Select pictures and search pictures locally

Software that can search hundreds of millions of images on your local computer by image and text without tagging. You can use text to search for content in pictures. For example, if there is an apple in the picture, search for "apple" to find the picture. You can also find the corresponding person through the face in the photo.

Advantages of local image search software

        • Hybrid index architecture: Combining Elasticsearch and graph database Neo4j

        • Parallel computing engine: CUDA-accelerated GPU inference (NVIDIA TensorRT optimization)

         • Storage optimization: Using PCA dimensionality reduction technology, the feature vector compression ratio reaches 1:32

         • Privacy Protection: Localized deployment solution, supporting GDPR compliance data desensitization

Image selection and local image search software application scenarios

【Enterprise level】

        • Integrated Media Center: Processing 500,000+ news image retrievals on average daily

         • E-commerce platform: SPU product image deduplication (accuracy rate 99.3%)

        • Security field: Real-time control of tens of millions of face databases

【Individual User】

        • Photographer: Intelligent management of RAW format work library

        • Home users: Reconstruction of character timelines from 10 years of photo library

• Designers: Semantic associative search of inspiration libraries

【Hybrid architecture design】

        • Dual-process communication model: Electron (front-end GUI) + Python microservice (computing engine)

         • IPC communication optimization: Based on ZeroMQ binary protocol transmission, achieving 10Gb/s level data throughput

         • Computing isolation mechanism: Key AI operations are encapsulated as independent gRPC services, supporting GPU resource pooling

【High performance computing architecture】

​​Multimodal reasoning engine

Integrated ONNX Runtime multi-platform acceleration framework (Windows/MacOS dual version adaptation)

Supports CUDA 12.2 and CoreML 5.0 heterogeneous computing

Model quantization compression technology: FP32→INT8 precision conversion (maintaining 98.7% accuracy)

Distributed feature engineering

FAISS trillion-level vector retrieval: IVF4096_PQ256 index strategy

​​Hybrid similarity algorithm: PHash + CNN feature weighted fusion (weight ratio 6:4)

Memory optimization: using mmap technology to load 40TB feature library

​​Professional-grade image pipeline

RAW format processing chain: 14bit color depth analysis based on rawpy

Multi-layer processing: PSD file layered analysis (supports Photoshop v23+)

HEIF efficient encoding and decoding: special optimization of Apple ProRAW format

【Engineering Innovation】

          • Cross-platform compilation system: Nuitka commercial-grade packaging solution (code confusion rate ≥92%)

           • Memory safety strategy: Rewrite key IO modules in Rust (blake3 hash acceleration by 300%)

         • Intelligent resource scheduling: dynamic memory recycling mechanism based on psutil (OOM risk reduced by 87%)

【Core technology components】

Text recognition core

RapidOCR-ONNX multi-language engine: supports mixed recognition of 87 languages

Document parsing extension: Excel/PDF structured data extraction (openpyxl+PyMuPDF)

AI model matrix

Image classification: EfficientNet-B7 (ImageNet Top-1 84.9%)

Target detection: YOLOv8s (COCO mAP@0.5 52.3)

Semantic segmentation: DeepLabv3+ (Cityscapes mIOU 82.1%)

​​Professional processing algorithms

Image background removal: U²-Net deeply optimized version (rembg 2.0 custom engine)

Perceptual hashing: Wavelet Hash + Difference Hash dual engine