معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

Face detection and recognition library that focuses on speed and ease of use.

ویژگی	مقدار
سیستم عامل	-
نام فایل	dface-1.1.5
نام	dface
نسخه کتابخانه	1.1.5
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	deepware
ایمیل نویسنده	dogan.kurt@dodobyte.com
آدرس صفحه اصلی	https://github.com/deepware/dface
آدرس اینترنتی	https://pypi.org/project/dface/
مجوز	-

## dFace Face detection and recognition library that focuses on speed and ease of use. This is a stripped down version of timesler's [facenet](https://github.com/timesler/facenet-pytorch) repo with some improvements notably on memory overflows. This repository is actively developed and used in production. [MTCNN](https://arxiv.org/abs/1604.02878) is used for face detection and [FaceNet](https://arxiv.org/abs/1503.03832) is used for generating face embeddings. ### Installation Both algorithms can be used directly by copying the source file into your code base. They are single file and single model implementations with no interdependence. Alternatively you can install with `pip3 install dface` command. Weights will be downloaded from github automatically in the first run. ### MTCNN This implementation takes a list of frames and returns a list of detected faces. The input and output lists always have the same size. If no face is detected for a frame, the corresponding list element is returned `None`, otherwise it's a tuple of three elements; `(bounding_boxes, probabilities, landmarks)`. ```python mtcnn = MTCNN('cuda') ... result = mtcnn.detect(frames) for res in result: if res is None: continue boxes, probs, lands = res ``` Bounding boxes is a list of four coordinates `(x1, y1, x2, y2)` for each face detected in the frame. Probabilities is a list of confidence scores between 0 and 1 for each face. Landmarks is a list of facial landmark points. There are five points for each face. The implementation is at [mtcnn.py](dface/mtcnn.py) ### FaceNet This implementation takes a list of cropped faces and returns a list of face embedding vector of 512 dimensions. These embeddings then can be used for detecting facial similarities, clustering identities and so on. ```python facenet = FaceNet('cuda') ... embeds = facenet.embedding(faces) ``` The implementation is at [facenet.py](dface/facenet.py) Both algorithms accept model path as the second argument if you want to load models from a custom location. ### Full working example The repo contains an [example](example.py) script that uses both MTCNN and Facenet to read a video and cluster the faces. It uses [OpenCV](https://pypi.org/project/opencv-python/) to read video frames and [DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) to cluster faces. You can run the example by simply typing: `python3 example.py video.mp4`. A directory will be created with the same name as video that contains clustered identities. The following is an example image. ![the dude](https://i.imgur.com/npt6W0l.png) The implementation is at [example.py](example.py)

نحوه نصب

نصب پکیج whl dface-1.1.5:

pip install dface-1.1.5.whl

نصب پکیج tar.gz dface-1.1.5:

pip install dface-1.1.5.tar.gz