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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)
# Load ResNet model
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 |