236 lines
7.6 KiB
Dart
236 lines
7.6 KiB
Dart
import 'dart:math' as math;
|
|
import 'package:opencv_dart/opencv_dart.dart' as cv;
|
|
|
|
class TargetDetectionResult {
|
|
final double centerX;
|
|
final double centerY;
|
|
final double radius;
|
|
final bool success;
|
|
|
|
TargetDetectionResult({
|
|
required this.centerX,
|
|
required this.centerY,
|
|
required this.radius,
|
|
this.success = true,
|
|
});
|
|
|
|
factory TargetDetectionResult.failure() {
|
|
return TargetDetectionResult(
|
|
centerX: 0.5,
|
|
centerY: 0.5,
|
|
radius: 0.4,
|
|
success: false,
|
|
);
|
|
}
|
|
}
|
|
|
|
class OpenCVTargetService {
|
|
/// Detect the main target (center and radius) from an image file
|
|
Future<TargetDetectionResult> detectTarget(String imagePath) async {
|
|
try {
|
|
// Read image
|
|
final img = cv.imread(imagePath, flags: cv.IMREAD_COLOR);
|
|
if (img.isEmpty) {
|
|
return TargetDetectionResult.failure();
|
|
}
|
|
|
|
// Convert to grayscale
|
|
final gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY);
|
|
|
|
// Apply Gaussian blur to reduce noise
|
|
final blurred = cv.gaussianBlur(gray, (9, 9), 2, sigmaY: 2);
|
|
|
|
// Detect circles using Hough Transform
|
|
// Parameters need to be tuned for the specific target type
|
|
final circles = cv.HoughCircles(
|
|
blurred,
|
|
cv.HOUGH_GRADIENT,
|
|
1, // dp: Inverse ratio of the accumulator resolution to the image resolution
|
|
(img.rows / 8)
|
|
.toDouble(), // minDist: Minimum distance between the centers of the detected circles
|
|
param1: 100, // param1: Gradient value for Canny edge detection
|
|
param2:
|
|
30, // param2: Accumulator threshold for the circle centers at the detection stage
|
|
minRadius: img.cols ~/ 20, // minRadius
|
|
maxRadius: img.cols ~/ 2, // maxRadius
|
|
);
|
|
|
|
// HoughCircles returns a Mat of shape (1, N, 3) where N is number of circles.
|
|
// In opencv_dart, we cannot iterate easily.
|
|
// However, we can access data via pointer if needed, or check if Vec3f is supported.
|
|
// Given the user report, `at<Vec3f>` likely failed compilation or runtime.
|
|
// Let's use a safer approach: assume standard memory layout (x, y, r, x, y, r...).
|
|
// Or use `at<double>` carefully.
|
|
|
|
// Better yet: try to use `circles.data` if available, but it returns a Pointer.
|
|
// Let's stick to `at` but use `double` and manual offset if Vec3f fails.
|
|
// actually, let's try to trust `at<double>` for flattened access OR `at<Vec3f>`.
|
|
// NOTE: `at<Vec3f>` was reported as "method at not defined for VecPoint2f" earlier, NOT for Mat.
|
|
// The user error was for `VecPoint2f`. `Mat` definitely has `at`.
|
|
// BUT `VecPoint2f` is a List-like structure in Dart wrapper.
|
|
// usage of `at` on `VecPoint2f` was the error.
|
|
// Here `circles` IS A MAT. So `at` IS defined.
|
|
// However, to be safe and robust, and to implement clustering...
|
|
|
|
if (circles.isEmpty) {
|
|
// Try with different parameters if first attempt fails (more lenient)
|
|
final looseCircles = cv.HoughCircles(
|
|
blurred,
|
|
cv.HOUGH_GRADIENT,
|
|
1,
|
|
(img.rows / 8).toDouble(),
|
|
param1: 50,
|
|
param2: 20,
|
|
minRadius: img.cols ~/ 20,
|
|
maxRadius: img.cols ~/ 2,
|
|
);
|
|
|
|
if (looseCircles.isEmpty) {
|
|
return TargetDetectionResult.failure();
|
|
}
|
|
return _findBestConcentricCircles(looseCircles, img.cols, img.rows);
|
|
}
|
|
|
|
return _findBestConcentricCircles(circles, img.cols, img.rows);
|
|
} catch (e) {
|
|
// print('Error detecting target with OpenCV: $e');
|
|
return TargetDetectionResult.failure();
|
|
}
|
|
}
|
|
|
|
TargetDetectionResult _findBestConcentricCircles(
|
|
cv.Mat circles,
|
|
int width,
|
|
int height,
|
|
) {
|
|
if (circles.rows == 0 || circles.cols == 0) {
|
|
return TargetDetectionResult.failure();
|
|
}
|
|
|
|
final int numCircles = circles.cols;
|
|
final List<({double x, double y, double r})> detected = [];
|
|
|
|
// Extract circles safely
|
|
// We'll use `at<double>` assuming the Mat is (1, N, 3) float32 (CV_32FC3 usually)
|
|
// Actually HoughCircles usually returns CV_32FC3.
|
|
// So we can access `at<cv.Vec3f>(0, i)`.
|
|
// If that fails, we can fall back. But since `Mat` has `at`, it should work unless generic is bad.
|
|
// Let's assume it works for Mat but checking boundaries.
|
|
|
|
// NOTE: If this throws "at not defined" (unlikely for Mat), we'd need another way.
|
|
// But since the previous error was on `VecPoint2f` (which is NOT a Mat), this should be fine.
|
|
|
|
for (int i = 0; i < numCircles; i++) {
|
|
// Access using Vec3f if possible, or try to interpret memory
|
|
// Using `at<cv.Vec3f>` is the standard way.
|
|
final vec = circles.at<cv.Vec3f>(0, i);
|
|
detected.add((x: vec.val1, y: vec.val2, r: vec.val3));
|
|
}
|
|
|
|
if (detected.isEmpty) return TargetDetectionResult.failure();
|
|
|
|
// Cluster circles by center position
|
|
// We consider circles "concentric" if their centers are within 5% of image min dimension
|
|
final double tolerance = math.min(width, height) * 0.05;
|
|
final List<List<({double x, double y, double r})>> clusters = [];
|
|
|
|
for (final circle in detected) {
|
|
bool added = false;
|
|
for (final cluster in clusters) {
|
|
// Check distance to cluster center (average of existing)
|
|
double clusterX = 0;
|
|
double clusterY = 0;
|
|
for (final c in cluster) {
|
|
clusterX += c.x;
|
|
clusterY += c.y;
|
|
}
|
|
clusterX /= cluster.length;
|
|
clusterY /= cluster.length;
|
|
|
|
final dist = math.sqrt(
|
|
math.pow(circle.x - clusterX, 2) + math.pow(circle.y - clusterY, 2),
|
|
);
|
|
|
|
if (dist < tolerance) {
|
|
cluster.add(circle);
|
|
added = true;
|
|
break;
|
|
}
|
|
}
|
|
if (!added) {
|
|
clusters.add([circle]);
|
|
}
|
|
}
|
|
|
|
// Find the best cluster
|
|
// 1. Prefer clusters with more circles (concentric rings)
|
|
// 2. Tie-break: closest to image center
|
|
|
|
List<({double x, double y, double r})> bestCluster = clusters.first;
|
|
double bestScore = -1.0;
|
|
|
|
for (final cluster in clusters) {
|
|
// Score calculation
|
|
// Base score = number of circles * 10
|
|
double score = cluster.length * 10.0;
|
|
|
|
// Penalize distance from center
|
|
double cx = 0, cy = 0;
|
|
for (final c in cluster) {
|
|
cx += c.x;
|
|
cy += c.y;
|
|
}
|
|
cx /= cluster.length;
|
|
cy /= cluster.length;
|
|
|
|
final distFromCenter = math.sqrt(
|
|
math.pow(cx - width / 2, 2) + math.pow(cy - height / 2, 2),
|
|
);
|
|
final relDist = distFromCenter / math.min(width, height);
|
|
|
|
score -= relDist * 5.0; // Moderate penalty for off-center
|
|
|
|
// Penalize very small clusters if they are just noise
|
|
// (Optional: check if radii are somewhat distributed?)
|
|
|
|
if (score > bestScore) {
|
|
bestScore = score;
|
|
bestCluster = cluster;
|
|
}
|
|
}
|
|
|
|
// Compute final result from best cluster
|
|
// Center: Use the smallest circle (bullseye) for best precision
|
|
// Radius: Use the largest circle (outer edge) for full coverage
|
|
|
|
double centerX = 0;
|
|
double centerY = 0;
|
|
double maxR = 0;
|
|
double minR = double.infinity;
|
|
|
|
for (final c in bestCluster) {
|
|
if (c.r > maxR) {
|
|
maxR = c.r;
|
|
}
|
|
if (c.r < minR) {
|
|
minR = c.r;
|
|
centerX = c.x;
|
|
centerY = c.y;
|
|
}
|
|
}
|
|
|
|
// Fallback if something went wrong (shouldn't happen with non-empty cluster)
|
|
if (minR == double.infinity) {
|
|
centerX = bestCluster.first.x;
|
|
centerY = bestCluster.first.y;
|
|
}
|
|
|
|
return TargetDetectionResult(
|
|
centerX: centerX / width,
|
|
centerY: centerY / height,
|
|
radius: maxR / math.min(width, height),
|
|
success: true,
|
|
);
|
|
}
|
|
}
|