Skip to content

Latest commit

 

History

History
195 lines (140 loc) · 6.25 KB

README.md

File metadata and controls

195 lines (140 loc) · 6.25 KB
Raw

FaceAI

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.

Update Record

  • 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.

Dependencies

  • Tensorflow 1.10.0 (and later versions)

  • Numpy 1.13.1 (and later versions)

  • OpenCV

  • Matplotlib

  • easydict

  • PIL

  • scipy

Demo

  • 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()

Installation

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

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

Pre-trained model

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

References