CIFilter黑白图像混合后呈蓝色

Question

将 CIFilter 应用于图像时，我得到了意想不到的结果。预期的结果是黑白图像，橙色通过边缘混合。相反，我得到了一个蓝色的图像，边缘混合了红色。我的功能是：

func sketch(with ciImage: CIImage) -> CIImage {

    var sourceCore = ciImage

    var convolutionValue_A:CGFloat = -0.0925937220454216
    var convolutionValue_B:CGFloat = -0.4166666567325592
    var convolutionValue_C:CGFloat = -1.8518532514572144
    var convolutionValue_D:CGFloat = 0.23148006200790405
    var convolutionValue_E:CGFloat = 4.5833334922790527
    var convolutionValue_F:CGFloat = 14.166666984558105

    var brightnessVal:CGFloat = 1.1041666269302368
    var contrastVal:CGFloat = 3.0555555820465088

    var weightsArr: [CGFloat] = [
        convolutionValue_A, convolutionValue_A, convolutionValue_B, convolutionValue_B, convolutionValue_B, convolutionValue_A, convolutionValue_A,
        convolutionValue_A, convolutionValue_B, convolutionValue_C, convolutionValue_C, convolutionValue_C, convolutionValue_B, convolutionValue_A,
        convolutionValue_B, convolutionValue_C, convolutionValue_D, convolutionValue_E, convolutionValue_D, convolutionValue_C, convolutionValue_B,
        convolutionValue_B, convolutionValue_C, convolutionValue_E, convolutionValue_F, convolutionValue_E, convolutionValue_C, convolutionValue_B,
        convolutionValue_B, convolutionValue_C, convolutionValue_D, convolutionValue_E, convolutionValue_D, convolutionValue_C, convolutionValue_B,
        convolutionValue_A, convolutionValue_B, convolutionValue_C, convolutionValue_C, convolutionValue_C, convolutionValue_B, convolutionValue_A,
        convolutionValue_A, convolutionValue_A, convolutionValue_B, convolutionValue_B, convolutionValue_B, convolutionValue_A, convolutionValue_A
    ]

    let inputWeights:CIVector = CIVector(values: weightsArr, count: weightsArr.count)

    sourceCore = sourceCore
        .applyingFilter("CIColorControls", parameters: [kCIInputImageKey: sourceCore,
                                                        kCIInputSaturationKey: 0.0,
                                                        kCIInputBrightnessKey: brightnessVal,
                                                        kCIInputContrastKey: contrastVal])

    // transforms image to only show edges in black and white
    sourceCore = sourceCore
        .applyingFilter("CIConvolution7X7", parameters: [kCIInputImageKey: sourceCore,
                                                         kCIInputWeightsKey: inputWeights]).cropped(to: sourceCore.extent)

    // give camera image a black and white Noir effect
    var ciImage = ciImage
        .applyingFilter("CIPhotoEffectNoir", parameters: [kCIInputImageKey: ciImage])


    // make solid color
    let color = CIColor(red: 0.819, green: 0.309, blue: 0.309)
    let colFilter = CIFilter(name: "CIConstantColorGenerator")!
    colFilter.setValue(color, forKey: kCIInputColorKey)
    var solidColor = colFilter.value(forKey: "outputImage") as! CIImage
    solidColor = solidColor.cropped(to: ciImage.extent)

    // color should only be shown through outlines 
    // for some reason the input image is blue-ish
    sourceCore = sourceCore
        .applyingFilter("CIBlendWithMask", parameters: [
            kCIInputImageKey: ciImage, // black and white image
            kCIInputBackgroundImageKey: solidColor, // solid color
            kCIInputMaskImageKey:sourceCore]) // edge work image

    ciImage = sourceCore

    return ciImage

}

以下是函数执行过程中每一步的图像：

我的来源，全彩图像

这就是我应用具有 0 饱和度和 7x7 卷积的 CIColorControls 时的结果。我用它作为我的面具，试图通过黑色轮廓区域显示橙色。

这是使用 CIFilter "CIPhotoEffectNoir" 后的黑白，在将其与蒙版和纯色混合时用作输入图像。

与蒙版混合时用作背景的纯橙色。

执行 CIBlendWithMask 后生成的图像看起来不正确。

首先，我希望背景颜色仅出现在蒙版图像上的轮廓所在的位置。背景颜色是橙色而不是红色，输入图像是蓝色而不是严格的黑白。

Answer 1

想通了，虽然我不确定为什么会这样。我猜这是Apple技术的错误。在 CIConvolution7X7 之后，我需要使用 CIColorDodgeBlendMode 或 CILinearDodgeBlendMode 将黑白图像与白色背景混合。

最终代码：

func sketch(with ciImage: CIImage) -> CIImage {

    var sourceCore = ciImage

    var convolutionValue_A:CGFloat = -0.0925937220454216
    var convolutionValue_B:CGFloat = -0.4166666567325592
    var convolutionValue_C:CGFloat = -1.8518532514572144
    var convolutionValue_D:CGFloat = 0.23148006200790405
    var convolutionValue_E:CGFloat = 4.5833334922790527
    var convolutionValue_F:CGFloat = 14.166666984558105

    var brightnessVal:CGFloat = 1.1041666269302368
    var contrastVal:CGFloat = 3.0555555820465088

    var weightsArr: [CGFloat] = [
        convolutionValue_A, convolutionValue_A, convolutionValue_B, convolutionValue_B, convolutionValue_B, convolutionValue_A, convolutionValue_A,
        convolutionValue_A, convolutionValue_B, convolutionValue_C, convolutionValue_C, convolutionValue_C, convolutionValue_B, convolutionValue_A,
        convolutionValue_B, convolutionValue_C, convolutionValue_D, convolutionValue_E, convolutionValue_D, convolutionValue_C, convolutionValue_B,
        convolutionValue_B, convolutionValue_C, convolutionValue_E, convolutionValue_F, convolutionValue_E, convolutionValue_C, convolutionValue_B,
        convolutionValue_B, convolutionValue_C, convolutionValue_D, convolutionValue_E, convolutionValue_D, convolutionValue_C, convolutionValue_B,
        convolutionValue_A, convolutionValue_B, convolutionValue_C, convolutionValue_C, convolutionValue_C, convolutionValue_B, convolutionValue_A,
        convolutionValue_A, convolutionValue_A, convolutionValue_B, convolutionValue_B, convolutionValue_B, convolutionValue_A, convolutionValue_A
    ]

    let inputWeights:CIVector = CIVector(values: weightsArr, count: weightsArr.count)

    sourceCore = sourceCore
        .applyingFilter("CIColorControls", parameters: [kCIInputImageKey: sourceCore,
                                                        kCIInputSaturationKey: 0.0,
                                                        kCIInputBrightnessKey: brightnessVal,
                                                        kCIInputContrastKey: contrastVal])

    // transforms image to only show edges in black and white
    sourceCore = sourceCore
        .applyingFilter("CIConvolution7X7", parameters: [kCIInputImageKey: sourceCore,

    let whiteCIColor = CIColor(red: 1, green: 1, blue: 1)

    let whiteColor = CIImage(color: whiteCIColor).cropped(to: ciImage.extent)

    // CIColorDodgeBlendMode, CILinearDodgeBlendMode

    sourceCore = sourceCore
            .applyingFilter("CIColorDodgeBlendMode", parameters: [kCIInputImageKey: sourceCore,
                                                                    kCIInputBackgroundImageKey: whiteColor])
                                                         kCIInputWeightsKey: inputWeights]).cropped(to: sourceCore.extent)

    // give camera image a black and white Noir effect
    var ciImage = ciImage
        .applyingFilter("CIPhotoEffectNoir", parameters: [kCIInputImageKey: ciImage])


    // make solid color
    let color = CIColor(red: 0.819, green: 0.309, blue: 0.309)
    let colFilter = CIFilter(name: "CIConstantColorGenerator")!
    colFilter.setValue(color, forKey: kCIInputColorKey)
    var solidColor = colFilter.value(forKey: "outputImage") as! CIImage
    solidColor = solidColor.cropped(to: ciImage.extent)

    // color is shown through outlines correctly, 
    // and image is black and white
    sourceCore = sourceCore
        .applyingFilter("CIBlendWithMask", parameters: [
            kCIInputImageKey: ciImage, // black and white image
            kCIInputBackgroundImageKey: solidColor, // solid color
            kCIInputMaskImageKey:sourceCore]) // edge work image

    ciImage = sourceCore

    return ciImage

}

Answer 2

我认为问题出在最后的混合步骤中。当您调用sourceCore.applyingFilter(...) 时，它会自动在该过滤器上将sourceCore 设置为inputImage。而且我不确定当您在 parameters 中重新定义它时会发生什么。

您可以像这样创建过滤器：

let blendFilter = CIFilter(name: "CIBlendWithMask", parameters: [
    kCIInputImageKey: ciImage,
    kCIInputBackgroundImageKey: solidColor,
    kCIInputMaskImageKey: sourceCore
])
return blendFilter.outputImage

顺便说一句，您可以像这样更轻松地创建纯色图像：

let solidColor = CIImage(color: color).cropped(to: ciImage.extent)