基于unity的卡通角色驱动(有问题可加Q群583442643)

# MTCNN `PyTorch` implementation of **inference 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 I found when using it for real-time detection task. * Mix torch operation and numpy operation together, which resulting in slow inference speed (Cannot run on GPU). * Based on the old version of pytorch (0.2). So I create this project and make some improvments: * Transfer all numpy operation to torch operation, so that it can benefit from GPU acceleration. * Automatic run on 'CPU' or 'GPU'. * Based on the latest version of pytorch (1.3) * Real-time face tracking ## Installation 1. Create virtual environment ``` # conda create -n face_detection ``` 2. Activate virtual environment ``` # source activate face_detection ``` 3. [Install PyTorch](https://pytorch.org/) ``` (face_detection)# conda install pytorch torchvision cudatoolkit=10.1 -c pytorch ``` 4. Install Jupyter Notebook ``` (face_detection)# conda install jupyter notebook ``` 5. Install `opencv` ``` (face_detection)# pip install opencv-python ``` ## How to use it Just download the repository and then do this ``` import sys import os sys.path.append(os.pardir) from importlib import import_module import cv2 from src.detect import FaceDetector # import camera driver if os.environ.get('CAMERA'): Camera = import_module('camera_' + os.environ['CAMERA']).Camera else: from camera import Camera if __name__ == "__main__": detector = FaceDetector() while True: frame = Camera().get_frame() image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) bounding_boxes = detector.detect(image) for i in range(len(bounding_boxes)): cv2.rectangle(frame, (int(bounding_boxes[i][0]), int(bounding_boxes[i][1])), (int(bounding_boxes[i][2]), int(bounding_boxes[i][3])), (255, 0, 0), 2) cv2.imshow('capture', frame) key = cv2.waitKey(1) if key & 0xFF == ord('q'): break ``` or just run: ``` (face_detection)# cd MTCNN_face_detection_alignment_pytorch/src/ (face_detection)# CAMERA=opencv python test.py ``` ## Tutorial [Detect step by step](./notebooks/try_mtcnn_step_by_step.ipynb) ## Credit This implementation is heavily inspired by: * [TropComplique/mtcnn-pytorch](https://github.com/TropComplique/mtcnn-pytorch) * [faciallab/FaceDetector](https://github.com/faciallab/FaceDetector) ## Citation ``` @article{7553523, author={K. Zhang and Z. Zhang and Z. Li and Y. Qiao}, journal={IEEE Signal Processing Letters}, title={Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks}, year={2016}, volume={23}, number={10}, pages={1499-1503}, keywords={Benchmark testing;Computer architecture;Convolution;Detectors;Face;Face detection;Training;Cascaded convolutional neural network (CNN);face alignment;face detection}, doi={10.1109/LSP.2016.2603342}, ISSN={1070-9908}, month={Oct} } ```