width, height = image.size |
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if max(width, height) > max_size: |
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if width > height: |
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new_width = max_size |
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new_height = int(height * (max_size / width)) |
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else: |
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new_height = max_size |
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new_width = int(width * (max_size / height)) |
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image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) |
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return image |
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print("✓ ImageProcessorManager defined") |
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