https://github.com/sarat-ravi/python-pytect

Easy Facial Detection and Recognition Library using OpenBR
https://github.com/sarat-ravi/python-pytect

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Easy Facial Detection and Recognition Library using OpenBR

• Host: GitHub
• URL: https://github.com/sarat-ravi/python-pytect
• Owner: sarat-ravi
• Created: 2015-04-11T23:46:40.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-04-12T20:24:09.000Z (over 9 years ago)
• Last Synced: 2024-04-14T19:51:58.685Z (9 months ago)
• Language: Python
• Homepage:
• Size: 5.55 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
#Overview

overview

##Recognizer

###Overview

This is the component that can detect and recognize faces, and the groups these faces belong to. The `make install` target installs the package in such a way that one can edit the source while developing in a completely random path.

###Install

```

cd recognizer

make install

```

###Uninstall

```

make clean

```

###Test

The test script trains on the `yalefaces` dataset, and tests the code by trying to detect and recognize the same faces it trained on, asserting that the confidence scores are greater than 0.95

```

make test

```

###Basic Usage

```python

import pytect

# create groups

yale = pytect.create_group(group_name="yale")

berkeley = pytect.create_group(group_name="berkeley")

# create people

bob = pytect.create_person(person_name="bob")

alice = pytect.create_person(person_name="alice")

aaron = pytect.create_person(person_name="aaron")

# add people to groups

yale.add_people([bob, alice])

berkeley.add_people([alice, aaron])

# train the images in the groups

pytect.train_group(yale)

pytect.train_group(berkeley)

# identify people and their groups from any image!

candidates, groups, scores = pytect.identify("bob.jpg", yale)

assert candidates[0] == bob and groups[0] == yale and scores[0] >= 0.95

candidates, groups, scores = pytect.identify("berkeley_class_2014.png", berkeley)

for candidate, group, score in zip(candidates, groups, scores):

    assert candidate in (alice, aaron)

    assert group == berkeley

    assert score >= 0.85

# clear all models and forget everything

pytect.clear()

# clear any cached data as well

pytect.clear_cache()

```