docs: examples

README.md

@@ -1,4 +1,9 @@
 # face
+
+[![GoDoc](https://pkg.go.dev/badge/github.com/dev6699/face)](https://pkg.go.dev/github.com/dev6699/face)
+[![Go Report Card](https://goreportcard.com/badge/github.com/dev6699/face)](https://goreportcard.com/report/github.com/dev6699/face)
+[![License](https://img.shields.io/github/license/dev6699/face)](LICENSE)
+
 A comprehensive collection of face AI models with integrated pre and post-processing steps, utilizing NVIDIA Triton Inference Server for seamless inference. This repository aims to provide easy-to-use face detection, recognition, and analysis tools.
 
 ## Table of Contents
@@ -21,28 +26,34 @@ This repository contains a suite of face AI models designed for various applicat
 - <b>Easy-to-Use Interface:</b>  Simple API for quick integration into various applications.
 
 ## Installation
-1. Clone the Repository:
+- ### Use `go get` to install this package:
+
+    ```bash
+    go get github.com/dev6699/face
+    ```
+
+- ### Clone the Repository:
 
     ```bash
     git clone https://github.com/dev6699/face.git
     cd face
     ```
 
-2. Open the repository in vscode devcontainer.
-
-3. Download and Prepare Models:
-    - Navigate to the [Available Models](#available-models) section to find the download links for each model.
-    - Download each model and rename the file to `model.onnx`.
-    - Place each `model.onnx` file into its respective directory within the model_repository folder.
-    - Example: Setting up the YOLOFace model:
+### Download and Prepare Models:
+- Navigate to the [Available Models](#available-models) section to find the download links for each model.
+- Download each model and rename the file to `model.onnx`.
+- Place each `model.onnx` file into its respective directory within the model_repository folder.
+- Example: Setting up the YOLOFace model:
 
-        ```bash
-        mkdir -p model_repository/yoloface/1
-        wget -O model_repository/yoloface/1/model.onnx <model_url>
-        ```
-        Ensure to replace <model_url> with the actual URL provided in the [Available Models](#available-models) section.
+    ```bash
+    mkdir -p model_repository/yoloface/1
+    wget -O model_repository/yoloface/1/model.onnx <model_url>
+    ```
+    Ensure to replace <model_url> with the actual URL provided in the [Available Models](#available-models) section.
 
 ## Usage
+Please refer to the [examples](examples) folder for more information on how to use the models and run various tasks.
+
 1. Start Triton Inference Server:
 
     ```bash

docker-compose.yml

@@ -15,3 +15,7 @@ services:
         reservations:
           devices:
             - capabilities: [gpu]
+
+networks:
+  default:
+    name: face_devcontainer_default

examples/2dfan4/main.go

@@ -0,0 +1,46 @@
+package main
+
+import (
+	"log"
+
+	"github.com/dev6699/face/client"
+	"github.com/dev6699/face/examples"
+	"github.com/dev6699/face/model"
+	_2dfan4 "github.com/dev6699/face/model/2dfan4"
+	"github.com/dev6699/face/model/yoloface"
+	"gocv.io/x/gocv"
+)
+
+func main() {
+	faceDetectorScore := float32(0.5)
+	iouThreshold := 0.4
+	yolofaceFactory := yoloface.NewFactory(faceDetectorScore, iouThreshold)
+	_2dfan4Factory := _2dfan4.NewFactory()
+	err := client.Init(
+		"tritonserver:8001",
+		[]model.ModelMeta{
+			yolofaceFactory(),
+			_2dfan4Factory(),
+		},
+	)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	img := gocv.IMRead("../image.jpg", gocv.IMReadColor)
+	yoloFaceOutput, err := client.Infer(yolofaceFactory, &yoloface.Input{Img: img})
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	for _, d := range yoloFaceOutput.Detections {
+		fanOutput, err := client.Infer(_2dfan4Factory, &_2dfan4.Input{Img: img, BoundingBox: d.BoundingBox})
+		if err != nil {
+			log.Fatal(err)
+		}
+
+		examples.DrawPoints(&img, fanOutput.FaceLandmark68.Data, examples.Red, 2)
+	}
+
+	gocv.IMWrite("output.jpg", img)
+}

examples/arcface/main.go

@@ -0,0 +1,52 @@
+package main
+
+import (
+	"fmt"
+	"log"
+
+	"github.com/dev6699/face/client"
+	"github.com/dev6699/face/model"
+	"github.com/dev6699/face/model/arcface"
+	"github.com/dev6699/face/model/yoloface"
+	"gocv.io/x/gocv"
+)
+
+func main() {
+	faceDetectorScore := float32(0.5)
+	iouThreshold := 0.4
+	yolofaceFactory := yoloface.NewFactory(faceDetectorScore, iouThreshold)
+	arcfaceFactory := arcface.NewFactory()
+	err := client.Init(
+		"tritonserver:8001",
+		[]model.ModelMeta{
+			yolofaceFactory(),
+			arcfaceFactory(),
+		},
+	)
+	if err != nil {
+		log.Fatal(err)
+	}
+	img := gocv.IMRead("../image.jpg", gocv.IMReadColor)
+	yoloFaceOutput, err := client.Infer(yolofaceFactory, &yoloface.Input{Img: img})
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	faceEmbeddings := [][]float32{}
+	for _, d := range yoloFaceOutput.Detections {
+		arcfaceOutput, err := client.Infer(arcfaceFactory, &arcface.Input{Img: img, FaceLandmark5: d.FaceLandmark5})
+		if err != nil {
+			log.Fatal(err)
+		}
+		faceEmbeddings = append(faceEmbeddings, arcfaceOutput.NormedEmbedding)
+	}
+
+	similarDistance := 0.6
+	for i, f1 := range faceEmbeddings {
+		for j, f2 := range faceEmbeddings {
+			faceDistance := model.CalcFaceDistance(f1, f2)
+			isSimilar := faceDistance < similarDistance
+			fmt.Printf("Face %d & %d: %.2f %v\n", i, j, faceDistance, isSimilar)
+		}
+	}
+}

examples/genderage/main.go

@@ -0,0 +1,51 @@
+package main
+
+import (
+	"fmt"
+	"log"
+
+	"github.com/dev6699/face/client"
+	"github.com/dev6699/face/examples"
+	"github.com/dev6699/face/model"
+	"github.com/dev6699/face/model/genderage"
+	"github.com/dev6699/face/model/yoloface"
+	"gocv.io/x/gocv"
+)
+
+func main() {
+	faceDetectorScore := float32(0.5)
+	iouThreshold := 0.4
+	yolofaceFactory := yoloface.NewFactory(faceDetectorScore, iouThreshold)
+	genderAgeFactory := genderage.NewFactory()
+	err := client.Init(
+		"tritonserver:8001",
+		[]model.ModelMeta{
+			yolofaceFactory(),
+			genderAgeFactory(),
+		},
+	)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	img := gocv.IMRead("../image.jpg", gocv.IMReadColor)
+	yoloFaceOutput, err := client.Infer(yolofaceFactory, &yoloface.Input{Img: img})
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	for _, d := range yoloFaceOutput.Detections {
+		genderAgeOutput, err := client.Infer(genderAgeFactory, &genderage.Input{Img: img, BoundingBox: d.BoundingBox})
+		if err != nil {
+			log.Fatal(err)
+		}
+
+		genderString := "M"
+		if genderAgeOutput.Gender == 0 {
+			genderString = "F"
+		}
+		examples.DrawBoundingBoxes(&img, d.BoundingBox, fmt.Sprintf("%s %d", genderString, genderAgeOutput.Age), examples.Green, examples.Red)
+	}
+
+	gocv.IMWrite("output.jpg", img)
+}

examples/gfpgan/main.go

@@ -0,0 +1,43 @@
+package main
+
+import (
+	"fmt"
+	"log"
+
+	"github.com/dev6699/face/client"
+	"github.com/dev6699/face/model"
+	"github.com/dev6699/face/model/gfpgan"
+	"github.com/dev6699/face/model/yoloface"
+	"gocv.io/x/gocv"
+)
+
+func main() {
+	faceDetectorScore := float32(0.5)
+	iouThreshold := 0.4
+	yolofaceFactory := yoloface.NewFactory(faceDetectorScore, iouThreshold)
+	gfpganFactory := gfpgan.NewFactory(80.0)
+	err := client.Init(
+		"tritonserver:8001",
+		[]model.ModelMeta{
+			yolofaceFactory(),
+			gfpganFactory(),
+		},
+	)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	img := gocv.IMRead("../image.jpg", gocv.IMReadColor)
+	yoloFaceOutput, err := client.Infer(yolofaceFactory, &yoloface.Input{Img: img})
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	for i, d := range yoloFaceOutput.Detections {
+		gfpganOutput, err := client.Infer(gfpganFactory, &gfpgan.Input{Img: img, FaceLandmark5: d.FaceLandmark5})
+		if err != nil {
+			log.Fatal(err)
+		}
+		gocv.IMWrite(fmt.Sprintf("output%d.jpg", i+1), gfpganOutput.OutFrame)
+	}
+}

examples/util.go

@@ -0,0 +1,26 @@
+package examples
+
+import (
+	"image"
+	"image/color"
+
+	"github.com/dev6699/face/model"
+	"gocv.io/x/gocv"
+)
+
+var (
+	Red   = color.RGBA{R: 255, G: 0, B: 0, A: 255}
+	Green = color.RGBA{R: 0, G: 255, B: 0, A: 255}
+	Blue  = color.RGBA{R: 0, G: 0, B: 255, A: 255}
+)
+
+func DrawBoundingBoxes(img *gocv.Mat, box model.BoundingBox, text string, boxColor, textColor color.RGBA) {
+	gocv.Rectangle(img, image.Rectangle{Min: image.Point{X: int(box.X1), Y: int(box.Y1)}, Max: image.Point{X: int(box.X2), Y: int(box.Y2)}}, boxColor, 2)
+	gocv.PutText(img, text, image.Point{X: int(box.X1), Y: int(box.Y1) - 5}, gocv.FontHersheySimplex, 0.5, textColor, 2)
+}
+
+func DrawPoints(img *gocv.Mat, points []gocv.Point2f, col color.RGBA, radius int) {
+	for _, pt := range points {
+		gocv.Circle(img, image.Pt(int(pt.X), int(pt.Y)), radius, col, -1)
+	}
+}

examples/yoloface/main.go

@@ -0,0 +1,40 @@
+package main
+
+import (
+	"fmt"
+	"log"
+
+	"github.com/dev6699/face/client"
+	"github.com/dev6699/face/examples"
+	"github.com/dev6699/face/model"
+	"github.com/dev6699/face/model/yoloface"
+	"gocv.io/x/gocv"
+)
+
+func main() {
+	faceDetectorScore := float32(0.5)
+	iouThreshold := 0.4
+	yolofaceFactory := yoloface.NewFactory(faceDetectorScore, iouThreshold)
+	err := client.Init(
+		"tritonserver:8001",
+		[]model.ModelMeta{
+			yolofaceFactory(),
+		},
+	)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	img := gocv.IMRead("../image.jpg", gocv.IMReadColor)
+	yoloFaceOutput, err := client.Infer(yolofaceFactory, &yoloface.Input{Img: img})
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	for _, d := range yoloFaceOutput.Detections {
+		examples.DrawBoundingBoxes(&img, d.BoundingBox, fmt.Sprintf("Score: %.2f", d.Confidence), examples.Green, examples.Green)
+		examples.DrawPoints(&img, d.FaceLandmark5, examples.Red, 3)
+	}
+
+	gocv.IMWrite("output.jpg", img)
+}

model/util.go

@@ -287,3 +287,25 @@ func getInverseVisionFrame(cropVisionFrame gocv.Mat, inverseMatrix gocv.Mat, tem
 	inverseVisionFrame.ConvertTo(&inverseVisionFrame, gocv.MatTypeCV64F)
 	return inverseVisionFrame
 }
+
+// CalcFaceDistance to calculate the distance between two face embeddings
+func CalcFaceDistance(faceEmbedding, referenceFaceEmbedding []float32) float64 {
+	if len(faceEmbedding) == 0 || len(referenceFaceEmbedding) == 0 || len(faceEmbedding) != len(referenceFaceEmbedding) {
+		return 0
+	}
+
+	dotProduct := dotProduct(faceEmbedding, referenceFaceEmbedding)
+	return 1.0 - float64(dotProduct)
+}
+
+func dotProduct(v1, v2 []float32) float32 {
+	if len(v1) != len(v2) {
+		return 0
+	}
+
+	var result float32
+	for i := 0; i < len(v1); i++ {
+		result += v1[i] * v2[i]
+	}
+	return result
+}