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@@ -1,9 +1,8 @@
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import { Component, OnInit, ElementRef, ViewChild } from '@angular/core';
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import { CommonModule } from '@angular/common';
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import { ImageProcessorService } from '../../services/image-processor.service';
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-import { LocalInferenceService } from '../../services/local-inference.service';
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import { LocalHistoryService } from '../../services/local-history.service';
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-import { InferenceService } from '../../core/services/inference.service';
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+import { InferenceService, InferenceMode, LocalEngine } from '../../core/services/inference.service';
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import { FormsModule } from '@angular/forms';
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@Component({
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@@ -19,7 +18,6 @@ export class AnalyzerComponent implements OnInit {
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previewUrl: string | null = null;
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results: any = null;
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loading = false;
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- modelType = 'onnx';
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confidence = 0.25;
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isDragging = false;
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@@ -30,7 +28,6 @@ export class AnalyzerComponent implements OnInit {
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) {}
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ngOnInit(): void {
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- // Confidence is now managed locally for the PoC
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this.confidence = 0.25;
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}
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@@ -76,6 +73,14 @@ export class AnalyzerComponent implements OnInit {
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reader.readAsDataURL(file);
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}
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+ onModeChange(newMode: any): void {
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+ this.inferenceService.mode.set(newMode as InferenceMode);
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+ }
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+
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+ onEngineChange(newEngine: any): void {
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+ this.inferenceService.localEngine.set(newEngine as LocalEngine);
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+ }
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+
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async analyze(): Promise<void> {
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if (!this.selectedFile || !this.previewUrl) return;
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@@ -83,30 +88,17 @@ export class AnalyzerComponent implements OnInit {
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const start = performance.now();
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try {
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- // Get image dimensions
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+ // Get image dimensions for scaling help
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const img = await this.loadImageDimensions(this.selectedFile);
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// Use the Master Inference Service Hub
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this.inferenceService.analyze(this.previewUrl, img.width, img.height).subscribe({
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next: (detections) => {
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- // Calculate Industrial Summary
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- const summary: any = {
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- 'Empty_Bunch': 0,
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- 'Underripe': 0,
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- 'Abnormal': 0,
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- 'Ripe': 0,
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- 'Unripe': 0,
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- 'Overripe': 0
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- };
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-
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- detections.forEach((d: any) => {
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- if (summary[d.class] !== undefined) summary[d.class]++;
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- });
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-
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this.results = {
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- industrial_summary: summary,
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+ industrial_summary: this.inferenceService.summary(),
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inference_ms: performance.now() - start,
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- detections: detections
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+ detections: detections,
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+ original_dimensions: img
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};
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// Persist to local vault
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@@ -132,7 +124,6 @@ export class AnalyzerComponent implements OnInit {
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}
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}
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- // Helper to get raw file dimensions
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private loadImageDimensions(file: File): Promise<{width: number, height: number}> {
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return new Promise((resolve) => {
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const img = new Image();
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@@ -146,43 +137,60 @@ export class AnalyzerComponent implements OnInit {
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}
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drawDetections(): void {
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- if (!this.results || !this.results.detections || !this.canvas) return;
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+ const detections = this.inferenceService.detections();
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+ if (!detections || !this.canvas || !this.previewUrl) return;
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const ctx = this.canvas.nativeElement.getContext('2d');
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if (!ctx) return;
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const img = new Image();
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- img.src = this.previewUrl!;
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+ img.src = this.previewUrl;
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img.onload = () => {
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const canvas = this.canvas.nativeElement;
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const containerWidth = canvas.parentElement!.clientWidth;
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- const scale = containerWidth / img.width;
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+
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+ // Calculate display scale factor
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+ const displayScale = containerWidth / img.width;
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canvas.width = containerWidth;
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- canvas.height = img.height * scale;
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+ canvas.height = img.height * displayScale;
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ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
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- this.results.detections.forEach((det: any) => {
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- const [x1, y1, x2, y2] = det.box.map((v: number) => v * scale);
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+ detections.forEach((det: any) => {
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+ let x1: number, y1: number, x2: number, y2: number;
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+
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+ if (this.inferenceService.mode() === 'api') {
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+ // API returns absolute pixels (e.g. 450 on a 640px base, but relative to original)
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+ // We need to scale them to the current canvas display size
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+ [x1, y1, x2, y2] = det.box.map((v: number) => v * displayScale);
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+ } else {
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+ // Local mode often uses normalized coordinates or absolute pixels depending on parser
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+ // Assuming the InferenceService already normalized or scaled these for us
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+ [x1, y1, x2, y2] = det.box.map((v: number) => v * displayScale);
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+ }
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- ctx.strokeStyle = det.color;
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+ ctx.strokeStyle = det.color || '#00A651';
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ctx.lineWidth = 3;
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ctx.strokeRect(x1, y1, x2 - x1, y2 - y1);
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- ctx.fillStyle = det.color;
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- ctx.font = '14px Outfit';
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+ // Drawing Label
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+ ctx.fillStyle = det.color || '#00A651';
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+ ctx.font = 'bold 14px Outfit';
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const label = `${det.class} ${Math.round(det.confidence * 100)}%`;
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const textWidth = ctx.measureText(label).width;
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- ctx.fillRect(x1, y1 - 20, textWidth + 10, 20);
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+
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+ // Background for text
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+ ctx.fillRect(x1, y1 - 25, textWidth + 10, 25);
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ctx.fillStyle = '#FFFFFF';
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- ctx.fillText(label, x1 + 5, y1 - 5);
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+ ctx.fillText(label, x1 + 5, y1 - 7);
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});
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};
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}
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getSummaryKeys(): string[] {
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- return this.results ? Object.keys(this.results.industrial_summary) : [];
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+ const summary = this.inferenceService.summary();
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+ return summary ? Object.keys(summary) : [];
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}
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}
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Dr-Swopt