基于Python+CNN+OpenCV人脸检测加表情识别源码+文档说明

# 说明 本仓库修改与仓库：[face_classification](https://github.com/oarriaga/face_classification/) 原仓库使用 OpenCV 进行人脸检测，但准确度不够高，因此后改用 [MTCNN](https://github.com/kpzhang93/MTCNN_face_detection_alignment) 增加检测准确率。 在原仓库中的改动如下： + 增加 MTCNN 模型： haarcascade_frontalface_default.xml + 增加人脸检测程序：detect_face.py + 更改程序 video_emotion_gender_demo.py, 使用 detect_face 来检测人脸。 下面是原始 README 内容： --- # Face classification and detection from the [B-IT-BOTS robotics team](https://mas-group.inf.h-brs.de/?page_id=622). Real-time face detection and emotion/gender classification using fer2013/IMDB datasets with a keras CNN model and openCV. * IMDB gender classification test accuracy: 96%. * fer2013 emotion classification test accuracy: 66%. For more information please consult the [publication](https://github.com/oarriaga/face_classification/blob/master/report.pdf) # Emotion/gender examples:  Guided back-prop  Real-time demo: <div align='center'> <img src='images/color_demo.gif' width='400px'> </div> [B-IT-BOTS](https://mas-group.inf.h-brs.de/?page_id=622) robotics team :)  ## Instructions ### Run real-time emotion demo: > python3 video_emotion_color_demo.py ### Run real-time guided back-prop demo: > python3 image_gradcam_demo.py ### Make inference on single images: > python3 image_emotion_gender_demo.py <image_path> e.g. > python3 image_emotion_gender_demo.py ../images/test_image.jpg ### Running with Docker With a few steps one can get its own face classification and detection running. Follow the commands below: * ```docker pull ekholabs/face-classifier``` * ```docker run -d -p 8084:8084 --name=face-classifier ekholabs/face-classifier``` * ```curl -v -F image=@[path_to_image] http://localhost:8084/classifyImage > image.png``` ### To train previous/new models for emotion classification: * Download the fer2013.tar.gz file from [here](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data) * Move the downloaded file to the datasets directory inside this repository. * Untar the file: > tar -xzf fer2013.tar * Run the train_emotion_classification.py file > python3 train_emotion_classifier.py ### To train previous/new models for gender classification: * Download the imdb_crop.tar file from [here](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/) (It's the 7GB button with the tittle Download faces only). * Move the downloaded file to the datasets directory inside this repository. * Untar the file: > tar -xfv imdb_crop.tar * Run the train_gender_classification.py file > python3 train_gender_classifier.py