LayerHashCalculator.GetChannelsHash
LayerHashCalculator.GetChannelsHash method
获取通道哈希。
public int GetChannelsHash()
返回值
所有层通道的哈希
例子
以下代码演示了用于获取不同文件中相似层的唯一哈希值的 API。
[C#]
/// <summary>
/// 获取层的名称。
/// </summary>
/// <typeparam name="T"></typeparam>;;;
/// <param name="image">图像。</param>;
/// <param name="name">名字。</param>;
/// <returns></returns>
private static T GetLayerByName<T>(PsdImage image, string name) where T : Layer
{
var layers = image.Layers;
foreach (var layer in layers)
{
if (layer.Name == name)
{
return (T) layer;
}
}
return null;
}
/// <summary>
/// 战神不平等。
/// </summary>
/// <typeparam name="T"></typeparam>;;;
/// <param name="expected">预期的。</param>;
/// <param name="actual">实际值。</param>
/// <exception cref="System.Exception">参数不能相等</exception>;
public static void AreNotEqual<T>(T expected, T actual)
{
if (expected != null && expected.Equals(actual))
{
throw new Exception("Arguments must not be equal");
}
}
/// <summary>
/// 平等的战神。
/// </summary>
/// <typeparam name="T"></typeparam>;;;
/// <param name="expected">预期的。</param>;
/// <param name="actual">实际值。</param>
/// <exception cref="System.Exception">参数必须相等</exception>;
public static void AreEqual<T>(T expected, T actual)
{
if (expected != null && !expected.Equals(actual))
{
throw new Exception("Arguments must be equal");
}
}
/// <summary>
/// 规范层内容哈希测试。
/// </summary>
/// <param name="fileName">文件名。</param>
public static void RegularLayerContentHashTest(string fileName)
{
using (var im = (PsdImage) Image.Load(fileName))
{
var layers = new Layer[9];
var hashers = new LayerHashCalculator[9];
for (int i = 0; i < layers.Length; i++)
{
layers[i] = GetLayerByName<Layer>(im, string.Format("Layer {0}", i + 1));
hashers[i] = new LayerHashCalculator(layers[i]);
}
AreNotEqual(hashers[0].GetChannelsHash(), hashers[1].GetChannelsHash());
AreNotEqual(hashers[1].GetChannelsHash(), hashers[2].GetChannelsHash());
AreNotEqual(hashers[0].GetChannelsHash(), hashers[2].GetChannelsHash());
AreNotEqual(hashers[5].GetChannelsHash(), hashers[7].GetChannelsHash());
AreNotEqual(hashers[0].GetChannelsHash(), hashers[8].GetChannelsHash());
// 这些层的哈希值相等
AreEqual(hashers[0].GetChannelsHash(), hashers[3].GetChannelsHash());
AreEqual(hashers[1].GetChannelsHash(), hashers[4].GetChannelsHash());
AreEqual(hashers[0].GetChannelsHash(), hashers[6].GetChannelsHash());
// 检查混合模式哈希
AreEqual(hashers[0].GetBlendingHash(), hashers[3].GetBlendingHash());
AreEqual(hashers[1].GetBlendingHash(), hashers[4].GetBlendingHash());
AreNotEqual(hashers[0].GetBlendingHash(), hashers[6].GetBlendingHash());
// 但指针不同
AreNotEqual(layers[0], layers[3]);
AreNotEqual(layers[1], layers[4]);
AreNotEqual(layers[0], layers[6]);
}
}
/// <summary>
/// 填充图层内容哈希测试。
/// </summary>
/// <param name="fileName">文件名。</param>
public static void FillLayerContentHashTest(string fileName)
{
using (var im = (PsdImage) Image.Load(fileName))
{
var fillLayersNames = new string[] { "Color Fill", "Gradient Fill", "Pattern Fill" };
var colorFillLayers = new Layer[4];
var colorFillHashers = new LayerHashCalculator[4];
for (int fillLayerIndex = 0; fillLayerIndex < fillLayersNames.Length; fillLayerIndex++)
{
for (int i = 0; i < 2; i++)
{
var index = 0 + i * 2;
colorFillLayers[index] = GetLayerByName<Layer>(im,
string.Format("{0} 1_{1}", fillLayersNames[fillLayerIndex], i + 1));
colorFillHashers[index] = new LayerHashCalculator(colorFillLayers[index]);
index = 1 + i * 2;
colorFillLayers[index] = GetLayerByName<Layer>(im,
string.Format("{0} 2_{1}", fillLayersNames[fillLayerIndex], i + 1));
colorFillHashers[index] = new LayerHashCalculator(colorFillLayers[index]);
}
// 相似的层总是在一个索引中
AreEqual(colorFillHashers[0].GetContentHash(), colorFillHashers[2].GetContentHash());
AreEqual(colorFillHashers[1].GetContentHash(), colorFillHashers[3].GetContentHash());
AreNotEqual(colorFillHashers[0].GetContentHash(), colorFillHashers[1].GetContentHash());
}
}
}
/// <summary>
/// 智能化对象层内容哈希测试。
/// </summary>
/// <param name="fileName">文件名。</param>
public static void SmartObjectLayerContentHashTest(string fileName)
{
using (var im = (PsdImage) Image.Load(fileName))
{
var smartObjects = new Layer[]
{
GetLayerByName<Layer>(im, "Regular1_1"),
GetLayerByName<Layer>(im, "Regular1_2"),
GetLayerByName<Layer>(im, "Regular2_1"),
GetLayerByName<Layer>(im, "Regular2_2"),
GetLayerByName<Layer>(im, "Smart1_1"),
GetLayerByName<Layer>(im, "Smart1_2"),
GetLayerByName<Layer>(im, "Smart2_1"),
GetLayerByName<Layer>(im, "Smart2_2"),
};
var hashers = new LayerHashCalculator[smartObjects.Length];
for (int i = 0; i < smartObjects.Length; i++)
{
hashers[i] = new LayerHashCalculator(smartObjects[i]);
}
// Channel 数据对于 Layer 和 Createad 来自它们的 Smart Objects 是相等的。
AreEqual(hashers[0].GetChannelsHash(), hashers[2].GetChannelsHash());
AreEqual(hashers[0].GetChannelsHash(), hashers[4].GetChannelsHash());
// Content Hash不同,因为Smart Object使用其他数据作为内容
AreNotEqual(hashers[0].GetContentHash(), hashers[4].GetContentHash());
// 但是混合哈希是相似的。两个层 - 智能层和常规层都具有正常混合模式和不透明度 255
AreEqual(hashers[0].GetBlendingHash(), hashers[4].GetBlendingHash());
// Channel 数据对于 Layer 和 Createad 来自它们的 Smart Objects 是相等的。
AreEqual(hashers[1].GetChannelsHash(), hashers[3].GetChannelsHash());
AreEqual(hashers[1].GetChannelsHash(), hashers[5].GetChannelsHash());
// Content Hash不同,因为Smart Object使用其他数据作为内容
AreNotEqual(hashers[1].GetContentHash(), hashers[5].GetContentHash());
// 但是混合哈希是相似的。两个层 - 智能层和常规层都具有正常混合模式和不透明度 255
AreEqual(hashers[1].GetBlendingHash(), hashers[5].GetBlendingHash());
AreNotEqual(hashers[0].GetChannelsHash(), hashers[1].GetChannelsHash());
AreNotEqual(hashers[2].GetChannelsHash(), hashers[3].GetChannelsHash());
AreNotEqual(hashers[4].GetChannelsHash(), hashers[5].GetChannelsHash());
}
}
/// <summary>
/// 调整图层内容哈希测试。
/// </summary>
/// <param name="fileName">文件名。</param>
public static void AdjustmentLayersContentHashTest(string fileName)
{
using (var im = (PsdImage) Image.Load(fileName))
{
var adjustments = new Layer[]
{
GetLayerByName<Layer>(im, "Brightness/Contrast 1"),
GetLayerByName<Layer>(im, "Levels 1"),
GetLayerByName<Layer>(im, "Curves 1"),
GetLayerByName<Layer>(im, "Exposure 1"),
GetLayerByName<Layer>(im, "Vibrance 1"),
GetLayerByName<Layer>(im, "Hue/Saturation 1"),
GetLayerByName<Layer>(im, "Color Balance 1"),
GetLayerByName<Layer>(im, "Black & White 1"),
GetLayerByName<Layer>(im, "Photo Filter 1"),
GetLayerByName<Layer>(im, "Channel Mixer 1"),
GetLayerByName<Layer>(im, "Invert 1"),
GetLayerByName<Layer>(im, "Posterize 1"),
};
var length = adjustments.Length;
var hashers = new LayerHashCalculator[length];
for (int i = 0; i < length; i++)
{
hashers[i] = new LayerHashCalculator(adjustments[i]);
}
// 所有哈希必须不同
for (int i = 0; i < length; i++)
{
for (int j = i + 1; j < length; j++)
{
AreNotEqual(hashers[i].GetContentHash(), hashers[j].GetContentHash());
AreEqual(hashers[i].GetBlendingHash(), hashers[j].GetBlendingHash());
}
}
}
}
/// <summary>
/// 文本层内容哈希测试。
/// </summary>
/// <param name="fileName">文件名。</param>
public static void TextLayersContentHashTest(string fileName)
{
using (var im = (PsdImage) Image.Load(fileName))
{
var textLayers1 = new TextLayer[]
{
GetLayerByName<TextLayer>(im, "Text 1"),
GetLayerByName<TextLayer>(im, "Text 1 Similar"),
GetLayerByName<TextLayer>(im, "Text 1 Changed"),
};
var textLayers2 = new TextLayer[]
{
GetLayerByName<TextLayer>(im, "Text 2"),
GetLayerByName<TextLayer>(im, "Text 2 Similar"),
GetLayerByName<TextLayer>(im, "Text 2 Changed 1"),
GetLayerByName<TextLayer>(im, "Text 2 Changed 2"),
GetLayerByName<TextLayer>(im, "Text 2 Rotated"),
};
var textHashers1 = new LayerHashCalculator[textLayers1.Length];
var textHashers2 = new LayerHashCalculator[textLayers2.Length];
for (int i = 0; i < textLayers1.Length; i++)
{
textHashers1[i] = new LayerHashCalculator(textLayers1[i]);
}
for (int i = 0; i < textLayers2.Length; i++)
{
textHashers2[i] = new LayerHashCalculator(textLayers2[i]);
}
AreEqual(textHashers1[0].GetContentHash(), textHashers1[1].GetContentHash());
AreNotEqual(textHashers1[0].GetContentHash(), textHashers1[2].GetContentHash());
AreEqual(textHashers2[0].GetContentHash(), textHashers2[1].GetContentHash());
AreNotEqual(textHashers2[0].GetContentHash(), textHashers2[2].GetContentHash());
AreNotEqual(textHashers2[0].GetContentHash(), textHashers2[3].GetContentHash());
// 哈希计算中不使用变换矩阵。你应该另外检查一下
AreEqual(textHashers2[0].GetContentHash(), textHashers2[4].GetContentHash());
// 在这种情况下,我们在矩阵中有一个旋转
AreNotEqual(textLayers2[0].TransformMatrix, textLayers2[4].TransformMatrix);
// 在这种情况下,我们只有翻译(下面的文本层移位)
AreNotEqual(textLayers2[0].TransformMatrix, textLayers2[1].TransformMatrix);
}
}
/// <summary>
/// 分组层内容哈希测试。
/// </summary>
/// <param name="fileName">文件名。</param>
public static void GroupLayerContentHashTest(string fileName)
{
using (var im = (PsdImage) Image.Load(fileName))
{
var fillLayersNames = new string[] { "Color Fill", "Gradient Fill", "Pattern Fill" };
var groupLayers = new Layer[2];
var groupLayersHashers = new LayerHashCalculator[2];
groupLayers[0] = GetLayerByName<Layer>(im, "Fill");
groupLayers[1] = GetLayerByName<Layer>(im, "Fill copy");
for (int i = 0; i < groupLayers.Length; i++)
{
groupLayersHashers[i] = new LayerHashCalculator(groupLayers[i]);
}
// Group Layer Hash是从里面的layerss计算出来的
AreEqual(groupLayersHashers[0].GetContentHash(), groupLayersHashers[1].GetContentHash());
AreNotEqual(groupLayers[0], groupLayers[1]);
}
}
/// <summary>
/// 调整来自不同文件哈希测试的层内容。
/// </summary>
/// <param name="fileName">文件名。</param>
public static void RegularLayerContentFromDifferentFilesHashTest(string fileName, string outputFile)
{
using (var im = (PsdImage) Image.Load(fileName, new PsdLoadOptions() { ReadOnlyMode = true }))
{
im.Save(outputFile);
}
using (var im = (PsdImage) Image.Load(fileName))
{
using (var imCopied = (PsdImage) Image.Load(outputFile))
{
for (int i = 0; i < im.Layers.Length; i++)
{
var layer = im.Layers[i];
var layer_copied = imCopied.Layers[i];
var hashCalc = new LayerHashCalculator(layer);
var hashCalc_copied = new LayerHashCalculator(layer_copied);
// 层有不同的指针
AreNotEqual(layer, layer_copied);
// 但是层的散列是相等的
AreEqual(hashCalc.GetChannelsHash(), hashCalc_copied.GetChannelsHash());
AreEqual(hashCalc.GetContentHash(), hashCalc_copied.GetContentHash());
}
}
}
File.Delete(outputFile);
}