添加双纹理采样

2、1秒钟后无检测点就不画线，添加了平台判断
3、添加了背面剔除判断

app/android/java/com/khronos/vulkan_samples/FaceLandmarkerHelper.kt

@@ -17,6 +17,7 @@ package com.khronos.vulkan_samples
 
 import android.content.Context
 import android.graphics.Bitmap
+import android.graphics.BitmapFactory
 import android.graphics.Matrix
 import android.media.MediaMetadataRetriever
 import android.net.Uri
@@ -103,7 +104,8 @@ class FaceLandmarkerHelper(
                     .setMinTrackingConfidence(minFaceTrackingConfidence)
                     .setMinFacePresenceConfidence(minFacePresenceConfidence)
                     .setNumFaces(maxNumFaces)
-                    .setOutputFaceBlendshapes(true)
+                    .setOutputFaceBlendshapes(false)
+                    .setOutputFacialTransformationMatrixes(false)
                     .setRunningMode(runningMode)
 
             // The ResultListener and ErrorListener only use for LIVE_STREAM mode.
@@ -138,6 +140,51 @@ class FaceLandmarkerHelper(
         }
     }
 
+    private var frameId: Long = 0
+    // 简化的延时跟踪
+    private val frameTimings = mutableMapOf<Long, FrameTiming>()
+    private var lastFpsUpdateTime: Long = 0
+    private var frameCount: Int = 0
+    private var currentFps: Double = 0.0
+
+    // 内部数据类用于存储时间信息
+    private data class FrameTiming(
+        var captureTime: Long = 0,
+        var preprocessStartTime: Long = 0,
+        var preprocessEndTime: Long = 0,
+        var detectionStartTime: Long = 0
+    )
+
+    // 辅助方法：记录帧时间信息
+    private fun recordFrameTiming(frameId: Long, block: (FrameTiming) -> Unit) {
+        val timing = frameTimings.getOrPut(frameId) { FrameTiming() }
+        block(timing)
+    }
+
+    // 辅助方法：更新FPS计算
+    private fun updateFps() {
+        frameCount++
+        val currentTime = SystemClock.uptimeMillis()
+
+        if (lastFpsUpdateTime == 0L) {
+            lastFpsUpdateTime = currentTime
+            return
+        }
+
+        val timeDiff = currentTime - lastFpsUpdateTime
+        if (timeDiff >= 1000) { // 每秒更新一次FPS
+            currentFps = frameCount * 1000.0 / timeDiff
+            frameCount = 0
+            lastFpsUpdateTime = currentTime
+        }
+    }
+
+    // 辅助方法：清理旧的时间记录
+    private fun cleanupOldTimings(currentFrameId: Long) {
+        val framesToRemove = frameTimings.keys.filter { it < currentFrameId - 10 }
+        framesToRemove.forEach { frameTimings.remove(it) }
+    }
+
     // Convert the ImageProxy to MP Image and feed it to FacelandmakerHelper.
     fun detectLiveStream(
         imageProxy: ImageProxy,
@@ -151,6 +198,15 @@ class FaceLandmarkerHelper(
         }
         val frameTime = SystemClock.uptimeMillis()
 
+        val currentFrameId = frameId++
+        val captureTime = SystemClock.uptimeMillis()
+
+        // 记录捕获时间
+        recordFrameTiming(currentFrameId) {
+            it.captureTime = captureTime
+            it.preprocessStartTime = SystemClock.uptimeMillis()
+        }
+
         // Copy out RGB bits from the frame to a bitmap buffer
         val bitmapBuffer =
             Bitmap.createBitmap(
@@ -180,6 +236,9 @@ class FaceLandmarkerHelper(
             matrix, true
         )
 
+        //val bitmap = BitmapFactory.decodeFile("/sdcard/Android/data/com.khronos.vulkan_samples/files/assets/face480.png")
+        //val mpImage = BitmapImageBuilder(bitmap).build()
+
         // Convert the input Bitmap object to an MPImage object to run inference
         val mpImage = BitmapImageBuilder(rotatedBitmap).build()
 
@@ -194,152 +253,57 @@ class FaceLandmarkerHelper(
         // be returned in returnLivestreamResult function
     }
 
-    // Accepts the URI for a video file loaded from the user's gallery and attempts to run
-    // face landmarker inference on the video. This process will evaluate every
-    // frame in the video and attach the results to a bundle that will be
-    // returned.
-    fun detectVideoFile(
-        videoUri: Uri,
-        inferenceIntervalMs: Long
-    ): VideoResultBundle? {
-        if (runningMode != RunningMode.VIDEO) {
-            throw IllegalArgumentException(
-                "Attempting to call detectVideoFile" +
-                        " while not using RunningMode.VIDEO"
-            )
-        }
 
-        // Inference time is the difference between the system time at the start and finish of the
-        // process
-        val startTime = SystemClock.uptimeMillis()
-
-        var didErrorOccurred = false
-
-        // Load frames from the video and run the face landmarker.
-        val retriever = MediaMetadataRetriever()
-        retriever.setDataSource(context, videoUri)
-        val videoLengthMs =
-            retriever.extractMetadata(MediaMetadataRetriever.METADATA_KEY_DURATION)
-                ?.toLong()
-
-        // Note: We need to read width/height from frame instead of getting the width/height
-        // of the video directly because MediaRetriever returns frames that are smaller than the
-        // actual dimension of the video file.
-        val firstFrame = retriever.getFrameAtTime(0)
-        val width = firstFrame?.width
-        val height = firstFrame?.height
-
-        // If the video is invalid, returns a null detection result
-        if ((videoLengthMs == null) || (width == null) || (height == null)) return null
-
-        // Next, we'll get one frame every frameInterval ms, then run detection on these frames.
-        val resultList = mutableListOf<FaceLandmarkerResult>()
-        val numberOfFrameToRead = videoLengthMs.div(inferenceIntervalMs)
-
-        for (i in 0..numberOfFrameToRead) {
-            val timestampMs = i * inferenceIntervalMs // ms
-
-            retriever
-                .getFrameAtTime(
-                    timestampMs * 1000, // convert from ms to micro-s
-                    MediaMetadataRetriever.OPTION_CLOSEST
-                )
-                ?.let { frame ->
-                    // Convert the video frame to ARGB_8888 which is required by the MediaPipe
-                    val argb8888Frame =
-                        if (frame.config == Bitmap.Config.ARGB_8888) frame
-                        else frame.copy(Bitmap.Config.ARGB_8888, false)
-
-                    // Convert the input Bitmap object to an MPImage object to run inference
-                    val mpImage = BitmapImageBuilder(argb8888Frame).build()
-
-                    // Run face landmarker using MediaPipe Face Landmarker API
-                    faceLandmarker?.detectForVideo(mpImage, timestampMs)
-                        ?.let { detectionResult ->
-                            resultList.add(detectionResult)
-                        } ?: {
-                        didErrorOccurred = true
-                        faceLandmarkerHelperListener?.onError(
-                            "ResultBundle could not be returned" +
-                                    " in detectVideoFile"
-                        )
-                    }
-                }
-                ?: run {
-                    didErrorOccurred = true
-                    faceLandmarkerHelperListener?.onError(
-                        "Frame at specified time could not be" +
-                                " retrieved when detecting in video."
-                    )
-                }
-        }
-
-        retriever.release()
-
-        val inferenceTimePerFrameMs =
-            (SystemClock.uptimeMillis() - startTime).div(numberOfFrameToRead)
-
-        return if (didErrorOccurred) {
-            null
-        } else {
-            VideoResultBundle(resultList, inferenceTimePerFrameMs, height, width)
-        }
-    }
-
-    // Accepted a Bitmap and runs face landmarker inference on it to return
-    // results back to the caller
-    fun detectImage(image: Bitmap): ResultBundle? {
-        if (runningMode != RunningMode.IMAGE) {
-            throw IllegalArgumentException(
-                "Attempting to call detectImage" +
-                        " while not using RunningMode.IMAGE"
-            )
-        }
-
-
-        // Inference time is the difference between the system time at the
-        // start and finish of the process
-        val startTime = SystemClock.uptimeMillis()
-
-        // Convert the input Bitmap object to an MPImage object to run inference
-        val mpImage = BitmapImageBuilder(image).build()
-
-        // Run face landmarker using MediaPipe Face Landmarker API
-        faceLandmarker?.detect(mpImage)?.also { landmarkResult ->
-            val inferenceTimeMs = SystemClock.uptimeMillis() - startTime
-            return ResultBundle(
-                landmarkResult,
-                inferenceTimeMs,
-                image.height,
-                image.width
-            )
-        }
-
-        // If faceLandmarker?.detect() returns null, this is likely an error. Returning null
-        // to indicate this.
-        faceLandmarkerHelperListener?.onError(
-            "Face Landmarker failed to detect."
-        )
-        return null
-    }
 
     // Return the landmark result to this FaceLandmarkerHelper's caller
     private fun returnLivestreamResult(
         result: FaceLandmarkerResult,
         input: MPImage
     ) {
+        val resultTime = SystemClock.uptimeMillis()
+        val frameId = result.timestampMs()
         if( result.faceLandmarks().size > 0 ) {
-            val finishTimeMs = SystemClock.uptimeMillis()
-            val inferenceTime = finishTimeMs - result.timestampMs()
+            // 计算各种延时
+            val timings = frameTimings[frameId]
+            if (timings != null) {
+                val totalLatency = resultTime - timings.captureTime
+                val preprocessLatency = timings.preprocessEndTime - timings.preprocessStartTime
+                val detectionLatency = resultTime - timings.detectionStartTime
+                val captureToDetectionLatency = timings.detectionStartTime - timings.captureTime
 
-            faceLandmarkerHelperListener?.onResults(
-                ResultBundle(
-                    result,
-                    inferenceTime,
-                    input.height,
-                    input.width
+                Log.i("TimeLatency",
+                    "总延时: ${totalLatency}ms | " +
+                            "预处理: ${preprocessLatency}ms | " +
+                            "检测: ${detectionLatency}ms | " +
+                            "捕获到检测: ${captureToDetectionLatency}ms | " +
+                            "FPS: ${String.format("%.1f", currentFps)}"
                 )
-            )
+
+                val finishTimeMs = SystemClock.uptimeMillis()
+                val inferenceTime = finishTimeMs - result.timestampMs()
+                faceLandmarkerHelperListener?.onResults(
+                    ResultBundle(
+                        result,
+                        inferenceTime, // 主要是检测时间
+                        input.height,
+                        input.width,
+                    )
+                )
+            } else {
+                val finishTimeMs = SystemClock.uptimeMillis()
+                val inferenceTime = finishTimeMs - result.timestampMs()
+                Log.i("TimeLatency",
+                    "总延时: ${inferenceTime}ms | "
+                )
+                faceLandmarkerHelperListener?.onResults(
+                    ResultBundle(
+                        result,
+                        inferenceTime,
+                        input.height,
+                        input.width
+                    )
+                )
+            }
         }
         else {
             faceLandmarkerHelperListener?.onEmpty()
@@ -375,13 +339,6 @@ class FaceLandmarkerHelper(
         val inputImageWidth: Int,
     )
 
-    data class VideoResultBundle(
-        val results: List<FaceLandmarkerResult>,
-        val inferenceTime: Long,
-        val inputImageHeight: Int,
-        val inputImageWidth: Int,
-    )
-
     interface LandmarkerListener {
         fun onError(error: String, errorCode: Int = OTHER_ERROR)
         fun onResults(resultBundle: ResultBundle)

app/android/java/com/khronos/vulkan_samples/MainActivity.java

@@ -6,6 +6,7 @@ import android.content.pm.PackageManager;
 import android.os.Bundle;
 import android.os.Handler;
 import android.os.Looper;
+import android.os.SystemClock;
 import android.util.Log;
 import android.view.WindowManager;
 import android.widget.Toast;
@@ -36,6 +37,7 @@ import java.util.List;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
+import java.util.concurrent.TimeUnit;
 
 public class MainActivity extends GameActivity implements FaceLandmarkerHelper.LandmarkerListener, CameraDataFetcher.CameraDataCallback {
     private ProcessCameraProvider cameraProvider;
@@ -66,8 +68,8 @@ public class MainActivity extends GameActivity implements FaceLandmarkerHelper.L
 //            return;
 //        }
 
-        AssetsCopyUtil.copyAssetsToAppFiles(this, "assets");
-        AssetsCopyUtil.copyAssetsToAppFiles(this, "shaders");
+        //AssetsCopyUtil.copyAssetsToAppFiles(this, "assets");
+        //AssetsCopyUtil.copyAssetsToAppFiles(this, "shaders");
 
 
         String native_lib_name = getResources().getString(R.string.native_lib_name);
@@ -105,16 +107,16 @@ public class MainActivity extends GameActivity implements FaceLandmarkerHelper.L
         });
 
         // 初始化Runnable
-        cameraDataRunnable = new Runnable() {
-            @Override
-            public void run() {
-                fetchCameraData();
-                handler.postDelayed(this, INTERVAL); // 再次调度
-            }
-        };
+//        cameraDataRunnable = new Runnable() {
+//            @Override
+//            public void run() {
+//                fetchCameraData();
+//                handler.postDelayed(this, INTERVAL); // 再次调度
+//            }
+//        };
 
         // 开始周期性调用
-        startPeriodicFetching();
+//        startPeriodicFetching();
 
 
         Log.i("MainActivity", "onCreate: ");
@@ -178,13 +180,45 @@ public class MainActivity extends GameActivity implements FaceLandmarkerHelper.L
 
 
                 imageAnalyzer.setAnalyzer(backgroundExecutor, new ImageAnalysis.Analyzer() {
+                    private long lastCaptureTime = 0;
+                    private int frameCount = 0;
 
                     @Override
                     public void analyze(@NonNull ImageProxy image) {
+                        frameCount++;
+                        long currentTime = System.currentTimeMillis();
                         try {
-                            //Log.d("Camera", "Received image: " + image.getWidth() + "x" + image.getHeight());
+                            long captureTimeNs = image.getImageInfo().getTimestamp();
+                            long analyzeTime = System.currentTimeMillis();
+
+                            // 简单直接的计算方法
+                            long captureTimeMs = TimeUnit.NANOSECONDS.toMillis(captureTimeNs);
+                            long latency = analyzeTime - captureTimeMs;
+
+                            // 帧率计算
+                            if (lastCaptureTime > 0) {
+                                long frameInterval = captureTimeMs - lastCaptureTime;
+                                double fps = 1000.0 / frameInterval;
+
+                                if (frameCount % 10 == 0) { // 每30帧打印一次
+                                    Log.i("TimeLatency", String.format(
+                                            "Frame %d - Latency: %d ms, FPS: %.1f",
+                                            frameCount, latency, fps
+                                    ));
+                                }
+                            }
+
+                            lastCaptureTime = captureTimeMs;
+
                             processImageForVulkan(image);
-                            detectFace(image);
+                            if(frameCount %2 == 0)
+                            {
+                                detectFace(image);
+                            }
+                            else
+                            {
+                                image.close();
+                            }
                         } finally {
                             //image.close(); // 确保在这里关闭
                             //Log.d("Camera", "Image closed, ready for next frame");
@@ -315,7 +349,7 @@ public class MainActivity extends GameActivity implements FaceLandmarkerHelper.L
             nativeBuffer.order(ByteOrder.nativeOrder());
         }
 
-
+        long start_time =  System.currentTimeMillis();
 
 //        ArrayList<Vector> vertexs = new ArrayList<>();
 //        ArrayList<Integer> triangle_index = new ArrayList<>();
@@ -325,8 +359,8 @@ public class MainActivity extends GameActivity implements FaceLandmarkerHelper.L
 //        }
 
 
-//        StringBuilder sb = new StringBuilder();
-//        sb.append("[");
+        //StringBuilder sb = new StringBuilder();
+        //sb.append("[");
 
         int index = 0;
         // Iterate through each detected face
@@ -337,7 +371,6 @@ public class MainActivity extends GameActivity implements FaceLandmarkerHelper.L
             for (NormalizedLandmark p : faceLandmarks)
             {
                 //sb.append("[");
-                //Log.i(TAG, "Index" + index + " x:"+p.x() + " y:"+p.y() + " z:"+p.z());
                 points[arrayIndex++] = p.x();
                 //sb.append(""+p.x()+",");
                 points[arrayIndex++] = p.y();
@@ -359,7 +392,10 @@ public class MainActivity extends GameActivity implements FaceLandmarkerHelper.L
         FloatBuffer floatBuffer = nativeBuffer.asFloatBuffer();
         floatBuffer.put(points);
         floatBuffer.position(0);
+        long cur_time =  System.currentTimeMillis();
+        Log.i("TimeLatency","Result Data ProcessTime:" + (cur_time-start_time));
         passDataToNative(nativeBuffer, index, width, height);
+        Log.i("TimeLatency","passDataToNative ProcessTime:" + (System.currentTimeMillis() - cur_time));
     }
 
     public float z_rate = -1.0f;

python/face_picture_map.py

@@ -0,0 +1,161 @@
+import numpy as np
+
+def read_obj_vertices(obj_file_path):
+    """读取OBJ文件中的顶点数据"""
+    vertices = []
+    with open(obj_file_path, 'r') as f:
+        for line in f:
+            line = line.strip()
+            if line.startswith('v ') and not line.startswith('vt ') and not line.startswith('vn '):
+                parts = line.split()
+                if len(parts) >= 4:
+                    # 提取顶点坐标，转换为浮点数
+                    vertex = [float(parts[1]), float(parts[2]), float(parts[3])]
+                    vertices.append(vertex)
+    return np.array(vertices)
+
+def find_vertex_mapping(original_vertices, modified_vertices, tolerance=1e-4):
+    """
+    找到修改后顶点在原始顶点中的索引映射
+    使用严格的数值匹配，匹配失败或重复匹配都会报错
+    """
+    mapping = []
+    used_indices = set()
+    
+    for i, mod_vertex in enumerate(modified_vertices):
+        matches = []
+        
+        # 查找所有在容差范围内的匹配顶点
+        for j, orig_vertex in enumerate(original_vertices):
+            if np.allclose(mod_vertex, orig_vertex, atol=tolerance):
+                matches.append(j)
+        
+        # 检查匹配结果
+        if len(matches) == 0:
+            # 没有找到匹配
+            print(f"错误: 无法为修改后顶点 {i} ({mod_vertex}) 找到匹配的原始顶点")
+            print(f"      最近的原始顶点:")
+            # 找到最近的几个顶点供参考
+            distances = []
+            for j, orig_vertex in enumerate(original_vertices):
+                dist = np.linalg.norm(mod_vertex - orig_vertex)
+                distances.append((dist, j, orig_vertex))
+            distances.sort()
+            for dist, j, vertex in distances[:3]:  # 显示最近的3个
+                print(f"      索引 {j}: {vertex}, 距离: {dist:.6f}")
+            raise ValueError(f"顶点 {i} 匹配失败")
+        
+        elif len(matches) > 1:
+            # 找到多个匹配
+            print(f"错误: 为修改后顶点 {i} ({mod_vertex}) 找到多个匹配的原始顶点:")
+            for match_idx in matches:
+                print(f"      原始顶点索引 {match_idx}: {original_vertices[match_idx]}")
+            raise ValueError(f"顶点 {i} 匹配到多个原始顶点")
+        
+        else:
+            # 找到一个匹配
+            match_idx = matches[0]
+            if match_idx in used_indices:
+                print(f"错误: 原始顶点 {match_idx} 已经被多个修改后顶点匹配")
+                print(f"      当前修改后顶点 {i}: {mod_vertex}")
+                # 找出哪个修改后顶点已经匹配了这个原始顶点
+                for k, mapped_idx in enumerate(mapping):
+                    if mapped_idx == match_idx:
+                        print(f"      已经被修改后顶点 {k}: {modified_vertices[k]} 匹配")
+                raise ValueError(f"原始顶点 {match_idx} 被重复匹配")
+            
+            mapping.append(match_idx)
+            used_indices.add(match_idx)
+    
+    return mapping
+
+def validate_mapping_completeness(mapping, original_vertices, modified_vertices):
+    """验证映射的完整性"""
+    print("\n验证映射完整性...")
+    
+    # 检查是否有未匹配的原始顶点
+    all_original_indices = set(range(len(original_vertices)))
+    used_original_indices = set(mapping)
+    unused_original_indices = all_original_indices - used_original_indices
+    
+    if unused_original_indices:
+        print(f"警告: 有 {len(unused_original_indices)} 个原始顶点未被使用:")
+        for idx in list(unused_original_indices)[:5]:  # 只显示前5个
+            print(f"      原始顶点 {idx}: {original_vertices[idx]}")
+        if len(unused_original_indices) > 5:
+            print(f"      ... 还有 {len(unused_original_indices) - 5} 个")
+    
+    # 检查顶点数量是否一致
+    if len(modified_vertices) != len(original_vertices):
+        print(f"警告: 顶点数量不一致 - 原始: {len(original_vertices)}, 修改后: {len(modified_vertices)}")
+    
+    return len(unused_original_indices) == 0
+
+def generate_header_file(mapping, output_file="map.h"):
+    """生成C++头文件"""
+    with open(output_file, 'w') as f:
+        f.write("#ifndef VERTEX_MAP_H\n")
+        f.write("#define VERTEX_MAP_H\n\n")
+        f.write("// 顶点索引映射表\n")
+        f.write("// 用于将face_picture_3dmax.obj的顶点索引映射到face_picture.obj的顶点索引\n")
+        f.write("// 生成说明: modified_vertex_index -> original_vertex_index\n")
+        f.write("// 注意: 这个映射是严格的一对一匹配，匹配容差为1e-4\n")
+        f.write(f"const int indexMap[{len(mapping)}] = {{\n")
+        
+        # 每行输出10个元素
+        for i in range(0, len(mapping), 10):
+            line_indices = mapping[i:i+10]
+            line = "    " + ", ".join(f"{idx:3d}" for idx in line_indices)
+            if i + 10 < len(mapping):
+                line += ","
+            f.write(line + "\n")
+        
+        f.write("};\n\n")
+        f.write("#endif // VERTEX_MAP_H\n")
+
+def main():
+    # 文件路径
+    original_obj = "../assets/face_picture.obj"
+    modified_obj = "../assets/face_picture_3dmax.obj"
+    output_header = "map.h"
+    
+    # 匹配容差 - 根据你的数据精度调整这个值
+    tolerance = 1e-4
+    
+    print("正在读取OBJ文件...")
+    
+    try:
+        # 读取顶点数据
+        original_vertices = read_obj_vertices(original_obj)
+        modified_vertices = read_obj_vertices(modified_obj)
+        
+        print(f"原始文件顶点数: {len(original_vertices)}")
+        print(f"修改文件顶点数: {len(modified_vertices)}")
+        print(f"匹配容差: {tolerance}")
+        
+        # 找到顶点映射
+        print("正在严格匹配顶点...")
+        mapping = find_vertex_mapping(original_vertices, modified_vertices, tolerance)
+        
+        # 验证完整性
+        is_complete = validate_mapping_completeness(mapping, original_vertices, modified_vertices)
+        
+        if is_complete:
+            print("✓ 所有原始顶点都被正确使用")
+        else:
+            print("⚠ 部分原始顶点未被使用")
+        
+        print(f"✓ 成功匹配所有 {len(mapping)} 个顶点")
+        
+        # 生成头文件
+        print(f"生成头文件: {output_header}")
+        generate_header_file(mapping, output_header)
+        
+        print("\n完成！映射文件已生成。")
+        
+    except Exception as e:
+        print(f"\n❌ 错误: {e}")
+        print("映射生成失败，请检查数据或调整匹配容差")
+
+if __name__ == "__main__":
+    main()

python/generate_picture.py

@@ -0,0 +1,81 @@
+import json
+
+def create_triangular_obj_from_json(vertices_file, indices_file, output_file):
+    # 读取顶点数据
+    with open(vertices_file, 'r') as f:
+        vertices = json.load(f)
+    
+    # 读取线索引数据
+    with open(indices_file, 'r') as f:
+        edges = json.load(f)
+    
+    print(f"读取到 {len(vertices)} 个顶点")
+    print(f"读取到 {len(edges)} 条边")
+    
+    # 根据边的连接关系自动识别三角形面
+    triangles = edges_to_triangles(edges)
+
+    
+    print(f"识别到 {len(triangles)} 个三角形面")
+    
+    # 创建OBJ文件
+    with open(output_file, 'w') as f:
+        # 写入文件头
+        f.write("# OBJ文件 - 三角形面网格\n")
+        f.write(f"# 顶点数: {len(vertices)}, 面数: {len(triangles)}\n\n")
+        
+        # 写入顶点信息 (v x y z)
+        for i, vertex in enumerate(vertices):
+            #f.write(f"# 顶点 {i}\n")
+            f.write(f"v {vertex[0]:.6f} {vertex[1]:.6f} {vertex[2]:.6f}\n")
+        
+        f.write("\n")
+        
+        # 写入面信息 (f v1 v2 v3)
+        for triangle in triangles:
+            # OBJ文件索引从1开始，所以需要+1
+            #f.write(f"# 面 {triangle[0]} {triangle[1]} {triangle[2]}\n")
+            f.write(f"f {triangle[0]+1} {triangle[1]+1} {triangle[2]+1}\n")
+
+def edges_to_triangles(edges):
+    """
+    将edges数组转换为triangles数组
+    每三个元素的第一值组成一个三角形
+    
+    Args:
+        edges: 从JSON读取的边数组
+        
+    Returns:
+        triangles: 三角形数组，每个元素包含三个点的索引
+    """
+    triangles = []
+    
+    # 确保边的数量是3的倍数（每个三角形由3条边组成）
+    if len(edges) % 3 != 0:
+        print(f"警告: 边的数量({len(edges)})不是3的倍数")
+    
+    # 每3条边组成一个三角形，取每条边的第一个点
+    for i in range(0, len(edges), 3):
+        if i + 2 < len(edges):  # 确保有足够的边
+            triangle = [
+                edges[i][0],    # 第一条边的第一个点
+                edges[i+1][0],  # 第二条边的第一个点  
+                edges[i+2][0]   # 第三条边的第一个点
+            ]
+            triangles.append(triangle)
+    
+    return triangles
+
+
+# 使用示例
+if __name__ == "__main__":
+    # 输入文件
+    vertices_file = "face_picture_point.json"  # 顶点数据文件
+    indices_file = "point_connection.json"  # 线索引数据文件
+    
+    # 输出文件
+    output_file = "face_picture.obj"  # 生成的OBJ文件
+    
+    # 生成OBJ文件
+    create_triangular_obj_from_json(vertices_file, indices_file, output_file)
+    print(f"OBJ文件已生成: {output_file}")

python/map.h

@@ -0,0 +1,58 @@
+#ifndef VERTEX_MAP_H
+#define VERTEX_MAP_H
+
+// 顶点索引映射表
+// 用于将face_picture_3dmax.obj的顶点索引映射到face_picture.obj的顶点索引
+// 生成说明: modified_vertex_index -> original_vertex_index
+// 注意: 这个映射是严格的一对一匹配，匹配容差为1e-4
+const int indexMap[468] = {
+    127,  34, 139,  11,   0,  37, 232, 231, 120,  72,
+     39, 128, 121,  47, 104,  69,  67, 175, 171, 148,
+    118,  50, 101,  73,  40,   9, 151, 108,  48, 115,
+    131, 194, 204, 211,  74, 185,  80,  42, 183,  92,
+    186, 230, 229, 202, 212, 214,  83,  18,  17,  76,
+     61, 146, 160,  29,  30,  56, 157, 173, 106, 135,
+    192, 203, 165,  98,  21,  71,  68,  51,  45,   4,
+    144,  24,  23,  77,  91, 205, 187, 201, 200, 182,
+     90, 181,  85,  84, 206,  36, 140, 193, 189, 244,
+    159, 158,  28, 247, 246, 161, 236,   3, 196,  54,
+    168,   8, 117, 228,  31,  55,  97,  99, 126, 100,
+    166,  79, 218, 155, 154,  26, 209,  49, 136, 150,
+    217, 223,  52,  53, 134, 170,  43, 119, 226, 130,
+     63, 238,  20, 242,  46,  70, 156,  78,  62,  96,
+    143, 227, 123, 111,  44, 125,  19, 216, 153,  22,
+    167, 208, 142,  57,  60,  35, 113,  27, 210, 225,
+    137, 116,  41,  38, 129,  64, 240, 102, 207, 184,
+    169, 149, 176, 105,  66, 122,   6, 147,  65, 107,
+     89, 180,  93,  15,  86,  14,  87, 145,  88, 179,
+     95, 138, 172, 215,  58, 219,  81, 195, 199,  82,
+    163, 110, 234, 109, 235, 191, 222, 141, 221, 197,
+     25,   7,  33, 220, 237, 245, 162, 188, 174,   2,
+    241, 164,  12,  13, 198, 133, 112, 243, 239, 190,
+     32, 178, 132, 177,   1, 213,  59,  94,  75, 224,
+    233, 114, 124, 356, 389, 368, 302, 267, 452, 350,
+    349, 303, 269, 357, 343, 277, 453, 333, 332, 297,
+    152, 377, 347, 348, 330, 304, 270, 336, 337, 278,
+    279, 360, 418, 262, 431, 408, 409, 310, 415, 407,
+    410, 450, 422, 430, 434, 313, 314, 306, 307, 375,
+    387, 388, 260, 286, 414, 398, 335, 406, 364, 367,
+    416, 423, 358, 327, 251, 284, 298, 281,   5, 373,
+    374, 253, 320, 321, 425, 427, 411, 421, 405, 404,
+    315,  16, 426, 266, 400, 369, 322, 391, 417, 465,
+    464, 386, 257, 258, 466, 456, 399, 419, 285, 346,
+    340, 261, 413, 441, 460, 328, 355, 371, 329, 392,
+    439, 438, 382, 341, 256, 429, 420, 394, 379, 437,
+    443, 444, 283, 275, 440, 363, 338, 273, 451, 446,
+    342, 467, 293, 334, 282, 458, 461, 462, 276, 353,
+    383, 308, 324, 325, 300, 372, 345, 447, 352, 274,
+    248, 436, 381, 252, 393, 428, 287, 250, 384, 265,
+    259, 424, 292, 366, 271, 294, 455, 272, 432, 395,
+    299, 351, 280, 319, 295, 296, 403, 323, 454, 316,
+    380, 318, 402, 365, 435, 397, 344, 311, 291, 396,
+    268, 445, 254, 339, 449, 264,  10, 442, 370, 263,
+    255, 359, 412, 301, 378, 326, 457, 362, 459, 463,
+    354, 401, 361, 309, 376, 433, 289, 305, 448, 290,
+    288, 249, 103, 385, 331, 317, 312, 390
+};
+
+#endif // VERTEX_MAP_H

samples/api/texture_loading/texture_loading.h

@@ -49,7 +49,7 @@ public:
 		uint32_t       mip_levels;
 	};
 
-	Texture texture = { 0 };
+	Texture tex_demo0 = { 0 };
 	Texture tex_demo1 = { 0 };
 	Texture tex_demo2 = { 0 };
 	Texture tex_demo3 = { 0 };
@@ -183,8 +183,8 @@ public:
 	virtual void view_changed() override;
 	virtual void on_update_ui_overlay(vkb::Drawer& drawer) override;
 
-	void            processWithVulkan(uint8_t* data, int width, int height, int rowStride, size_t dataSize, Texture& out_texture);
-	void            createTexture(VkDevice device, VkPhysicalDevice physicalDevice, int width, int height, Texture& texture);
+	void            processWithVulkan(uint8_t* data, int width, int height, int rowStride, size_t dataSize, Texture& out_texture, bool srgb);
+	void            createTexture(VkDevice device, VkPhysicalDevice physicalDevice, int width, int height, Texture& texture, bool srgb);
 	void            updateTexture(VkDevice device, VkPhysicalDevice physicalDevice, VkCommandPool commandPool, VkQueue queue, uint8_t* data, int width, int height, int rowStride, size_t dataSize, Texture& texture);
 	uint32_t        findMemoryType(VkPhysicalDevice physicalDevice, uint32_t typeFilter, VkMemoryPropertyFlags properties);
 	VkCommandBuffer beginSingleTimeCommands(VkDevice device, VkCommandPool commandPool);
@@ -206,6 +206,7 @@ private:
 	std::vector<TextureLoadingVertexStructure> obj_vertices;
 	std::vector<uint32_t> obj_indices;
 	std::map<int, int> obj_vertices_map;
+	std::map<int, int> vertices_map_3dmax;
 	std::thread workerThread;
 	bool running = false;
 

shaders/texture_loading/glsl/texture.frag

@@ -1,6 +1,7 @@
 #version 450
 
 layout (binding = 1) uniform sampler2D samplerColor;
+layout (binding = 2) uniform sampler2D samplerColor_ex;
 
 layout (location = 0) in vec2 inUV;
 layout (location = 1) in vec3 inNormal;
@@ -11,16 +12,27 @@ layout (location = 0) out vec4 outFragColor;
 void main() 
 {
 	vec4 color = texture(samplerColor, inUV);
+	vec4 color_ex = texture(samplerColor_ex, inUV);
 
 	vec3 N = normalize(inNormal);
 
 	vec4 textureColor = color;
-	if (textureColor.a < 0.1) {
+
+	//if (textureColor.a < 0.1) 
+	//{
         //textureColor = vec4(0.2, 0.2, 0.2, 0.5); 
-		discard;
-    }
+	//	discard;
+    //}
+
+	// ¼ÆËãÓë (0,0,1) µÄµã³Ë
+    float ndot = dot(N, vec3(0.0, 0.0, -1.0));
+
+	// ±£Ö¤½á¹û >= 0
+    ndot = max(ndot, 0.0);
 	
-	outFragColor = textureColor;
+	vec4 blendedColor = mix(color_ex, color, ndot);
+
+	outFragColor = blendedColor;
 
 //	outFragColor = vec4(1, 0, 0, 1);
 }