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ViewController.swift
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ViewController.swift
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// Ultralytics YOLO 🚀 - AGPL-3.0 License
//
// Main View Controller for Ultralytics YOLO App
// This file is part of the Ultralytics YOLO app, enabling real-time object detection using YOLOv8 models on iOS devices.
// Licensed under AGPL-3.0. For commercial use, refer to Ultralytics licensing: https://ultralytics.com/license
// Access the source code: https://github.com/ultralytics/yolo-ios-app
//
// This ViewController manages the app's main screen, handling video capture, model selection, detection visualization,
// and user interactions. It sets up and controls the video preview layer, handles model switching via a segmented control,
// manages UI elements like sliders for confidence and IoU thresholds, and displays detection results on the video feed.
// It leverages CoreML, Vision, and AVFoundation frameworks to perform real-time object detection and to interface with
// the device's camera.
import AVFoundation
import CoreMedia
import CoreML
import UIKit
import Vision
var mlModel = try! yolov8m(configuration: .init()).model
class ViewController: UIViewController {
@IBOutlet var videoPreview: UIView!
@IBOutlet var View0: UIView!
@IBOutlet var playButtonOutlet: UIBarButtonItem!
@IBOutlet var pauseButtonOutlet: UIBarButtonItem!
@IBOutlet weak var labelName: UILabel!
@IBOutlet weak var labelFPS: UILabel!
@IBOutlet weak var labelZoom: UILabel!
@IBOutlet weak var labelVersion: UILabel!
@IBOutlet weak var activityIndicator: UIActivityIndicatorView!
let selection = UISelectionFeedbackGenerator()
var detector = try! VNCoreMLModel(for: mlModel)
var session: AVCaptureSession!
var videoCapture: VideoCapture!
var currentBuffer: CVPixelBuffer?
var framesDone = 0
var t0 = 0.0 // inference start
var t1 = 0.0 // inference dt
var t2 = 0.0 // inference dt smoothed
var t3 = CACurrentMediaTime() // FPS start
var t4 = 0.0 // FPS dt smoothed
// var cameraOutput: AVCapturePhotoOutput!
// Developer mode
let developerMode = UserDefaults.standard.bool(forKey: "developer_mode") // developer mode selected in settings
let save_detections = false // write every detection to detections.txt
let save_frames = false // write every frame to frames.txt
// Global ORTSession initialized in the viewDidLoad
var ortSession: ORTSession?
var poseUtil: OnnxPoseUtils?
func getOnnxModelPath() -> String{
guard let modelPath = Bundle.main.path(forResource: "yolov8n-pose-pre", ofType: "onnx") else { fatalError("Error in finding model") }
return modelPath
}
func setModelOnnx() {
do {
guard let modelPath = Bundle.main.path(forResource: "yolov8n-pose-pre", ofType: "onnx") else { fatalError("Error in finding model") }
let ortEnv = try ORTEnv(loggingLevel: ORTLoggingLevel.info)
let ortSessionOptions = try ORTSessionOptions()
try ortSessionOptions.registerCustomOps(functionPointer: RegisterCustomOps) // Register the bridging-header in Build settings
ortSession = try ORTSession(
env: ortEnv, modelPath: modelPath, sessionOptions: ortSessionOptions)
} catch {
print(error)
fatalError("Error in instantiating the ONNX model")
}
t2 = 0.0 // inference dt smoothed
t3 = CACurrentMediaTime() // FPS start
t4 = 0.0 // FPS dt smoothed
}
override func viewDidLoad() {
super.viewDidLoad()
//load the ONNX model
setLabels()
setUpBoundingBoxViews()
startVideo()
//setModel()
poseUtil = OnnxPoseUtils()
setModelOnnx()
}
@IBAction func vibrate(_ sender: Any) {
selection.selectionChanged()
}
@IBAction func takePhoto(_ sender: Any?) {
let t0 = DispatchTime.now().uptimeNanoseconds
// 1. captureSession and cameraOutput
// session = videoCapture.captureSession // session = AVCaptureSession()
// session.sessionPreset = AVCaptureSession.Preset.photo
// cameraOutput = AVCapturePhotoOutput()
// cameraOutput.isHighResolutionCaptureEnabled = true
// cameraOutput.isDualCameraDualPhotoDeliveryEnabled = true
// print("1 Done: ", Double(DispatchTime.now().uptimeNanoseconds - t0) / 1E9)
// 2. Settings
let settings = AVCapturePhotoSettings()
// settings.flashMode = .off
// settings.isHighResolutionPhotoEnabled = cameraOutput.isHighResolutionCaptureEnabled
// settings.isDualCameraDualPhotoDeliveryEnabled = self.videoCapture.cameraOutput.isDualCameraDualPhotoDeliveryEnabled
// 3. Capture Photo
usleep(20_000) // short 10 ms delay to allow camera to focus
self.videoCapture.cameraOutput.capturePhoto(with: settings, delegate: self as AVCapturePhotoCaptureDelegate)
print("3 Done: ", Double(DispatchTime.now().uptimeNanoseconds - t0) / 1E9)
}
@IBAction func logoButton(_ sender: Any) {
selection.selectionChanged()
if let link = URL(string: "https://www.ultralytics.com") {
UIApplication.shared.open(link)
}
}
func setLabels() {
self.labelName.text = "YOLOv8n Pose"
self.labelVersion.text = "Version " + UserDefaults.standard.string(forKey: "app_version")!
}
@IBAction func playButton(_ sender: Any) {
selection.selectionChanged()
self.videoCapture.start()
playButtonOutlet.isEnabled = false
pauseButtonOutlet.isEnabled = true
}
@IBAction func pauseButton(_ sender: Any?) {
selection.selectionChanged()
self.videoCapture.stop()
playButtonOutlet.isEnabled = true
pauseButtonOutlet.isEnabled = false
}
@IBAction func switchCameraTapped(_ sender: Any) {
self.videoCapture.captureSession.beginConfiguration()
let currentInput = self.videoCapture.captureSession.inputs.first as? AVCaptureDeviceInput
self.videoCapture.captureSession.removeInput(currentInput!)
// let newCameraDevice = currentInput?.device == .builtInWideAngleCamera ? getCamera(with: .front) : getCamera(with: .back)
// let device = AVCaptureDevice.default(.builtInWideAngleCamera, for: .video, position: .back)!
let device = AVCaptureDevice.default(.builtInWideAngleCamera, for: .video, position: .front)!
guard let videoInput1 = try? AVCaptureDeviceInput(device: device) else {
return
}
self.videoCapture.captureSession.addInput(videoInput1)
self.videoCapture.captureSession.commitConfiguration()
}
// share image
@IBAction func shareButton(_ sender: Any) {
selection.selectionChanged()
let bounds = UIScreen.main.bounds
//let bounds = self.View0.bounds
UIGraphicsBeginImageContextWithOptions(bounds.size, true, 0.0)
self.View0.drawHierarchy(in: bounds, afterScreenUpdates: false)
let img = UIGraphicsGetImageFromCurrentImageContext()
UIGraphicsEndImageContext()
let activityViewController = UIActivityViewController(activityItems: [img!], applicationActivities: nil)
activityViewController.popoverPresentationController?.sourceView = self.View0
self.present(activityViewController, animated: true, completion: nil)
// playButton("")
}
// share screenshot
@IBAction func saveScreenshotButton(_ shouldSave: Bool = true) {
// let layer = UIApplication.shared.keyWindow!.layer
// let scale = UIScreen.main.scale
// UIGraphicsBeginImageContextWithOptions(layer.frame.size, false, scale);
// layer.render(in: UIGraphicsGetCurrentContext()!)
// let screenshot = UIGraphicsGetImageFromCurrentImageContext()
// UIGraphicsEndImageContext()
// let screenshot = UIApplication.shared.screenShot
// UIImageWriteToSavedPhotosAlbum(screenshot!, nil, nil, nil)
}
let maxBoundingBoxViews = 1
var boundingBoxViews = [BoundingBoxView]()
var colors: [String: UIColor] = [:]
func setUpBoundingBoxViews() {
// Ensure all bounding box views are initialized up to the maximum allowed.
while boundingBoxViews.count < maxBoundingBoxViews {
boundingBoxViews.append(BoundingBoxView())
}
}
func startVideo() {
videoCapture = VideoCapture()
videoCapture.delegate = self
videoCapture.setUp(sessionPreset: .photo) { success in
// .hd4K3840x2160 or .photo (4032x3024) Warning: 4k may not work on all devices i.e. 2019 iPod
if success {
// Add the video preview into the UI.
if let previewLayer = self.videoCapture.previewLayer {
self.videoPreview.layer.addSublayer(previewLayer)
self.videoCapture.previewLayer?.frame = self.videoPreview.bounds // resize preview layer
}
// Add the bounding box layers to the UI, on top of the video preview.
for box in self.boundingBoxViews {
box.addToLayer(self.videoPreview.layer)
}
// Once everything is set up, we can start capturing live video.
self.videoCapture.start()
}
}
}
func predict(sampleBuffer: CMSampleBuffer) {
if currentBuffer == nil, let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) {
currentBuffer = pixelBuffer
let onnxHandler = VNOnnxHandler(cvImageBufffer: pixelBuffer, session: ortSession!)
DispatchQueue.main.async {
if UIDevice.current.orientation != .faceUp { // stop if placed down on a table
self.t0 = CACurrentMediaTime() // inference start
do {
self.videoPreview.layer.sublayers = nil // Remove all previous layers to avoid an OOM problem
//let outputTensor = try onnxHandler.perform()
let outputImage = try onnxHandler.performImage(poseUtil: self.poseUtil!)
if self.t1 < 10.0 { // valid dt
self.t2 = self.t1 * 0.05 + self.t2 * 0.95 // smoothed inference time
}
self.t4 = (CACurrentMediaTime() - self.t3) * 0.05 + self.t4 * 0.95 // smoothed delivered FPS
self.labelFPS.text = String(format: "%.1f FPS - %.1f ms", 1 / self.t4, self.t2 * 1000) // t2 seconds to ms
self.t3 = CACurrentMediaTime()
let l = CALayer()
l.contents = outputImage.cgImage
l.contentsGravity = .resizeAspect
l.isHidden = true
l.frame = self.videoPreview.bounds
self.videoPreview.layer.addSublayer(l)
l.isHidden = false
//self.processOnnxObservations(for: outputTensor, inputImage: UIImage(cgImage: CGImage.create(from: pixelBuffer)!))
} catch {
print("Error in model execution \(error)")
}
self.t1 = CACurrentMediaTime() - self.t0 // inference dt
}
self.currentBuffer = nil
}
}
}
/// Convert the outputTensor values into a layer for super imposing on the videoPreview layer
/// - Params
/// - request: The output tensor after processing the image
/// - inputImage: The original image that was processed by the onnx model
func processOnnxObservations(for request: ORTValue, inputImage: UIImage) {
DispatchQueue.main.async {
self.showOnnx(opTensor: request, inputImage: inputImage)
// Measure FPS
if self.t1 < 10.0 { // valid dt
self.t2 = self.t1 * 0.05 + self.t2 * 0.95 // smoothed inference time
}
self.t4 = (CACurrentMediaTime() - self.t3) * 0.05 + self.t4 * 0.95 // smoothed delivered FPS
self.labelFPS.text = String(format: "%.1f FPS - %.1f ms", 1 / self.t4, self.t2 * 1000) // t2 seconds to ms
self.t3 = CACurrentMediaTime()
}
}
func showOnnx(opTensor: ORTValue, inputImage: UIImage) {
let targetWidth = videoPreview.bounds.width // 375 pix
let targetHeight = videoPreview.bounds.height // 812 pix
var str = ""
// ratio = videoPreview AR divided by sessionPreset AR
var ratio: CGFloat = 1.0
if videoCapture.captureSession.sessionPreset == .photo {
ratio = (targetHeight / targetWidth) / (4.0 / 3.0) // .photo
} else {
ratio = (targetHeight / targetWidth) / (16.0 / 9.0) // .hd4K3840x2160, .hd1920x1080, .hd1280x720 etc.
}
let widthRatio = targetWidth / inputImage.size.width
let heightRatio = targetHeight / inputImage.size.height
let scaleFactor = min(widthRatio, heightRatio)
// date for developer mode
let date = Date()
let calendar = Calendar.current
let hour = calendar.component(.hour, from: date)
let minutes = calendar.component(.minute, from: date)
let seconds = calendar.component(.second, from: date)
let nanoseconds = calendar.component(.nanosecond, from: date)
let sec_day = Double(hour) * 3600.0 + Double(minutes) * 60.0 + Double(seconds) + Double(nanoseconds) / 1E9 // seconds in the day
// pose datapoints
var keypoints:[Float32] = Array()
do {
let output = try opTensor.tensorData()
var arr2 = Array<Float32>(repeating: 0, count: output.count/MemoryLayout<Float32>.stride) // Do not change the datatype Float32
_ = arr2.withUnsafeMutableBytes { output.copyBytes(to: $0) }
// 57 is hardcoded based on the keypoints returned from the model. Refer to the Netron visualisation for the output shape
if (!arr2.isEmpty) {
for i in stride(from: arr2.count-57, to: arr2.count, by: 1) {
keypoints.append(arr2[i])
}
}
} catch {
print(error)
fatalError("Output tensor processing failed")
}
if (keypoints.count > 0) {
let box = keypoints[0..<4] // The first 4 points are the bounding box co-ords.
// Refer yolov8_pose_e2e.py run_inference method under the https://onnxruntime.ai/docs/tutorials/mobile/pose-detection.html
let half_w = Double(box[2] / 2 )
let half_h = Double(box[3] / 2 )
let x = (Double(box[0]) - Double(half_w)) * widthRatio
let y = (Double(box[1]) - Double(half_h)) * heightRatio
//let rect = CGRect(x: -x, y: y, width: Double(half_w * 2), height: Double(half_h * 2))
var keypointsWithoutBoxes = Array(keypoints[6..<keypoints.count]) // Based on 17 key
//keypointsWithoutBoxes = keypointsWithoutBoxes.map { Float($0) * Float(scaleFactor) }
for i in 0..<boundingBoxViews.count {
//var rect = prediction.boundingBox // normalized xywh, origin lower left
let rect = CGRect(x: x, y: y, width: Double(half_w), height: Double(half_h))
// This part is commented because I am unable to figure out the scaling part
/*
switch UIDevice.current.orientation {
case .portraitUpsideDown:
rect = CGRect(x: 1.0 - rect.origin.x - rect.width,
y: 1.0 - rect.origin.y - rect.height,
width: rect.width,
height: rect.height)
case .landscapeLeft:
rect = CGRect(x: rect.origin.y,
y: 1.0 - rect.origin.x - rect.width,
width: rect.height,
height: rect.width)
case .landscapeRight:
rect = CGRect(x: 1.0 - rect.origin.y - rect.height,
y: rect.origin.x,
width: rect.height,
height: rect.width)
case .unknown:
print("The device orientation is unknown, the predictions may be affected")
fallthrough
default: break
}
if ratio >= 1 { // iPhone ratio = 1.218
let offset = (1 - ratio) * (0.5 - rect.minX)
let transform = CGAffineTransform(scaleX: 1, y: -1).translatedBy(x: offset, y: -1)
rect = rect.applying(transform)
rect.size.width *= ratio
} else { // iPad ratio = 0.75
let offset = (ratio - 1) * (0.5 - rect.maxY)
let transform = CGAffineTransform(scaleX: 1, y: -1).translatedBy(x: 0, y: offset - 1)
rect = rect.applying(transform)
rect.size.height /= ratio
}
*/
NSLog("Rect origin \(rect.origin.debugDescription)")
NSLog("Rect size \(rect.size.debugDescription)")
NSLog("Input image : \(inputImage.size.debugDescription)")
NSLog("Video frame \(videoPreview.frame)")
// Scale normalized to pixels [375, 812] [width, height]
//rect = VNImageRectForNormalizedRect(rect, Int(width), Int(height))
// The labels array is a list of VNClassificationObservation objects,
// with the highest scoring class first in the list.
let bestClass = "class"
let confidence = 0.1
// print(confidence, rect) // debug (confidence, xywh) with xywh origin top left (pixels)
// Show the bounding box.
boundingBoxViews[i].showOnnx(frame: rect,
keypoints: keypointsWithoutBoxes, widthRatio: Float(widthRatio), heightRatio: Float(heightRatio)) // alpha 0 (transparent) to 1 (opaque) for conf threshold 0.2 to 1.0)
if developerMode {
if save_detections {
str += String(format: "%.3f %.3f %.3f %@ %.2f %.1f %.1f %.1f %.1f\n",
sec_day, freeSpace(), UIDevice.current.batteryLevel, bestClass, confidence,
rect.origin.x, rect.origin.y, rect.size.width, rect.size.height)
}
}
}
}
// Write
if developerMode {
if save_detections {
saveText(text: str, file: "detections.txt") // Write stats for each detection
}
if save_frames {
str = String(format: "%.3f %.3f %.3f %.3f %.1f %.1f %.1f\n",
sec_day, freeSpace(), memoryUsage(), UIDevice.current.batteryLevel,
self.t1 * 1000, self.t2 * 1000, 1 / self.t4)
saveText(text: str, file: "frames.txt") // Write stats for each image
}
}
// Debug
// print(str)
// print(UIDevice.current.identifierForVendor!)
// saveImage()
}
// Save text file
func saveText(text: String, file: String = "saved.txt") {
if let dir = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask).first {
let fileURL = dir.appendingPathComponent(file)
// Writing
do { // Append to file if it exists
let fileHandle = try FileHandle(forWritingTo: fileURL)
fileHandle.seekToEndOfFile()
fileHandle.write(text.data(using: .utf8)!)
fileHandle.closeFile()
} catch { // Create new file and write
do {
try text.write(to: fileURL, atomically: false, encoding: .utf8)
} catch {
print("no file written")
}
}
// Reading
// do {let text2 = try String(contentsOf: fileURL, encoding: .utf8)} catch {/* error handling here */}
}
}
// Save image file
func saveImage() {
let dir = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask).first
let fileURL = dir!.appendingPathComponent("saved.jpg")
let image = UIImage(named: "ultralytics_yolo_logotype.png")
FileManager.default.createFile(atPath: fileURL.path, contents: image!.jpegData(compressionQuality: 0.5), attributes: nil)
}
// Return hard drive space (GB)
func freeSpace() -> Double {
let fileURL = URL(fileURLWithPath: NSHomeDirectory() as String)
do {
let values = try fileURL.resourceValues(forKeys: [.volumeAvailableCapacityForImportantUsageKey])
return Double(values.volumeAvailableCapacityForImportantUsage!) / 1E9 // Bytes to GB
} catch {
print("Error retrieving storage capacity: \(error.localizedDescription)")
}
return 0
}
// Return RAM usage (GB)
func memoryUsage() -> Double {
var taskInfo = mach_task_basic_info()
var count = mach_msg_type_number_t(MemoryLayout<mach_task_basic_info>.size) / 4
let kerr: kern_return_t = withUnsafeMutablePointer(to: &taskInfo) {
$0.withMemoryRebound(to: integer_t.self, capacity: 1) {
task_info(mach_task_self_, task_flavor_t(MACH_TASK_BASIC_INFO), $0, &count)
}
}
if kerr == KERN_SUCCESS {
return Double(taskInfo.resident_size) / 1E9 // Bytes to GB
} else {
return 0
}
}
// Pinch to Zoom Start ---------------------------------------------------------------------------------------------
let minimumZoom: CGFloat = 1.0
let maximumZoom: CGFloat = 10.0
var lastZoomFactor: CGFloat = 1.0
@IBAction func pinch(_ pinch: UIPinchGestureRecognizer) {
let device = videoCapture.captureDevice
// Return zoom value between the minimum and maximum zoom values
func minMaxZoom(_ factor: CGFloat) -> CGFloat {
return min(min(max(factor, minimumZoom), maximumZoom), device.activeFormat.videoMaxZoomFactor)
}
func update(scale factor: CGFloat) {
do {
try device.lockForConfiguration()
defer {
device.unlockForConfiguration()
}
device.videoZoomFactor = factor
} catch {
print("\(error.localizedDescription)")
}
}
let newScaleFactor = minMaxZoom(pinch.scale * lastZoomFactor)
switch pinch.state {
case .began: fallthrough
case .changed:
update(scale: newScaleFactor)
self.labelZoom.text = String(format: "%.2fx", newScaleFactor)
self.labelZoom.font = UIFont.preferredFont(forTextStyle: .title2)
case .ended:
lastZoomFactor = minMaxZoom(newScaleFactor)
update(scale: lastZoomFactor)
self.labelZoom.font = UIFont.preferredFont(forTextStyle: .body)
default: break
}
} // Pinch to Zoom Start ------------------------------------------------------------------------------------------
} // ViewController class End
extension ViewController: VideoCaptureDelegate {
func videoCapture(_ capture: VideoCapture, didCaptureVideoFrame sampleBuffer: CMSampleBuffer) {
if let buffer = sampleBuffer.imageBuffer {
predict(sampleBuffer: sampleBuffer)
}
}
}
// Programmatically save image
extension ViewController: AVCapturePhotoCaptureDelegate {
func photoOutput(_ output: AVCapturePhotoOutput, didFinishProcessingPhoto photo: AVCapturePhoto, error: Error?) {
if let error = error {
print("error occurred : \(error.localizedDescription)")
}
if let dataImage = photo.fileDataRepresentation() {
print(UIImage(data: dataImage)?.size as Any)
let dataProvider = CGDataProvider(data: dataImage as CFData)
let cgImageRef: CGImage! = CGImage(jpegDataProviderSource: dataProvider!, decode: nil, shouldInterpolate: true, intent: .defaultIntent)
let image = UIImage(cgImage: cgImageRef, scale: 0.5, orientation: UIImage.Orientation.right)
// Save to camera roll
UIImageWriteToSavedPhotosAlbum(image, nil, nil, nil);
} else {
print("AVCapturePhotoCaptureDelegate Error")
}
}
}