A python library built to realize face detection and face landmark with Deep Learning using simple and few lines of code.And my website will follow Latest paper and poster some my personal comment.

Now FaceAI is an original version and only support MTCCN to detect face. Eventually, FaceAI will offer others various kind of applications about face.

• V0.3.0 : Add 3D face restruction module.
• V0.2.0 : Add download pre-trained automatically from google drive.
• V0.1.0 : Add landmarks detection module.
• V0.0.1 : Initialize the file structure of FaceAI and add MTCCN model.

• Tensorflow 1.10.0 (and later versions)

• Numpy 1.13.1 (and later versions)

• OpenCV

• Matplotlib

• easydict

• PIL

• scipy

• Face Detection input image:

  

  output image:

  

  code:

  from faceai.Detection import *
  import os
  import cv2
  
  def main():
      execution_path = os.getcwd()
      model_path = ['MTCNN_model/PNet_landmark/PNet-30', 'MTCNN_model/RNet_landmark/RNet-22',
              'MTCNN_model/ONet_landmark/ONet-22']
      input_path = os.path.join(execution_path, "image.jpg")
      output_path = os.path.join(execution_path, "imagenew.jpg")
      # image_arr = cv2.imread(input_path)
      # image_arr = cv2.cvtColor(image_arr, cv2.COLOR_BGR2RGB)
      facedetector = FacesDetection()
      facedetector.setModelTypeAsMTCNN()
      facedetector.setModelPath(model_path)
      facedetector.loadModel(min_face_size=24)
  
      img,dets = facedetector.detectFacesFromImage(input_image=input_path,box_mark=True)
      cv2.imwrite("imagenew.jpg", img)
      print('the number of faces: {:0>3d}'.format((len(dets))))
  
  if __name__ == '__main__':
      main()
  

• Face Landmarks Detection input image:

  

  output image:

  

  code:

  from faceai.Detection import *
  from faceai.Alignment import LandmarksDetection
  import os
  import cv2
  
  def main():
      execution_path = os.getcwd()
      model_path = ['MTCNN_model/PNet_landmark/PNet-30', 'MTCNN_model/RNet_landmark/RNet-22',
                  'MTCNN_model/ONet_landmark/ONet-22']
      input_path = os.path.join(execution_path, "land.jpg")
      output_path = os.path.join(execution_path, "landnew.jpg")
  
      facedetector = FacesDetection()
      facedetector.setModelTypeAsMTCNN()
      facedetector.setModelPath(model_path)
      facedetector.loadModel(min_face_size=48)
  
      img,infs = facedetector.detectFacesFromImage(input_image=input_path,box_mark=False)
      dets = []
      for inf in infs:
          dets.append(inf["detection_details"])
      print('the number of faces: {:0>3d}'.format((len(infs))))
  
      landsdetector = LandmarksDetection()
      landsdetector.setModelTypeAsDAN()
      landsdetector.setModelPath("./Model/Model")
      landsdetector.loadModel()
  
      img,lands = landsdetector.detectLandmarksFromImage(img,dets,points_mark = True)
      cv2.imshow("t",img)
      cv2.imwrite("landnew.jpg", img)
  
  if __name__ == '__main__':
      main()
  

• 3D Face Restruction

  input image:

  

  output image: dense image pose image depth image

  code:

  from faceai.Detection import FacesDetection
  from faceai.Alignment import LandmarksDetection
  from faceai.ThrDFace import ThreeDimRestructure
  import os
  import cv2
  
  def main():
    execution_path = os.getcwd()
    input_path = os.path.join(execution_path, "3d.jpg")
    output_path = os.path.join(execution_path, "landnew.jpg")
  
    facedetector = FacesDetection()
    facedetector.setModelTypeAsMTCNN()
    facedetector.loadModel(detection_speed='fast',min_face_size=12)
  
    img,infs = facedetector.detectFacesFromImage(input_image=input_path,box_mark=False)
    dets = []
    for inf in infs:
        dets.append(inf["detection_details"])
    print('the number of faces: {:0>3d}'.format((len(infs))))
  
    landsdetector = ThreeDimRestructure()
    landsdetector.setModelTypeAsPRNet()
    landsdetector.loadModel()
  
    img_3d = landsdetector.restructure3DFaceFromImage(img,dets=dets,depth=True,pose=True)
    for img_ in img_3d:
        cv2.imshow("vertices",img_['img_show']['vertices'])
        cv2.imshow("pose", img_['img_show']['pose'])
        cv2.imshow("depth", img_['img_show']['depth'])
        cv2.imwrite("3d_vertices.jpg", img_['img_show']['vertices'])
        cv2.imwrite("3d_pose.jpg", img_['img_show']['pose'])
        cv2.imwrite("3d_depth.jpg", img_['img_show']['depth'])
  
  if __name__ == '__main__':
    main()
  

To install ImageAI, run the python installation instruction below in the command line:

pip install faceai-0.1.2-py2.py3-none-any.whl

if you can not download model ,please download by hand,and code will remind you where you should uncompress these file.

*mtcnn pre-trained model will download at C:/Users/Administrator/.faceai/mtcnn

• DAN

• MTCNN

• Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

• Deep Alignment Network: A convolutional neural network for robust face alignment

• Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network [code]