faciallab / Facedetector
A re-implementation of mtcnn. Joint training, tutorial and deployment together.
Stars: ✭ 99
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MTCNN
pytorch implementation of inference and training stage of face detection algorithm described in
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks.
Why this projects
mtcnn-pytorch This is the most popular pytorch implementation of mtcnn. There are some disadvantages we found when using it for real-time detection task.
- No training code.
- Mix torch operation and numpy operation together, which resulting in slow inference speed.
- No unified interface for setting computation device. ('cpu' or 'gpu')
- Based on the old version of pytorch (0.2).
So we create this project and add these features:
- Add code for training stage, you can train model by your own datasets.
- Transfer all numpy operation to torch operation, so that it can benefit from gpu acceleration. It's 10 times faster than the original repo mtcnn-pytorch.
- Provide unified interface to assign 'cpu' or 'gpu'.
- Based on the latest version of pytorch (1.0) and we will provide long-term support.
- It's is a component of our FaceLab ecosystem.
- Real-time face tracking.
- Friendly tutorial for beginner.
Installation
Create virtual env use conda (recommend)
conda create -n face_detection python=3
source activate face_detection
Installation dependency package
pip install opencv-python numpy easydict Cython progressbar2 torch tensorboardX
If you have gpu on your mechine, you can follow the official instruction and install pytorch gpu version.
Compile the cython code
Compile with gpu support
python setup.py build_ext --inplace
Compile with cpu only
python setup.py build_ext --inplace --disable_gpu
Also, you can install mtcnn as a package
python setup.py install
Test the code by example
We assume all these command running in the $SOURCE_ROOT directory.
Detect on example picture
python -m unittest tests.test_detection.TestDetection.test_detection
Detect on video
python scripts/detect_on_video.py --video_path ./tests/asset/video/school.avi --device cuda:0 --minsize 24
you can set device to 'cpu' if you have no valid gpu on your machine
Basic Usage
import cv2
import mtcnn
# First we create pnet, rnet, onet, and load weights from caffe model.
pnet, rnet, onet = mtcnn.get_net_caffe('output/converted')
# Then we create a detector
detector = mtcnn.FaceDetector(pnet, rnet, onet, device='cuda:0')
# Then we can detect faces from image
img = 'tests/asset/images/office5.jpg'
boxes, landmarks = detector.detect(img)
# Then we draw bounding boxes and landmarks on image
image = cv2.imread(img)
image = mtcnn.utils.draw.draw_boxes2(image, boxes)
image = mtcnn.utils.draw.batch_draw_landmarks(image, landmarks)
# Show the result
cv2.imshwow("Detected image.", image)
cv2.waitKey(0)
Doc
Train your own model from scratch
Tutorial
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