premier app version beta
This commit is contained in:
775
lib/services/image_processing_service.dart
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775
lib/services/image_processing_service.dart
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import 'dart:io';
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import 'dart:math' as math;
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import 'package:image/image.dart' as img;
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class DetectedCircle {
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final double centerX;
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final double centerY;
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final double radius;
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DetectedCircle({
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required this.centerX,
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required this.centerY,
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required this.radius,
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});
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}
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class DetectedImpact {
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final double x;
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final double y;
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final double radius;
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DetectedImpact({
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required this.x,
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required this.y,
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required this.radius,
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});
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}
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/// Image processing settings for impact detection
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class ImpactDetectionSettings {
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/// Threshold for dark spot detection (0-255, lower = darker)
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final int darkThreshold;
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/// Minimum impact size in pixels
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final int minImpactSize;
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/// Maximum impact size in pixels
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final int maxImpactSize;
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/// Blur radius for noise reduction
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final int blurRadius;
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/// Contrast enhancement factor
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final double contrastFactor;
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/// Minimum circularity (0-1, 1 = perfect circle)
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/// Used to filter out non-circular shapes like numbers
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final double minCircularity;
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/// Maximum aspect ratio (width/height or height/width)
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/// Used to filter out elongated shapes like numbers
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final double maxAspectRatio;
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/// Minimum fill ratio (0-1, ~0.7 for filled circle, lower for rings/hollow shapes)
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/// A bullet hole is FILLED, numbers on target are hollow rings
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final double minFillRatio;
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const ImpactDetectionSettings({
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this.darkThreshold = 80,
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this.minImpactSize = 20,
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this.maxImpactSize = 500,
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this.blurRadius = 2,
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this.contrastFactor = 1.2,
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this.minCircularity = 0.6,
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this.maxAspectRatio = 2.0,
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this.minFillRatio = 0.5, // Filled circles should have ratio > 0.5
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});
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}
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/// Reference impact for calibrated detection
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class ReferenceImpact {
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final double x; // Normalized 0-1
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final double y; // Normalized 0-1
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const ReferenceImpact({required this.x, required this.y});
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}
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/// Characteristics learned from reference impacts
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class ImpactCharacteristics {
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final double avgLuminance;
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final double luminanceStdDev;
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final double avgSize;
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final double sizeStdDev;
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final double avgCircularity;
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final double avgFillRatio; // How filled is the blob vs its bounding circle
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final double avgDarkThreshold; // The threshold used to detect the blob
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const ImpactCharacteristics({
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required this.avgLuminance,
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required this.luminanceStdDev,
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required this.avgSize,
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required this.sizeStdDev,
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required this.avgCircularity,
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required this.avgFillRatio,
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required this.avgDarkThreshold,
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});
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@override
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String toString() {
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return 'ImpactCharacteristics(lum: ${avgLuminance.toStringAsFixed(1)} ± ${luminanceStdDev.toStringAsFixed(1)}, '
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'size: ${avgSize.toStringAsFixed(1)} ± ${sizeStdDev.toStringAsFixed(1)}, '
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'circ: ${avgCircularity.toStringAsFixed(2)}, fill: ${avgFillRatio.toStringAsFixed(2)})';
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}
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}
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/// Service for image processing and impact detection
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class ImageProcessingService {
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/// Detect the main target circle from an image
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DetectedCircle? detectMainTarget(String imagePath) {
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// Return center of image as target (basic implementation)
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// Could be enhanced with circle detection algorithm
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return DetectedCircle(
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centerX: 0.5,
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centerY: 0.5,
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radius: 0.4,
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);
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}
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/// Detect impacts (bullet holes) from an image file
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List<DetectedImpact> detectImpacts(String imagePath) {
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return detectImpactsWithSettings(
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imagePath,
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const ImpactDetectionSettings(),
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);
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}
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/// Detect impacts with custom settings
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List<DetectedImpact> detectImpactsWithSettings(
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String imagePath,
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ImpactDetectionSettings settings,
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) {
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try {
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// Load the image
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final file = File(imagePath);
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final bytes = file.readAsBytesSync();
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final originalImage = img.decodeImage(bytes);
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if (originalImage == null) {
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return [];
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}
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// Resize for faster processing if image is too large
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img.Image image;
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final maxDimension = 1000;
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if (originalImage.width > maxDimension || originalImage.height > maxDimension) {
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final scale = maxDimension / math.max(originalImage.width, originalImage.height);
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image = img.copyResize(
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originalImage,
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width: (originalImage.width * scale).round(),
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height: (originalImage.height * scale).round(),
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);
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} else {
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image = originalImage;
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}
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// Convert to grayscale
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final grayscale = img.grayscale(image);
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// Apply gaussian blur to reduce noise
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final blurred = img.gaussianBlur(grayscale, radius: settings.blurRadius);
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// Enhance contrast
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final enhanced = img.adjustColor(
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blurred,
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contrast: settings.contrastFactor,
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);
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// Detect dark spots (potential impacts)
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// Filter by circularity and fill ratio to avoid detecting numbers (hollow rings)
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final impacts = _detectDarkSpots(
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enhanced,
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settings.darkThreshold,
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settings.minImpactSize,
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settings.maxImpactSize,
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minCircularity: settings.minCircularity,
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maxAspectRatio: settings.maxAspectRatio,
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minFillRatio: settings.minFillRatio,
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);
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// Convert to relative coordinates
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final width = originalImage.width.toDouble();
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final height = originalImage.height.toDouble();
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return impacts.map((impact) {
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return DetectedImpact(
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x: impact.x / width,
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y: impact.y / height,
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radius: impact.radius,
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);
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}).toList();
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} catch (e) {
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print('Error detecting impacts: $e');
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return [];
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}
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}
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/// Analyze reference impacts to learn their characteristics
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/// This actually finds the blob at each reference point and extracts its real properties
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ImpactCharacteristics? analyzeReferenceImpacts(
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String imagePath,
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List<ReferenceImpact> references, {
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int searchRadius = 30,
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}) {
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if (references.length < 2) return null;
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try {
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final file = File(imagePath);
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final bytes = file.readAsBytesSync();
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final originalImage = img.decodeImage(bytes);
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if (originalImage == null) return null;
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// Resize for faster processing
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img.Image image;
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double scale = 1.0;
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final maxDimension = 1000;
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if (originalImage.width > maxDimension || originalImage.height > maxDimension) {
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scale = maxDimension / math.max(originalImage.width, originalImage.height);
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image = img.copyResize(
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originalImage,
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width: (originalImage.width * scale).round(),
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height: (originalImage.height * scale).round(),
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);
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} else {
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image = originalImage;
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}
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final grayscale = img.grayscale(image);
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final blurred = img.gaussianBlur(grayscale, radius: 2);
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final width = image.width;
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final height = image.height;
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final luminances = <double>[];
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final sizes = <double>[];
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final circularities = <double>[];
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final fillRatios = <double>[];
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final thresholds = <double>[];
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for (final ref in references) {
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final centerX = (ref.x * width).round().clamp(0, width - 1);
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final centerY = (ref.y * height).round().clamp(0, height - 1);
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// Find the darkest point in the search area (the center of the impact)
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int darkestX = centerX;
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int darkestY = centerY;
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double darkestLum = 255;
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for (int dy = -searchRadius; dy <= searchRadius; dy++) {
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for (int dx = -searchRadius; dx <= searchRadius; dx++) {
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final px = centerX + dx;
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final py = centerY + dy;
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if (px < 0 || px >= width || py < 0 || py >= height) continue;
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final pixel = blurred.getPixel(px, py);
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final lum = img.getLuminance(pixel).toDouble();
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if (lum < darkestLum) {
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darkestLum = lum;
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darkestX = px;
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darkestY = py;
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}
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}
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}
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// Now find the blob at the darkest point using adaptive threshold
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// Start from the darkest point and expand until we find the boundary
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final blobResult = _findBlobAtPoint(blurred, darkestX, darkestY, width, height);
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if (blobResult != null) {
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luminances.add(blobResult.avgLuminance);
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sizes.add(blobResult.size.toDouble());
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circularities.add(blobResult.circularity);
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fillRatios.add(blobResult.fillRatio);
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thresholds.add(blobResult.threshold);
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}
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}
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if (luminances.isEmpty) return null;
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// Calculate statistics
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final avgLum = luminances.reduce((a, b) => a + b) / luminances.length;
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final avgSize = sizes.reduce((a, b) => a + b) / sizes.length;
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final avgCirc = circularities.reduce((a, b) => a + b) / circularities.length;
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final avgFill = fillRatios.reduce((a, b) => a + b) / fillRatios.length;
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final avgThreshold = thresholds.reduce((a, b) => a + b) / thresholds.length;
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// Calculate standard deviations
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double lumVariance = 0;
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double sizeVariance = 0;
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for (int i = 0; i < luminances.length; i++) {
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lumVariance += math.pow(luminances[i] - avgLum, 2);
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sizeVariance += math.pow(sizes[i] - avgSize, 2);
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}
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final lumStdDev = math.sqrt(lumVariance / luminances.length);
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final sizeStdDev = math.sqrt(sizeVariance / sizes.length);
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return ImpactCharacteristics(
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avgLuminance: avgLum,
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luminanceStdDev: lumStdDev,
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avgSize: avgSize,
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sizeStdDev: sizeStdDev,
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avgCircularity: avgCirc,
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avgFillRatio: avgFill,
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avgDarkThreshold: avgThreshold,
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);
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} catch (e) {
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print('Error analyzing reference impacts: $e');
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return null;
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}
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}
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/// Find a blob at a specific point and extract its characteristics
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_BlobAnalysis? _findBlobAtPoint(img.Image image, int startX, int startY, int width, int height) {
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// Get the luminance at the center point
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final centerPixel = image.getPixel(startX, startY);
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final centerLum = img.getLuminance(centerPixel).toDouble();
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// Find the threshold by looking at the luminance gradient around the point
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// Sample in expanding circles to find where the blob ends
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double sumLum = centerLum;
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int pixelCount = 1;
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double maxRadius = 0;
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// Sample at different radii to find the edge
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for (int r = 1; r <= 50; r++) {
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double ringSum = 0;
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int ringCount = 0;
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// Sample points on a ring
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for (int i = 0; i < 16; i++) {
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final angle = (i / 16) * 2 * math.pi;
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final px = startX + (r * math.cos(angle)).round();
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final py = startY + (r * math.sin(angle)).round();
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if (px < 0 || px >= width || py < 0 || py >= height) continue;
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final pixel = image.getPixel(px, py);
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final lum = img.getLuminance(pixel).toDouble();
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ringSum += lum;
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ringCount++;
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}
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if (ringCount > 0) {
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final avgRingLum = ringSum / ringCount;
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// If the ring is significantly brighter than the center, we've found the edge
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if (avgRingLum > centerLum + 40) {
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maxRadius = r.toDouble();
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break;
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}
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sumLum += ringSum;
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pixelCount += ringCount;
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}
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}
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if (maxRadius < 3) return null; // Too small to be a valid blob
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// Calculate threshold as the midpoint between center and edge luminance
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final edgeRadius = (maxRadius * 1.2).round();
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double edgeLum = 0;
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int edgeCount = 0;
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for (int i = 0; i < 16; i++) {
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final angle = (i / 16) * 2 * math.pi;
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final px = startX + (edgeRadius * math.cos(angle)).round();
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final py = startY + (edgeRadius * math.sin(angle)).round();
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if (px < 0 || px >= width || py < 0 || py >= height) continue;
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final pixel = image.getPixel(px, py);
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edgeLum += img.getLuminance(pixel).toDouble();
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edgeCount++;
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}
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if (edgeCount > 0) {
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edgeLum /= edgeCount;
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}
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final threshold = ((centerLum + edgeLum) / 2).round();
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// Now do a flood fill with this threshold to get the actual blob
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final mask = List.generate(height, (_) => List.filled(width, false));
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for (int y = 0; y < height; y++) {
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for (int x = 0; x < width; x++) {
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final pixel = image.getPixel(x, y);
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final lum = img.getLuminance(pixel);
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mask[y][x] = lum < threshold;
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}
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}
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final visited = List.generate(height, (_) => List.filled(width, false));
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// Find the blob containing the start point
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if (!mask[startY][startX]) {
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// Start point might not be in mask, find nearest point that is
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for (int r = 1; r <= 10; r++) {
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bool found = false;
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for (int dy = -r; dy <= r && !found; dy++) {
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for (int dx = -r; dx <= r && !found; dx++) {
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final px = startX + dx;
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final py = startY + dy;
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if (px >= 0 && px < width && py >= 0 && py < height && mask[py][px]) {
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final blob = _floodFill(mask, visited, px, py, width, height);
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// Calculate fill ratio: actual pixels / bounding circle area
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final boundingRadius = math.max(blob.radius, 1);
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final boundingCircleArea = math.pi * boundingRadius * boundingRadius;
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final fillRatio = (blob.size / boundingCircleArea).clamp(0.0, 1.0);
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return _BlobAnalysis(
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avgLuminance: sumLum / pixelCount,
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size: blob.size,
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circularity: blob.circularity,
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fillRatio: fillRatio,
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threshold: threshold.toDouble(),
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);
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}
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}
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}
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}
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return null;
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}
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final blob = _floodFill(mask, visited, startX, startY, width, height);
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// Calculate fill ratio
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final boundingRadius = math.max(blob.radius, 1);
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final boundingCircleArea = math.pi * boundingRadius * boundingRadius;
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final fillRatio = (blob.size / boundingCircleArea).clamp(0.0, 1.0);
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return _BlobAnalysis(
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avgLuminance: sumLum / pixelCount,
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size: blob.size,
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circularity: blob.circularity,
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fillRatio: fillRatio,
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threshold: threshold.toDouble(),
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);
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}
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/// Detect impacts based on reference characteristics with tolerance
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List<DetectedImpact> detectImpactsFromReferences(
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String imagePath,
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ImpactCharacteristics characteristics, {
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double tolerance = 2.0, // Number of standard deviations
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double minCircularity = 0.4,
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}) {
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try {
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final file = File(imagePath);
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final bytes = file.readAsBytesSync();
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final originalImage = img.decodeImage(bytes);
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if (originalImage == null) return [];
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// Resize for faster processing
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img.Image image;
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double scale = 1.0;
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final maxDimension = 1000;
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if (originalImage.width > maxDimension || originalImage.height > maxDimension) {
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scale = maxDimension / math.max(originalImage.width, originalImage.height);
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image = img.copyResize(
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originalImage,
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width: (originalImage.width * scale).round(),
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height: (originalImage.height * scale).round(),
|
||||
);
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} else {
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image = originalImage;
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}
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final grayscale = img.grayscale(image);
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final blurred = img.gaussianBlur(grayscale, radius: 2);
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// Use the threshold learned from references
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final threshold = characteristics.avgDarkThreshold.round();
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// Calculate size range based on learned characteristics
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final minSize = (characteristics.avgSize / (tolerance * 2)).clamp(5, 10000).round();
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final maxSize = (characteristics.avgSize * tolerance * 2).clamp(10, 10000).round();
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// Calculate minimum fill ratio based on learned characteristics
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// Allow some variance but still filter out hollow shapes
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final minFillRatio = (characteristics.avgFillRatio - 0.2).clamp(0.3, 0.9);
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// Detect blobs using the learned threshold
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final impacts = _detectDarkSpots(
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blurred,
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threshold,
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||||
minSize,
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||||
maxSize,
|
||||
minCircularity: math.max(characteristics.avgCircularity - 0.2, minCircularity),
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||||
minFillRatio: minFillRatio,
|
||||
);
|
||||
|
||||
// Convert to relative coordinates
|
||||
final width = originalImage.width.toDouble();
|
||||
final height = originalImage.height.toDouble();
|
||||
|
||||
return impacts.map((impact) {
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||||
return DetectedImpact(
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||||
x: impact.x / image.width,
|
||||
y: impact.y / image.height,
|
||||
radius: impact.radius / scale,
|
||||
);
|
||||
}).toList();
|
||||
} catch (e) {
|
||||
print('Error detecting impacts from references: $e');
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
/// Detect dark spots with adaptive luminance range
|
||||
List<_Blob> _detectDarkSpotsAdaptive(
|
||||
img.Image image,
|
||||
int minLuminance,
|
||||
int maxLuminance,
|
||||
int minSize,
|
||||
int maxSize, {
|
||||
double minCircularity = 0.5,
|
||||
double minFillRatio = 0.5,
|
||||
}) {
|
||||
final width = image.width;
|
||||
final height = image.height;
|
||||
|
||||
// Create binary mask of pixels within luminance range
|
||||
final mask = List.generate(height, (_) => List.filled(width, false));
|
||||
|
||||
for (int y = 0; y < height; y++) {
|
||||
for (int x = 0; x < width; x++) {
|
||||
final pixel = image.getPixel(x, y);
|
||||
final luminance = img.getLuminance(pixel);
|
||||
mask[y][x] = luminance >= minLuminance && luminance <= maxLuminance;
|
||||
}
|
||||
}
|
||||
|
||||
// Find connected components
|
||||
final visited = List.generate(height, (_) => List.filled(width, false));
|
||||
final blobs = <_Blob>[];
|
||||
|
||||
for (int y = 0; y < height; y++) {
|
||||
for (int x = 0; x < width; x++) {
|
||||
if (mask[y][x] && !visited[y][x]) {
|
||||
final blob = _floodFill(mask, visited, x, y, width, height);
|
||||
if (blob.size >= minSize &&
|
||||
blob.size <= maxSize &&
|
||||
blob.circularity >= minCircularity &&
|
||||
blob.fillRatio >= minFillRatio) {
|
||||
blobs.add(blob);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return _filterOverlappingBlobs(blobs);
|
||||
}
|
||||
|
||||
/// Detect dark spots in a grayscale image using blob detection
|
||||
List<_Blob> _detectDarkSpots(
|
||||
img.Image image,
|
||||
int threshold,
|
||||
int minSize,
|
||||
int maxSize, {
|
||||
double minCircularity = 0.6,
|
||||
double maxAspectRatio = 2.0,
|
||||
double minFillRatio = 0.5,
|
||||
}) {
|
||||
final width = image.width;
|
||||
final height = image.height;
|
||||
|
||||
// Create binary mask of dark pixels
|
||||
final mask = List.generate(height, (_) => List.filled(width, false));
|
||||
|
||||
for (int y = 0; y < height; y++) {
|
||||
for (int x = 0; x < width; x++) {
|
||||
final pixel = image.getPixel(x, y);
|
||||
final luminance = img.getLuminance(pixel);
|
||||
mask[y][x] = luminance < threshold;
|
||||
}
|
||||
}
|
||||
|
||||
// Find connected components (blobs)
|
||||
final visited = List.generate(height, (_) => List.filled(width, false));
|
||||
final blobs = <_Blob>[];
|
||||
|
||||
for (int y = 0; y < height; y++) {
|
||||
for (int x = 0; x < width; x++) {
|
||||
if (mask[y][x] && !visited[y][x]) {
|
||||
final blob = _floodFill(mask, visited, x, y, width, height);
|
||||
|
||||
// Filter by size
|
||||
if (blob.size < minSize || blob.size > maxSize) continue;
|
||||
|
||||
// Filter by circularity (reject non-circular shapes like numbers)
|
||||
if (blob.circularity < minCircularity) continue;
|
||||
|
||||
// Filter by aspect ratio (reject elongated shapes)
|
||||
if (blob.aspectRatio > maxAspectRatio) continue;
|
||||
|
||||
// Filter by fill ratio (reject hollow rings - numbers on target)
|
||||
// A filled bullet hole should have fill ratio > 0.5
|
||||
// A hollow ring (like number "0" or "8") has a much lower fill ratio
|
||||
if (blob.fillRatio < minFillRatio) continue;
|
||||
|
||||
blobs.add(blob);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Filter overlapping blobs (keep larger ones)
|
||||
final filteredBlobs = _filterOverlappingBlobs(blobs);
|
||||
|
||||
return filteredBlobs;
|
||||
}
|
||||
|
||||
/// Flood fill to find connected component
|
||||
_Blob _floodFill(
|
||||
List<List<bool>> mask,
|
||||
List<List<bool>> visited,
|
||||
int startX,
|
||||
int startY,
|
||||
int width,
|
||||
int height,
|
||||
) {
|
||||
final stack = <_Point>[_Point(startX, startY)];
|
||||
final points = <_Point>[];
|
||||
|
||||
int minX = startX, maxX = startX;
|
||||
int minY = startY, maxY = startY;
|
||||
int perimeterCount = 0;
|
||||
|
||||
while (stack.isNotEmpty) {
|
||||
final point = stack.removeLast();
|
||||
final x = point.x;
|
||||
final y = point.y;
|
||||
|
||||
if (x < 0 || x >= width || y < 0 || y >= height) continue;
|
||||
if (visited[y][x] || !mask[y][x]) continue;
|
||||
|
||||
visited[y][x] = true;
|
||||
points.add(point);
|
||||
|
||||
minX = math.min(minX, x);
|
||||
maxX = math.max(maxX, x);
|
||||
minY = math.min(minY, y);
|
||||
maxY = math.max(maxY, y);
|
||||
|
||||
// Check if this is a perimeter pixel (has at least one non-blob neighbor)
|
||||
bool isPerimeter = false;
|
||||
for (final delta in [[-1, 0], [1, 0], [0, -1], [0, 1]]) {
|
||||
final nx = x + delta[0];
|
||||
final ny = y + delta[1];
|
||||
if (nx < 0 || nx >= width || ny < 0 || ny >= height || !mask[ny][nx]) {
|
||||
isPerimeter = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (isPerimeter) perimeterCount++;
|
||||
|
||||
// Add neighbors (4-connectivity)
|
||||
stack.add(_Point(x + 1, y));
|
||||
stack.add(_Point(x - 1, y));
|
||||
stack.add(_Point(x, y + 1));
|
||||
stack.add(_Point(x, y - 1));
|
||||
}
|
||||
|
||||
// Calculate centroid
|
||||
double sumX = 0, sumY = 0;
|
||||
for (final p in points) {
|
||||
sumX += p.x;
|
||||
sumY += p.y;
|
||||
}
|
||||
|
||||
final centerX = points.isNotEmpty ? sumX / points.length : startX.toDouble();
|
||||
final centerY = points.isNotEmpty ? sumY / points.length : startY.toDouble();
|
||||
|
||||
// Calculate bounding box dimensions
|
||||
final blobWidth = (maxX - minX + 1).toDouble();
|
||||
final blobHeight = (maxY - minY + 1).toDouble();
|
||||
|
||||
// Calculate approximate radius based on bounding box
|
||||
final radius = math.max(blobWidth, blobHeight) / 2.0;
|
||||
|
||||
// Calculate circularity: 4 * pi * area / perimeter^2
|
||||
// For a perfect circle, this equals 1
|
||||
final area = points.length.toDouble();
|
||||
final perimeter = perimeterCount.toDouble();
|
||||
final circularity = perimeter > 0
|
||||
? (4 * math.pi * area) / (perimeter * perimeter)
|
||||
: 0.0;
|
||||
|
||||
// Calculate aspect ratio (always >= 1)
|
||||
final aspectRatio = blobWidth > blobHeight
|
||||
? blobWidth / blobHeight
|
||||
: blobHeight / blobWidth;
|
||||
|
||||
// Calculate fill ratio: actual area vs bounding circle area
|
||||
// A filled circle has fill ratio ~0.78 (pi/4), a ring/hollow circle has much lower
|
||||
final boundingCircleArea = math.pi * radius * radius;
|
||||
final fillRatio = boundingCircleArea > 0 ? (area / boundingCircleArea).clamp(0.0, 1.0) : 0.0;
|
||||
|
||||
return _Blob(
|
||||
x: centerX,
|
||||
y: centerY,
|
||||
radius: radius,
|
||||
size: points.length,
|
||||
circularity: circularity.clamp(0.0, 1.0),
|
||||
aspectRatio: aspectRatio,
|
||||
fillRatio: fillRatio,
|
||||
);
|
||||
}
|
||||
|
||||
/// Filter overlapping blobs, keeping the larger ones
|
||||
List<_Blob> _filterOverlappingBlobs(List<_Blob> blobs) {
|
||||
if (blobs.isEmpty) return [];
|
||||
|
||||
// Sort by size (largest first)
|
||||
blobs.sort((a, b) => b.size.compareTo(a.size));
|
||||
|
||||
final filtered = <_Blob>[];
|
||||
|
||||
for (final blob in blobs) {
|
||||
bool overlaps = false;
|
||||
|
||||
for (final existing in filtered) {
|
||||
final dx = blob.x - existing.x;
|
||||
final dy = blob.y - existing.y;
|
||||
final distance = math.sqrt(dx * dx + dy * dy);
|
||||
|
||||
// Check if blobs overlap
|
||||
if (distance < (blob.radius + existing.radius) * 0.8) {
|
||||
overlaps = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (!overlaps) {
|
||||
filtered.add(blob);
|
||||
}
|
||||
}
|
||||
|
||||
return filtered;
|
||||
}
|
||||
}
|
||||
|
||||
class _Point {
|
||||
final int x;
|
||||
final int y;
|
||||
|
||||
_Point(this.x, this.y);
|
||||
}
|
||||
|
||||
class _Blob {
|
||||
final double x;
|
||||
final double y;
|
||||
final double radius;
|
||||
final int size;
|
||||
final double circularity; // 0-1, 1 = perfect circle
|
||||
final double aspectRatio; // width/height ratio
|
||||
final double fillRatio; // How filled vs hollow the blob is
|
||||
|
||||
_Blob({
|
||||
required this.x,
|
||||
required this.y,
|
||||
required this.radius,
|
||||
required this.size,
|
||||
required this.circularity,
|
||||
required this.aspectRatio,
|
||||
this.fillRatio = 1.0,
|
||||
});
|
||||
}
|
||||
|
||||
class _BlobAnalysis {
|
||||
final double avgLuminance;
|
||||
final int size;
|
||||
final double circularity;
|
||||
final double fillRatio;
|
||||
final double threshold;
|
||||
|
||||
_BlobAnalysis({
|
||||
required this.avgLuminance,
|
||||
required this.size,
|
||||
required this.circularity,
|
||||
required this.fillRatio,
|
||||
required this.threshold,
|
||||
});
|
||||
}
|
||||
Reference in New Issue
Block a user