我需要与应用即时心率相同的功能。
基本流程要求用户:
这可以通过打开闪光灯并在血液通过食指时观察光线变化来实现。
如何从视频捕获中获取光照数据?我应该在哪里寻找这个?
我查看了班级AVCaptureDevice
,但没有找到任何有用的东西。
我还发现了AVCaptureDeviceSubjectAreaDidChangeNotification
,这会有用吗?
答案 0 :(得分:25)
看看这个..
// switch on the flash in torch mode
if([camera isTorchModeSupported:AVCaptureTorchModeOn]) {
[camera lockForConfiguration:nil];
camera.torchMode=AVCaptureTorchModeOn;
[camera unlockForConfiguration];
}
[session setSessionPreset:AVCaptureSessionPresetLow];
// Create the AVCapture Session
session = [[AVCaptureSession alloc] init];
// Get the default camera device
AVCaptureDevice* camera = [AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];
if([camera isTorchModeSupported:AVCaptureTorchModeOn]) {
[camera lockForConfiguration:nil];
camera.torchMode=AVCaptureTorchModeOn;
[camera unlockForConfiguration];
}
// Create a AVCaptureInput with the camera device
NSError *error=nil;
AVCaptureInput* cameraInput = [[AVCaptureDeviceInput alloc] initWithDevice:camera error:&error];
if (cameraInput == nil) {
NSLog(@"Error to create camera capture:%@",error);
}
// Set the output
AVCaptureVideoDataOutput* videoOutput = [[AVCaptureVideoDataOutput alloc] init];
// create a queue to run the capture on
dispatch_queue_t captureQueue=dispatch_queue_create("catpureQueue", NULL);
// setup our delegate
[videoOutput setSampleBufferDelegate:self queue:captureQueue];
// configure the pixel format
videoOutput.videoSettings = [NSDictionary dictionaryWithObjectsAndKeys:[NSNumber numberWithUnsignedInt:kCVPixelFormatType_32BGRA], (id)kCVPixelBufferPixelFormatTypeKey,
nil];
// cap the framerate
videoOutput.minFrameDuration=CMTimeMake(1, 10);
// and the size of the frames we want
[session setSessionPreset:AVCaptureSessionPresetLow];
// Add the input and output
[session addInput:cameraInput];
[session addOutput:videoOutput];
// Start the session
[session startRunning];
- (void)captureOutput:(AVCaptureOutput *)captureOutput didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer fromConnection:(AVCaptureConnection *)connection {
// this is the image buffer
CVImageBufferRef cvimgRef = CMSampleBufferGetImageBuffer(sampleBuffer);
// Lock the image buffer
CVPixelBufferLockBaseAddress(cvimgRef,0);
// access the data
int width=CVPixelBufferGetWidth(cvimgRef);
int height=CVPixelBufferGetHeight(cvimgRef);
// get the raw image bytes
uint8_t *buf=(uint8_t *) CVPixelBufferGetBaseAddress(cvimgRef);
size_t bprow=CVPixelBufferGetBytesPerRow(cvimgRef);
// get the average red green and blue values from the image
float r=0,g=0,b=0;
for(int y=0; y<height; y++) {
for(int x=0; x<width*4; x+=4) {
b+=buf[x];
g+=buf[x+1];
r+=buf[x+2];
}
buf+=bprow;
}
r/=255*(float) (width*height);
g/=255*(float) (width*height);
b/=255*(float) (width*height);
NSLog(@"%f,%f,%f", r, g, b);
}
示例代码 Here
答案 1 :(得分:3)
实际上可以很简单,你必须分析捕获图像的像素值。一个简单的算法是:在图像中心选择和区域,转换为灰度,获得每个图像的像素的中值,最后得到2D函数,并在此函数上计算最小值之间的距离或最大限度地解决了问题。
如果您在5秒钟内查看所采集图像的直方图,您会注意到灰度分布的变化。如果您想要更稳健的计算,请分析直方图。
答案 2 :(得分:3)
作为旁注,您可能对this research paper感兴趣。这种方法甚至不需要手指(或任何东西)直接在镜头上。