| from PIL import Image |
| import pytesseract |
| import torch |
| from torchvision import transforms |
| from transformers import ResNetForImageClassification |
| import logging |
|
|
| def resize_image(image, max_size=(800, 600)): |
| """Resize an image to the specified maximum size.""" |
| image.thumbnail(max_size) |
| return image |
|
|
| def extract_text_from_image(image): |
| """Extract text from an image using OCR.""" |
| try: |
| image = resize_image(image) |
| text = pytesseract.image_to_string(image) |
| return text |
| except Exception as e: |
| logging.error(f"Error extracting text from image: {e}") |
| return "" |
|
|
| def analyze_image(image): |
| """Analyze image content using a pre-trained model.""" |
| try: |
| preprocess = 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]), |
| ]) |
| image_tensor = preprocess(image).unsqueeze(0) |
| |
| |
| model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50") |
| model.eval() |
| |
| with torch.no_grad(): |
| output = model(image_tensor) |
| return output |
| except Exception as e: |
| logging.error(f"Error analyzing image: {e}") |
| return None |
