Ilovecphfjziywno Onion 005 Jpg %28%28new%29%29

# Load and preprocess image transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

return features

def generate_basic_features(image_path): try: img = Image.open(image_path) features = { 'width': img.width, 'height': img.height, 'mode': img.mode, 'file_size': os.path.getsize(image_path) } return features except Exception as e: print(f"An error occurred: {e}") return None Ilovecphfjziywno Onion 005 jpg %28%28NEW%29%29

img = Image.open(image_path).convert('RGB') img = transform(img) img = img.unsqueeze(0) # Add batch dimension

# Usage image_path = 'Ilovecphfjziywno Onion 005 jpg (NEW).jpg' features = generate_basic_features(image_path) print(features) You would typically use libraries like TensorFlow or PyTorch for this. Here's a very simplified example with PyTorch: # Load and preprocess image transform = transforms

# Generate features with torch.no_grad(): features = model(img)

# Usage image_path = 'Ilovecphfjziywno Onion 005 jpg (NEW).jpg' features = generate_cnn_features(image_path) print(features.shape) These examples are quite basic. The kind of features you generate will heavily depend on your specific requirements and the nature of your project. import torch import torchvision import torchvision

import torch import torchvision import torchvision.transforms as transforms