iOS8 Core Image In Swift：自动改善图像以及内置滤镜的使用
iOS8 Core Image In Swift：更复杂的滤镜
iOS8 Core Image In Swift：人脸检测以及马赛克
iOS8 Core Image In Swift：视频实时滤镜
Core Image不仅内置了诸多滤镜，还能检测图像中的人脸，不过Core Image只是检测，并非识别，检测人脸是指在图像中寻找符合人脸特征（只要是个人脸）的区域，识别是指在图像中寻找指定的人脸（比如某某某的脸）。Core Image在找到符合人脸特征的区域后，会返回该特征的信息，比如人脸的范围、眼睛和嘴巴的位置等。
人脸检测并标记检测到的区域
- 新建一个Single View Application工程
- 然后在Storyboard里放入UIImageView，ContentMode设置为Aspect Fit
- 将UIImageView连接到VC里
- 放入一个名为“人脸检测”的UIButton，然后连接到VC的faceDetecting方法上
- 关闭Auto Layout以及Size Classes
class ViewController: UIViewController {
@IBOutlet var imageView: UIImageView!
lazy var originalImage: UIImage = {
return UIImage(named: "Image")
}()
lazy var context: CIContext = {
return CIContext(options: nil)
}()
......
override func viewDidLoad() {
super.viewDidLoad()
// Do any additional setup after loading the view, typically from a nib.
self.imageView.image = originalImage
}
@IBAction func faceDetecing() {
let inputImage = CIImage(image: originalImage)
let detector = CIDetector(ofType: CIDetectorTypeFace,
context: context,
options: [CIDetectorAccuracy: CIDetectorAccuracyHigh])
var faceFeatures: [CIFaceFeature]!
if let orientation: AnyObject = inputImage.properties()?[kCGImagePropertyOrientation] {
faceFeatures = detector.featuresInImage(inputImage,
options: [CIDetectorImageOrientation: orientation]
) as [CIFaceFeature]
} else {
faceFeatures = detector.featuresInImage(inputImage) as [CIFaceFeature]
}
println(faceFeatures)
......
| 使用kCGImagePropertyOrientation的时候，可能需要导入ImageIO框架 |
- 获取所有的面部特征
- 用bounds实例化一个UIView
- 把View显示出来
@IBAction func faceDetecing() {
let inputImage = CIImage(image: originalImage)
let detector = CIDetector(ofType: CIDetectorTypeFace,
context: context,
options: [CIDetectorAccuracy: CIDetectorAccuracyHigh])
var faceFeatures: [CIFaceFeature]!
if let orientation: AnyObject = inputImage.properties()?[kCGImagePropertyOrientation] {
faceFeatures = detector.featuresInImage(inputImage, options: [CIDetectorImageOrientation: orientation]) as [CIFaceFeature]
} else {
faceFeatures = detector.featuresInImage(inputImage) as [CIFaceFeature]
}
println(faceFeatures)
for faceFeature in faceFeatures {
let faceView = UIView(frame: faceFeature.bounds)
faceView.layer.borderColor = UIColor.orangeColor().CGColor
faceView.layer.borderWidth = 2
imageView.addSubview(faceView)
}
}
- 调整transform，让它正过来
- 缩放bounds，让它适配imageView
@IBAction func faceDetecing() {
let inputImage = CIImage(image: originalImage)
let detector = CIDetector(ofType: CIDetectorTypeFace,
context: context,
options: [CIDetectorAccuracy: CIDetectorAccuracyHigh])
var faceFeatures: [CIFaceFeature]!
if let orientation: AnyObject = inputImage.properties()?[kCGImagePropertyOrientation] {
faceFeatures = detector.featuresInImage(inputImage, options: [CIDetectorImageOrientation: orientation]) as [CIFaceFeature]
} else {
faceFeatures = detector.featuresInImage(inputImage) as [CIFaceFeature]
}
println(faceFeatures)
// 1.
let inputImageSize = inputImage.extent().size
var transform = CGAffineTransformIdentity
transform = CGAffineTransformScale(transform, 1, -1)
transform = CGAffineTransformTranslate(transform, 0, -inputImageSize.height)
for faceFeature in faceFeatures {
var faceViewBounds = CGRectApplyAffineTransform(faceFeature.bounds, transform)
// 2.
let scaleTransform = CGAffineTransformMakeScale(0.5, 0.5)
faceViewBounds = CGRectApplyAffineTransform(faceViewBounds, scaleTransform)
let faceView = UIView(frame: faceViewBounds)
faceView.layer.borderColor = UIColor.orangeColor().CGColor
faceView.layer.borderWidth = 2
imageView.addSubview(faceView)
}
}
@IBAction func faceDetecing() {
let inputImage = CIImage(image: originalImage)
let detector = CIDetector(ofType: CIDetectorTypeFace,
context: context,
options: [CIDetectorAccuracy: CIDetectorAccuracyHigh])
var faceFeatures: [CIFaceFeature]!
if let orientation: AnyObject = inputImage.properties()?[kCGImagePropertyOrientation] {
faceFeatures = detector.featuresInImage(inputImage, options: [CIDetectorImageOrientation: orientation]) as [CIFaceFeature]
} else {
faceFeatures = detector.featuresInImage(inputImage) as [CIFaceFeature]
}
println(faceFeatures)
// 1.
let inputImageSize = inputImage.extent().size
var transform = CGAffineTransformIdentity
transform = CGAffineTransformScale(transform, 1, -1)
transform = CGAffineTransformTranslate(transform, 0, -inputImageSize.height)
for faceFeature in faceFeatures {
var faceViewBounds = CGRectApplyAffineTransform(faceFeature.bounds, transform)
// 2.
var scale = min(imageView.bounds.size.width / inputImageSize.width,
imageView.bounds.size.height / inputImageSize.height)
var offsetX = (imageView.bounds.size.width - inputImageSize.width * scale) / 2
var offsetY = (imageView.bounds.size.height - inputImageSize.height * scale) / 2
faceViewBounds = CGRectApplyAffineTransform(faceViewBounds, CGAffineTransformMakeScale(scale, scale))
faceViewBounds.origin.x += offsetX
faceViewBounds.origin.y += offsetY
let faceView = UIView(frame: faceViewBounds)
faceView.layer.borderColor = UIColor.orangeColor().CGColor
faceView.layer.borderWidth = 2
imageView.addSubview(faceView)
}
}
面部马赛克
- 基于原图，创建一个将所有部分都马赛克的图片
- 为检测到的人脸创建一张蒙版图
- 用蒙版图，将完全马赛克的图和原图混合起来
创建完全马赛克的图
- 设置inputImage为原图
- 可以根据自己的需要，选择设置inputScale参数，inputScale取值为1到100，取值越大，马赛克就越大
为检测到的人脸创建蒙版图
- 使用CIRadialGradient滤镜创建一个把脸包围起来的圆
- 使用CISourceOverCompositing滤镜把各个蒙版（有几张脸其实就有几个蒙版）组合起来
混合马赛克图、蒙版图以及原图
- 设置inputImage为马赛克图
- 设置inputBackground为原图
- 设置inputMaskImage为蒙版图
@IBAction func pixellated() {
// 1.
var filter = CIFilter(name: "CIPixellate")
println(filter.attributes())
let inputImage = CIImage(image: originalImage)
filter.setValue(inputImage, forKey: kCIInputImageKey)
// filter.setValue(max(inputImage.extent().size.width, inputImage.extent().size.height) / 60, forKey: kCIInputScaleKey)
let fullPixellatedImage = filter.outputImage
// let cgImage = context.createCGImage(fullPixellatedImage, fromRect: fullPixellatedImage.extent())
// imageView.image = UIImage(CGImage: cgImage)
// 2.
let detector = CIDetector(ofType: CIDetectorTypeFace,
context: context,
options: nil)
let faceFeatures = detector.featuresInImage(inputImage)
// 3.
var maskImage: CIImage!
for faceFeature in faceFeatures {
println(faceFeature.bounds)
// 4.
let centerX = faceFeature.bounds.origin.x + faceFeature.bounds.size.width / 2
let centerY = faceFeature.bounds.origin.y + faceFeature.bounds.size.height / 2
let radius = min(faceFeature.bounds.size.width, faceFeature.bounds.size.height)
let radialGradient = CIFilter(name: "CIRadialGradient",
withInputParameters: [
"inputRadius0" : radius,
"inputRadius1" : radius + 1,
"inputColor0" : CIColor(red: 0, green: 1, blue: 0, alpha: 1),
"inputColor1" : CIColor(red: 0, green: 0, blue: 0, alpha: 0),
kCIInputCenterKey : CIVector(x: centerX, y: centerY)
])
println(radialGradient.attributes())
// 5.
let radialGradientOutputImage = radialGradient.outputImage.imageByCroppingToRect(inputImage.extent())
if maskImage == nil {
maskImage = radialGradientOutputImage
} else {
println(radialGradientOutputImage)
maskImage = CIFilter(name: "CISourceOverCompositing",
withInputParameters: [
kCIInputImageKey : radialGradientOutputImage,
kCIInputBackgroundImageKey : maskImage
]).outputImage
}
}
// 6.
let blendFilter = CIFilter(name: "CIBlendWithMask")
blendFilter.setValue(fullPixellatedImage, forKey: kCIInputImageKey)
blendFilter.setValue(inputImage, forKey: kCIInputBackgroundImageKey)
blendFilter.setValue(maskImage, forKey: kCIInputMaskImageKey)
// 7.
let blendOutputImage = blendFilter.outputImage
let blendCGImage = context.createCGImage(blendOutputImage, fromRect: blendOutputImage.extent())
imageView.image = UIImage(CGImage: blendCGImage)
}
- 用CIPixellate滤镜对原图先做个完全马赛克
- 检测人脸，并保存在faceFeatures中
- 初始化蒙版图，并开始遍历检测到的所有人脸
- 由于我们要基于人脸的位置，为每一张脸都单独创建一个蒙版，所以要先计算出脸的中心点，对应为x、y轴坐标，再基于脸的宽度或高度给一个半径，最后用这些计算结果初始化一个CIRadialGradient滤镜（我将inputColor1的alpha赋值为0，表示将这些颜色值设为透明，因为我不关心除了蒙版以外的颜色，这点和苹果官网中的例子有太一样，苹果将其赋值为了1）
- 由于CIRadialGradient滤镜创建的是一张无限大小的图，所以在使用之前先对它进行裁剪（苹果官网例子中没有对其裁剪。。），然后把每一张脸的蒙版图合在一起
- 用CIBlendWithMask滤镜把马赛克图、原图、蒙版图混合起来
- 输出，在界面上显示
GitHub下载地址
UPDATED:
var scale = min(imageView.bounds.size.width / inputImage.extent().size.width,
imageView.bounds.size.height / inputImage.extent().size.height)
let radius = min(faceFeature.bounds.size.width, faceFeature.bounds.size.height) * scale
参考资料：
