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face-detection-0.2.2
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مشاهده بیشترتوضیحات
A simple and lightweight package for state of the art face detection with GPU support.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | face-detection-0.2.2 |
| نام | face-detection |
| نسخه کتابخانه | 0.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Håkon Hukkelås |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/hukkelas/DSFD-Pytorch-Inference |
| آدرس اینترنتی | https://pypi.org/project/face-detection/ |
| مجوز | apache-2.0 |
# State of the Art Face Detection in Pytorch with DSFD and RetinaFace
This repository includes:
- A High-Performance Pytorch Implementation of the paper "[DSFD: Dual Shot Face Detector" (CVPR 2019).](http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_DSFD_Dual_Shot_Face_Detector_CVPR_2019_paper.pdf) adapted from the [original source code](https://github.com/TencentYoutuResearch/FaceDetection-DSFD).
- Lightweight single-shot face detection from the paper [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://arxiv.org/abs/1905.00641) adapted from https://github.com/biubug6/Pytorch_Retinaface.
**NOTE** This implementation can only be used for inference of a selection of models and all training scripts are removed. If you want to finetune any models, we recommend you to use the original source code.
## Install
You can install this repository with pip (requires python>=3.6);
```bash
pip install git+https://github.com/hukkelas/DSFD-Pytorch-Inference.git
```
You can also install with the `setup.py`
```bash
python3 setup.py install
```
## Getting started
Run
```
python3 test.py
```
This will look for images in the `images/` folder, and save the results in the same folder with an ending `_out.jpg`
## Simple API
To perform detection you can simple use the following lines:
```python
import cv2
import face_detection
print(face_detection.available_detectors)
detector = face_detection.build_detector(
"DSFDDetector", confidence_threshold=.5, nms_iou_threshold=.3)
# BGR to RGB
im = cv2.imread("path_to_im.jpg")[:, :, ::-1]
detections = detector.detect(im)
```
This will return a tensor with shape `[N, 5]`, where N is number of faces and the five elements are `[xmin, ymin, xmax, ymax, detection_confidence]`
### Batched inference
```python
import numpy as np
import face_detection
print(face_detection.available_detectors)
detector = face_detection.build_detector(
"DSFDDetector", confidence_threshold=.5, nms_iou_threshold=.3)
# [batch size, height, width, 3]
images_dummy = np.zeros((2, 512, 512, 3))
detections = detector.batched_detect(im)
```
## Improvements
### Difference from DSFD
For the original source code, see [here](https://github.com/TencentYoutuResearch/FaceDetection-DSFD).
- Removal of all unnecessary files for training / loading VGG models.
- Improve the inference time by about 30x (from ~6s to 0.2) with rough estimates using `time` (Measured on a V100-32GB GPU).
The main improvements in inference time comes from:
- Replacing non-maximum-suppression to a [highly optimized torchvision version](https://github.com/pytorch/vision/blob/19315e313511fead3597e23075552255d07fcb2a/torchvision/ops/boxes.py#L5)
- Refactoring `init_priors`in the [SSD model](dsfd/face_ssd.py) to cache previous prior sizes (no need to generate this per forward pass).
- Refactoring the forward pass in `Detect` in [`utils.py`](dsfd/utils.py) to perform confidence thresholding before non-maximum suppression
- Minor changes in the forward pass to use pytorch 1.0 features
### Difference from RetinaFace
For the original source code, see [here](https://github.com/biubug6/Pytorch_Retinaface).
We've done the following improvements:
- Remove gradient computation for inference (`torch.no_grad`).
- Replacing non-maximum-suppression to a [highly optimized torchvision version](https://github.com/pytorch/vision/blob/19315e313511fead3597e23075552255d07fcb2a/torchvision/ops/boxes.py#L5)
## Inference time
This is **very roughly** estimated on a 1024x687 image. The reported time is the average over 1000 forward passes on a single image. (With no cudnn benchmarking and no fp16 computation).
| | DSFDDetector | RetinaNetResNet50 | RetinaNetMobileNetV1 |
| -|-|-|-|
| CPU (Intel 2.2GHz i7) *| 17,496 ms (0.06 FPS) | 2970ms (0.33 FPS) | 270ms (3.7 FPS) |
| NVIDIA V100-32GB | 100ms (10 FPS) | | |
| NVIDIA GTX 1060 6GB | 341ms (2.9 FPS) | 76.6ms (13 FPS) | 48.2ms (20.7 FPS) |
| NVIDIA T4 16 GB | 482 ms (2.1 FPS) | 181ms (5.5 FPS) | 178ms (5.6 FPS) |
*Done over 100 forward passes on a MacOS Mid 2014, 15-Inch.
## Changelog
- September 1st 2020: added support for fp16/mixed precision inference
- September 24th 2020: added support for TensorRT.
## TensorRT Inference (Experimental)
You can run RetinaFace ResNet-50 with TensorRT:
```python
from face_detection.retinaface.tensorrt_wrap import TensorRTRetinaFace
inference_imshape =(480, 640) # Input to the CNN
input_imshape = (1080, 1920) # Input for original video source
detector = TensorRTRetinaFace(input_imshape, imshape)
boxes, landmarks, scores = detector.infer(image)
```
## Citation
If you find this code useful, remember to cite the original authors:
```
@inproceedings{li2018dsfd,
title={DSFD: Dual Shot Face Detector},
author={Li, Jian and Wang, Yabiao and Wang, Changan and Tai, Ying and Qian, Jianjun and Yang, Jian and Wang, Chengjie and Li, Jilin and Huang, Feiyue},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}
}
@inproceedings{deng2019retinaface,
title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
booktitle={arxiv},
year={2019}
```
نیازمندی
| مقدار | نام |
|---|---|
| >=1.6 | torch |
| >=0.3.0 | torchvision |
| - | numpy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl face-detection-0.2.2:
pip install face-detection-0.2.2.whl
نصب پکیج tar.gz face-detection-0.2.2:
pip install face-detection-0.2.2.tar.gz