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import torch |
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from transformers import BlipProcessor, BlipForConditionalGeneration |
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from PIL import Image |
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import re |
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import asyncio |
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from typing import Dict, Any |
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class AttributeExtractor: |
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def __init__(self): |
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self.model_name = "Salesforce/blip-image-captioning-base" |
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self.processor = None |
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self.model = None |
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self._load_model() |
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self.style_patterns = { |
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"formal": ["suit", "blazer", "dress shirt", "tie", "formal", "business", "elegant"], |
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"casual": ["t-shirt", "jeans", "sneakers", "hoodie", "casual", "relaxed", "comfortable", "leggings"], |
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"sports": ["athletic", "sports", "gym", "workout", "running", "training"] |
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} |
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self.texture_patterns = { |
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"cotton": ["cotton", "soft", "comfortable"], |
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"denim": ["denim", "jeans", "rugged"], |
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"silk": ["silk", "smooth", "shiny", "lustrous", "leggings", "velvet"], |
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"wool": ["wool", "warm", "thick"], |
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"leather": ["leather", "tough", "durable"], |
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"synthetic": ["polyester", "synthetic", "artificial"] |
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} |
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def _load_model(self): |
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"""Load the BLIP model for image captioning""" |
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try: |
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print("Loading BLIP model for attribute extraction...") |
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self.processor = BlipProcessor.from_pretrained(self.model_name) |
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self.model = BlipForConditionalGeneration.from_pretrained(self.model_name) |
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self.model.eval() |
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print("BLIP model loaded successfully!") |
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except Exception as e: |
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print(f"Error loading BLIP model: {e}") |
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raise |
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async def extract_attributes(self, image: Image.Image) -> Dict[str, Any]: |
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"""Extract clothing attributes from image""" |
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try: |
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loop = asyncio.get_event_loop() |
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tasks = [ |
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loop.run_in_executor(None, self._generate_caption, image, "a photo of"), |
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loop.run_in_executor(None, self._generate_caption, image, "clothing style:"), |
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loop.run_in_executor(None, self._generate_caption, image, "fabric texture:") |
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] |
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captions = await asyncio.gather(*tasks) |
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attributes = self._analyze_captions(captions) |
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return attributes |
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except Exception as e: |
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print(f"Attribute extraction error: {e}") |
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return {"style": "unknown", "formality": "unknown", "texture": "unknown"} |
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def _generate_caption(self, image: Image.Image, prompt: str = "") -> str: |
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"""Generate caption for the image""" |
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try: |
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if prompt: |
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inputs = self.processor(image, prompt, return_tensors="pt") |
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else: |
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inputs = self.processor(image, return_tensors="pt") |
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with torch.no_grad(): |
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out = self.model.generate(**inputs, max_length=50, num_beams=4) |
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caption = self.processor.decode(out[0], skip_special_tokens=True) |
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return caption.lower() |
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except Exception as e: |
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print(f"Caption generation error: {e}") |
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return "" |
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def _analyze_captions(self, captions: list) -> Dict[str, Any]: |
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"""Analyze captions to extract structured attributes""" |
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combined_text = " ".join(captions).lower() |
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formal_score = sum(1 for word in self.style_patterns["formal"] if word in combined_text) |
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casual_score = sum(1 for word in self.style_patterns["casual"] if word in combined_text) |
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sports_score = sum(1 for word in self.style_patterns["sports"] if word in combined_text) |
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if formal_score > casual_score and formal_score > sports_score: |
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style = "formal" |
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formality = "formal" |
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elif sports_score > casual_score: |
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style = "athletic" |
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formality = "casual" |
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else: |
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style = "casual" |
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formality = "casual" |
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texture_scores = {} |
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for texture, patterns in self.texture_patterns.items(): |
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texture_scores[texture] = sum(1 for word in patterns if word in combined_text) |
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detected_texture = max(texture_scores, key=texture_scores.get) if max(texture_scores.values()) > 0 else "unknown" |
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return { |
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"style": style, |
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"formality": formality, |
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"texture": detected_texture, |
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"confidence": 0.8, |
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"raw_captions": captions, |
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"detected_keywords": [word for word in combined_text.split() if any(word in patterns for patterns in self.style_patterns.values())] |
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} |
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