Python API for bob.ip.mtcnn

bob.ip.mtcnn.get_config()[source]

Returns a string containing the configuration information.

class bob.ip.mtcnn.FaceDetector[source]

Bases: object

Detects face and 5 landmarks using the MTCNN (https://github.com/kpzhang93/MTCNN_face_detection_alignment) from the paper.

Zhang, Kaipeng, et al. “Joint face detection and alignment using multitask cascaded convolutional networks.” IEEE Signal Processing Letters 23.10 (2016): 1499-1503.

detect_all_faces(image, return_bob_bb=True)[source]

Detect all the faces with its respective landmarks, if any, in a COLORED image

Parameters:
Returns:

Returns two lists; the first on contains the bounding boxes with the detected faces and the second one contains list with the faces landmarks. The CNN returns 5 facial landmarks (leye, reye, nose, mouthleft, mouthright). If there’s no face, None will be returned
Return type:

object
Raises:

ValueError – When image.ndim is not 3.
detect_crop(image, final_image_size=(182, 182), margin=44)[source]

Detects the biggest face and crop it

Parameters
image: numpy array with color image [c, w, h] final_image_size: Image dimensions [w, h]
Returns
The cropped image. If there’s no face, None will be returned
detect_crop_align(image, final_image_size=(160, 160))[source]

Detects the biggest face and crop it based in the eyes location using py:class:bob.ip.base.FaceEyesNorm.

Final eyes location was inspired here: https://gitlab.idiap.ch/bob/bob.bio.caffe_face/blob/master/bob/bio/caffe_face/config/preprocessor/vgg_preprocessor.py

Parameters
image: numpy array with color image [c, w, h] final_image_size: Image dimensions [w, h]
Returns
The cropped image. If there’s no face, None will be returned
detect_single_face(image)[source]

Returns the biggest face in a COLORED image, if any.

Parameters:image (numpy.array) – numpy array with color image [c, w, h]
Returns:
  • The face bounding box and its respective 5 landmarks (leye, reye, nose,
  • mouthleft, mouthright). If there’s no face, None will be returned
static get_mtcnn_model_path()[source]