vrpwrp 0.0.5

Vision-algorithms Requests Processing Wrappers for deep-learning Computer Vision algorithms on the cloud.

VRPWRP (Vision-algorithm Requests Processing Wrappers) is a package that wraps an API-REST for Computer Vision deep-learning algorithms. Currently, it supports state-of-the-art face-detection and face-recognition algorithms out-of-the-box.

Installation

Currently it is only supported Python 3.4.1 onwards:

sudo pip3 install vrpwrp

Face detection

Face detection allows you to retrieve the location of faces inside images in the form of bounding boxes (left, top, width, height). The algorihm is a deep-learning based algorithm, composed by a cascade of Convolutional Neural Networks. It is based on the paper Zhang et al. (2016) [ZHANG2016].

A simple example for retrieving the bounding boxes of faces from an image:

>>> from vrpwrp.wrappers.face_detection import FaceDetection
>>> face_detection = FaceDetection()
>>> bounding_boxes = face_detection.analyze_file("route/to/image.jpg")
>>> for bb in bounding_boxes: print(bb)
...
[162, 79, 114, 146]

FaceDetection has methods for analyzing images also from bytes, URLs and pillow images directly:

>>> bounding_boxes = face_detection.analyze_bytes(image_bytes)
>>> bounding_boxes = face_detection.analyze_url(image_url)
>>> bounding_boxes = face_detection.analyze_pil(pillow_image)
...

Face Recognition

Face recognition allows extracting the identity of a face within a given image of the face. The identity is a set of float numbers (since it is deep-learning-based, it is the output of the last convolution layer of a Convolutional Neural Network). The algorithm is based on the papers Schroff et al. (2015) [SCHROFF2015], Wen et al. (2016) [WEN2016]. and Parkhi et al. (2015) [PARKHI2015].

In vrpwrp, the identity of a face is also known as embeddings.

A simple example for retrieving the embeddings of a face is:

>>> from vrpwrp.wrappers.face_recognition import FaceRecognition
>>> face_recognition = FaceRecognition()
>>> face_embeddings = face_recognition.get_embeddings_from_file("route/to/image_of_face.jpg")
>>> print(face_embeddings)
[-0.05258641 -0.14807236  0.21828972  0.00097196  0.08881456  0.01356898 -0.01393933 -0.09459263 -0.07305822  0.00354048  0.1649337  -0.05636634  0.03599492 -0.02649886 ...]

Like in FaceDetection, it allows to analyze images from different sources:

>>> embeddings = face_recognition.get_embeddings_from_bytes(image_bytes)
>>> embeddings = face_recognition.get_embeddings_from_url(image_url)
>>> embeddings = face_recognition.get_embeddings_from_pil(pillow_image)
...

The embeddings of two faces can be easily compared to see how close they are:

>>> face1_embeddings = face_recognition.get_embeddings_from_file("route/to/image_of_face1.jpg")
>>> face2_embeddings = face_recognition.get_embeddings_from_file("route/to/image_of_face2.jpg")
>>> print(face1_embeddings - face2_embeddings)
0.5634614628831894

A value close to 0 indicates that two faces might be of the same person. In this example, image_of_face1.jpg and image_of_face2.jpg are likely to be of the same person. Otherwise, a value over 1.0 might indicate that two faces are not likely to be of the same person.

This might lead to a scenario where you store lot of embeddings and want to compare a single one with each of them, resulting in a loop like the following:

faces_embeddings = [emb1, emb2, ..., embN]

new_embedding = face_recognition.get_embeddings_from_file("route/to/image_of_face1.jpg")

for embedding in faces_embeddings:
     distance = embedding - new_embedding

Rather than using a loop (even if it is a list-comprehension), there is an optimized and preferred way of performing such a comparison that can be used instead:

faces_embeddings = [emb1, emb2, ..., embN]

new_embedding = face_recognition.get_embedding_from_file("route/to/image_of_face1.jpg")
distances = face_recognition.get_embeddings_distances(new_embedding, faces_embeddings)

References

[ZHANG2016]

Zhang, K., Zhang, Z., Li, Z., and Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10):1499–1503.

[SCHROFF2015]

Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on CVPR (pp. 815-823).

[WEN2016]

Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016, October). A discriminative feature learning approach for deep face recognition. In ECCV (pp. 499-515). Springer International Publishing.

[PARKHI2015]

Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015, September). Deep Face Recognition. In BMVC (Vol. 1, No. 3, p. 6).