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1.6 KiB
Retinaface Face Detection (Pytorch)
Authors: David Wang
Desription
This opertator detects faces in the images by using RetinaFace Detector[1]. It will returns the bounding box positions and the confidence scores of detected faces. This repo is a adopataion from [2].
from towhee import ops
model = ops.face_detection.retinaface()
embedding = model(img)
Factory Constructor
Create the operator via the following factory method
ops.face_detection.retinaface()
Interface
A face detection operator takes an image as input. it generates the bounding box position and confidence score back to ndarray.
Args:
framework
the framework of the model
supported types: str
, default is 'pytorch'
Parameters:
image
the image to detect faces.
supported types: numpy.ndarray
Returns::
numpy.ndarray
The detected face bounding boxes.
numpy.ndarray
The detected face bounding boxes confident scores.
Code Example
get detected face bounding boxes from './img1.jpg'.
Write the pipeline in simplified style:
import towhee.DataCollection as dc
dc.glob('./img1.jpg')
.image_decode.cv2()
.face_detection.retinaface()
.to_list()
Write a same pipeline with explicit inputs/outputs name specifications:
import towhee.DataCollection as dc
dc.glob['path']('./img1.jpg')
.image_decode.cv2['path', 'img']()
.face_detection.retinaface()
.to_list()
Reference
[1]. https://arxiv.org/abs/1905.00641
[2]. https://github.com/biubug6/Pytorch_Retinaface
1.6 KiB
Retinaface Face Detection (Pytorch)
Authors: David Wang
Desription
This opertator detects faces in the images by using RetinaFace Detector[1]. It will returns the bounding box positions and the confidence scores of detected faces. This repo is a adopataion from [2].
from towhee import ops
model = ops.face_detection.retinaface()
embedding = model(img)
Factory Constructor
Create the operator via the following factory method
ops.face_detection.retinaface()
Interface
A face detection operator takes an image as input. it generates the bounding box position and confidence score back to ndarray.
Args:
framework
the framework of the model
supported types: str
, default is 'pytorch'
Parameters:
image
the image to detect faces.
supported types: numpy.ndarray
Returns::
numpy.ndarray
The detected face bounding boxes.
numpy.ndarray
The detected face bounding boxes confident scores.
Code Example
get detected face bounding boxes from './img1.jpg'.
Write the pipeline in simplified style:
import towhee.DataCollection as dc
dc.glob('./img1.jpg')
.image_decode.cv2()
.face_detection.retinaface()
.to_list()
Write a same pipeline with explicit inputs/outputs name specifications:
import towhee.DataCollection as dc
dc.glob['path']('./img1.jpg')
.image_decode.cv2['path', 'img']()
.face_detection.retinaface()
.to_list()
Reference
[1]. https://arxiv.org/abs/1905.00641
[2]. https://github.com/biubug6/Pytorch_Retinaface