一般短视频项目中会使用类似Face++这样的商业sdk实现瘦脸大眼特效，想到苹果的原生框架Vision也可以进行人脸识别，提取人脸特征点，应该也能实现。没想到挺顺利，参考了网上的相关算法，个把小时就实现了效果。
Vision与Face++对比：
1.Vision原生框架，体积小，免费；Face++需要付费,包大概50M左右。
2.Vision要求在ios11以上，Face++貌似没有。
3.Vision检测人脸关键点数量为74个。Face++检测人脸关键点数量为106个。
4.Vision特征点貌似有点飘（稳定性一般），边缘检测不是很准。Face++特征点相对贴合的要准一点。
1.使用GPUImageVideoCamera采集摄像头数据。
2.将采集到的数据CMSampleBufferRef送入Vision处理，拿到人脸特征点。
3.自定义的瘦脸大眼滤镜，添加到GPUImage的滤镜链上。
4.在自定义滤镜中重写- (void)renderToTextureWithVertices:(const GLfloat *)vertices textureCoordinates:(const GLfloat *)textureCoordinates方法，将特征点送入片元着色器中处理。
5.使用瘦脸大眼相关算法：圆内放大算法，圆内缩小算法，定点拉伸算法。算法原理解析
1.Vision发送识别请求
我要评论+ (void)detectImageWithType:(DSDetectionType)type pixelBuffer:(CVPixelBufferRef)pixelBuffer complete:(detectImageHandler _Nullable )complete
{
// 创建处理requestHandler
VNImageRequestHandler *detectFaceRequestHandler = [[VNImageRequestHandler alloc]initWithCVPixelBuffer:pixelBuffer orientation:kCGImagePropertyOrientationLeftMirrored options:@{}];
// 创建BaseRequest
VNImageBasedRequest *detectRequest = [[VNImageBasedRequest alloc]init];
// 设置回调
CompletionHandler completionHandler = ^(VNRequest *request, NSError * _Nullable error) {
NSArray *observations = request.results;
[self handleImageWithType:type image:nil observations:observations complete:complete];
};
switch (type) {
case DSDetectionTypeFace:
detectRequest = [[VNDetectFaceRectanglesRequest alloc]initWithCompletionHandler:completionHandler];
break;
case DSDetectionTypeLandmark:
detectRequest = [[VNDetectFaceLandmarksRequest alloc]initWithCompletionHandler:completionHandler];
break;
case DSDetectionTypeTextRectangles:
detectRequest = [[VNDetectTextRectanglesRequest alloc]initWithCompletionHandler:completionHandler];
[detectRequest setValue:@(YES) forKey:@"reportCharacterBoxes"]; // 设置识别具体文字
break;
default:
break;
}
// 发送识别请求
[detectFaceRequestHandler performRequests:@[detectRequest] error:nil];
}
// 处理人脸识别回调
+ (void)faceRectangles:(NSArray *)observations image:(UIImage *_Nullable)image complete:(detectImageHandler _Nullable )complete{
NSMutableArray *tempArray = @[].mutableCopy;
DSDetectData *detectFaceData = [[DSDetectData alloc]init];
for (VNFaceObservation *observation in observations) {
NSValue *ractValue = [NSValue valueWithCGRect:[self convertRect:observation.boundingBox imageSize:image.size]];
[tempArray addObject:ractValue];
}
detectFaceData.faceAllRect = tempArray;
if (complete) {
complete(detectFaceData);
}
}
2.Vision提取人脸特征点，需要注意的是特征点的坐标转换。
- (void)handleFaceData:(DSDetectFaceData *)faceData{
while (self.gpuImageView.subviews.count) {
[self.gpuImageView.subviews.lastObject removeFromSuperview];
}
// 遍历位置信息
CGFloat faceRectWidth = kScreenWidth * faceData.observation.boundingBox.size.width;
CGFloat faceRectHeight = kScreenHeight * faceData.observation.boundingBox.size.height;
CGFloat faceRectX = faceData.observation.boundingBox.origin.x * kScreenWidth;
// Y默认的位置是左下角
CGFloat faceRectY = faceData.observation.boundingBox.origin.y * kScreenHeight;
__block int index = 0;
NSMutableArray *array = [NSMutableArray array];
[faceData.allPoints enumerateObjectsUsingBlock:^(VNFaceLandmarkRegion2D *obj, NSUInteger idx, BOOL * _Nonnull stop) {
// VNFaceLandmarkRegion2D *obj 是一个对象. 表示当前的一个部位
// 遍历当前部分所有的点
for (int i=0; i<obj.pointCount; i++) {
// 取出点
CGPoint point = obj.normalizedPoints[i];
// 计算出center
/*
* 这里的 point 的 x,y 表示也比例, 表示当前点在脸的比例值
* 因为Y点是在左下角， 所以我们需要转换成左上角
* 这里的center 关键点 可以根据需求保存起来
*/
CGPoint center = CGPointMake(faceRectX + faceRectWidth * point.x, kScreenHeight -
(faceRectY + faceRectHeight * point.y));
[array addObject:[NSValue valueWithCGPoint:CGPointMake(center.x/kScreenWidth, center.y/kScreenHeight)]];
// 将点显示出来
UIView *point_view = [[UIView alloc] initWithFrame:CGRectMake(0, 0, 3, 3)];
point_view.backgroundColor = UIColorRGBA(0xFF0000, 0.8);
point_view.center = center;
// 将点添加到imageView上即可 需要注意，当前image的bounds 应该和图片大小一样大
[self.gpuImageView addSubview:point_view];
UILabel *label = [[UILabel alloc] initWithFrame:CGRectMake(0, 0, 24, 12)];
label.font = [UIFont systemFontOfSize:8.0];
label.textColor = UIColorRGBA(0x3333FF, 0.8);
label.center = CGPointMake(center.x, center.y+5);
label.text = [NSString stringWithFormat:@"%d",index];
[self.gpuImageView addSubview:label];
index++;
}
}];
[FaceDetector shareInstance].landmarks = [array copy];
// NSLog(@"index == %d",index);
}
3.送入片元着色器处理。
- (void)setUniformsWithLandmarks:(NSArray <NSValue *>*)landmarks{
if (!landmarks.count) {
[self setInteger:0 forUniform:hasFaceUniform program:filterProgram];
return;
}
[self setInteger:1 forUniform:hasFaceUniform program:filterProgram];
CGFloat aspect = inputTextureSize.width/inputTextureSize.height;
[self setFloat:aspect forUniform:aspectRatioUniform program:filterProgram];
[self setFloat:self.thinFaceDelta forUniform:thinFaceDeltaUniform program:filterProgram];
[self setFloat:self.bigEyeDelta forUniform:bigEyeDeltaUniform program:filterProgram];
GLsizei size = 74 * 2;
GLfloat *facePoints = malloc(size*sizeof(GLfloat));
int index = 0;
for (NSValue *value in landmarks) {
CGPoint point = [value CGPointValue];
*(facePoints + index) = point.x;
*(facePoints + index + 1) = point.y;
index += 2;
if (index == size) {
break;
}
}
[self setFloatArray:facePoints length:size forUniform:facePointsUniform program:filterProgram];
free(facePoints);
}
4.片元着色器算法实现。
NSString *const kGPUImageThinFaceFragmentShaderString = SHADER_STRING
(
precision highp float;
varying highp vec2 textureCoordinate;
uniform sampler2D inputImageTexture;
uniform int hasFace;
uniform float facePoints[74 * 2];
uniform highp float aspectRatio;
uniform float thinFaceDelta;
uniform float bigEyeDelta;
//圓內放大
vec2 enlargeEye(vec2 textureCoord, vec2 originPosition, float radius, float delta) {
float weight = distance(vec2(textureCoord.x, textureCoord.y / aspectRatio), vec2(originPosition.x, originPosition.y / aspectRatio)) / radius;
weight = 1.0 - (1.0 - weight * weight) * delta;
weight = clamp(weight,0.0,1.0);
textureCoord = originPosition + (textureCoord - originPosition) * weight;
return textureCoord;
}
// 曲线形变处理
vec2 curveWarp(vec2 textureCoord, vec2 originPosition, vec2 targetPosition, float delta) {
vec2 offset = vec2(0.0);
vec2 result = vec2(0.0);
vec2 direction = (targetPosition - originPosition) * delta;
float radius = distance(vec2(targetPosition.x, targetPosition.y / aspectRatio), vec2(originPosition.x, originPosition.y / aspectRatio));
float ratio = distance(vec2(textureCoord.x, textureCoord.y / aspectRatio), vec2(originPosition.x, originPosition.y / aspectRatio)) / radius;
ratio = 1.0 - ratio;
ratio = clamp(ratio, 0.0, 1.0);
offset = direction * ratio;
result = textureCoord - offset;
return result;
}
vec2 thinFace(vec2 currentCoordinate){
vec2 faceIndexs[8];
// faceIndexs[0] = vec2(0., 45.);
// faceIndexs[1] = vec2(10.,45.);
faceIndexs[0] = vec2(1., 46.);
faceIndexs[1] = vec2(9., 46.);
faceIndexs[2] = vec2(2., 50.);
faceIndexs[3] = vec2(8., 50.);
faceIndexs[4] = vec2(3., 50.);
faceIndexs[5] = vec2(7., 50.);
faceIndexs[6] = vec2(4., 50.);
faceIndexs[7] = vec2(6., 50.);
for(int i = 0;i < 8;i++){
int originIndex = int(faceIndexs[i].x);
int targetIndex = int(faceIndexs[i].y);
vec2 originPoint = vec2(facePoints[originIndex * 2],
facePoints[originIndex *2 + 1]);
vec2 targetPoint = vec2(facePoints[targetIndex * 2],
facePoints[targetIndex *2 + 1]);
currentCoordinate = curveWarp(currentCoordinate,originPoint,targetPoint,thinFaceDelta);
}
return currentCoordinate;
}
vec2 bigEye(vec2 currentCoordinate) {
vec2 faceIndexs[2];
faceIndexs[0] = vec2(72., 13.);
faceIndexs[1] = vec2(73., 21.);
for(int i = 0; i < 2; i++)
{
int originIndex = int(faceIndexs[i].x);
int targetIndex = int(faceIndexs[i].y);
vec2 originPoint = vec2(facePoints[originIndex * 2], facePoints[originIndex * 2 + 1]);
vec2 targetPoint = vec2(facePoints[targetIndex * 2], facePoints[targetIndex * 2 + 1]);
float radius = distance(vec2(targetPoint.x, targetPoint.y / aspectRatio), vec2(originPoint.x, originPoint.y / aspectRatio));
radius = radius * 5.;
currentCoordinate = enlargeEye(currentCoordinate, originPoint, radius, bigEyeDelta);
}
return currentCoordinate;
}
void main()
{
vec2 positionToUse = textureCoordinate;
if (hasFace == 1) {
positionToUse = thinFace(positionToUse);
positionToUse = bigEye(positionToUse);
}
gl_FragColor = texture2D(inputImageTexture,positionToUse);
}
);
第一张为原图，第二张为瘦脸大眼效果。可以看到，大眼效果不太自然，原因是系数设置的较大。（为了技术，牺牲挺大- - !）
1.如图所示，取出左眼瞳孔特征点72的坐标和上方特征点13的坐标。
2.以瞳孔72为圆心，以72和13的距离的5倍为半径，确定放大范围。
3.按照圆内放大算法，离圆心越近的像素向圆圈外部偏移量越大，离圆心越远的像素偏移量越小。所以眼睛的纵向被拉伸的程度比较明显。而且又能让放大区域和未放大区域实现平滑过渡。
4.其他圆内缩小，定点拉伸的算法其实也是类似，就不再赘述。
（demo待上传）
本文地址：https://blog.csdn.net/weixin_40290106/article/details/107572089
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