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

python setup.py build_ext --inplace

Olso, 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

Detect step by step.

face_alignment step by step