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.
Currently it is only supported Python 3.4.1 onwards:
sudo pip3 install vrpwrp
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]. The backend runs a Caffe-based MTCNN influenced by this python MTCNN version .
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 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]. The backend is influenced by Facenet, using TensorFlow.
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)
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.
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).
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.
Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015, September). Deep Face Recognition. In BMVC (Vol. 1, No. 3, p. 6).
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