Objective C 中 iOS 的图像模糊检测
Image blur detection for iOS in Objective C
我正在尝试确定从相机拍摄的图像 iOS 是否模糊。
我在拍照前已经检查过相机对焦，但如果图像模糊，这似乎是不同的。
我使用 Open CV 在 Android 上完成了这项工作，
最后是，
int soglia = -6118750;
if (maxLap <= soglia) { // blurry
我玩了一下，减少到-6718750。
对于 iOS 来说，关于这样做的信息似乎较少。我看到几个 post 人试图在 iOS 上为此使用 Open CV，但他们似乎没有成功。
我看到 post 在 iOS 上使用金属来做到这一点，
https://medium.com/better-programming/blur-detection-via-metal-on-ios-16dd02cb1558
这是在Swift中，所以我手动将它逐行转换为Objective C。
我认为可能代码是正确的翻译，但不确定原始代码是否正确或一般适用于相机拍摄的图像？
基本在我的测试中它总是给我 2 的结果，无论是平均值还是方差，这如何用于检测模糊图像或任何其他想法？
- (BOOL) detectBlur: (CGImageRef)image {
NSLog(@"detectBlur: %@", image);
// Initialize MTL
device = MTLCreateSystemDefaultDevice();
queue = [device newCommandQueue];
// Create a command buffer for the transformation pipeline
id <MTLCommandBuffer> commandBuffer = [queue commandBuffer];
// These are the two built-in shaders we will use
MPSImageLaplacian* laplacian = [[MPSImageLaplacian alloc] initWithDevice: device];
MPSImageStatisticsMeanAndVariance* meanAndVariance = [[MPSImageStatisticsMeanAndVariance alloc] initWithDevice: device];
// Load the captured pixel buffer as a texture
MTKTextureLoader* textureLoader = [[MTKTextureLoader alloc] initWithDevice: device];
id <MTLTexture> sourceTexture = [textureLoader newTextureWithCGImage: image options: nil error: nil];
// Create the destination texture for the laplacian transformation
MTLTextureDescriptor* lapDesc = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat: sourceTexture.pixelFormat width: sourceTexture.width height: sourceTexture.height mipmapped: false];
lapDesc.usage = MTLTextureUsageShaderWrite | MTLTextureUsageShaderRead;
id <MTLTexture> lapTex = [device newTextureWithDescriptor: lapDesc];
// Encode this as the first transformation to perform
[laplacian encodeToCommandBuffer: commandBuffer sourceTexture: sourceTexture destinationTexture: lapTex];
// Create the destination texture for storing the variance.
MTLTextureDescriptor* varianceTextureDescriptor = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat: sourceTexture.pixelFormat width: 2 height: 1 mipmapped: false];
varianceTextureDescriptor.usage = MTLTextureUsageShaderWrite | MTLTextureUsageShaderRead;
id <MTLTexture> varianceTexture = [device newTextureWithDescriptor: varianceTextureDescriptor];
// Encode this as the second transformation
[meanAndVariance encodeToCommandBuffer: commandBuffer sourceTexture: lapTex destinationTexture: varianceTexture];
// Run the command buffer on the GPU and wait for the results
[commandBuffer commit];
[commandBuffer waitUntilCompleted];
// The output will be just 2 pixels, one with the mean, the other the variance.
NSMutableData* result = [NSMutableData dataWithLength: 2];
void* resultBytes = result.mutableBytes;
//var result = [Int8](repeatElement(0, count: 2));
MTLRegion region = MTLRegionMake2D(0, 0, 2, 1);
const char* bytes = resultBytes;
NSLog(@"***resultBytes: %d", bytes[0]);
NSLog(@"***resultBytes: %d", bytes[1]);
[varianceTexture getBytes: resultBytes bytesPerRow: 1 * 2 * 4 fromRegion: region mipmapLevel: 0];
NSLog(@"resultBytes: %d", bytes[0]);
NSLog(@"resultBytes: %d", bytes[1]);
int variance = (int)bytes[1];
return variance < 2;
}
Apple 提供了一个示例项目，展示了如何find the sharpest image in a sequence of captured images 使用 Accelerate 框架。这可能有助于作为起点。
您的代码暗示您假定一个 varianceTexture 具有 4 个通道，每个通道一个字节。但是对于您的 varianceTextureDescriptor，您可能希望使用浮点值，这也是由于方差的值范围，请参见下面的代码。还有，好像是想和OpenCV比较，有可比性的值。
无论如何，让我们从 MPSImageLaplacian 的 Apple 文档开始：
This filter uses an optimized convolution filter with a 3x3 kernel with the following weights:
在 Python 中可以这样做，例如喜欢：
import cv2
import np
from PIL import Image
img = np.array(Image.open('forrest.jpg'))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
laplacian_kernel = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
print(img.dtype)
print(img.shape)
laplacian = cv2.filter2D(img, -1, laplacian_kernel)
print('mean', np.mean(laplacian))
print('variance', np.var(laplacian, axis=(0, 1)))
cv2.imshow('laplacian', laplacian)
key = cv2.waitKey(0)
请注意，我们完全使用 Apple 文档中给出的值。
这为我的测试图像提供了以下输出：
uint8
(4032, 3024)
mean 14.531123203525237
variance 975.6843631756923
MPSImageStatisticsMeanAndVariance
我们现在想要使用 Apple 的 Metal Performance Shader MPSImageStatisticsMeanAndVariance 获得相同的值。
将输入图像转换为灰度图像很有用。然后应用 MPSImageLaplacian 图像内核。
一个字节也只能有 0 到 255 之间的值。因此对于生成的均值或方差值，我们希望有浮点值。我们可以独立于输入图像的像素格式来指定它。所以我们应该使用 MTLPixelFormatR32Float 如下：
MTLTextureDescriptor *varianceTextureDescriptor = [MTLTextureDescriptor
texture2DDescriptorWithPixelFormat:MTLPixelFormatR32Float
width:2
height:1
mipmapped:NO];
然后我们想将结果纹理中的 8 个字节解释为两个浮点数。我们可以通过工会很好地做到这一点。这可能看起来像这样：
union {
float f[2];
unsigned char bytes[8];
} u1;
MTLRegion region = MTLRegionMake2D(0, 0, 2, 1);
[varianceTexture getBytes:u1.bytes bytesPerRow:2 * 4 fromRegion:region mipmapLevel: 0];
最后，我们需要知道计算是用 0 到 1 之间的浮点值完成的，这实际上意味着我们要乘以 255 或 255*255 的方差以使其进入可比较的值范围:
NSLog(@"mean: %f", u1.f[0] * 255);
NSLog(@"variance: %f", u1.f[1] * 255 * 255);
为了完整起见，整个Objective-C代码：
id<MTLDevice> device = MTLCreateSystemDefaultDevice();
id<MTLCommandQueue> queue = [device newCommandQueue];
id<MTLCommandBuffer> commandBuffer = [queue commandBuffer];
MTKTextureLoader *textureLoader = [[MTKTextureLoader alloc] initWithDevice:device];
id<MTLTexture> sourceTexture = [textureLoader newTextureWithCGImage:image.CGImage options:nil error:nil];
CGColorSpaceRef srcColorSpace = CGColorSpaceCreateDeviceRGB();
CGColorSpaceRef dstColorSpace = CGColorSpaceCreateDeviceGray();
CGColorConversionInfoRef conversionInfo = CGColorConversionInfoCreate(srcColorSpace, dstColorSpace);
MPSImageConversion *conversion = [[MPSImageConversion alloc] initWithDevice:device
srcAlpha:MPSAlphaTypeAlphaIsOne
destAlpha:MPSAlphaTypeAlphaIsOne
backgroundColor:nil
conversionInfo:conversionInfo];
MTLTextureDescriptor *grayTextureDescriptor = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat:MTLPixelFormatR16Unorm
width:sourceTexture.width
height:sourceTexture.height
mipmapped:false];
grayTextureDescriptor.usage = MTLTextureUsageShaderWrite | MTLTextureUsageShaderRead;
id<MTLTexture> grayTexture = [device newTextureWithDescriptor:grayTextureDescriptor];
[conversion encodeToCommandBuffer:commandBuffer sourceTexture:sourceTexture destinationTexture:grayTexture];
MTLTextureDescriptor *textureDescriptor = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat:grayTexture.pixelFormat
width:sourceTexture.width
height:sourceTexture.height
mipmapped:false];
textureDescriptor.usage = MTLTextureUsageShaderWrite | MTLTextureUsageShaderRead;
id<MTLTexture> texture = [device newTextureWithDescriptor:textureDescriptor];
MPSImageLaplacian *imageKernel = [[MPSImageLaplacian alloc] initWithDevice:device];
[imageKernel encodeToCommandBuffer:commandBuffer sourceTexture:grayTexture destinationTexture:texture];
MPSImageStatisticsMeanAndVariance *meanAndVariance = [[MPSImageStatisticsMeanAndVariance alloc] initWithDevice:device];
MTLTextureDescriptor *varianceTextureDescriptor = [MTLTextureDescriptor
texture2DDescriptorWithPixelFormat:MTLPixelFormatR32Float
width:2
height:1
mipmapped:NO];
varianceTextureDescriptor.usage = MTLTextureUsageShaderWrite;
id<MTLTexture> varianceTexture = [device newTextureWithDescriptor:varianceTextureDescriptor];
[meanAndVariance encodeToCommandBuffer:commandBuffer sourceTexture:texture destinationTexture:varianceTexture];
[commandBuffer commit];
[commandBuffer waitUntilCompleted];
union {
float f[2];
unsigned char bytes[8];
} u;
MTLRegion region = MTLRegionMake2D(0, 0, 2, 1);
[varianceTexture getBytes:u.bytes bytesPerRow:2 * 4 fromRegion:region mipmapLevel: 0];
NSLog(@"mean: %f", u.f[0] * 255);
NSLog(@"variance: %f", u.f[1] * 255 * 255);
最终输出给出了与 Python 程序类似的值：
mean: 14.528159
variance: 974.630615
Python 代码和 Objective-C 代码也计算其他图像的相似值。
即使这不是直接问的，也应该注意方差值当然也非常依赖于主题。如果您有一系列具有相同主题的图像，那么该值肯定是有意义的。为了说明这一点，这里有一个小测试，有两个不同的图案，它们都很清晰，但在方差值上显示出明显的差异：
在上部区域，您可以看到应用拉普拉斯滤镜后转换为灰色的相应图像，在下部区域。在图像之间的中间可以看到相应的中值或方差值。
Swift版本
一些注意事项。内核大小为 3x3 的拉普拉斯函数 (MPSImageLaplacian) 可以根据图像的大小给出一些不同的结果。如果您比较相似的图像以获得一致的结果，您可能希望将图像缩放到恒定大小。
guard let image = uiImage.cgImage, let device = MTLCreateSystemDefaultDevice(), let queue = device.makeCommandQueue(), let commandBuffer = queue.makeCommandBuffer() else {
return
}
let textureLoader = MTKTextureLoader(device: device)
let sourceColorSpace = CGColorSpaceCreateDeviceRGB()
let destinationColorSpace = CGColorSpaceCreateDeviceGray()
let conversionInfo = CGColorConversionInfo(src: sourceColorSpace, dst: destinationColorSpace)
let conversion = MPSImageConversion(device: device, srcAlpha: MPSAlphaType.alphaIsOne, destAlpha: MPSAlphaType.alphaIsOne, backgroundColor: nil, conversionInfo: conversionInfo)
guard let sourceTexture = try? textureLoader.newTexture(cgImage: image, options: [.origin: MTKTextureLoader.Origin.flippedVertically]) else {
return
}
let grayscaleTextureDescriptor = MTLTextureDescriptor.texture2DDescriptor(pixelFormat: MTLPixelFormat.r16Snorm, width: sourceTexture.width, height: sourceTexture.height, mipmapped: false)
grayscaleTextureDescriptor.usage = [.shaderWrite, .shaderRead]
guard let grayscaleTexture = device.makeTexture(descriptor: grayscaleTextureDescriptor) else {
return
}
conversion.encode(commandBuffer: commandBuffer, sourceTexture: sourceTexture, destinationTexture: grayscaleTexture)
let textureDescriptor = MTLTextureDescriptor.texture2DDescriptor(pixelFormat: grayscaleTexture.pixelFormat, width: sourceTexture.width, height: sourceTexture.height, mipmapped: false)
textureDescriptor.usage = [.shaderWrite, .shaderRead]
guard let texture = device.makeTexture(descriptor: textureDescriptor) else {
return
}
let laplacian = MPSImageLaplacian(device: device)
laplacian.encode(commandBuffer: commandBuffer, sourceTexture: grayscaleTexture, destinationTexture: texture)
let meanAndVariance = MPSImageStatisticsMeanAndVariance(device: device)
let varianceTextureDescriptor = MTLTextureDescriptor.texture2DDescriptor(pixelFormat: MTLPixelFormat.r32Float, width: 2, height: 1, mipmapped: false)
varianceTextureDescriptor.usage = [.shaderWrite]
guard let varianceTexture = device.makeTexture(descriptor: varianceTextureDescriptor) else {
return
}
meanAndVariance.encode(commandBuffer: commandBuffer, sourceTexture: texture, destinationTexture: varianceTexture)
commandBuffer.commit()
commandBuffer.waitUntilCompleted()
var bytes = [Int8](repeatElement(0, count: 8))
let region = MTLRegionMake2D(0, 0, 2, 1)
varianceTexture.getBytes(&bytes, bytesPerRow: 4 * 2, from: region, mipmapLevel: 0)
var result = [Float32](repeating: 0, count: 2)
memcpy(&result, &bytes, 8)
let mean = Double(result[0] * 255.0)
let variance = Double(result[1] * 255.0 * 255.0)
let standardDeviation = sqrt(variance)
我正在尝试确定从相机拍摄的图像 iOS 是否模糊。 我在拍照前已经检查过相机对焦，但如果图像模糊，这似乎是不同的。
我使用 Open CV 在 Android 上完成了这项工作，
最后是，
int soglia = -6118750;
if (maxLap <= soglia) { // blurry
我玩了一下，减少到-6718750。
对于 iOS 来说，关于这样做的信息似乎较少。我看到几个 post 人试图在 iOS 上为此使用 Open CV，但他们似乎没有成功。
我看到 post 在 iOS 上使用金属来做到这一点， https://medium.com/better-programming/blur-detection-via-metal-on-ios-16dd02cb1558
这是在Swift中，所以我手动将它逐行转换为Objective C。 我认为可能代码是正确的翻译，但不确定原始代码是否正确或一般适用于相机拍摄的图像？
基本在我的测试中它总是给我 2 的结果，无论是平均值还是方差，这如何用于检测模糊图像或任何其他想法？
- (BOOL) detectBlur: (CGImageRef)image {
NSLog(@"detectBlur: %@", image);
// Initialize MTL
device = MTLCreateSystemDefaultDevice();
queue = [device newCommandQueue];
// Create a command buffer for the transformation pipeline
id <MTLCommandBuffer> commandBuffer = [queue commandBuffer];
// These are the two built-in shaders we will use
MPSImageLaplacian* laplacian = [[MPSImageLaplacian alloc] initWithDevice: device];
MPSImageStatisticsMeanAndVariance* meanAndVariance = [[MPSImageStatisticsMeanAndVariance alloc] initWithDevice: device];
// Load the captured pixel buffer as a texture
MTKTextureLoader* textureLoader = [[MTKTextureLoader alloc] initWithDevice: device];
id <MTLTexture> sourceTexture = [textureLoader newTextureWithCGImage: image options: nil error: nil];
// Create the destination texture for the laplacian transformation
MTLTextureDescriptor* lapDesc = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat: sourceTexture.pixelFormat width: sourceTexture.width height: sourceTexture.height mipmapped: false];
lapDesc.usage = MTLTextureUsageShaderWrite | MTLTextureUsageShaderRead;
id <MTLTexture> lapTex = [device newTextureWithDescriptor: lapDesc];
// Encode this as the first transformation to perform
[laplacian encodeToCommandBuffer: commandBuffer sourceTexture: sourceTexture destinationTexture: lapTex];
// Create the destination texture for storing the variance.
MTLTextureDescriptor* varianceTextureDescriptor = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat: sourceTexture.pixelFormat width: 2 height: 1 mipmapped: false];
varianceTextureDescriptor.usage = MTLTextureUsageShaderWrite | MTLTextureUsageShaderRead;
id <MTLTexture> varianceTexture = [device newTextureWithDescriptor: varianceTextureDescriptor];
// Encode this as the second transformation
[meanAndVariance encodeToCommandBuffer: commandBuffer sourceTexture: lapTex destinationTexture: varianceTexture];
// Run the command buffer on the GPU and wait for the results
[commandBuffer commit];
[commandBuffer waitUntilCompleted];
// The output will be just 2 pixels, one with the mean, the other the variance.
NSMutableData* result = [NSMutableData dataWithLength: 2];
void* resultBytes = result.mutableBytes;
//var result = [Int8](repeatElement(0, count: 2));
MTLRegion region = MTLRegionMake2D(0, 0, 2, 1);
const char* bytes = resultBytes;
NSLog(@"***resultBytes: %d", bytes[0]);
NSLog(@"***resultBytes: %d", bytes[1]);
[varianceTexture getBytes: resultBytes bytesPerRow: 1 * 2 * 4 fromRegion: region mipmapLevel: 0];
NSLog(@"resultBytes: %d", bytes[0]);
NSLog(@"resultBytes: %d", bytes[1]);
int variance = (int)bytes[1];
return variance < 2;
}
Apple 提供了一个示例项目，展示了如何find the sharpest image in a sequence of captured images 使用 Accelerate 框架。这可能有助于作为起点。
您的代码暗示您假定一个 varianceTexture 具有 4 个通道，每个通道一个字节。但是对于您的 varianceTextureDescriptor，您可能希望使用浮点值，这也是由于方差的值范围，请参见下面的代码。还有，好像是想和OpenCV比较，有可比性的值。
无论如何，让我们从 MPSImageLaplacian 的 Apple 文档开始：
This filter uses an optimized convolution filter with a 3x3 kernel with the following weights:
在 Python 中可以这样做，例如喜欢：
import cv2
import np
from PIL import Image
img = np.array(Image.open('forrest.jpg'))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
laplacian_kernel = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
print(img.dtype)
print(img.shape)
laplacian = cv2.filter2D(img, -1, laplacian_kernel)
print('mean', np.mean(laplacian))
print('variance', np.var(laplacian, axis=(0, 1)))
cv2.imshow('laplacian', laplacian)
key = cv2.waitKey(0)
请注意，我们完全使用 Apple 文档中给出的值。
这为我的测试图像提供了以下输出：
uint8
(4032, 3024)
mean 14.531123203525237
variance 975.6843631756923
MPSImageStatisticsMeanAndVariance
我们现在想要使用 Apple 的 Metal Performance Shader MPSImageStatisticsMeanAndVariance 获得相同的值。
将输入图像转换为灰度图像很有用。然后应用 MPSImageLaplacian 图像内核。
一个字节也只能有 0 到 255 之间的值。因此对于生成的均值或方差值，我们希望有浮点值。我们可以独立于输入图像的像素格式来指定它。所以我们应该使用 MTLPixelFormatR32Float 如下：
MTLTextureDescriptor *varianceTextureDescriptor = [MTLTextureDescriptor
texture2DDescriptorWithPixelFormat:MTLPixelFormatR32Float
width:2
height:1
mipmapped:NO];
然后我们想将结果纹理中的 8 个字节解释为两个浮点数。我们可以通过工会很好地做到这一点。这可能看起来像这样：
union {
float f[2];
unsigned char bytes[8];
} u1;
MTLRegion region = MTLRegionMake2D(0, 0, 2, 1);
[varianceTexture getBytes:u1.bytes bytesPerRow:2 * 4 fromRegion:region mipmapLevel: 0];
最后，我们需要知道计算是用 0 到 1 之间的浮点值完成的，这实际上意味着我们要乘以 255 或 255*255 的方差以使其进入可比较的值范围:
NSLog(@"mean: %f", u1.f[0] * 255);
NSLog(@"variance: %f", u1.f[1] * 255 * 255);
为了完整起见，整个Objective-C代码：
id<MTLDevice> device = MTLCreateSystemDefaultDevice();
id<MTLCommandQueue> queue = [device newCommandQueue];
id<MTLCommandBuffer> commandBuffer = [queue commandBuffer];
MTKTextureLoader *textureLoader = [[MTKTextureLoader alloc] initWithDevice:device];
id<MTLTexture> sourceTexture = [textureLoader newTextureWithCGImage:image.CGImage options:nil error:nil];
CGColorSpaceRef srcColorSpace = CGColorSpaceCreateDeviceRGB();
CGColorSpaceRef dstColorSpace = CGColorSpaceCreateDeviceGray();
CGColorConversionInfoRef conversionInfo = CGColorConversionInfoCreate(srcColorSpace, dstColorSpace);
MPSImageConversion *conversion = [[MPSImageConversion alloc] initWithDevice:device
srcAlpha:MPSAlphaTypeAlphaIsOne
destAlpha:MPSAlphaTypeAlphaIsOne
backgroundColor:nil
conversionInfo:conversionInfo];
MTLTextureDescriptor *grayTextureDescriptor = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat:MTLPixelFormatR16Unorm
width:sourceTexture.width
height:sourceTexture.height
mipmapped:false];
grayTextureDescriptor.usage = MTLTextureUsageShaderWrite | MTLTextureUsageShaderRead;
id<MTLTexture> grayTexture = [device newTextureWithDescriptor:grayTextureDescriptor];
[conversion encodeToCommandBuffer:commandBuffer sourceTexture:sourceTexture destinationTexture:grayTexture];
MTLTextureDescriptor *textureDescriptor = [MTLTextureDescriptor texture2DDescriptorWithPixelFormat:grayTexture.pixelFormat
width:sourceTexture.width
height:sourceTexture.height
mipmapped:false];
textureDescriptor.usage = MTLTextureUsageShaderWrite | MTLTextureUsageShaderRead;
id<MTLTexture> texture = [device newTextureWithDescriptor:textureDescriptor];
MPSImageLaplacian *imageKernel = [[MPSImageLaplacian alloc] initWithDevice:device];
[imageKernel encodeToCommandBuffer:commandBuffer sourceTexture:grayTexture destinationTexture:texture];
MPSImageStatisticsMeanAndVariance *meanAndVariance = [[MPSImageStatisticsMeanAndVariance alloc] initWithDevice:device];
MTLTextureDescriptor *varianceTextureDescriptor = [MTLTextureDescriptor
texture2DDescriptorWithPixelFormat:MTLPixelFormatR32Float
width:2
height:1
mipmapped:NO];
varianceTextureDescriptor.usage = MTLTextureUsageShaderWrite;
id<MTLTexture> varianceTexture = [device newTextureWithDescriptor:varianceTextureDescriptor];
[meanAndVariance encodeToCommandBuffer:commandBuffer sourceTexture:texture destinationTexture:varianceTexture];
[commandBuffer commit];
[commandBuffer waitUntilCompleted];
union {
float f[2];
unsigned char bytes[8];
} u;
MTLRegion region = MTLRegionMake2D(0, 0, 2, 1);
[varianceTexture getBytes:u.bytes bytesPerRow:2 * 4 fromRegion:region mipmapLevel: 0];
NSLog(@"mean: %f", u.f[0] * 255);
NSLog(@"variance: %f", u.f[1] * 255 * 255);
最终输出给出了与 Python 程序类似的值：
mean: 14.528159
variance: 974.630615
Python 代码和 Objective-C 代码也计算其他图像的相似值。
即使这不是直接问的，也应该注意方差值当然也非常依赖于主题。如果您有一系列具有相同主题的图像，那么该值肯定是有意义的。为了说明这一点，这里有一个小测试，有两个不同的图案，它们都很清晰，但在方差值上显示出明显的差异：
在上部区域，您可以看到应用拉普拉斯滤镜后转换为灰色的相应图像，在下部区域。在图像之间的中间可以看到相应的中值或方差值。
Swift版本
一些注意事项。内核大小为 3x3 的拉普拉斯函数 (MPSImageLaplacian) 可以根据图像的大小给出一些不同的结果。如果您比较相似的图像以获得一致的结果，您可能希望将图像缩放到恒定大小。
guard let image = uiImage.cgImage, let device = MTLCreateSystemDefaultDevice(), let queue = device.makeCommandQueue(), let commandBuffer = queue.makeCommandBuffer() else {
return
}
let textureLoader = MTKTextureLoader(device: device)
let sourceColorSpace = CGColorSpaceCreateDeviceRGB()
let destinationColorSpace = CGColorSpaceCreateDeviceGray()
let conversionInfo = CGColorConversionInfo(src: sourceColorSpace, dst: destinationColorSpace)
let conversion = MPSImageConversion(device: device, srcAlpha: MPSAlphaType.alphaIsOne, destAlpha: MPSAlphaType.alphaIsOne, backgroundColor: nil, conversionInfo: conversionInfo)
guard let sourceTexture = try? textureLoader.newTexture(cgImage: image, options: [.origin: MTKTextureLoader.Origin.flippedVertically]) else {
return
}
let grayscaleTextureDescriptor = MTLTextureDescriptor.texture2DDescriptor(pixelFormat: MTLPixelFormat.r16Snorm, width: sourceTexture.width, height: sourceTexture.height, mipmapped: false)
grayscaleTextureDescriptor.usage = [.shaderWrite, .shaderRead]
guard let grayscaleTexture = device.makeTexture(descriptor: grayscaleTextureDescriptor) else {
return
}
conversion.encode(commandBuffer: commandBuffer, sourceTexture: sourceTexture, destinationTexture: grayscaleTexture)
let textureDescriptor = MTLTextureDescriptor.texture2DDescriptor(pixelFormat: grayscaleTexture.pixelFormat, width: sourceTexture.width, height: sourceTexture.height, mipmapped: false)
textureDescriptor.usage = [.shaderWrite, .shaderRead]
guard let texture = device.makeTexture(descriptor: textureDescriptor) else {
return
}
let laplacian = MPSImageLaplacian(device: device)
laplacian.encode(commandBuffer: commandBuffer, sourceTexture: grayscaleTexture, destinationTexture: texture)
let meanAndVariance = MPSImageStatisticsMeanAndVariance(device: device)
let varianceTextureDescriptor = MTLTextureDescriptor.texture2DDescriptor(pixelFormat: MTLPixelFormat.r32Float, width: 2, height: 1, mipmapped: false)
varianceTextureDescriptor.usage = [.shaderWrite]
guard let varianceTexture = device.makeTexture(descriptor: varianceTextureDescriptor) else {
return
}
meanAndVariance.encode(commandBuffer: commandBuffer, sourceTexture: texture, destinationTexture: varianceTexture)
commandBuffer.commit()
commandBuffer.waitUntilCompleted()
var bytes = [Int8](repeatElement(0, count: 8))
let region = MTLRegionMake2D(0, 0, 2, 1)
varianceTexture.getBytes(&bytes, bytesPerRow: 4 * 2, from: region, mipmapLevel: 0)
var result = [Float32](repeating: 0, count: 2)
memcpy(&result, &bytes, 8)
let mean = Double(result[0] * 255.0)
let variance = Double(result[1] * 255.0 * 255.0)
let standardDeviation = sqrt(variance)