Metadata-Version: 2.1
Name: face-detection
Version: 0.2.2
Summary: A simple and lightweight package for state of the art face detection with GPU support.
Home-page: https://github.com/hukkelas/DSFD-Pytorch-Inference
Author: Håkon Hukkelås
License: apache-2.0
Description: # 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.
        
        ![](example_det.jpg)
        
        **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}
        
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
