yhshu/PoseEstimation-CoreML-master

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.

한국어 README

Jointed Keypoints	Concatenated heatmap	Still Image

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

How it works

Requirements

• Xcode 9.2+
• iOS 11.0+
• Swift 4

Download 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

Matadata

	cpm	hourglass
Input shape	[1, 192, 192, 3]	[1, 192, 192, 3]
Output shape	[1, 96, 96, 14]	[1, 48, 48, 14]
Input node name	image	image
Output node name	Convolutional_Pose_Machine/stage_5_out	hourglass_out_3
Model size	2.6 MB	2.0 MB

Inference Time

Device	cpm	hourglass
iPhone XS	(TODO)	(TODO)
iPhone XS Max	(TODO)	(TODO)
iPad Pro (3rd generation)	21 ms	11 ms
iPhone X	51 ms	49 ms
iPhone 8+	49 ms	46 ms
iPhone 8	(TODO)	(TODO)
iPhone 7	(TODO)	(TODO)
iPhone 6+	200 ms	180 ms

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
• 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.