feat(vision): add TensorFlow.js column classifier model and improve detection
- Add trained CNN model for abacus column digit classification - model.json: TensorFlow.js layers model (fixed for Keras 3 compatibility) - group1-shard1of1.bin: quantized model weights (~2.2MB) - Improve detection performance and stability - Throttle inference to 5fps (was running every animation frame) - Lower stability threshold: 3 consecutive frames (was 10) - Lower confidence threshold: 50% (was 70%) - Clean up debug logging from development Note: Model trained on synthetic data, accuracy on real images is limited. Future work: retrain on real abacus photos for better accuracy. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Thomas Hallock
parent
7a9185eadb
commit
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||||
"scale": 0.0008689317048764697,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "batch_normalization_3/beta",
|
||||
"shape": [128],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.05253423078387391,
|
||||
"scale": 0.00032833894239921196,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "batch_normalization_3/moving_mean",
|
||||
"shape": [128],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": 2.3402893845059225e-8,
|
||||
"scale": 0.124165194550534,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "batch_normalization_3/moving_variance",
|
||||
"shape": [128],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": 0.000532600621227175,
|
||||
"scale": 0.8092722632006888,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "conv2d/kernel",
|
||||
"shape": [3, 3, 1, 32],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.1684967933916578,
|
||||
"scale": 0.0012961291799358293,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "conv2d/bias",
|
||||
"shape": [32],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.014791351323034248,
|
||||
"scale": 0.00019462304372413485,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "conv2d_1/kernel",
|
||||
"shape": [3, 3, 32, 64],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.14185832411635155,
|
||||
"scale": 0.0010912178778180888,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "conv2d_1/bias",
|
||||
"shape": [64],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.052345379924072934,
|
||||
"scale": 0.00033341006321065564,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "conv2d_2/kernel",
|
||||
"shape": [3, 3, 64, 128],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.09215074052997664,
|
||||
"scale": 0.0007199276603904425,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "conv2d_2/bias",
|
||||
"shape": [128],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.052666782806901374,
|
||||
"scale": 0.00035346834098591524,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "dense/kernel",
|
||||
"shape": [16384, 128],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.1078803108311167,
|
||||
"scale": 0.0006960020053620432,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "dense/bias",
|
||||
"shape": [128],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.010696043731535184,
|
||||
"scale": 0.00013539295862702763,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "dense_1/kernel",
|
||||
"shape": [128, 10],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.26071277062098186,
|
||||
"scale": 0.002190863618663713,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "dense_1/bias",
|
||||
"shape": [10],
|
||||
"dtype": "float32",
|
||||
"quantization": {
|
||||
"dtype": "uint8",
|
||||
"min": -0.020677046455881174,
|
||||
"scale": 0.00016028718182853623,
|
||||
"original_dtype": "float32"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -83,6 +83,10 @@ export function useAbacusVision(options: UseAbacusVisionOptions = {}): UseAbacus
|
|||
// Track previous stable value to avoid duplicate callbacks
|
||||
const lastStableValueRef = useRef<number | null>(null)
|
||||
|
||||
// Throttle inference to 5fps for performance
|
||||
const lastInferenceTimeRef = useRef<number>(0)
|
||||
const INFERENCE_INTERVAL_MS = 200 // 5fps
|
||||
|
||||
// Ref for calibration functions to avoid infinite loop in auto-calibration effect
|
||||
const calibrationRef = useRef(calibration)
|
||||
calibrationRef.current = calibration
|
||||
|
|
@ -274,6 +278,13 @@ export function useAbacusVision(options: UseAbacusVisionOptions = {}): UseAbacus
|
|||
* Process a video frame for detection using TensorFlow.js classifier
|
||||
*/
|
||||
const processFrame = useCallback(async () => {
|
||||
// Throttle inference for performance (5fps instead of 60fps)
|
||||
const now = performance.now()
|
||||
if (now - lastInferenceTimeRef.current < INFERENCE_INTERVAL_MS) {
|
||||
return
|
||||
}
|
||||
lastInferenceTimeRef.current = now
|
||||
|
||||
// Get video element from camera stream
|
||||
const videoElements = document.querySelectorAll('video')
|
||||
let video: HTMLVideoElement | null = null
|
||||
|
|
@ -292,12 +303,10 @@ export function useAbacusVision(options: UseAbacusVisionOptions = {}): UseAbacus
|
|||
|
||||
// Process video frame into column strips
|
||||
const columnImages = processVideoFrame(video, calibration.calibration)
|
||||
|
||||
if (columnImages.length === 0) return
|
||||
|
||||
// Run classification
|
||||
const result = await classifier.classifyColumns(columnImages)
|
||||
|
||||
if (!result) return
|
||||
|
||||
// Update column confidences
|
||||
|
|
|
|||
|
|
@ -86,7 +86,6 @@ export function useColumnClassifier(): UseColumnClassifierReturn {
|
|||
setIsModelLoaded(true)
|
||||
return true
|
||||
} else {
|
||||
// Model doesn't exist - not an error, just unavailable
|
||||
setIsModelUnavailable(true)
|
||||
return false
|
||||
}
|
||||
|
|
|
|||
|
|
@ -187,8 +187,8 @@ export interface FrameStabilityConfig {
|
|||
* Default stability configuration
|
||||
*/
|
||||
export const DEFAULT_STABILITY_CONFIG: FrameStabilityConfig = {
|
||||
minConsecutiveFrames: 10, // ~300ms at 30fps
|
||||
minConfidence: 0.7,
|
||||
minConsecutiveFrames: 3, // 600ms at 5fps inference rate
|
||||
minConfidence: 0.5, // Lower threshold - model confidence is often 60-80%
|
||||
handMotionThreshold: 0.3,
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue