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TURBO-565-浮雕为3D算子

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李伟
2026-04-20 09:56:46 +08:00
parent 4390ad1e9f
commit 78c21c21f0
24 changed files with 79115 additions and 42 deletions
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XP.ImageProcessing.Processors/图像增强/SuperResolutionProcessor.cs
+42 -41
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@@ -32,7 +32,9 @@ public class SuperResolutionProcessor : ImageProcessorBase
// 会话缓存，避免重复加载
private static InferenceSession? _cachedSession;
private static string _cachedModelKey = string.Empty;
private static readonly object _sessionLock = new();
public SuperResolutionProcessor()
{
@@ -89,32 +91,33 @@ public class SuperResolutionProcessor : ImageProcessorBase
// 加载或复用会话
string modelKey = $"{model}_{scale}";
InferenceSession session;
if (_cachedModelKey == modelKey && _cachedSession != null)
lock (_sessionLock)
{
session = _cachedSession;
_logger.Debug("Reusing cached session: {ModelKey}", modelKey);
}
else
{
_cachedSession?.Dispose();
var options = new SessionOptions();
options.GraphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_ALL;
try
if (_cachedModelKey == modelKey && _cachedSession != null)
{
options.AppendExecutionProvider_CUDA(0);
_logger.Information("Using CUDA GPU for inference");
session = _cachedSession;
_logger.Debug("Reusing cached session: {ModelKey}", modelKey);
}
catch
else
{
_logger.Warning("CUDA not available, falling back to CPU");
_cachedSession?.Dispose();
var options = new SessionOptions();
options.GraphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_ALL;
bool cudaEnabled = false;
try
{
options.AppendExecutionProvider_CUDA(0);
cudaEnabled = true;
}
catch (Exception ex)
{
_logger.Warning(ex, "CUDA EP unavailable (check CUDA/cuDNN version match), falling back to CPU");
}
session = new InferenceSession(modelPath, options);
_cachedSession = session;
_cachedModelKey = modelKey;
_logger.Information("Loaded ONNX model: {ModelPath}, CUDA={CudaEnabled}", modelPath, cudaEnabled);
}
session = new InferenceSession(modelPath, options);
_cachedSession = session;
_cachedModelKey = modelKey;
// 记录实际使用的 Execution Provider
var providers = session.ModelMetadata?.CustomMetadataMap;
_logger.Information("Loaded ONNX model: {ModelPath}, Providers: {Providers}",
modelPath, string.Join(", ", session.GetType().Name));
}
int h = inputImage.Height;
@@ -178,7 +181,7 @@ public class SuperResolutionProcessor : ImageProcessorBase
for (int x = 0; x < w; x++)
{
int px = rowOffset + x * 3;
buf[px] = imgData[y, x, 0];
buf[px] = imgData[y, x, 0];
buf[px + 1] = imgData[y, x, 1];
buf[px + 2] = imgData[y, x, 2];
}
@@ -193,44 +196,42 @@ public class SuperResolutionProcessor : ImageProcessorBase
};
using var results = session.Run(inputs);
var outputTensor = results.First().AsTensor<float>();
var outputTensor = (DenseTensor<float>)results.First().AsTensor<float>();
// 输出 shape: [1, C, H*scale, W*scale] (NCHW，模型输出经过 Transpose)
// 输出 shape: [1, C, H*scale, W*scale] (NCHW)
var shape = outputTensor.Dimensions;
int outC = shape[1];
int outH = shape[2];
int outW = shape[3];
var outBuf = outputTensor.Buffer.ToArray(); // Span 不能跨 lambda 捕获，转为数组
Image<Gray, byte> result = new(outW, outH);
var outData = result.Data;
int planeSize = outH * outW;
// 转换为灰度图像
// 使用 Parallel.For + 直接内存操作
Image<Gray, byte> result;
if (outC == 1)
{
// FSRCNN: 单通道输出 [1, 1, outH, outW]
result = new Image<Gray, byte>(outW, outH);
var outData = result.Data;
// FSRCNN: [1, 1, outH, outW]
Parallel.For(0, outH, y =>
{
int rowOffset = y * outW;
for (int x = 0; x < outW; x++)
outData[y, x, 0] = (byte)Math.Clamp((int)outputTensor[0, 0, y, x], 0, 255);
outData[y, x, 0] = (byte)Math.Clamp((int)outBuf[rowOffset + x], 0, 255);
});
}
else
{
// EDSR: 三通道输出 [1, 3, outH, outW] → 灰度
// 直接计算灰度值，跳过中间 BGR 图像分配
result = new Image<Gray, byte>(outW, outH);
var outData = result.Data;
// EDSR: [1, 3, outH, outW] → 灰度，BT.601
Parallel.For(0, outH, y =>
{
int rowOffset = y * outW;
for (int x = 0; x < outW; x++)
{
float b = outputTensor[0, 0, y, x];
float g = outputTensor[0, 1, y, x];
float r = outputTensor[0, 2, y, x];
// BT.601 灰度公式: 0.299*R + 0.587*G + 0.114*B
int gray = (int)(0.299f * r + 0.587f * g + 0.114f * b);
outData[y, x, 0] = (byte)Math.Clamp(gray, 0, 255);
int i = rowOffset + x;
float b = outBuf[i];
float g = outBuf[planeSize + i];
float r = outBuf[planeSize * 2 + i];
outData[y, x, 0] = (byte)Math.Clamp((int)(0.299f * r + 0.587f * g + 0.114f * b), 0, 255);
}
});
}