94db5bd40b
- Update DetectLogosDETR to support both CLIP and DINOv2 models - Rename clip_model parameter to embedding_model - Add model type detection for different embedding extraction - DINOv2 uses CLS token, CLIP uses get_image_features() - Add -e/--embedding-model argument to test_logo_detection.py - Include model name in file output header - Add run_threshold_tests.sh for testing various threshold/margin values - Add run_model_comparison.sh for comparing CLIP vs DINOv2 models
650 lines
24 KiB
Python
650 lines
24 KiB
Python
"""
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Logo detection using DETR for object detection and vision models for feature matching.
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This module provides a class for detecting logos in images using:
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1. DETR (DEtection TRansformer) for initial logo region detection
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2. Vision models (CLIP, DINOv2, etc.) for feature extraction and matching
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The class supports caching of embeddings for efficient reprocessing.
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The class automatically uses local models if available, otherwise falls back to HuggingFace.
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Supported embedding models:
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- CLIP models (openai/clip-vit-*): Text-image alignment, good general features
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- DINOv2 models (facebook/dinov2-*): Self-supervised, excellent for visual similarity
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"""
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import os
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import torch
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import torch.nn.functional as F
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from transformers import pipeline, CLIPProcessor, CLIPModel, AutoImageProcessor, AutoModel
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from PIL import Image
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import cv2
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import numpy as np
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from pathlib import Path
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from typing import List, Tuple, Dict, Optional, Any
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class DetectLogosDETR:
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"""
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Logo detection class using DETR and vision embedding models.
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This class detects logos in images by:
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1. Using DETR to find potential logo regions (bounding boxes)
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2. Extracting embeddings for each detected region (CLIP, DINOv2, etc.)
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3. Comparing embeddings with reference logos for identification
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The class automatically checks for local models before downloading from HuggingFace.
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Supported embedding models:
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- CLIP models (openai/clip-vit-*): Text-image alignment
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- DINOv2 models (facebook/dinov2-*): Self-supervised visual features
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"""
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def __init__(
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self,
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logger,
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detr_model: str = "Pravallika6/detr-finetuned-logo-detection_v2",
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embedding_model: str = "openai/clip-vit-large-patch14",
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detr_threshold: float = 0.5,
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min_box_size: int = 20,
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nms_iou_threshold: float = 0.5,
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):
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"""
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Initialize DETR and embedding models.
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The class will automatically check for local models in the default directories
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before downloading from HuggingFace. You can override this by providing absolute
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paths to local models.
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Args:
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logger: Logger instance for logging
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detr_model: HuggingFace model name or local path for DETR object detection
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embedding_model: HuggingFace model name for embeddings (CLIP or DINOv2)
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detr_threshold: Confidence threshold for DETR detections (0-1)
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min_box_size: Minimum width/height in pixels for detected boxes (filters noise)
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nms_iou_threshold: IoU threshold for Non-Maximum Suppression
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"""
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self.logger = logger
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self.detr_threshold = detr_threshold
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self.min_box_size = min_box_size
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self.nms_iou_threshold = nms_iou_threshold
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self.embedding_model_name = embedding_model
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# Set device
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self.device_str = "cuda:0" if torch.cuda.is_available() else "cpu"
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self.device_index = 0 if torch.cuda.is_available() else -1
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self.device = torch.device(self.device_str)
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self.logger.info(f"Initializing DetectLogosDETR on device: {self.device_str}")
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# Get default model directories from environment variables
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default_detr_dir = os.environ.get('LOGO_DETR_MODEL_DIR', 'models/logo_detection/detr')
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default_embedding_dir = os.environ.get('LOGO_EMBEDDING_MODEL_DIR', 'models/logo_detection/embedding')
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# Resolve DETR model path (check local first, then use HuggingFace name)
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detr_model_path = self._resolve_model_path(
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detr_model, default_detr_dir, "DETR"
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)
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# Initialize DETR pipeline for logo detection
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self.logger.info(f"Loading DETR model: {detr_model_path}")
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self.detr_pipe = pipeline(
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task="object-detection",
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model=detr_model_path,
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device=self.device_index,
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use_fast=True,
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)
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# Resolve embedding model path
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embedding_model_path = self._resolve_model_path(
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embedding_model, default_embedding_dir, "Embedding"
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)
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# Detect model type and initialize accordingly
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self.model_type = self._detect_model_type(embedding_model)
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self.logger.info(f"Loading {self.model_type} embedding model: {embedding_model_path}")
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if self.model_type == "clip":
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self.embedding_model = CLIPModel.from_pretrained(embedding_model_path).to(self.device)
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self.embedding_processor = CLIPProcessor.from_pretrained(embedding_model_path)
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else: # dinov2 or other transformer models
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self.embedding_model = AutoModel.from_pretrained(embedding_model_path).to(self.device)
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self.embedding_processor = AutoImageProcessor.from_pretrained(embedding_model_path)
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self.logger.info("DetectLogosDETR initialization complete")
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def _detect_model_type(self, model_name: str) -> str:
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"""Detect the type of embedding model based on name."""
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model_name_lower = model_name.lower()
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if "clip" in model_name_lower:
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return "clip"
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elif "dino" in model_name_lower:
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return "dinov2"
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else:
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# Default to generic transformer for unknown models
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return "transformer"
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def _resolve_model_path(
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self, model_name_or_path: str, default_local_dir: str, model_type: str
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) -> str:
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"""
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Resolve model path, checking for local models before using HuggingFace.
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Args:
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model_name_or_path: HuggingFace model name or absolute path
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default_local_dir: Default local directory to check
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model_type: Type of model (for logging, e.g., "DETR" or "CLIP")
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Returns:
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Resolved model path (local path or HuggingFace model name)
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"""
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# If it's an absolute path, use it directly
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if os.path.isabs(model_name_or_path):
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if os.path.exists(model_name_or_path):
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self.logger.info(
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f"{model_type} model: Using local model at {model_name_or_path}"
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)
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return model_name_or_path
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else:
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self.logger.warning(
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f"{model_type} model: Local path {model_name_or_path} does not exist, "
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f"falling back to HuggingFace"
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)
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return model_name_or_path
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# Check if default local directory exists
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if os.path.exists(default_local_dir):
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# Verify it's a valid model directory (has config.json)
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config_file = os.path.join(default_local_dir, "config.json")
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if os.path.exists(config_file):
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abs_path = os.path.abspath(default_local_dir)
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self.logger.info(
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f"{model_type} model: Found local model at {abs_path}"
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)
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return abs_path
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else:
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self.logger.warning(
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f"{model_type} model: Local directory {default_local_dir} exists but "
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f"is not a valid model (missing config.json)"
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)
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# Use HuggingFace model name
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self.logger.info(
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f"{model_type} model: No local model found, will download from HuggingFace: "
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f"{model_name_or_path}"
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)
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return model_name_or_path
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def detect(self, image: np.ndarray) -> List[Dict[str, Any]]:
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"""
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Detect logos in an image and return bounding boxes with CLIP embeddings.
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Args:
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image: OpenCV image (BGR format, numpy array)
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Returns:
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List of dictionaries, each containing:
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- 'box': dict with 'xmin', 'ymin', 'xmax', 'ymax' (pixel coordinates)
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- 'score': DETR confidence score (float 0-1)
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- 'embedding': CLIP feature embedding (torch.Tensor)
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- 'label': DETR predicted label (string)
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"""
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# Convert OpenCV BGR to RGB PIL Image
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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pil_image = Image.fromarray(image_rgb)
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# Run DETR detection
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predictions = self.detr_pipe(pil_image)
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# Filter by threshold and size, then add CLIP embeddings
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detections = []
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for pred in predictions:
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score = pred.get("score", 0.0)
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if score < self.detr_threshold:
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continue
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box = pred.get("box", {})
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xmin = box.get("xmin", 0)
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ymin = box.get("ymin", 0)
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xmax = box.get("xmax", 0)
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ymax = box.get("ymax", 0)
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# Filter by minimum box size
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box_width = xmax - xmin
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box_height = ymax - ymin
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if box_width < self.min_box_size or box_height < self.min_box_size:
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continue
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# Extract bounding box region
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bbox_crop = pil_image.crop((xmin, ymin, xmax, ymax))
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# Get embedding for this region
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embedding = self._get_embedding_pil(bbox_crop)
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detections.append(
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{
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"box": {"xmin": xmin, "ymin": ymin, "xmax": xmax, "ymax": ymax},
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"score": score,
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"embedding": embedding,
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"label": pred.get("label", "logo"),
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}
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)
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# Apply Non-Maximum Suppression to remove overlapping detections
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detections = self._apply_nms(detections, self.nms_iou_threshold)
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self.logger.debug(f"Detected {len(detections)} logos (threshold: {self.detr_threshold})")
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return detections
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def _apply_nms(self, predictions: List[Dict], iou_threshold: float) -> List[Dict]:
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"""
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Apply Non-Maximum Suppression to remove overlapping detections.
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Args:
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predictions: List of prediction dictionaries with 'box' and 'score'
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iou_threshold: IoU threshold for considering boxes as overlapping
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Returns:
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Filtered list of predictions after NMS
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"""
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if len(predictions) == 0:
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return []
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# Extract boxes and scores
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boxes = []
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scores = []
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for pred in predictions:
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box = pred.get("box", {})
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boxes.append([
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box.get("xmin", 0),
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box.get("ymin", 0),
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box.get("xmax", 0),
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box.get("ymax", 0)
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])
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scores.append(pred.get("score", 0.0))
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# Convert to numpy arrays
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boxes = np.array(boxes, dtype=np.float32)
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scores = np.array(scores, dtype=np.float32)
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# Sort by scores (descending)
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sorted_indices = np.argsort(scores)[::-1]
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keep_indices = []
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while len(sorted_indices) > 0:
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# Keep the box with highest score
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current_idx = sorted_indices[0]
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keep_indices.append(current_idx)
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if len(sorted_indices) == 1:
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break
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# Calculate IoU with remaining boxes
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current_box = boxes[current_idx]
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remaining_boxes = boxes[sorted_indices[1:]]
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ious = self._calculate_iou_batch(current_box, remaining_boxes)
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# Keep only boxes with IoU below threshold
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mask = ious < iou_threshold
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sorted_indices = sorted_indices[1:][mask]
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# Return predictions for kept indices
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return [predictions[i] for i in keep_indices]
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def _calculate_iou_batch(self, box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
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"""
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Calculate IoU between one box and multiple boxes.
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Args:
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box: Single box [xmin, ymin, xmax, ymax]
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boxes: Multiple boxes [[xmin, ymin, xmax, ymax], ...]
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Returns:
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Array of IoU values
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"""
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# Calculate intersection coordinates
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x1 = np.maximum(box[0], boxes[:, 0])
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y1 = np.maximum(box[1], boxes[:, 1])
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x2 = np.minimum(box[2], boxes[:, 2])
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y2 = np.minimum(box[3], boxes[:, 3])
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# Calculate intersection area
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intersection = np.maximum(0, x2 - x1) * np.maximum(0, y2 - y1)
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# Calculate union area
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box_area = (box[2] - box[0]) * (box[3] - box[1])
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boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
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union = box_area + boxes_area - intersection
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# Calculate IoU
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iou = intersection / (union + 1e-6) # Add small epsilon to avoid division by zero
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return iou
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def get_embedding(self, image: np.ndarray) -> torch.Tensor:
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"""
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Get embedding for a reference logo image.
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This method is used to compute embeddings for reference logos
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that will be compared against detected regions.
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Args:
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image: OpenCV image (BGR format, numpy array)
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Returns:
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Normalized feature embedding (torch.Tensor)
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"""
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# Convert OpenCV BGR to RGB PIL Image
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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pil_image = Image.fromarray(image_rgb)
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return self._get_embedding_pil(pil_image)
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def _get_embedding_pil(self, pil_image: Image.Image) -> torch.Tensor:
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"""
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Internal method to get embedding from PIL image.
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Handles both CLIP and DINOv2 model types.
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Args:
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pil_image: PIL Image (RGB format)
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Returns:
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Normalized feature embedding (torch.Tensor)
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"""
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# Process image through the embedding model
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inputs = self.embedding_processor(images=pil_image, return_tensors="pt").to(self.device)
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with torch.no_grad():
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if self.model_type == "clip":
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# CLIP has a dedicated method for image features
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features = self.embedding_model.get_image_features(**inputs)
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else:
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# DINOv2 and other transformers use the CLS token or pooled output
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outputs = self.embedding_model(**inputs)
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# Use the CLS token (first token) from last hidden state
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if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None:
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features = outputs.pooler_output
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else:
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# Use CLS token from last_hidden_state
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features = outputs.last_hidden_state[:, 0, :]
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# Normalize for cosine similarity
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features = F.normalize(features, dim=-1)
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return features
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def compare_embeddings(
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self, embedding1: torch.Tensor, embedding2: torch.Tensor
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) -> float:
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"""
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Compute cosine similarity between two CLIP embeddings.
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Args:
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embedding1: First CLIP embedding (torch.Tensor)
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embedding2: Second CLIP embedding (torch.Tensor)
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Returns:
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Cosine similarity score (float, range: -1 to 1, typically 0 to 1)
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"""
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# Ensure tensors are on the same device
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if embedding1.device != embedding2.device:
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embedding2 = embedding2.to(embedding1.device)
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# Compute cosine similarity
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similarity = F.cosine_similarity(embedding1, embedding2, dim=-1)
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# Return as Python float
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return similarity.item()
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def find_best_match(
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self,
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detected_embedding: torch.Tensor,
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reference_embeddings: List[Tuple[str, torch.Tensor]],
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similarity_threshold: float = 0.7,
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) -> Optional[Tuple[str, float]]:
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"""
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Find the best matching reference logo for a detected embedding.
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Args:
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detected_embedding: CLIP embedding from detected logo region
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reference_embeddings: List of (label, embedding) tuples for reference logos
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similarity_threshold: Minimum similarity to consider a match (0-1)
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Returns:
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Tuple of (label, similarity) for best match, or None if no match above threshold
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"""
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if not reference_embeddings:
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return None
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best_similarity = -1.0
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best_label = None
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for label, ref_embedding in reference_embeddings:
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similarity = self.compare_embeddings(detected_embedding, ref_embedding)
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if similarity > best_similarity:
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best_similarity = similarity
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best_label = label
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if best_similarity >= similarity_threshold:
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return (best_label, best_similarity)
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else:
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return None
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def find_all_matches(
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self,
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detected_embedding: torch.Tensor,
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reference_embeddings: List[Tuple[str, torch.Tensor]],
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similarity_threshold: float = 0.7,
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) -> List[Tuple[str, float]]:
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"""
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Find all matching reference logos above the similarity threshold.
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Unlike find_best_match, this returns ALL logos that have at least one
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reference above threshold. Each unique logo is returned once with its
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highest similarity score.
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Args:
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detected_embedding: CLIP embedding from detected logo region
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reference_embeddings: List of (label, embedding) tuples for reference logos
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similarity_threshold: Minimum similarity to consider a match (0-1)
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Returns:
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List of (label, similarity) tuples for all matches above threshold,
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sorted by similarity descending. Each logo appears at most once.
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"""
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if not reference_embeddings:
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return []
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# Track best similarity for each logo
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logo_best_sim: Dict[str, float] = {}
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for label, ref_embedding in reference_embeddings:
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similarity = self.compare_embeddings(detected_embedding, ref_embedding)
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if similarity >= similarity_threshold:
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if label not in logo_best_sim or similarity > logo_best_sim[label]:
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logo_best_sim[label] = similarity
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# Convert to list and sort by similarity descending
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matches = [(label, sim) for label, sim in logo_best_sim.items()]
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matches.sort(key=lambda x: x[1], reverse=True)
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return matches
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def find_best_match_multi_ref(
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self,
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detected_embedding: torch.Tensor,
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reference_embeddings: Dict[str, List[torch.Tensor]],
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similarity_threshold: float = 0.85,
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min_matching_refs: int = 1,
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use_mean_similarity: bool = True,
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margin: float = 0.0,
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) -> Optional[Tuple[str, float, int]]:
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"""
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Find the best matching reference logo using multiple reference embeddings per logo.
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This method improves accuracy by using multiple reference images for each logo
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and requiring consistency across references.
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Args:
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detected_embedding: CLIP embedding from detected logo region
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reference_embeddings: Dict mapping logo name to list of embeddings
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similarity_threshold: Minimum similarity to consider a match (0-1)
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min_matching_refs: Minimum number of references that must match above threshold
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|
use_mean_similarity: If True, use mean similarity across all refs; if False, use max
|
|
margin: Required margin between best and second-best logo scores (0-1)
|
|
|
|
Returns:
|
|
Tuple of (label, similarity, num_matching_refs) for best match,
|
|
or None if no match meets criteria
|
|
"""
|
|
if not reference_embeddings:
|
|
return None
|
|
|
|
# Calculate scores for all logos that meet the min_matching_refs requirement
|
|
logo_scores = []
|
|
|
|
for label, ref_embedding_list in reference_embeddings.items():
|
|
if not ref_embedding_list:
|
|
continue
|
|
|
|
# Calculate similarity to each reference embedding
|
|
similarities = []
|
|
for ref_embedding in ref_embedding_list:
|
|
sim = self.compare_embeddings(detected_embedding, ref_embedding)
|
|
similarities.append(sim)
|
|
|
|
# Count how many references match above threshold
|
|
num_matches = sum(1 for s in similarities if s >= similarity_threshold)
|
|
|
|
# Calculate aggregate score
|
|
if use_mean_similarity:
|
|
score = sum(similarities) / len(similarities)
|
|
else:
|
|
score = max(similarities)
|
|
|
|
# Only consider logos that meet the minimum matching refs requirement
|
|
if num_matches >= min_matching_refs:
|
|
logo_scores.append((label, score, num_matches))
|
|
|
|
if not logo_scores:
|
|
return None
|
|
|
|
# Sort by score descending
|
|
logo_scores.sort(key=lambda x: x[1], reverse=True)
|
|
|
|
best_label, best_score, best_num_matches = logo_scores[0]
|
|
|
|
# Check if best score meets threshold
|
|
if best_score < similarity_threshold:
|
|
return None
|
|
|
|
# Check margin against second-best logo (if exists)
|
|
if margin > 0 and len(logo_scores) > 1:
|
|
second_best_score = logo_scores[1][1]
|
|
if best_score - second_best_score < margin:
|
|
return None # Not confident enough
|
|
|
|
return (best_label, best_score, best_num_matches)
|
|
|
|
def find_best_match_with_margin(
|
|
self,
|
|
detected_embedding: torch.Tensor,
|
|
reference_embeddings: List[Tuple[str, torch.Tensor]],
|
|
similarity_threshold: float = 0.85,
|
|
margin: float = 0.05,
|
|
) -> Optional[Tuple[str, float]]:
|
|
"""
|
|
Find best match with a confidence margin over the second-best match.
|
|
|
|
This reduces false positives by requiring the best match to be
|
|
significantly better than alternatives.
|
|
|
|
Args:
|
|
detected_embedding: CLIP embedding from detected logo region
|
|
reference_embeddings: List of (label, embedding) tuples for reference logos
|
|
similarity_threshold: Minimum similarity to consider a match (0-1)
|
|
margin: Required margin between best and second-best match
|
|
|
|
Returns:
|
|
Tuple of (label, similarity) for best match, or None if no confident match
|
|
"""
|
|
if not reference_embeddings:
|
|
return None
|
|
|
|
# Calculate all similarities
|
|
similarities = []
|
|
for label, ref_embedding in reference_embeddings:
|
|
sim = self.compare_embeddings(detected_embedding, ref_embedding)
|
|
similarities.append((label, sim))
|
|
|
|
# Sort by similarity descending
|
|
similarities.sort(key=lambda x: x[1], reverse=True)
|
|
|
|
best_label, best_sim = similarities[0]
|
|
|
|
# Check if best is above threshold
|
|
if best_sim < similarity_threshold:
|
|
return None
|
|
|
|
# Check margin against second best (if exists)
|
|
if len(similarities) > 1:
|
|
second_best_sim = similarities[1][1]
|
|
if best_sim - second_best_sim < margin:
|
|
return None # Not confident enough
|
|
|
|
return (best_label, best_sim)
|
|
|
|
def detect_and_match(
|
|
self,
|
|
image: np.ndarray,
|
|
reference_embeddings: List[Tuple[str, torch.Tensor]],
|
|
similarity_threshold: float = 0.7,
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Detect logos and match them against reference embeddings in one step.
|
|
|
|
This is a convenience method that combines detection and matching.
|
|
|
|
Args:
|
|
image: OpenCV image (BGR format, numpy array)
|
|
reference_embeddings: List of (label, embedding) tuples for reference logos
|
|
similarity_threshold: Minimum similarity to consider a match (0-1)
|
|
|
|
Returns:
|
|
List of matched detections, each containing:
|
|
- 'box': bounding box coordinates
|
|
- 'detr_score': DETR confidence score
|
|
- 'clip_similarity': CLIP similarity score
|
|
- 'label': matched reference logo label
|
|
"""
|
|
# Detect all logos
|
|
detections = self.detect(image)
|
|
|
|
# Match each detection against references
|
|
matched_detections = []
|
|
for detection in detections:
|
|
match_result = self.find_best_match(
|
|
detection["embedding"], reference_embeddings, similarity_threshold
|
|
)
|
|
|
|
if match_result is not None:
|
|
label, similarity = match_result
|
|
matched_detections.append(
|
|
{
|
|
"box": detection["box"],
|
|
"detr_score": detection["score"],
|
|
"clip_similarity": similarity,
|
|
"label": label,
|
|
}
|
|
)
|
|
|
|
self.logger.debug(
|
|
f"Matched {len(matched_detections)}/{len(detections)} detections "
|
|
f"(threshold: {similarity_threshold})"
|
|
)
|
|
|
|
return matched_detections |