ne101_camera component architecture deep-dive: 5-layer decomposition of the 1972-line IIFE (helper / template / sub-component / main / export), 3 exported components (NE101CameraPanel / ConfigPanel / AdvancedPanel), the WebSocket-priority + REST-fallback data flow, the ROI overlay pipeline, and an architectural comparison with metric_card
Learn how to monitor urban waste bin overflow using NeoEyes NE301 and Home Assistant. Transform city sanitation from scheduled cleaning to demand-driven efficiency.
ne101_camera component build: IIFE injection pattern (window.React + jsxRuntime), named export object, five-layer module structure, three React-in-IIFE pitfalls (#310 hook order, frozen input, conditional useState), ConfigPanel/AdvancedPanel/ExtDropdown sub-components, shadcn CSS class replica
ne101_camera data contract: MQTT device telemetry, extension response normalization (boxes_x1y1x2y2 / objects_bbox / detections_bbox / ocr_text_blocks), JSON string detections parsing, ROI Sutherland-Hodgman clipping algorithm, virtual metrics output prefix mapping
ne101_camera deep dive: 133-commit version evolution, the rise and fall of Transform lifecycle debug traces (4 debug commits finally cleaned up by 00a59cc), the Boa engine console.log crash incident, the _configHash performance optimization, source hygiene recap (3 files, zero backups)
ne101_camera extension-side contract: the processingExtensionId generic AI processing paradigm, the AI_EXT_IDS whitelist, the EXT_MODES mode catalog (imageArg/responseType/command triple), the __imageData injection mechanism, the locate-anything-v2 NMS threshold special-case, and the extension degradation fallback
ne101_camera frontend consumption: detections fetching, JSON string parsing, per-class coloring (golden-angle HSV), SVG overlay rendering (polygon + rect fallback), object-cover coordinate transform, ResizeObserver callback-ref pattern, Transform tiered lifecycle
ne101_camera integration tests: end-to-end test matrix (test_bundle.js 35KB), ROI overlay verification (Sutherland-Hodgman clipping + object-cover mapping), multi-extension switching tests (locate-anything-v2 / image-analyzer-v2 / yolo-device-inference / ocr-device-inference), source_ts alignment verification, WS+REST dual-channel tests
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NeoMind's most complex streaming extension: Push-mode real-time video processing, YOLOv11 detection, ROI/line-crossing/smart-capture, ffmpeg-next + nokhwa dual backends, cross-platform ONNX Runtime dylib governance, and frontend MJPEG integration — a complete engineering dissection