Flutter图像识别与处理插件sift的使用
Flutter图像识别与处理插件sift的使用
Sift 是一个轻量级的地图读取库，可以用于 Dart 和 Flutter 项目。
使用
Map<String, dynamic> _data = {'age': 21, 'name': null, 'address': '41 Main drive'};
// 读取必需的值 - 如果出错会抛出异常
try {
var age = _sift.readNumberFromMap(_data, 'age');
var address = _sift.readStringFromMap(_data, 'address');
print(age); // 打印 21
print(address); // 打印 41 Main Drive
} on SiftException catch (exception) {
print(exception.errorMessage);
}
// 通过提供默认值来读取可选值
var name = _sift.readStringFromMapWithDefaultValue(_data, 'name', 'John Doe');
print(name); // 打印 John Doe
示例代码
以下是一个完整的示例代码，展示了如何在 Flutter 应用程序中使用 sift 插件：
import 'package:flutter/material.dart';
import 'package:sift/sift.dart'; // 导入 sift 库
void main() => runApp(MyApp());
class MyApp extends StatelessWidget {
[@override](/user/override)
Widget build(BuildContext context) {
return MaterialApp(
home: Scaffold(
appBar: AppBar(title: Text('Sift 示例')),
body: Center(
child: SiftExample(),
),
),
);
}
}
class SiftExample extends StatefulWidget {
[@override](/user/override)
_SiftExampleState createState() => _SiftExampleState();
}
class _SiftExampleState extends State<SiftExample> {
final _sift = Sift(); // 初始化 sift 实例
Map<String, dynamic> _data = {'age': 21, 'name': null, 'address': '41 Main drive'};
void readRequiredValues() {
// 读取必需的值 - 如果出错会抛出异常
try {
var age = _sift.readNumberFromMap(_data, 'age');
var address = _sift.readStringFromMap(_data, 'address');
print(age); // 打印 21
print(address); // 打印 41 Main Drive
} on SiftException catch (exception) {
print(exception.errorMessage);
}
}
void readOptionalValues() {
// 通过提供默认值来读取可选值
var name = _sift.readStringFromMapWithDefaultValue(_data, 'name', 'John Doe');
print(name); // 打印 John Doe
}
[@override](/user/override)
Widget build(BuildContext context) {
return Column(
mainAxisAlignment: MainAxisAlignment.center,
children: [
ElevatedButton(
onPressed: readRequiredValues,
child: Text('读取必需值'),
),
ElevatedButton(
onPressed: readOptionalValues,
child: Text('读取可选值'),
),
],
);
}
}
更多关于Flutter图像识别与处理插件sift的使用的实战系列教程也可以访问 https://www.itying.com/category-92-b0.html
1 回复
更多关于Flutter图像识别与处理插件sift的使用的实战系列教程也可以访问 https://www.itying.com/category-92-b0.html
在Flutter中进行图像识别与处理时，使用Scale-Invariant Feature Transform (SIFT) 算法是一个强大的工具，但它通常不是直接在Flutter层实现的，因为SIFT算法的计算复杂度和资源需求较高，更适合在原生代码层（如Dart通过Platform Channels调用的Android/iOS原生代码）实现。
Flutter本身并没有直接提供SIFT算法的插件，但你可以通过Platform Channels调用原生代码来实现这一功能。以下是一个大致的实现思路，包括如何在Flutter中调用原生Android和iOS代码进行SIFT特征提取。
1. 设置Flutter项目
首先，确保你有一个Flutter项目。如果没有，可以通过以下命令创建一个新的Flutter项目：
flutter create sift_example
cd sift_example
2. 创建Flutter插件
你可以创建一个自定义的Flutter插件来封装SIFT算法的实现。不过为了简化，这里假设你直接修改lib目录下的MainActivity.kt（Android）和AppDelegate.swift（iOS），并通过MethodChannel进行通信。
3. 在Android上实现SIFT
3.1 添加依赖
在android/app/build.gradle中添加对OpenCV的依赖（因为SIFT通常通过OpenCV实现）：
dependencies {
implementation project(':opencv')
}
你还需要下载并配置OpenCV Android SDK，将其作为一个模块导入到你的Android项目中。
3.2 实现SIFT特征提取
在MainActivity.kt中，添加以下代码来设置MethodChannel并处理来自Flutter的调用：
import android.content.Context
import io.flutter.embedding.android.FlutterActivity
import io.flutter.embedding.engine.FlutterEngine
import io.flutter.plugin.common.MethodChannel
import org.opencv.android.Utils
import org.opencv.core.Core
import org.opencv.core.CvType
import org.opencv.core.Mat
import org.opencv.core.MatOfKeyPoint
import org.opencv.core.MatOfDMatch
import org.opencv.core.Scalar
import org.opencv.core.Size
import org.opencv.features2d.Features2d
import org.opencv.features2d.SIFT
import org.opencv.imgcodecs.Imgcodecs
import java.io.File
class MainActivity: FlutterActivity() {
private val CHANNEL = "com.example.sift_example/sift"
override fun configureFlutterEngine(flutterEngine: FlutterEngine) {
super.configureFlutterEngine(flutterEngine)
MethodChannel(flutterEngine.dartExecutor.binaryMessenger, CHANNEL).setMethodCallHandler { call, result ->
if (call.method == "extractSIFT") {
val imagePath = call.argument<String>("imagePath") ?: return@setMethodCallHandler result.error("INVALID_ARGUMENT", "imagePath missing", null)
// Load OpenCV library
if (!OpenCVLoader.initDebug()) {
Log.e("OpenCV", "OpenCV library not loaded")
return@setMethodCallHandler result.error("NATIVE_LIBRARY_FAILED", "OpenCV library not loaded", null)
}
// Load image using OpenCV
val mat = Mat()
val imgFile = File(imagePath)
if (!imgFile.exists()) {
return@setMethodCallHandler result.error("FILE_NOT_FOUND", "Image file not found", null)
}
Utils.loadImage(mat, imgFile.absolutePath)
// Create SIFT detector
val sift = SIFT.create()
val keyPoints = MatOfKeyPoint()
val descriptors = Mat()
sift.detectAndCompute(mat, null, keyPoints, descriptors)
// Convert key points and descriptors to a format suitable for Flutter
// (This part depends on how you want to send the data back to Flutter)
val keyPointList = ArrayList<Map<String, Any>>()
for (i in 0 until keyPoints.rows()) {
val kp = keyPoints.toKeyPointArray()[i]
keyPointList.add(mapOf(
"pt" to listOf(kp.pt.x, kp.pt.y),
"size" to kp.size,
"angle" to kp.angle,
"response" to kp.response,
"octave" to kp.octave
))
}
// Assuming descriptors are stored as a list of floats
val descriptorList = descriptors.reshape(1, 1).convertTo(Mat(), CvType.CV_32F).toList<Float>().toList()
// Send result back to Flutter
result.success(mapOf(
"keyPoints" to keyPointList,
"descriptors" to descriptorList
))
} else {
result.notImplemented()
}
}
}
}
4. 在iOS上实现SIFT
4.1 添加OpenCV依赖
你需要将OpenCV的iOS框架添加到你的Xcode项目中。下载OpenCV iOS框架，并将其添加到Xcode的Linked Frameworks and Libraries中。
4.2 实现SIFT特征提取
在AppDelegate.swift中，添加以下代码来设置FlutterMethodChannel并处理来自Flutter的调用：
import UIKit
import Flutter
import opencv2
@UIApplicationMain
@objc class AppDelegate: FlutterAppDelegate {
override func application(
_ application: UIApplication,
didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey: Any]?
) -> Bool {
GeneratedPluginRegistrant.register(with: self)
let controller : FlutterViewController = window?.rootViewController as! FlutterViewController
let channel = FlutterMethodChannel(name: "com.example.sift_example/sift", binaryMessenger: controller)
channel.setMethodCallHandler({
(call: FlutterMethodCall, result: @escaping FlutterResult) in
guard let imagePath = call.arguments as? String else {
result(.error(withCode: "INVALID_ARGUMENT", message: "imagePath missing", details: nil))
return
}
guard let imageUrl = URL(fileURLWithPath: imagePath) else {
result(.error(withCode: "FILE_NOT_FOUND", message: "Image file not found", details: nil))
return
}
guard let cgImage = UIImage(contentsOfFile: imagePath)?.cgImage else {
result(.error(withCode: "IMAGE_LOAD_FAILED", message: "Failed to load image", details: nil))
return
}
let mat = cv::Mat(cv::Size(cgImage.width, cgImage.height), CV_8UC4)
UIImageToMat(UIImage(cgImage: cgImage), mat)
let sift = cv::SIFT_create()
var keyPoints = cv::MatOfKeyPoint()
var descriptors = cv::Mat()
sift.detectAndCompute(mat, nil, &keyPoints, &descriptors)
// Convert key points and descriptors to a format suitable for Flutter
let keyPointList: [[Double]] = keyPoints.toArray().map { kp in
[kp.pt.x, kp.pt.y, kp.size, kp.angle, kp.response, Double(kp.octave)]
}
let descriptorList = descriptors.reshape(1, 1).convert(to: .cv32F).data.map { $0.doubleValue }
result(success: [
"keyPoints": keyPointList,
"descriptors": descriptorList
])
})
return super.application(application, didFinishLaunchingWithOptions: launchOptions)
}
}
// Helper function to convert UIImage to cv::Mat
func UIImageToMat(_ uiImage: UIImage, _ mat: inout cv::Mat) {
let cgImage = uiImage.cgImage!
let dataProvider = cgImage.dataProvider!
let data = dataProvider.data!
let ptr = CFDataGetBytePtr(data)
let width = cgImage.width
let height = cgImage.height
let bytesPerRow = cgImage.bytesPerRow
let bitsPerComponent = cgImage.bitsPerComponent
let bitsPerPixel = cgImage.bitsPerPixel
let bytesPerPixel = bitsPerPixel / bitsPerComponent
let colorSpace = CGColorSpaceCreateDeviceRGB()
let bitmapInfo = CGBitmapInfo(rawValue: CGImageAlphaInfo.noneSkipLast.rawValue)
cv::Mat(height, width, CV_8UC4, UnsafeMutableRawPointer(mutating: ptr).assumingMemoryBound(to: UInt8.self)).copyTo(&mat)
}
