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"""
Aesthetic metrics for image quality assessment using AI models.
These metrics evaluate subjective aspects of images like aesthetic appeal, composition, etc.
"""
import torch
import numpy as np
from PIL import Image
from transformers import AutoFeatureExtractor, AutoModelForImageClassification, CLIPProcessor, CLIPModel
import torchvision.transforms as transforms
class AestheticMetrics:
"""Class for computing aesthetic image quality metrics using AI models."""
def __init__(self):
"""Initialize models for aesthetic evaluation."""
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self._initialize_models()
def _initialize_models(self):
"""Initialize all required models."""
# Initialize CLIP model for text-image similarity using transformers
try:
self.clip_model_name = "openai/clip-vit-base-patch32"
self.clip_processor = CLIPProcessor.from_pretrained(self.clip_model_name)
self.clip_model = CLIPModel.from_pretrained(self.clip_model_name)
self.clip_model.to(self.device)
self.clip_loaded = True
except Exception as e:
print(f"Warning: Could not load CLIP model: {e}")
self.clip_loaded = False
# Initialize aesthetic predictor model (LAION Aesthetic Predictor v2)
try:
self.aesthetic_model_name = "cafeai/cafe_aesthetic"
self.aesthetic_extractor = AutoFeatureExtractor.from_pretrained(self.aesthetic_model_name)
self.aesthetic_model = AutoModelForImageClassification.from_pretrained(self.aesthetic_model_name)
self.aesthetic_model.to(self.device)
self.aesthetic_loaded = True
except Exception as e:
print(f"Warning: Could not load aesthetic model: {e}")
self.aesthetic_loaded = False
# Initialize transforms for preprocessing
self.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]),
])
def calculate_aesthetic_score(self, image_path):
"""
Calculate aesthetic score using a pre-trained model.
Args:
image_path: path to the image file
Returns:
float: aesthetic score between 0 and 10
"""
if not self.aesthetic_loaded:
return 5.0 # Default middle score if model not loaded
try:
image = Image.open(image_path).convert('RGB')
inputs = self.aesthetic_extractor(images=image, return_tensors="pt").to(self.device)
with torch.no_grad():
outputs = self.aesthetic_model(**inputs)
# Get predicted class probabilities
probs = torch.nn.functional.softmax(outputs.logits, dim=1)
# Calculate weighted score (0-10 scale)
score_weights = torch.tensor([i for i in range(10)]).to(self.device).float()
aesthetic_score = torch.sum(probs * score_weights).item()
return aesthetic_score
except Exception as e:
print(f"Error calculating aesthetic score: {e}")
return 5.0
def calculate_composition_score(self, image_path):
"""
Estimate composition quality using rule of thirds and symmetry analysis.
Args:
image_path: path to the image file
Returns:
float: composition score between 0 and 10
"""
try:
# Load image
image = Image.open(image_path).convert('RGB')
img_array = np.array(image)
# Calculate rule of thirds score
h, w = img_array.shape[:2]
third_h, third_w = h // 3, w // 3
# Define rule of thirds points
thirds_points = [
(third_w, third_h), (2*third_w, third_h),
(third_w, 2*third_h), (2*third_w, 2*third_h)
]
# Calculate edge detection to find important elements
gray = np.mean(img_array, axis=2).astype(np.uint8)
edges = np.abs(np.diff(gray, axis=0, append=0)) + np.abs(np.diff(gray, axis=1, append=0))
# Calculate score based on edge concentration near thirds points
thirds_score = 0
for px, py in thirds_points:
# Get region around thirds point
region = edges[max(0, py-50):min(h, py+50), max(0, px-50):min(w, px+50)]
thirds_score += np.mean(region)
# Normalize score
thirds_score = min(10, thirds_score / 100)
# Calculate symmetry score
flipped = np.fliplr(img_array)
symmetry_diff = np.mean(np.abs(img_array.astype(float) - flipped.astype(float)))
symmetry_score = 10 * (1 - symmetry_diff / 255)
# Combine scores (weighted average)
composition_score = 0.7 * thirds_score + 0.3 * symmetry_score
return min(10, max(0, composition_score))
except Exception as e:
print(f"Error calculating composition score: {e}")
return 5.0
def calculate_color_harmony(self, image_path):
"""
Calculate color harmony score based on color theory.
Args:
image_path: path to the image file
Returns:
float: color harmony score between 0 and 10
"""
try:
# Load image
image = Image.open(image_path).convert('RGB')
img_array = np.array(image)
# Convert to HSV for better color analysis
hsv = np.array(image.convert('HSV'))
# Extract hue channel and create histogram
hue = hsv[:,:,0].flatten()
hist, _ = np.histogram(hue, bins=36, range=(0, 255))
hist = hist / np.sum(hist)
# Calculate entropy of hue distribution
entropy = -np.sum(hist * np.log2(hist + 1e-10))
# Calculate complementary color usage
complementary_score = 0
for i in range(18):
complementary_i = (i + 18) % 36
complementary_score += min(hist[i], hist[complementary_i])
# Calculate analogous color usage
analogous_score = 0
for i in range(36):
analogous_i1 = (i + 1) % 36
analogous_i2 = (i + 35) % 36
analogous_score += min(hist[i], max(hist[analogous_i1], hist[analogous_i2]))
# Calculate saturation variance as a measure of color interest
saturation = hsv[:,:,1].flatten()
saturation_variance = np.var(saturation)
# Combine metrics into final score
harmony_score = (
3 * (1 - min(1, entropy/5)) + # Lower entropy is better for harmony
3 * complementary_score + # Complementary colors
2 * analogous_score + # Analogous colors
2 * min(1, saturation_variance/2000) # Saturation variance
)
return min(10, max(0, harmony_score))
except Exception as e:
print(f"Error calculating color harmony: {e}")
return 5.0
def calculate_prompt_similarity(self, image_path, prompt):
"""
Calculate similarity between image and text prompt using CLIP.
Args:
image_path: path to the image file
prompt: text prompt used to generate the image
Returns:
float: similarity score between 0 and 10
"""
if not self.clip_loaded or not prompt:
return 5.0 # Default middle score if model not loaded or no prompt
try:
# Load image
image = Image.open(image_path).convert('RGB')
# Process inputs with CLIP processor
inputs = self.clip_processor(
text=[prompt],
images=image,
return_tensors="pt",
padding=True
).to(self.device)
# Calculate similarity
with torch.no_grad():
outputs = self.clip_model(**inputs)
logits_per_image = outputs.logits_per_image
similarity = logits_per_image.item()
# Convert to 0-10 scale (CLIP similarity is typically in 0-100 range)
return min(10, max(0, similarity / 10))
except Exception as e:
print(f"Error calculating prompt similarity: {e}")
return 5.0
def calculate_all_metrics(self, image_path, prompt=None):
"""
Calculate all aesthetic metrics for an image.
Args:
image_path: path to the image file
prompt: optional text prompt used to generate the image
Returns:
dict: dictionary with all metric scores
"""
metrics = {
'aesthetic_score': self.calculate_aesthetic_score(image_path),
'composition_score': self.calculate_composition_score(image_path),
'color_harmony': self.calculate_color_harmony(image_path),
}
# Add prompt similarity if prompt is provided
if prompt:
metrics['prompt_similarity'] = self.calculate_prompt_similarity(image_path, prompt)
return metrics
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