# Batch Face for Modern Research
## 🚧Documentation under construction, check tests folder for more details. 🚧
This repo provides the out-of-box face detection and face alignment with batch input support and enables real-time application on CPU.
## Features
1. Batch input support for faster data processing.
2. Smart API.
3. Ultrafast with inference runtime acceleration.
4. Automatically download pre-trained weights.
5. Minimal dependencies.
### Requirements
- Linux, Windows or macOS
- Python 3.5+ (it may work with other versions too)
- opencv-python
- PyTorch (>=1.0)
- ONNX (optional)
While not required, for optimal performance it is highly recommended to run the code using a CUDA enabled GPU.
## Install
The easiest way to install it is using pip:
```bash
pip install git+https://github.com/elliottzheng/batch-face.git@master
```
No extra setup needs, most of the pretrained weights will be downloaded automatically.
## Usage
You can clone the repo and run tests like this
```
python -m tests.camera
```
### Face Detection
##### Detect face and five landmarks on single image
```python
import cv2
from batch_face import RetinaFace
detector = RetinaFace(gpu_id=0)
img = cv2.imread("examples/obama.jpg")
faces = detector(img, cv=True) # set cv to False for rgb input, the default value of cv is False
box, landmarks, score = faces[0]
```
##### Running on CPU/GPU
In order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device id.
```python
from batch_face import RetinaFace
# 0 means using GPU with id 0 for inference
# default -1: means using cpu for inference
detector = RetinaFace(gpu_id=0)
```
| | GPU(GTX 1080TI,batch size=1) | GPU(GTX 1080TI，batch size=750) | CPU(Intel(R) Core(TM) i7-7800X CPU @ 3.50GHz) |
| ---- | ---------------------------- | ------------------------------- | --------------------------------------------- |
| FPS | 44.02405810720893 | 96.64058005582535 | 15.452635835550483 |
| SPF | 0.022714852809906007 | 0.010347620010375976 | 0.0647138786315918 |
##### Batch input for faster detection
**Detector with CUDA process batch input faster than the same amount of single input.**
```python
import cv2
from batch_face import RetinaFace
detector = RetinaFace()
img= cv2.imread('examples/obama.jpg')[...,::-1]
all_faces = detector([img,img]) # return faces list of all images
box, landmarks, score = all_faces[0][0]
```
Note: All the input images must of the same size, for input images with different size, please use `detector.pseudo_batch_detect`.
### Face Alignment
##### face alignment on single image
```python
from batch_face import drawLandmark_multiple, LandmarkPredictor, RetinaFace
predictor = LandmarkPredictor(0)
detector = RetinaFace(0)
imgname = "examples/obama.jpg"
img = cv2.imread(imgname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
faces = detector(img)
if len(faces) == 0:
print("NO face is detected!")
exit(-1)
# the first input for the predictor is a list of face boxes. [[x1,y1,x2,y2]]
results = predictor(faces, img, from_fd=True) # from_fd=True to convert results from our detection results to simple boxes
for face, landmarks in zip(faces, results):
img = drawLandmark_multiple(img, face[0], landmarks)
```
## References
- Face Detection Network and pretrained model are from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)
- Face Alignment Network and pretrained model are from [cunjian/pytorch_face_landmark](https://github.com/cunjian/pytorch_face_landmark)
- Face Reconstruction Network and pretrained model are from [cleardusk/3DDFA](https://github.com/cleardusk/3DDFA)
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"description": "# Batch Face for Modern Research\n\n## \ud83d\udea7Documentation under construction, check tests folder for more details. \ud83d\udea7\n\nThis repo provides the out-of-box face detection and face alignment with batch input support and enables real-time application on CPU.\n\n## Features\n1. Batch input support for faster data processing.\n2. Smart API.\n3. Ultrafast with inference runtime acceleration.\n4. Automatically download pre-trained weights.\n5. Minimal dependencies.\n\n### Requirements\n\n- Linux, Windows or macOS\n- Python 3.5+ (it may work with other versions too)\n- opencv-python\n- PyTorch (>=1.0) \n- ONNX (optional)\n\nWhile not required, for optimal performance it is highly recommended to run the code using a CUDA enabled GPU.\n\n## Install\n\nThe easiest way to install it is using pip:\n\n```bash\npip install git+https://github.com/elliottzheng/batch-face.git@master\n```\nNo extra setup needs, most of the pretrained weights will be downloaded automatically.\n\n## Usage\nYou can clone the repo and run tests like this\n```\npython -m tests.camera\n```\n### Face Detection\n\n##### Detect face and five landmarks on single image\n```python\nimport cv2\nfrom batch_face import RetinaFace\n\ndetector = RetinaFace(gpu_id=0)\nimg = cv2.imread(\"examples/obama.jpg\")\nfaces = detector(img, cv=True) # set cv to False for rgb input, the default value of cv is False\nbox, landmarks, score = faces[0]\n```\n##### Running on CPU/GPU\n\nIn order to specify the device (GPU or CPU) on which the code will run one can explicitly pass the device id.\n```python\nfrom batch_face import RetinaFace\n# 0 means using GPU with id 0 for inference\n# default -1: means using cpu for inference\ndetector = RetinaFace(gpu_id=0) \n```\n| | GPU(GTX 1080TI,batch size=1) | GPU(GTX 1080TI\uff0cbatch size=750) | CPU(Intel(R) Core(TM) i7-7800X CPU @ 3.50GHz) |\n| ---- | ---------------------------- | ------------------------------- | --------------------------------------------- |\n| FPS | 44.02405810720893 | 96.64058005582535 | 15.452635835550483 |\n| SPF | 0.022714852809906007 | 0.010347620010375976 | 0.0647138786315918 |\n\n\n##### Batch input for faster detection\n\n**Detector with CUDA process batch input faster than the same amount of single input.** \n\n```python\nimport cv2\nfrom batch_face import RetinaFace\n\ndetector = RetinaFace()\nimg= cv2.imread('examples/obama.jpg')[...,::-1]\nall_faces = detector([img,img]) # return faces list of all images\nbox, landmarks, score = all_faces[0][0]\n```\n\nNote: All the input images must of the same size, for input images with different size, please use `detector.pseudo_batch_detect`.\n\n\n\n### Face Alignment\n##### face alignment on single image\n\n```python \nfrom batch_face import drawLandmark_multiple, LandmarkPredictor, RetinaFace\n\npredictor = LandmarkPredictor(0)\ndetector = RetinaFace(0)\n\nimgname = \"examples/obama.jpg\"\nimg = cv2.imread(imgname)\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\nfaces = detector(img)\n\nif len(faces) == 0:\n print(\"NO face is detected!\")\n exit(-1)\n\n# the first input for the predictor is a list of face boxes. [[x1,y1,x2,y2]]\nresults = predictor(faces, img, from_fd=True) # from_fd=True to convert results from our detection results to simple boxes\n\nfor face, landmarks in zip(faces, results):\n img = drawLandmark_multiple(img, face[0], landmarks)\n```\n\n\n## References\n\n- Face Detection Network and pretrained model are from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)\n- Face Alignment Network and pretrained model are from [cunjian/pytorch_face_landmark](https://github.com/cunjian/pytorch_face_landmark)\n- Face Reconstruction Network and pretrained model are from [cleardusk/3DDFA](https://github.com/cleardusk/3DDFA)\n",
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