基于MTCNN实现人脸检测完整代码

# 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](https://arxiv.org/abs/1604.02878). ## Why this projects [mtcnn-pytorch](https://github.com/TropComplique/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](https://github.com/TropComplique/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](https://github.com/faciallab) 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 ```bash pip install opencv-python numpy easydict Cython progressbar2 torch tensorboardX ``` If you have gpu on your mechine, you can follow the [official instruction](https://pytorch.org/) and install pytorch gpu version. ### Compile the cython code Compile with gpu support ```bash python setup.py build_ext --inplace ``` Compile with cpu only ```bash 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 ```bash python -m unittest tests.test_detection.TestDetection.test_detection ``` #### Detect on video ```bash 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 ```python 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](./doc/TRAIN.md) ## Tutorial [Detect step by step](./tutorial/detect_step_by_step.ipynb). [face_alignment step by step](./tutorial/face_align.ipynb)