ddccf653d2
Add DETR+CLIP based logo detection library and test framework: - DetectLogosDETR class for logo detection and matching - Test script with margin-based and multi-ref matching methods - Data preparation script for test database - Documentation for API usage and test methodology
53 lines
1.8 KiB
Markdown
53 lines
1.8 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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Logo detection system using deep learning models:
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- **DETR** (DEtection TRansformer) for logo region detection
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- **CLIP** (Contrastive Language-Image Pre-training) for feature extraction and matching
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## Development Commands
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```bash
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# Install dependencies (uses uv package manager)
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uv sync
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# Run main script
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uv run python main.py
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# Run logo detection module directly
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uv run python logo_detection_detr.py
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```
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## Architecture
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### Core Module: `logo_detection_detr.py`
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The `DetectLogosDETR` class provides the main detection pipeline:
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1. **Detection Flow**: OpenCV image (BGR) → DETR detects bounding boxes → CLIP extracts embeddings for each region
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2. **Matching Flow**: Compare detected embeddings against reference logo embeddings using cosine similarity
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**Key Methods:**
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- `detect(image)` - Detect logos, returns boxes + CLIP embeddings
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- `get_embedding(image)` - Get CLIP embedding for a reference logo
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- `compare_embeddings(emb1, emb2)` - Cosine similarity between embeddings
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- `detect_and_match(image, references, threshold)` - Combined detection and matching
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### Model Configuration
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Models are resolved in this order:
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1. Absolute path if provided
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2. Local directory from environment variables (`LOGO_DETR_MODEL_DIR`, `LOGO_CLIP_MODEL_DIR`)
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3. Default local paths: `models/logo_detection/detr`, `models/logo_detection/clip`
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4. HuggingFace download as fallback
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Default models:
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- DETR: `Pravallika6/detr-finetuned-logo-detection_v2`
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- CLIP: `openai/clip-vit-large-patch14`
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### Reference Dataset
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`LogoDet-3K/` contains logo images organized by category: Clothes, Electronic, Food, Leisure, Medical, Necessities, Others, Sports, Transportation. |