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# models/image_analysis.py
from PIL import Image
import numpy as np
from transformers import AutoImageProcessor, AutoModelForImageClassification
from .logging_config import logger
# Initialize real estate classification model
try:
processor = AutoImageProcessor.from_pretrained("andupets/real-estate-image-classification")
model = AutoModelForImageClassification.from_pretrained("andupets/real-estate-image-classification")
has_model = True
logger.info("Real estate classification model loaded successfully")
except Exception as e:
logger.error(f"Error loading real estate classification model: {str(e)}")
has_model = False
def analyze_image(image):
try:
if image is None:
logger.error("No image provided to analyze_image.")
return {
'is_property_related': False,
'property_confidence': 0.0,
'predicted_label': 'no_image',
'top_predictions': [],
'image_quality': {'resolution': 'unknown', 'quality_score': 0},
'is_ai_generated': False,
'authenticity_score': 0.0,
'error': 'No image provided'
}
if has_model:
try:
img_rgb = image.convert('RGB')
inputs = processor(images=img_rgb, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
probs = logits.softmax(dim=1).detach().numpy()[0]
max_prob_idx = probs.argmax()
max_prob = probs[max_prob_idx]
predicted_label = model.config.id2label[max_prob_idx]
is_real_estate = max_prob > 0.5
quality = assess_image_quality(image)
is_ai_generated = detect_ai_generated_image(image)
return {
'is_property_related': is_real_estate,
'property_confidence': float(max_prob),
'predicted_label': predicted_label,
'top_predictions': [
{'label': model.config.id2label[i], 'confidence': float(prob)}
for i, prob in enumerate(probs)
],
'image_quality': quality,
'is_ai_generated': is_ai_generated,
'authenticity_score': 0.95 if not is_ai_generated else 0.60
}
except Exception as e:
logger.error(f"Error in model-based image analysis: {str(e)}")
return {
'is_property_related': False,
'property_confidence': 0.0,
'predicted_label': 'error',
'top_predictions': [],
'image_quality': assess_image_quality(image),
'is_ai_generated': False,
'authenticity_score': 0.0,
'error': str(e)
}
else:
logger.warning("Real estate classification model unavailable")
return {
'is_property_related': False,
'property_confidence': 0.0,
'predicted_label': 'unknown',
'top_predictions': [],
'image_quality': assess_image_quality(image),
'is_ai_generated': False,
'authenticity_score': 0.5
}
except Exception as e:
logger.error(f"Error analyzing image: {str(e)}")
return {
'is_property_related': False,
'property_confidence': 0.0,
'predicted_label': 'error',
'top_predictions': [],
'image_quality': {'resolution': 'unknown', 'quality_score': 0},
'is_ai_generated': False,
'authenticity_score': 0.0,
'error': str(e)
}
def detect_ai_generated_image(image):
try:
img_array = np.array(image)
if len(img_array.shape) == 3:
gray = np.mean(img_array, axis=2)
else:
gray = img_array
noise = gray - np.mean(gray)
noise_std = np.std(noise)
width, height = image.size
perfect_dimensions = (width % 64 == 0 and height % 64 == 0)
has_exif = hasattr(image, '_getexif') and image._getexif() is not None
return noise_std < 0.05 or perfect_dimensions or not has_exif
except Exception as e:
logger.error(f"Error detecting AI-generated image: {str(e)}")
return False
def assess_image_quality(img):
try:
width, height = img.size
resolution = width * height
quality_score = min(100, resolution // 20000)
return {
'resolution': f"{width}x{height}",
'quality_score': quality_score
}
except Exception as e:
logger.error(f"Error assessing image quality: {str(e)}")
return {
'resolution': 'unknown',
'quality_score': 0
}
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