PoseEstimation-CoreML

This project is Pose Estimation on iOS with Core ML.
If you are interested in iOS + Machine Learning, visit here you can see various DEMOs.

README

Jointed Keypoints	Hatmaps	Still Image

Video source:

• https://www.youtube.com/watch?v=EM16LBKBEgI

Pose Capturing	Pose Matching

Features

• Estimate body pose on a image
• Inference with camera's pixel buffer on real-time
• Inference with a photo library's image
• Visualize as heatmaps
• Visualize as lines and points
• Pose capturing and pose matching

How it works

Requirements

• Xcode 9.2+
• iOS 11.0+
• Swift 4

Model

Get PoseEstimationForMobile's model

Download this temporary models from following link.

• cpm
• hourglass

Or

☞ Download Core ML model model_cpm.mlmodel or hourglass.mlmodel.

input_name_shape_dict = {"image:0":[1,192,192,3]} image_input_names=["image:0"]
output_feature_names = ['Convolutional_Pose_Machine/stage_5_out:0']

－in https://github.com/tucan9389/pose-estimation-for-mobile

Model Size, Minimum iOS Version, Input/Output Shape

Model	Size (MB)	Minimum iOS Version	Input Shape	Output Shape
cpm	2.6	iOS11	[1, 192, 192, 3]	[1, 96, 96, 14]
hourhglass	2	iOS11	[1, 192, 192, 3]	[1, 48, 48, 14]

Infernece Time (ms)

Model vs. Device	11 Pro	XS Max	XR	X	8	8+	7	7+	6S+	6+
cpm	5	27	27	32	31	31	39	37	44	115
hourhglass	3	6	7	29	31	32	37	42	48	94

Total Time (ms)

Model vs. Device	11 Pro	XS Max	XR	X	8	8+	7	7+	6S+	6+
cpm	23	39	40	46	47	45	55	58	56	139
hourhglass	23	15	15	38	40	40	48	55	58	106

FPS

Model vs. Device	11 Pro	XS Max	XR	X	8	8+	7	7+	6S+	6+
cpm	15	23	23	20	20	21	17	16	16	6
hourhglass	15	23	23	24	23	23	19	16	15	8

Get your own model

Or you can use your own PoseEstimation model

Build & Run

1. Prerequisites

1.1 Import pose estimation model

Once you import the model, compiler generates model helper class on build path automatically. You can access the model through model helper class by creating an instance, not through build path.

1.2 Add permission in info.plist for device's camera access

2. Dependencies

No external library yet.

3. Code

3.1 Import Vision framework

import Vision

3.2 Define properties for Core ML

// properties on ViewController
typealias EstimationModel = model_cpm // model name(model_cpm) must be equal with mlmodel file name
var request: VNCoreMLRequest!
var visionModel: VNCoreMLModel!

3.3 Configure and prepare the model

override func viewDidLoad() {
    super.viewDidLoad()

    visionModel = try? VNCoreMLModel(for: EstimationModel().model)
	request = VNCoreMLRequest(model: visionModel, completionHandler: visionRequestDidComplete)
	request.imageCropAndScaleOption = .scaleFill
}

func visionRequestDidComplete(request: VNRequest, error: Error?) {
    /* ------------------------------------------------------ */
    /* something postprocessing what you want after inference */
    /* ------------------------------------------------------ */
}

3.4 Inference 🏃‍♂️

// on the inference point
let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer)
try? handler.perform([request])

Performance Test

1. Import the model

You can download cpm or hourglass model for Core ML from tucan9389/pose-estimation-for-mobile repo.

2. Fix the model name on PoseEstimation_CoreMLTests.swift

3. Run the test

Hit the ⌘ + U or click the Build for Testing icon.

See also

• motlabs/iOS-Proejcts-with-ML-Models
  : The challenge using machine learning model created from tensorflow on iOS
• tucan9389/PoseEstimation-TFLiteSwift
  : The pose estimation with TensorFlowLiteSwift pod for iOS (Preparing...)
• edvardHua/PoseEstimationForMobile
  : TensorFlow project for pose estimation for mobile
• tucan9389/pose-estimation-for-mobile
  : forked from edvardHua/PoseEstimationForMobile
• tucan9389/FingertipEstimation-CoreML
  : iOS project for fingertip estimation using CoreML.