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b/android/.gradle/config.properties @@ -1,2 +1,2 @@ -#Fri Apr 25 19:53:31 CST 2025 -java.home=/Applications/Android Studio.app/Contents/jbr/Contents/Home +#Mon May 26 14:33:47 GMT+08:00 2025 +java.home=D\:\\Android\\AS_INstall\\jbr diff --git a/android/.gradle/file-system.probe b/android/.gradle/file-system.probe index f771f1f..61691d3 100644 Binary files a/android/.gradle/file-system.probe and b/android/.gradle/file-system.probe differ diff --git a/android/.idea/android.iml b/android/.idea/android.iml index 12bbf74..d6ebd48 100644 --- a/android/.idea/android.iml +++ b/android/.idea/android.iml @@ -1 +1,9 @@ + + + + + + + + \ No newline at end of file diff --git a/android/.idea/codeStyles/Project.xml b/android/.idea/codeStyles/Project.xml new file mode 100644 index 0000000..7643783 --- /dev/null +++ b/android/.idea/codeStyles/Project.xml @@ -0,0 +1,123 @@ + + + + + + + + + + \ No newline at end of file diff --git a/android/.idea/codeStyles/codeStyleConfig.xml b/android/.idea/codeStyles/codeStyleConfig.xml new file mode 100644 index 0000000..79ee123 --- /dev/null +++ b/android/.idea/codeStyles/codeStyleConfig.xml @@ -0,0 +1,5 @@ + + + + \ No newline at end of file diff --git a/android/.idea/deploymentTargetSelector.xml b/android/.idea/deploymentTargetSelector.xml index b268ef3..15e2a68 100644 --- a/android/.idea/deploymentTargetSelector.xml +++ b/android/.idea/deploymentTargetSelector.xml @@ -4,6 +4,14 @@ diff --git a/android/.idea/misc.xml b/android/.idea/misc.xml index e756225..d15a481 100644 --- a/android/.idea/misc.xml +++ b/android/.idea/misc.xml @@ -1,3 +1,4 @@ + diff --git a/android/app/build.gradle b/android/app/build.gradle index d1545ae..5bada6c 100644 --- a/android/app/build.gradle +++ b/android/app/build.gradle @@ -49,8 +49,11 @@ dependencies { implementation 'org.tensorflow:tensorflow-lite:2.14.0' implementation 'org.tensorflow:tensorflow-lite-gpu:2.5.0' implementation 'org.tensorflow:tensorflow-lite-support:0.3.0' + implementation 'org.mindrot:jbcrypt:0.4' androidTestImplementation 'androidx.test.ext:junit:1.1.2' androidTestImplementation 'androidx.test.espresso:espresso-core:3.3.0' androidTestImplementation "com.google.truth:truth:1.1.3" + androidTestImplementation 'androidx.test.ext:junit:1.1.3' + androidTestImplementation 'androidx.test.espresso:espresso-core:3.4.0' } diff --git a/android/app/src/androidTest/java/org/tensorflow/lite/examples/poseestimation/ml/MovenetLightningTest.kt b/android/app/src/androidTest/java/org/tensorflow/lite/examples/poseestimation/ml/MovenetLightningTest.kt index 7941b5e..1baf4e7 100644 --- a/android/app/src/androidTest/java/org/tensorflow/lite/examples/poseestimation/ml/MovenetLightningTest.kt +++ b/android/app/src/androidTest/java/org/tensorflow/lite/examples/poseestimation/ml/MovenetLightningTest.kt @@ -9,8 +9,7 @@ You may obtain a copy of the License at Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. +See the License for the specific language governing permissions and limitations under the License. ============================================================================== */ @@ -26,37 +25,49 @@ import org.junit.runner.RunWith import org.tensorflow.lite.examples.poseestimation.data.BodyPart import org.tensorflow.lite.examples.poseestimation.data.Device +// 使用 AndroidJUnit4 测试框架运行测试 @RunWith(AndroidJUnit4::class) class MovenetLightningTest { + // 常量定义，用于测试 companion object { - private const val TEST_INPUT_IMAGE1 = "image1.png" - private const val TEST_INPUT_IMAGE2 = "image2.jpg" - private const val ACCEPTABLE_ERROR = 21f + private const val TEST_INPUT_IMAGE1 = "image1.png" // 测试用输入图像1 + private const val TEST_INPUT_IMAGE2 = "image2.jpg" // 测试用输入图像2 + private const val ACCEPTABLE_ERROR = 21f // 可接受的误差阈值 } + // 用于检测姿势的类实例 private lateinit var poseDetector: PoseDetector + // 应用上下文 private lateinit var appContext: Context + // 预期的检测结果 private lateinit var expectedDetectionResult: List> + // 在每个测试执行前进行初始化 @Before fun setup() { + // 获取应用的上下文 appContext = InstrumentationRegistry.getInstrumentation().targetContext + // 创建 PoseDetector 实例，使用 Movenet 模型的 Lightening 版本，并指定使用 CPU 设备 poseDetector = MoveNet.create(appContext, Device.CPU, ModelType.Lightning) + // 加载预期的检测结果（CSV 文件中的数据） expectedDetectionResult = EvaluationUtils.loadCSVAsset("pose_landmark_truth.csv") } + // 测试第一个输入图像的姿势估计结果 @Test fun testPoseEstimationResultWithImage1() { + // 加载输入图像1 val input = EvaluationUtils.loadBitmapAssetByName(TEST_INPUT_IMAGE1) - // As Movenet use previous frame to optimize detection result, we run it multiple times - // using the same image to improve result. + // Movenet 使用前一帧的信息优化检测结果，因此多次运行相同图像以改善检测结果 poseDetector.estimatePoses(input) poseDetector.estimatePoses(input) poseDetector.estimatePoses(input) + // 获取姿势估计的第一个人的结果 val person = poseDetector.estimatePoses(input)[0] + // 校验检测结果与预期值的误差是否在可接受范围内 EvaluationUtils.assertPoseDetectionResult( person, expectedDetectionResult[0], @@ -64,20 +75,23 @@ class MovenetLightningTest { ) } + // 测试第二个输入图像的姿势估计结果 @Test fun testPoseEstimationResultWithImage2() { + // 加载输入图像2 val input = EvaluationUtils.loadBitmapAssetByName(TEST_INPUT_IMAGE2) - // As Movenet use previous frame to optimize detection result, we run it multiple times - // using the same image to improve result. + // Movenet 使用前一帧的信息优化检测结果，因此多次运行相同图像以改善检测结果 poseDetector.estimatePoses(input) poseDetector.estimatePoses(input) poseDetector.estimatePoses(input) + // 获取姿势估计的第一个人的结果 val person = poseDetector.estimatePoses(input)[0] + // 校验检测结果与预期值的误差是否在可接受范围内 EvaluationUtils.assertPoseDetectionResult( person, expectedDetectionResult[1], ACCEPTABLE_ERROR ) } -} \ No newline at end of file +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/LoginActivity.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/LoginActivity.kt index e718156..5ca3ab3 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/LoginActivity.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/LoginActivity.kt @@ -2,18 +2,24 @@ package org.tensorflow.lite.examples.poseestimation import android.content.Intent import android.os.Bundle -import android.widget.Toast -import androidx.appcompat.app.AppCompatActivity -import com.google.android.material.textfield.TextInputEditText import android.widget.Button -import android.widget.TextView import android.widget.EditText +import android.widget.TextView +import android.widget.Toast +import androidx.appcompat.app.AppCompatActivity +import org.tensorflow.lite.examples.poseestimation.auth.UserDao +import org.tensorflow.lite.examples.poseestimation.utils.PasswordUtils class LoginActivity : AppCompatActivity() { + private lateinit var userDao: UserDao override fun onCreate(savedInstanceState: Bundle?) { super.onCreate(savedInstanceState) setContentView(R.layout.activity_login) + // 初始化 UserDao + userDao = UserDao(this) + + // 获取从上一个页面传递的数据 val selectedGender = intent.getStringExtra("selected_gender") val selectedAge = intent.getIntExtra("selected_age", 0) @@ -42,16 +48,44 @@ class LoginActivity : AppCompatActivity() { return@setOnClickListener } + // 执行数据库查询验证 + val user = userDao.getUserByUsername(email) + if (user != null) { + // 验证密码 + + + if (PasswordUtils.verifyPassword(password, user.passwordHash)) { + // 登录成功 + Toast.makeText(this, "登录成功", Toast.LENGTH_SHORT).show() + + val intent = Intent(this, MainActivity::class.java) + intent.putExtra("selected_gender", selectedGender) + intent.putExtra("selected_age", selectedAge) + intent.putExtra("selected_weight", selectedWeight) + intent.putExtra("selected_height", selectedHeight) + //intent.putExtra("user_id", user.id) // 传递用户ID + startActivity(intent) + finish() + } else { + //密码错误 + passwordEdit.error = "密码不正确" + } + + }else { + //用户不存在 + emailEdit.error = "用户不存在" + } + // TODO: 这里添加实际的登录验证逻辑 // 目前仅做演示，直接跳转到主页面 - val intent = Intent(this, MainActivity::class.java) - intent.putExtra("selected_gender", selectedGender) - intent.putExtra("selected_age", selectedAge) - intent.putExtra("selected_weight", selectedWeight) - intent.putExtra("selected_height", selectedHeight) - startActivity(intent) - finish() - } +// val intent = Intent(this, MainActivity::class.java) +// intent.putExtra("selected_gender", selectedGender) +// intent.putExtra("selected_age", selectedAge) +// intent.putExtra("selected_weight", selectedWeight) +// intent.putExtra("selected_height", selectedHeight) +// startActivity(intent) +// finish() + } // 忘记密码点击事件 forgotPassword.setOnClickListener { diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/MainActivity.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/MainActivity.kt index e3088fe..8075ff4 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/MainActivity.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/MainActivity.kt @@ -25,7 +25,12 @@ import android.os.Process import android.view.SurfaceView import android.view.View import android.view.WindowManager -import android.widget.* +import android.widget.AdapterView +import android.widget.ArrayAdapter +import android.widget.CompoundButton +import android.widget.Spinner +import android.widget.TextView +import android.widget.Toast import androidx.activity.result.contract.ActivityResultContracts import androidx.appcompat.app.AppCompatActivity import androidx.appcompat.widget.SwitchCompat @@ -36,7 +41,13 @@ import kotlinx.coroutines.Dispatchers import kotlinx.coroutines.launch import org.tensorflow.lite.examples.poseestimation.camera.CameraSource import org.tensorflow.lite.examples.poseestimation.data.Device -import org.tensorflow.lite.examples.poseestimation.ml.* +import org.tensorflow.lite.examples.poseestimation.ml.ModelType +import org.tensorflow.lite.examples.poseestimation.ml.MoveNet +import org.tensorflow.lite.examples.poseestimation.ml.MoveNetMultiPose +import org.tensorflow.lite.examples.poseestimation.ml.PoseClassifier +import org.tensorflow.lite.examples.poseestimation.ml.PoseNet +import org.tensorflow.lite.examples.poseestimation.ml.TrackerType +import org.tensorflow.lite.examples.poseestimation.ml.Type class MainActivity : AppCompatActivity() { companion object { @@ -267,99 +278,95 @@ class MainActivity : AppCompatActivity() { } } - // Change model when app is running + // 在应用运行时更改模型 private fun changeModel(position: Int) { if (modelPos == position) return modelPos = position - createPoseEstimator() + createPoseEstimator() // 重新创建姿势估计器 } - // Change device (accelerator) type when app is running + // 在应用运行时更改设备（加速器）类型 private fun changeDevice(position: Int) { val targetDevice = when (position) { - 0 -> Device.CPU - 1 -> Device.GPU - else -> Device.NNAPI + 0 -> Device.CPU // 使用CPU + 1 -> Device.GPU // 使用GPU + else -> Device.NNAPI // 使用NNAPI } if (device == targetDevice) return device = targetDevice - createPoseEstimator() + createPoseEstimator() // 重新创建姿势估计器 } - // Change tracker for Movenet MultiPose model + // 更改Movenet MultiPose模型的跟踪器 private fun changeTracker(position: Int) { cameraSource?.setTracker( when (position) { - 1 -> TrackerType.BOUNDING_BOX - 2 -> TrackerType.KEYPOINTS - else -> TrackerType.OFF + 1 -> TrackerType.BOUNDING_BOX // 选择边界框跟踪器 + 2 -> TrackerType.KEYPOINTS // 选择关键点跟踪器 + else -> TrackerType.OFF // 关闭跟踪器 } ) } + // 创建姿势估计器，根据不同的模型类型配置不同的设置 private fun createPoseEstimator() { - // For MoveNet MultiPose, hide score and disable pose classifier as the model returns - // multiple Person instances. + // 对于MoveNet MultiPose，隐藏分数并禁用姿势分类器，因为该模型返回多个Person实例。 val poseDetector = when (modelPos) { 0 -> { - // MoveNet Lightning (SinglePose) + // MoveNet Lightning (单人姿势) showPoseClassifier(true) showDetectionScore(true) showTracker(false) MoveNet.create(this, device, ModelType.Lightning) } 1 -> { - // MoveNet Thunder (SinglePose) + // MoveNet Thunder (单人姿势) showPoseClassifier(true) showDetectionScore(true) showTracker(false) MoveNet.create(this, device, ModelType.Thunder) } 2 -> { - // MoveNet (Lightning) MultiPose + // MoveNet (Lightning) MultiPose (多人姿势) showPoseClassifier(false) showDetectionScore(false) - // Movenet MultiPose Dynamic does not support GPUDelegate + // Movenet MultiPose Dynamic不支持GPUDelegate if (device == Device.GPU) { - showToast(getString(R.string.tfe_pe_gpu_error)) + showToast(getString(R.string.tfe_pe_gpu_error)) // 如果是GPU设备，显示错误提示 } showTracker(true) - MoveNetMultiPose.create( - this, - device, - Type.Dynamic - ) + MoveNetMultiPose.create(this, device, Type.Dynamic) } 3 -> { - // PoseNet (SinglePose) + // PoseNet (单人姿势) showPoseClassifier(true) showDetectionScore(true) showTracker(false) PoseNet.create(this, device) } else -> { - null + null // 如果没有匹配的模型，则返回null } } poseDetector?.let { detector -> - cameraSource?.setDetector(detector) + cameraSource?.setDetector(detector) // 设置相机源的检测器 } } - // Show/hide the pose classification option. + // 显示/隐藏姿势分类选项 private fun showPoseClassifier(isVisible: Boolean) { vClassificationOption.visibility = if (isVisible) View.VISIBLE else View.GONE if (!isVisible) { - swClassification.isChecked = false + swClassification.isChecked = false // 如果不可见，取消选择分类开关 } } - // Show/hide the detection score. + // 显示/隐藏检测分数 private fun showDetectionScore(isVisible: Boolean) { tvScore.visibility = if (isVisible) View.VISIBLE else View.GONE } - // Show/hide classification result. + // 显示/隐藏分类结果 private fun showClassificationResult(isVisible: Boolean) { val visibility = if (isVisible) View.VISIBLE else View.GONE tvClassificationValue1.visibility = visibility @@ -367,31 +374,31 @@ class MainActivity : AppCompatActivity() { tvClassificationValue3.visibility = visibility } - // Show/hide the tracking options. + // 显示/隐藏跟踪选项 private fun showTracker(isVisible: Boolean) { if (isVisible) { - // Show tracker options and enable Bounding Box tracker. + // 显示跟踪器选项并启用边界框跟踪器 vTrackerOption.visibility = View.VISIBLE spnTracker.setSelection(1) } else { - // Set tracker type to off and hide tracker option. + // 设置跟踪器类型为关闭，并隐藏跟踪选项 vTrackerOption.visibility = View.GONE spnTracker.setSelection(0) } } + // 请求权限 private fun requestPermission() { when (PackageManager.PERMISSION_GRANTED) { ContextCompat.checkSelfPermission( this, Manifest.permission.CAMERA ) -> { - // You can use the API that requires the permission. + // 如果已获得权限，可以直接使用需要权限的API openCamera() } else -> { - // You can directly ask for the permission. - // The registered ActivityResultCallback gets the result of this request. + // 否则，直接请求权限 requestPermissionLauncher.launch( Manifest.permission.CAMERA ) @@ -399,6 +406,7 @@ class MainActivity : AppCompatActivity() { } } + // 显示Toast消息 private fun showToast(message: String) { Toast.makeText(this, message, Toast.LENGTH_LONG).show() } diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/SignupActivity.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/SignupActivity.kt index 6c60c2e..9c5b85c 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/SignupActivity.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/SignupActivity.kt @@ -6,12 +6,18 @@ import android.widget.EditText import android.widget.TextView import android.widget.Toast import androidx.appcompat.app.AppCompatActivity +import org.tensorflow.lite.examples.poseestimation.auth.UserDao +import org.tensorflow.lite.examples.poseestimation.auth.User +import org.tensorflow.lite.examples.poseestimation.utils.PasswordUtils class SignupActivity : AppCompatActivity() { override fun onCreate(savedInstanceState: Bundle?) { super.onCreate(savedInstanceState) setContentView(R.layout.activity_signup) + // 初始化 UserDao + var userDao = UserDao(this) + // 绑定控件 val usernameEdit = findViewById(R.id.editUsername) val passwordEdit = findViewById(R.id.editPassword) @@ -46,9 +52,29 @@ class SignupActivity : AppCompatActivity() { return@setOnClickListener } - // TODO: 这里添加实际的注册逻辑 - Toast.makeText(this, "注册成功", Toast.LENGTH_SHORT).show() - finish() + // 检查用户名是否已存在 + if (userDao.checkUsernameExists(username)) { + usernameEdit.error = "用户名已存在" + Toast.makeText(this, "用户名已存在", Toast.LENGTH_SHORT).show() + return@setOnClickListener + } + // 加密密码 + val passwordHash = PasswordUtils.hashPassword(password) + + val user = User() + user.setUsername(username) + user.setPasswordHash(passwordHash) + + + +// 保存到数据库 + val userId = userDao.addUser(user) + if (userId != (-1).toLong()) { + Toast.makeText(this, "注册成功", Toast.LENGTH_SHORT).show() + finish() + } else { + Toast.makeText(this, "注册失败，请重试", Toast.LENGTH_SHORT).show() + } } // Login Tab点击事件 diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/DatabaseHelper.java b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/DatabaseHelper.java new file mode 100644 index 0000000..35aea9f --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/DatabaseHelper.java @@ -0,0 +1,49 @@ +package org.tensorflow.lite.examples.poseestimation.auth; + +import android.content.Context; +import android.database.sqlite.SQLiteDatabase; +import android.database.sqlite.SQLiteOpenHelper; +public class DatabaseHelper extends SQLiteOpenHelper{ + + // 数据库信息 + private static final String DATABASE_NAME = "user_database"; + private static final int DATABASE_VERSION = 1; + + // 表名和列名 + public static final String TABLE_USERS = "users"; + public static final String COLUMN_ID = "_id"; + public static final String COLUMN_USERNAME = "username"; + public static final String COLUMN_PASSWORD_HASH = "password_hash"; + public static final String COLUMN_AGE = "age"; + public static final String COLUMN_WEIGHT = "weight"; + public static final String COLUMN_GENDER = "gender"; + public static final String COLUMN_HEIGHT = "height"; + + // 创建表的SQL语句 + private static final String CREATE_TABLE_USERS = + "CREATE TABLE " + TABLE_USERS + "(" + + COLUMN_ID + " INTEGER PRIMARY KEY AUTOINCREMENT," + + COLUMN_USERNAME + " TEXT UNIQUE NOT NULL," + + COLUMN_PASSWORD_HASH + " TEXT NOT NULL" +// + COLUMN_AGE + " INTEGER," +// + COLUMN_WEIGHT + " REAL," +// + COLUMN_GENDER + " TEXT," +// + COLUMN_HEIGHT + " REAL" + + ")"; + + // 构造函数 + public DatabaseHelper(Context context) { + super(context, DATABASE_NAME, null, DATABASE_VERSION); + } + + @Override + public void onCreate(SQLiteDatabase db) { + db.execSQL(CREATE_TABLE_USERS); + } + + @Override + public void onUpgrade(SQLiteDatabase db, int oldVersion, int newVersion) { + db.execSQL("DROP TABLE IF EXISTS " + TABLE_USERS); + onCreate(db); + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/User.java b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/User.java new file mode 100644 index 0000000..7ec6a62 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/User.java @@ -0,0 +1,46 @@ +package org.tensorflow.lite.examples.poseestimation.auth; + +import java.io.Serializable; +public class User implements Serializable{ + private long id; + private String username; + private String passwordHash; +// private int age; +// private float weight; +// private String gender; +// private float height; + + // 构造函数、Getter和Setter方法 + public User() {} + + public User(String username, String passwordHash) { + this.username = username; + this.passwordHash = passwordHash; +// this.age = age; +// this.weight = weight; +// this.gender = gender; +// this.height = height; + } + + // Getter和Setter方法 + public long getId() { return id; } + public void setId(long id) { this.id = id; } + + public String getUsername() { return username; } + public void setUsername(String username) { this.username = username; } + + public String getPasswordHash() { return passwordHash; } + public void setPasswordHash(String passwordHash) { this.passwordHash = passwordHash; } + +// public int getAge() { return age; } +// public void setAge(int age) { this.age = age; } +// +// public float getWeight() { return weight; } +// public void setWeight(float weight) { this.weight = weight; } +// +// public String getGender() { return gender; } +// public void setGender(String gender) { this.gender = gender; } +// +// public float getHeight() { return height; } +// public void setHeight(float height) { this.height = height; } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/UserDao.java b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/UserDao.java new file mode 100644 index 0000000..e719854 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/auth/UserDao.java @@ -0,0 +1,130 @@ +package org.tensorflow.lite.examples.poseestimation.auth; + +import android.content.ContentValues; +import android.content.Context; +import android.database.Cursor; +import android.database.sqlite.SQLiteDatabase; +import org.tensorflow.lite.examples.poseestimation.auth.User; +import java.util.ArrayList; +import java.util.List; +public class UserDao { + private DatabaseHelper databaseHelper; + + public UserDao(Context context) { + databaseHelper = new DatabaseHelper(context); + } + + // 添加用户 + public long addUser(User user) { + SQLiteDatabase db = databaseHelper.getWritableDatabase(); + + ContentValues values = new ContentValues(); + values.put(DatabaseHelper.COLUMN_USERNAME, user.getUsername()); + values.put(DatabaseHelper.COLUMN_PASSWORD_HASH, user.getPasswordHash()); +// values.put(DatabaseHelper.COLUMN_AGE, user.getAge()); +// values.put(DatabaseHelper.COLUMN_WEIGHT, user.getWeight()); +// values.put(DatabaseHelper.COLUMN_GENDER, user.getGender()); +// values.put(DatabaseHelper.COLUMN_HEIGHT, user.getHeight()); + + long id = db.insert(DatabaseHelper.TABLE_USERS, null, values); + db.close(); + return id; + } + + // 根据用户名获取用户 + public User getUserByUsername(String username) { + SQLiteDatabase db = databaseHelper.getReadableDatabase(); + + String[] projection = { + DatabaseHelper.COLUMN_ID, + DatabaseHelper.COLUMN_USERNAME, + DatabaseHelper.COLUMN_PASSWORD_HASH, +// DatabaseHelper.COLUMN_AGE, +// DatabaseHelper.COLUMN_WEIGHT, +// DatabaseHelper.COLUMN_GENDER, +// DatabaseHelper.COLUMN_HEIGHT + }; + + String selection = DatabaseHelper.COLUMN_USERNAME + " = ?"; + String[] selectionArgs = { username }; + + Cursor cursor = db.query( + DatabaseHelper.TABLE_USERS, + projection, + selection, + selectionArgs, + null, + null, + null + ); + + User user = null; + if (cursor.moveToFirst()) { + user = new User(); + user.setId(cursor.getLong(cursor.getColumnIndex(DatabaseHelper.COLUMN_ID))); + user.setUsername(cursor.getString(cursor.getColumnIndex(DatabaseHelper.COLUMN_USERNAME))); + user.setPasswordHash(cursor.getString(cursor.getColumnIndex(DatabaseHelper.COLUMN_PASSWORD_HASH))); +// user.setAge(cursor.getInt(cursor.getColumnIndex(DatabaseHelper.COLUMN_AGE))); +// user.setWeight(cursor.getFloat(cursor.getColumnIndex(DatabaseHelper.COLUMN_WEIGHT))); +// user.setGender(cursor.getString(cursor.getColumnIndex(DatabaseHelper.COLUMN_GENDER))); +// user.setHeight(cursor.getFloat(cursor.getColumnIndex(DatabaseHelper.COLUMN_HEIGHT))); + } + + cursor.close(); + db.close(); + return user; + } + + // 检查用户名是否存在 + public boolean checkUsernameExists(String username) { + SQLiteDatabase db = databaseHelper.getReadableDatabase(); + + String[] projection = { DatabaseHelper.COLUMN_USERNAME }; + String selection = DatabaseHelper.COLUMN_USERNAME + " = ?"; + String[] selectionArgs = { username }; + + Cursor cursor = db.query( + DatabaseHelper.TABLE_USERS, + projection, + selection, + selectionArgs, + null, + null, + null + ); + + boolean exists = cursor.moveToFirst(); + cursor.close(); + db.close(); + + return exists; + } + + // 获取所有用户 + public List getAllUsers() { + List userList = new ArrayList<>(); + String selectQuery = "SELECT * FROM " + DatabaseHelper.TABLE_USERS; + + SQLiteDatabase db = databaseHelper.getReadableDatabase(); + Cursor cursor = db.rawQuery(selectQuery, null); + + if (cursor.moveToFirst()) { + do { + User user = new User(); + user.setId(cursor.getLong(cursor.getColumnIndex(DatabaseHelper.COLUMN_ID))); + user.setUsername(cursor.getString(cursor.getColumnIndex(DatabaseHelper.COLUMN_USERNAME))); + user.setPasswordHash(cursor.getString(cursor.getColumnIndex(DatabaseHelper.COLUMN_PASSWORD_HASH))); +// user.setAge(cursor.getInt(cursor.getColumnIndex(DatabaseHelper.COLUMN_AGE))); +// user.setWeight(cursor.getFloat(cursor.getColumnIndex(DatabaseHelper.COLUMN_WEIGHT))); +// user.setGender(cursor.getString(cursor.getColumnIndex(DatabaseHelper.COLUMN_GENDER))); +// user.setHeight(cursor.getFloat(cursor.getColumnIndex(DatabaseHelper.COLUMN_HEIGHT))); + + userList.add(user); + } while (cursor.moveToNext()); + } + + cursor.close(); + db.close(); + return userList; + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/BodyPart.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/BodyPart.kt index 9e105f1..d8fa44e 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/BodyPart.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/BodyPart.kt @@ -1,41 +1,51 @@ /* Copyright 2021 The TensorFlow Authors. All Rights Reserved. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 + http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. ============================================================================== */ package org.tensorflow.lite.examples.poseestimation.data +// 定义一个枚举类 BodyPart，用于表示人体的各个部位 +// 每个部位都关联一个整数值，表示该部位在模型中的位置 + +// BodyPart 枚举类，其中每个枚举值代表一个人体部位 +// 枚举类的构造函数接收一个整数值 position，表示该部位的位置信息 enum class BodyPart(val position: Int) { - NOSE(0), - LEFT_EYE(1), - RIGHT_EYE(2), - LEFT_EAR(3), - RIGHT_EAR(4), - LEFT_SHOULDER(5), - RIGHT_SHOULDER(6), - LEFT_ELBOW(7), - RIGHT_ELBOW(8), - LEFT_WRIST(9), - RIGHT_WRIST(10), - LEFT_HIP(11), - RIGHT_HIP(12), - LEFT_KNEE(13), - RIGHT_KNEE(14), - LEFT_ANKLE(15), - RIGHT_ANKLE(16); - companion object{ + NOSE(0), // 鼻子，位置 0 + LEFT_EYE(1), // 左眼，位置 1 + RIGHT_EYE(2), // 右眼，位置 2 + LEFT_EAR(3), // 左耳，位置 3 + RIGHT_EAR(4), // 右耳，位置 4 + LEFT_SHOULDER(5), // 左肩，位置 5 + RIGHT_SHOULDER(6),// 右肩，位置 6 + LEFT_ELBOW(7), // 左肘，位置 7 + RIGHT_ELBOW(8), // 右肘，位置 8 + LEFT_WRIST(9), // 左手腕，位置 9 + RIGHT_WRIST(10), // 右手腕，位置 10 + LEFT_HIP(11), // 左髋，位置 11 + RIGHT_HIP(12), // 右髋，位置 12 + LEFT_KNEE(13), // 左膝，位置 13 + RIGHT_KNEE(14), // 右膝，位置 14 + LEFT_ANKLE(15), // 左踝，位置 15 + RIGHT_ANKLE(16); // 右踝，位置 16 + + companion object { + // 使用一个映射表，将每个部位的位置与 BodyPart 进行关联 private val map = values().associateBy(BodyPart::position) + + // 根据位置返回对应的 BodyPart + // 如果传入的位置没有对应的 BodyPart，则抛出异常 fun fromInt(position: Int): BodyPart = map.getValue(position) } } diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/KeyPoint.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/KeyPoint.kt index 99a89cd..4075a80 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/KeyPoint.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/KeyPoint.kt @@ -17,5 +17,56 @@ limitations under the License. package org.tensorflow.lite.examples.poseestimation.data import android.graphics.PointF +import kotlin.math.acos +import kotlin.math.atan2 +import kotlin.math.sqrt -data class KeyPoint(val bodyPart: BodyPart, var coordinate: PointF, val score: Float) +data class KeyPoint( + // bodyPart 表示该关键点对应的人体部位，是一个 BodyPart 枚举类型 + val bodyPart: BodyPart, + + // coordinate 表示该关键点的坐标位置，是一个 PointF 对象，包含了 x 和 y 坐标 + var coordinate: PointF, + + // score 表示该关键点的置信度分数，值越高表示模型对该关键点预测的准确性越高 + val score: Float +){ + + // 函数1: 计算两点连线与竖直方向和水平方向的夹角 + fun abtPoints(other: KeyPoint): Pair { + val dx = other.coordinate.x - this.coordinate.x + val dy = other.coordinate.y - this.coordinate.y + + // 计算连线与水平方向的夹角（atan2返回的是弧度，转成角度） + val angleX = Math.toDegrees(atan2(dy.toDouble(), dx.toDouble())).toFloat() + + // 计算连线与竖直方向的夹角 + val angleY = Math.toDegrees(atan2(dx.toDouble(), dy.toDouble())).toFloat() + + return Pair(angleX, angleY) + } + + // 函数2: 计算角ABC + fun abttPoints(A: KeyPoint, B: KeyPoint, C: KeyPoint): Float { + // 计算向量BA和BC的坐标 + val BAx = A.coordinate.x - B.coordinate.x + val BAy = A.coordinate.y - B.coordinate.y + val BCx = C.coordinate.x - B.coordinate.x + val BCy = C.coordinate.y - B.coordinate.y + + // 计算BA和BC的点积 + val dotProduct = BAx * BCx + BAy * BCy + + // 计算BA和BC的模长 + val magBA = sqrt(BAx * BAx + BAy * BAy) + val magBC = sqrt(BCx * BCx + BCy * BCy) + + // 计算夹角的cos值 + val cosAngle = dotProduct / (magBA * magBC) + + // 计算夹角的角度（弧度转角度） + val angle = Math.toDegrees(acos(cosAngle.toDouble())).toFloat() + + return angle + } +} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/Person.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/Person.kt index 07133ff..4ab1c8d 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/Person.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/Person.kt @@ -16,11 +16,24 @@ limitations under the License. package org.tensorflow.lite.examples.poseestimation.data +// 导入 Android 的 RectF 类，用于表示矩形区域 import android.graphics.RectF +// Person 数据类用于表示一个人体，包含其识别信息、关键点、边界框等 +// 适用于多人体姿态估计应用场景 data class Person( - var id: Int = -1, // default id is -1 + // id 表示该人体的唯一标识符，默认为 -1。每个人体有一个唯一的 id，便于区分不同的人体 + var id: Int = -1, + + // keyPoints 表示该人体的关键点集合，是一个包含多个 KeyPoint 对象的列表 + // 每个 KeyPoint 表示人体的一个部位及其坐标和置信度 val keyPoints: List, - val boundingBox: RectF? = null, // Only MoveNet MultiPose return bounding box. + + // boundingBox 表示该人体的边界框，是一个 RectF 对象 + // 仅在使用 MoveNet MultiPose 模型时返回边界框信息 + // 这个矩形框用于表示人体的大致位置和区域 + val boundingBox: RectF? = null, // MoveNet MultiPose 返回的边界框，可为 null + + // score 表示该人体的置信度分数，值越高表示模型对该人体的识别和预测的可信度越高 val score: Float ) diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/TorsoAndBodyDistance.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/TorsoAndBodyDistance.kt index 644069c..334a2ce 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/TorsoAndBodyDistance.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/data/TorsoAndBodyDistance.kt @@ -15,10 +15,23 @@ limitations under the License. */ package org.tensorflow.lite.examples.poseestimation.data - +// TorsoAndBodyDistance 数据类用于表示躯干和身体在不同方向上的最大距离 +// 该类通常用于计算和表示躯干（如躯干的上半部分）和身体整体在不同坐标轴上的位移或距离差异 data class TorsoAndBodyDistance( + // maxTorsoYDistance 表示躯干在 Y 轴（垂直方向）上的最大距离 + // 这个值用于衡量躯干在上下方向上的移动范围或位移 val maxTorsoYDistance: Float, + + // maxTorsoXDistance 表示躯干在 X 轴（水平方向）上的最大距离 + // 这个值用于衡量躯干在左右方向上的移动范围或位移 val maxTorsoXDistance: Float, + + // maxBodyYDistance 表示身体整体在 Y 轴（垂直方向）上的最大距离 + // 这个值用于衡量整个身体在上下方向上的移动范围或位移 val maxBodyYDistance: Float, + + // maxBodyXDistance 表示身体整体在 X 轴（水平方向）上的最大距离 + // 这个值用于衡量整个身体在左右方向上的移动范围或位移 val maxBodyXDistance: Float ) + diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/MoveNet.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/MoveNet.kt index 9e7722d..a1c9482 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/MoveNet.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/MoveNet.kt @@ -33,40 +33,41 @@ import kotlin.math.abs import kotlin.math.max import kotlin.math.min +// 定义模型类型，支持Lightning和Thunder两种 enum class ModelType { Lightning, Thunder } +// MoveNet类实现了PoseDetector接口，用于姿势估计 class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: GpuDelegate?) : PoseDetector { companion object { - private const val MIN_CROP_KEYPOINT_SCORE = .2f - private const val CPU_NUM_THREADS = 4 + private const val MIN_CROP_KEYPOINT_SCORE = .2f // 关键点最低得分 + private const val CPU_NUM_THREADS = 4 // CPU线程数 - // Parameters that control how large crop region should be expanded from previous frames' - // body keypoints. - private const val TORSO_EXPANSION_RATIO = 1.9f - private const val BODY_EXPANSION_RATIO = 1.2f + // 控制如何从上一帧的人体关键点扩展裁剪区域的参数 + private const val TORSO_EXPANSION_RATIO = 1.9f // 胸部扩展比率 + private const val BODY_EXPANSION_RATIO = 1.2f // 身体扩展比率 - // TFLite file names. + // TFLite模型文件名称 private const val LIGHTNING_FILENAME = "movenet_lightning.tflite" private const val THUNDER_FILENAME = "movenet_thunder.tflite" - // allow specifying model type. + // 根据设备类型创建MoveNet实例 fun create(context: Context, device: Device, modelType: ModelType): MoveNet { val options = Interpreter.Options() var gpuDelegate: GpuDelegate? = null - options.setNumThreads(CPU_NUM_THREADS) + options.setNumThreads(CPU_NUM_THREADS) // 设置CPU线程数 when (device) { Device.CPU -> { } Device.GPU -> { - gpuDelegate = GpuDelegate() + gpuDelegate = GpuDelegate() // 使用GPU加速 options.addDelegate(gpuDelegate) } - Device.NNAPI -> options.setUseNNAPI(true) + Device.NNAPI -> options.setUseNNAPI(true) // 使用NNAPI加速 } return MoveNet( Interpreter( @@ -80,26 +81,27 @@ class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu ) } - // default to lightning. + // 默认使用Lightning模型 fun create(context: Context, device: Device): MoveNet = create(context, device, ModelType.Lightning) } - private var cropRegion: RectF? = null - private var lastInferenceTimeNanos: Long = -1 - private val inputWidth = interpreter.getInputTensor(0).shape()[1] - private val inputHeight = interpreter.getInputTensor(0).shape()[2] - private var outputShape: IntArray = interpreter.getOutputTensor(0).shape() + private var cropRegion: RectF? = null // 用于存储裁剪区域 + private var lastInferenceTimeNanos: Long = -1 // 上次推理的时间 + private val inputWidth = interpreter.getInputTensor(0).shape()[1] // 输入图像的宽度 + private val inputHeight = interpreter.getInputTensor(0).shape()[2] // 输入图像的高度 + private var outputShape: IntArray = interpreter.getOutputTensor(0).shape() // 输出的形状 + // 估算姿势 override fun estimatePoses(bitmap: Bitmap): List { - val inferenceStartTimeNanos = SystemClock.elapsedRealtimeNanos() + val inferenceStartTimeNanos = SystemClock.elapsedRealtimeNanos() // 获取当前时间 if (cropRegion == null) { - cropRegion = initRectF(bitmap.width, bitmap.height) + cropRegion = initRectF(bitmap.width, bitmap.height) // 初始化裁剪区域 } - var totalScore = 0f + var totalScore = 0f // 总得分 - val numKeyPoints = outputShape[2] - val keyPoints = mutableListOf() + val numKeyPoints = outputShape[2] // 关键点的数量 + val keyPoints = mutableListOf() // 存储关键点 cropRegion?.run { val rect = RectF( @@ -107,35 +109,35 @@ class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu (top * bitmap.height), (right * bitmap.width), (bottom * bitmap.height) - ) + ) // 根据裁剪区域计算裁剪矩形 val detectBitmap = Bitmap.createBitmap( rect.width().toInt(), rect.height().toInt(), Bitmap.Config.ARGB_8888 - ) + ) // 创建裁剪后的Bitmap Canvas(detectBitmap).drawBitmap( bitmap, -rect.left, -rect.top, null ) - val inputTensor = processInputImage(detectBitmap, inputWidth, inputHeight) - val outputTensor = TensorBuffer.createFixedSize(outputShape, DataType.FLOAT32) + val inputTensor = processInputImage(detectBitmap, inputWidth, inputHeight) // 处理输入图像 + val outputTensor = TensorBuffer.createFixedSize(outputShape, DataType.FLOAT32) // 创建输出张量 val widthRatio = detectBitmap.width.toFloat() / inputWidth val heightRatio = detectBitmap.height.toFloat() / inputHeight val positions = mutableListOf() inputTensor?.let { input -> - interpreter.run(input.buffer, outputTensor.buffer.rewind()) + interpreter.run(input.buffer, outputTensor.buffer.rewind()) // 运行推理 val output = outputTensor.floatArray for (idx in 0 until numKeyPoints) { - val x = output[idx * 3 + 1] * inputWidth * widthRatio - val y = output[idx * 3 + 0] * inputHeight * heightRatio + val x = output[idx * 3 + 1] * inputWidth * widthRatio // 获取x坐标 + val y = output[idx * 3 + 0] * inputHeight * heightRatio // 获取y坐标 positions.add(x) positions.add(y) - val score = output[idx * 3 + 2] + val score = output[idx * 3 + 2] // 获取得分 keyPoints.add( KeyPoint( BodyPart.fromInt(idx), @@ -152,8 +154,8 @@ class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu val matrix = Matrix() val points = positions.toFloatArray() - matrix.postTranslate(rect.left, rect.top) - matrix.mapPoints(points) + matrix.postTranslate(rect.left, rect.top) // 计算偏移 + matrix.mapPoints(points) // 映射坐标 keyPoints.forEachIndexed { index, keyPoint -> keyPoint.coordinate = PointF( @@ -161,16 +163,18 @@ class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu points[index * 2 + 1] ) } - // new crop region + // 更新裁剪区域 cropRegion = determineRectF(keyPoints, bitmap.width, bitmap.height) } lastInferenceTimeNanos = - SystemClock.elapsedRealtimeNanos() - inferenceStartTimeNanos - return listOf(Person(keyPoints = keyPoints, score = totalScore / numKeyPoints)) + SystemClock.elapsedRealtimeNanos() - inferenceStartTimeNanos // 计算推理时间 + return listOf(Person(keyPoints = keyPoints, score = totalScore / numKeyPoints)) // 返回姿势信息 } + // 获取上次推理时间 override fun lastInferenceTimeNanos(): Long = lastInferenceTimeNanos + // 关闭资源 override fun close() { gpuDelegate?.close() interpreter.close() @@ -178,27 +182,25 @@ class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu } /** - * Prepare input image for detection + * 准备输入图像进行检测 */ private fun processInputImage(bitmap: Bitmap, inputWidth: Int, inputHeight: Int): TensorImage? { val width: Int = bitmap.width val height: Int = bitmap.height - val size = if (height > width) width else height + val size = if (height > width) width else height // 选择较小的一边 val imageProcessor = ImageProcessor.Builder().apply { - add(ResizeWithCropOrPadOp(size, size)) - add(ResizeOp(inputWidth, inputHeight, ResizeOp.ResizeMethod.BILINEAR)) + add(ResizeWithCropOrPadOp(size, size)) // 裁剪或填充为正方形 + add(ResizeOp(inputWidth, inputHeight, ResizeOp.ResizeMethod.BILINEAR)) // 调整大小 }.build() val tensorImage = TensorImage(DataType.UINT8) - tensorImage.load(bitmap) - return imageProcessor.process(tensorImage) + tensorImage.load(bitmap) // 加载图像 + return imageProcessor.process(tensorImage) // 返回处理后的图像 } /** - * Defines the default crop region. - * The function provides the initial crop region (pads the full image from both - * sides to make it a square image) when the algorithm cannot reliably determine - * the crop region from the previous frame. + * 初始化裁剪区域。 + * 当算法无法从上一帧可靠地确定裁剪区域时，提供初始裁剪区域（将图像从两边填充为正方形）。 */ private fun initRectF(imageWidth: Int, imageHeight: Int): RectF { val xMin: Float @@ -225,9 +227,8 @@ class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu } /** - * Checks whether there are enough torso keypoints. - * This function checks whether the model is confident at predicting one of the - * shoulders/hips which is required to determine a good crop region. + * 检查是否有足够的躯干关键点。 + * 该函数检查模型是否能够自信地预测一个肩膀/臀部，这是确定良好裁剪区域所必需的。 */ private fun torsoVisible(keyPoints: List): Boolean { return ((keyPoints[BodyPart.LEFT_HIP.position].score > MIN_CROP_KEYPOINT_SCORE).or( @@ -240,13 +241,9 @@ class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu } /** - * Determines the region to crop the image for the model to run inference on. - * The algorithm uses the detected joints from the previous frame to estimate - * the square region that encloses the full body of the target person and - * centers at the midpoint of two hip joints. The crop size is determined by - * the distances between each joints and the center point. - * When the model is not confident with the four torso joint predictions, the - * function returns a default crop which is the full image padded to square. + * 根据上一帧的关键点来确定裁剪区域。 + * 该算法使用上一帧检测到的关节来估算围绕目标人体的正方形区域，并以两个臀部关节的中点为中心。裁剪大小由每个关节与中心点的距离决定。 + * 当模型对四个躯干关节的预测不自信时，函数返回默认的裁剪区域，即将整个图像填充为正方形。 */ private fun determineRectF( keyPoints: List, @@ -306,10 +303,9 @@ class MoveNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu } /** - * Calculates the maximum distance from each keypoints to the center location. - * The function returns the maximum distances from the two sets of keypoints: - * full 17 keypoints and 4 torso keypoints. The returned information will be - * used to determine the crop size. See determineRectF for more detail. + * * 计算每个关键点到中心位置的最大距离。 + * * 该函数返回来自两组关键点的最大距离： + * * 全部17个关键点和4个躯干关键点。返回的信息将用于确定裁剪大小。 */ private fun determineTorsoAndBodyDistances( keyPoints: List, diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseClassifier.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseClassifier.kt index 0a67189..d8ac228 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseClassifier.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseClassifier.kt @@ -21,52 +21,60 @@ import org.tensorflow.lite.Interpreter import org.tensorflow.lite.examples.poseestimation.data.Person import org.tensorflow.lite.support.common.FileUtil +// 定义PoseClassifier类，用于进行姿势分类 class PoseClassifier( - private val interpreter: Interpreter, - private val labels: List + private val interpreter: Interpreter, // TensorFlow Lite解释器实例 + private val labels: List // 标签列表，用于模型输出后转换为具体的类别名称 ) { - private val input = interpreter.getInputTensor(0).shape() - private val output = interpreter.getOutputTensor(0).shape() + private val input = interpreter.getInputTensor(0).shape() // 获取输入张量的形状 + private val output = interpreter.getOutputTensor(0).shape() // 获取输出张量的形状 companion object { - private const val MODEL_FILENAME = "classifier.tflite" - private const val LABELS_FILENAME = "labels.txt" - private const val CPU_NUM_THREADS = 4 + private const val MODEL_FILENAME = "classifier.tflite" // 模型文件名 + private const val LABELS_FILENAME = "labels.txt" // 标签文件名 + private const val CPU_NUM_THREADS = 4 // 设置使用的线程数 + // 创建PoseClassifier的工厂方法 fun create(context: Context): PoseClassifier { + // 设置解释器的选项，包括线程数 val options = Interpreter.Options().apply { setNumThreads(CPU_NUM_THREADS) } + // 返回一个新的PoseClassifier实例 return PoseClassifier( Interpreter( FileUtil.loadMappedFile( - context, MODEL_FILENAME + context, MODEL_FILENAME // 加载模型文件 ), options ), - FileUtil.loadLabels(context, LABELS_FILENAME) + FileUtil.loadLabels(context, LABELS_FILENAME) // 加载标签文件 ) } } + // 姿势分类方法 fun classify(person: Person?): List> { - // Preprocess the pose estimation result to a flat array - val inputVector = FloatArray(input[1]) + // 预处理姿势估计结果，转换为一维数组 + val inputVector = FloatArray(input[1]) // 创建输入向量 person?.keyPoints?.forEachIndexed { index, keyPoint -> + // 将每个关键点的坐标（y, x）和得分（score）填充到输入向量中 inputVector[index * 3] = keyPoint.coordinate.y inputVector[index * 3 + 1] = keyPoint.coordinate.x inputVector[index * 3 + 2] = keyPoint.score } - // Postprocess the model output to human readable class names - val outputTensor = FloatArray(output[1]) - interpreter.run(arrayOf(inputVector), arrayOf(outputTensor)) - val output = mutableListOf>() + // 处理模型输出，将结果转换为可读的类别名称 + val outputTensor = FloatArray(output[1]) // 创建输出张量 + interpreter.run(arrayOf(inputVector), arrayOf(outputTensor)) // 运行模型进行推理 + val output = mutableListOf>() // 存储输出结果的列表 outputTensor.forEachIndexed { index, score -> + // 将每个输出结果与标签配对 output.add(Pair(labels[index], score)) } - return output + return output // 返回输出结果 } + // 关闭解释器 fun close() { interpreter.close() } diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseDetector.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseDetector.kt index f8e81a8..89c19a4 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseDetector.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseDetector.kt @@ -19,9 +19,12 @@ package org.tensorflow.lite.examples.poseestimation.ml import android.graphics.Bitmap import org.tensorflow.lite.examples.poseestimation.data.Person +// 定义一个PoseDetector接口，继承自AutoCloseable，表示此接口的实现类需要提供关闭资源的功能 interface PoseDetector : AutoCloseable { + // 使用输入的位图（Bitmap）估计图像中的姿势，返回一个Person对象的列表，每个Person表示一个姿势估计的结果 fun estimatePoses(bitmap: Bitmap): List + // 获取上一次推理的耗时（以纳秒为单位） fun lastInferenceTimeNanos(): Long } diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseNet.kt b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseNet.kt index fff4470..868320f 100644 --- a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseNet.kt +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseNet.kt @@ -34,18 +34,25 @@ import org.tensorflow.lite.support.image.ImageProcessor import org.tensorflow.lite.support.image.TensorImage import org.tensorflow.lite.support.image.ops.ResizeOp import org.tensorflow.lite.support.image.ops.ResizeWithCropOrPadOp +import kotlin.math.acos +import kotlin.math.atan2 import kotlin.math.exp +import kotlin.math.sqrt -class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: GpuDelegate?) : - PoseDetector { +// 定义PoseNet类，继承自PoseDetector接口，负责姿势检测 +class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: GpuDelegate?) : PoseDetector { companion object { + // 设置CPU线程数 private const val CPU_NUM_THREADS = 4 + // 图像标准化时的均值和标准差 private const val MEAN = 127.5f private const val STD = 127.5f private const val TAG = "Posenet" + // 模型文件名称 private const val MODEL_FILENAME = "posenet.tflite" + // 创建PoseNet实例的方法，根据设备类型选择相应的计算方式（CPU、GPU或NNAPI） fun create(context: Context, device: Device): PoseNet { val options = Interpreter.Options() var gpuDelegate: GpuDelegate? = null @@ -54,33 +61,35 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu Device.CPU -> { } Device.GPU -> { - gpuDelegate = GpuDelegate() + gpuDelegate = GpuDelegate() // 使用GPU加速 options.addDelegate(gpuDelegate) } - Device.NNAPI -> options.setUseNNAPI(true) + Device.NNAPI -> options.setUseNNAPI(true) // 使用NNAPI加速 } return PoseNet( Interpreter( - FileUtil.loadMappedFile( - context, - MODEL_FILENAME - ), options + FileUtil.loadMappedFile(context, MODEL_FILENAME), options ), gpuDelegate ) } } + // 保存上一次推理的时间 private var lastInferenceTimeNanos: Long = -1 + // 获取输入张量的宽度和高度 private val inputWidth = interpreter.getInputTensor(0).shape()[1] private val inputHeight = interpreter.getInputTensor(0).shape()[2] private var cropHeight = 0f private var cropWidth = 0f private var cropSize = 0 + // 重写estimatePoses方法，用于估算图像中的姿势 @Suppress("UNCHECKED_CAST") override fun estimatePoses(bitmap: Bitmap): List { + // 记录估算开始时间 val estimationStartTimeNanos = SystemClock.elapsedRealtimeNanos() + // 处理输入图像并转换为TensorBuffer val inputArray = arrayOf(processInputImage(bitmap).tensorBuffer.buffer) Log.i( TAG, @@ -90,20 +99,27 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu ) ) + // 初始化输出map val outputMap = initOutputMap(interpreter) + // 记录推理开始时间 val inferenceStartTimeNanos = SystemClock.elapsedRealtimeNanos() + // 运行模型进行推理 interpreter.runForMultipleInputsOutputs(inputArray, outputMap) + // 记录推理时间 lastInferenceTimeNanos = SystemClock.elapsedRealtimeNanos() - inferenceStartTimeNanos Log.i( TAG, String.format("Interpreter took %.2f ms", 1.0f * lastInferenceTimeNanos / 1_000_000) ) + // 获取热图和偏移量输出 val heatmaps = outputMap[0] as Array>> val offsets = outputMap[1] as Array>> + // 记录后处理开始时间 val postProcessingStartTimeNanos = SystemClock.elapsedRealtimeNanos() + // 进行后处理，提取关键点 val person = postProcessModelOuputs(heatmaps, offsets) Log.i( TAG, @@ -113,12 +129,19 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu ) ) + // 返回结果，封装成一个Person对象 return listOf(person) } /** - * Convert heatmaps and offsets output of Posenet into a list of keypoints + * 将Posenet的输出（热图和偏移量）转换为关键点列表 */ + //函数1: 计算两点连线倾斜程度 + fun calculateAngle(x1: Float, y1: Float, x2: Float, y2: Float): Float { + val Y = y2 - y1 + val X = x2 - x1 + return Math.toDegrees(atan2(Y.toDouble(),X.toDouble())).toFloat() + } private fun postProcessModelOuputs( heatmaps: Array>>, offsets: Array>> @@ -127,7 +150,7 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu val width = heatmaps[0][0].size val numKeypoints = heatmaps[0][0][0].size - // Finds the (row, col) locations of where the keypoints are most likely to be. + // 寻找每个关键点最可能出现的（行，列）位置 val keypointPositions = Array(numKeypoints) { Pair(0, 0) } for (keypoint in 0 until numKeypoints) { var maxVal = heatmaps[0][0][0][keypoint] @@ -145,7 +168,7 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu keypointPositions[keypoint] = Pair(maxRow, maxCol) } - // Calculating the x and y coordinates of the keypoints with offset adjustment. + // 计算关键点的x和y坐标并进行偏移调整 val xCoords = IntArray(numKeypoints) val yCoords = IntArray(numKeypoints) val confidenceScores = FloatArray(numKeypoints) @@ -168,6 +191,7 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu confidenceScores[idx] = sigmoid(heatmaps[0][positionY][positionX][idx]) } + // 创建关键点列表并计算总分数 val keypointList = mutableListOf() var totalScore = 0.0f enumValues().forEachIndexed { idx, it -> @@ -180,21 +204,116 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu ) totalScore += confidenceScores[idx] } + //计算并得到关键点之间的角度关系 + val keypoint = KeypointAngles() + keypoint.csAngles(keypointList.toList()) + + // 返回一个包含关键点和总分数的Person对象 return Person(keyPoints = keypointList.toList(), score = totalScore / numKeypoints) } + class KeypointAngles { + // 用来存储角度的列表 + val angles = mutableListOf() + + // 计算三点之间的夹角 + fun abttPoints(A: KeyPoint, B: KeyPoint, C: KeyPoint): Float { + val BAx = A.coordinate.x - B.coordinate.x + val BAy = A.coordinate.y - B.coordinate.y + val BCx = C.coordinate.x - B.coordinate.x + val BCy = C.coordinate.y - B.coordinate.y + + val dotProduct = BAx * BCx + BAy * BCy + val magBA = sqrt(BAx * BAx + BAy * BAy) + val magBC = sqrt(BCx * BCx + BCy * BCy) + + val cosAngle = dotProduct / (magBA * magBC) + return Math.toDegrees(acos(cosAngle.toDouble())).toFloat() + } + + // 添加角度并控制角度列表长度 + fun addAngle(angle: Float) { + angles.add(angle) + if (angles.size > 8) { + angles.removeAt(0) + } + } + + // 通用方法：根据三点计算并添加角度 + fun calculateAndAddAngle(A: KeyPoint, B: KeyPoint, C: KeyPoint) { + val angle = abttPoints(A, B, C) + addAngle(angle) + } + + // 遍历keypointList并计算夹角 + fun csAngles(keypointList: List) { + keypointList.forEach { keypoint -> + when (keypoint.bodyPart) { + BodyPart.LEFT_SHOULDER -> { + val leftShoulder = keypoint + val leftElbow = keypointList.first { it.bodyPart == BodyPart.LEFT_ELBOW } + val leftWrist = keypointList.first { it.bodyPart == BodyPart.LEFT_WRIST } + calculateAndAddAngle(leftShoulder, leftElbow, leftWrist) + + val rightShoulder = keypointList.first { it.bodyPart == BodyPart.RIGHT_SHOULDER } + val rightElbow = keypointList.first { it.bodyPart == BodyPart.RIGHT_ELBOW } + calculateAndAddAngle(leftShoulder, rightShoulder, rightElbow) + } + BodyPart.RIGHT_SHOULDER -> { + val rightShoulder = keypoint + val rightElbow = keypointList.first { it.bodyPart == BodyPart.RIGHT_ELBOW } + val rightWrist = keypointList.first { it.bodyPart == BodyPart.RIGHT_WRIST } + calculateAndAddAngle(rightShoulder, rightElbow, rightWrist) + + val leftShoulder = keypointList.first { it.bodyPart == BodyPart.LEFT_SHOULDER } + val leftElbow = keypointList.first { it.bodyPart == BodyPart.LEFT_ELBOW } + calculateAndAddAngle(rightShoulder, leftShoulder, leftElbow) + } + BodyPart.LEFT_HIP -> { + val leftHip = keypoint + val rightHip = keypointList.first { it.bodyPart == BodyPart.RIGHT_HIP } + val leftKnee = keypointList.first { it.bodyPart == BodyPart.LEFT_KNEE } + calculateAndAddAngle(rightHip, leftHip, leftKnee) + + val rightKnee = keypointList.first { it.bodyPart == BodyPart.RIGHT_KNEE } + calculateAndAddAngle(leftHip, rightHip, rightKnee) + + val leftAnkle = keypointList.first { it.bodyPart == BodyPart.LEFT_ANKLE } + calculateAndAddAngle(leftHip, leftKnee, leftAnkle) + } + BodyPart.RIGHT_HIP -> { + val rightHip = keypoint + val leftHip = keypointList.first { it.bodyPart == BodyPart.LEFT_HIP } + val rightKnee = keypointList.first { it.bodyPart == BodyPart.RIGHT_KNEE } + calculateAndAddAngle(rightHip, leftHip, rightKnee) + + val rightAnkle = keypointList.first { it.bodyPart == BodyPart.RIGHT_ANKLE } + calculateAndAddAngle(rightHip, rightKnee, rightAnkle) + } + // 这里可以继续添加其他 BodyPart 的处理分支 + else -> { + // 如果遇到其他没有显式列出的bodyPart，进行处理 + println("Unknown body part: ${keypoint.bodyPart}") + } + } + } + } + } + + // 返回上一次推理的时间 override fun lastInferenceTimeNanos(): Long = lastInferenceTimeNanos + // 关闭PoseNet模型和GPU代理 override fun close() { gpuDelegate?.close() interpreter.close() } /** - * Scale and crop the input image to a TensorImage. + * 将输入的位图图像进行缩放和裁剪，转换为TensorImage */ private fun processInputImage(bitmap: Bitmap): TensorImage { - // reset crop width and height + // 重置裁剪的宽度和高度 cropWidth = 0f cropHeight = 0f cropSize = if (bitmap.width > bitmap.height) { @@ -205,15 +324,21 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu bitmap.height } + // 设置图像处理器，执行缩放、裁剪和标准化操作 val imageProcessor = ImageProcessor.Builder().apply { add(ResizeWithCropOrPadOp(cropSize, cropSize)) add(ResizeOp(inputWidth, inputHeight, ResizeOp.ResizeMethod.BILINEAR)) add(NormalizeOp(MEAN, STD)) }.build() + + // 创建TensorImage并加载图像 val tensorImage = TensorImage(DataType.FLOAT32) tensorImage.load(bitmap) + // 处理图像并返回 return imageProcessor.process(tensorImage) } +} + /** * Initializes an outputMap of 1 * x * y * z FloatArrays for the model processing to populate. @@ -258,4 +383,3 @@ class PoseNet(private val interpreter: Interpreter, private var gpuDelegate: Gpu private fun sigmoid(x: Float): Float { return (1.0f / (1.0f + exp(-x))) } -} diff --git a/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/utils/PasswordUtils.java b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/utils/PasswordUtils.java new file mode 100644 index 0000000..8758521 --- /dev/null +++ b/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/utils/PasswordUtils.java @@ -0,0 +1,40 @@ +package org.tensorflow.lite.examples.poseestimation.utils; + +import android.util.Base64; +import java.security.MessageDigest; +import java.security.NoSuchAlgorithmException; +import java.security.SecureRandom; +import javax.crypto.Cipher; +import javax.crypto.spec.IvParameterSpec; +import javax.crypto.spec.SecretKeySpec; +public class PasswordUtils { + private static final String ALGORITHM = "SHA-256"; + private static final String SECRET_KEY = "YourSecretKey123"; // 实际应用中应存储在安全位置 + + // 生成密码哈希 + public static String hashPassword(String password) { + try { + MessageDigest digest = MessageDigest.getInstance(ALGORITHM); + byte[] encodedHash = digest.digest(password.getBytes()); + + StringBuilder hexString = new StringBuilder(2 * encodedHash.length); + for (byte b : encodedHash) { + String hex = Integer.toHexString(0xff & b); + if (hex.length() == 1) { + hexString.append('0'); + } + hexString.append(hex); + } + return hexString.toString(); + } catch (NoSuchAlgorithmException e) { + e.printStackTrace(); + return null; + } + } + + // 验证密码 + public static boolean verifyPassword(String password, String hashedPassword) { + String hashedInput = hashPassword(password); + return hashedInput != null && hashedInput.equals(hashedPassword); + } +} diff --git a/android/local.properties b/android/local.properties index bc2a713..70f5ae8 100644 --- a/android/local.properties +++ b/android/local.properties @@ -4,5 +4,5 @@ # Location of the SDK. This is only used by Gradle. # For customization when using a Version Control System, please read the # header note. -#Fri Apr 25 19:53:17 CST 2025 -sdk.dir=/Users/ziyue/Library/Android/sdk +#Mon May 26 14:34:26 GMT+08:00 2025 +sdk.dir=D\:\\wangjiachen\\sdk