github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-06 19:45:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

order

Browse Source
This commit is contained in:
Sefik Ilkin Serengil 2022-07-15 11:04:47 +01:00 committed by GitHub
parent ff7368052f
commit 44855ed29a
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 22 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

43
README.md
View File

@ -143,27 +143,6 @@ obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race'
Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its [tutorial](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/).
**Streaming and Real Time Analysis** - [`Demo`](https://youtu.be/-c9sSJcx6wI)
You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.
```python
DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")
```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-3.jpg" width="90%" height="90%"></p>
Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.
```bash
user
├── database
│ ├── Alice
│ │ ├── Alice1.jpg
│ │ ├── Alice2.jpg
│ ├── Bob
│ │ ├── Bob.jpg
```
**Face Detectors** - [`Demo`](https://youtu.be/GZ2p2hj2H5k)
@ -204,6 +183,28 @@ The performance of RetinaFace is very satisfactory even in the crowd as seen in
You can find out more about RetinaFace on this [repo](https://github.com/serengil/retinaface).
**Real Time Analysis** - [`Demo`](https://youtu.be/-c9sSJcx6wI)
You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.
```python
DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")
```
<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-3.jpg" width="90%" height="90%"></p>
Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.
```bash
user
├── database
│ ├── Alice
│ │ ├── Alice1.jpg
│ │ ├── Alice2.jpg
│ ├── Bob
│ │ ├── Bob.jpg
```
**API** - [`Demo`](https://youtu.be/HeKCQ6U9XmI)
Deepface serves an API as well. You can clone [`/api/api.py`](https://github.com/serengil/deepface/tree/master/api/api.py) and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.