README
¶
gofaces
This repository is an implementation of this article.
It is a toy to run face detection on a picture with a neural network.
The neural network is based on Tiny YOLO v2 and is encoded in the onnx format. It decocded with onnx-go and executed by Gorgonia.
Installation an usage
Requirements
- Go with modules support,
1.12+recommended. - Git LFS. It's needed in order to get the file model.onnx when cloning the repo. If you don’t have this, running will fail with an error: proto: can’t skip unknown wire type 6
Usage
git clone https://github.com/owulveryck/gofaces
cd gofaces
git lfs install
git lfs fetch
git lfs checkout model/model.onnx
cd cmd
go run main.go -h
Documentation
¶
Overview ¶
Package gofaces is a set of functions to handle the input and output of a Tint YOLO v2 model.
Index ¶
Constants ¶
const ( // HSize is the height of the input picture HSize = 416 // WSize is the width of the input picture WSize = 416 )
Variables ¶
This section is empty.
Functions ¶
Types ¶
type Box ¶
type Box struct {
R image.Rectangle
Confidence float64 // The confidence the model has that there is at least one element in this box
Elements []Element
// contains filtered or unexported fields
}
Box is holding a bounding box A bunding box is a rectangle R containing an object with Confidence. The object is one of the Elements (most likely the one with the highest probability)
func ProcessOutput ¶
ProcessOutput analyze the tensor dense and output the bouding boxes filled with the predictions
func Sanitize ¶
Sanitize the output from https://medium.com/@jonathan_hui/real-time-object-detection-with-yolo-yolov2-28b1b93e2088
- Sort the predictions by the confidence scores.
- Start from the top scores, ignore any current prediction if we find any previous predictions that have the same class and IoU > 0.5 with the current prediction.
- Repeat step 2 until all predictions are checked.
Source Files
Directories