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|
%global _empty_manifest_terminate_build 0
Name: python-auto-face-recognition
Version: 0.0.3
Release: 1
Summary: auto_face_recognition is Tensorflow based python library for fast face recognition
License: MIT License
URL: https://github.com/Dipeshpal/auto_face_recognition
Source0: https://mirrors.aliyun.com/pypi/web/packages/ce/80/b2bfb193f9adc38f8b60995d2c23f4746a72c2f46e33e153c31a37e5680a/auto_face_recognition-0.0.3.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-opencv-contrib-python
Requires: python3-tensorflow
Requires: python3-matplotlib
%description
# [auto_face_recognition](https://github.com/Dipeshpal/auto_face_recognition)
***Last Upadted: 19 November, 2020***
1. What is auto_face_recognition?
2. Prerequisite
3. Getting Started- How to use it?
4. Future?
## 1. What is auto_face_recognition?
It is a python library for the Face Recognition. This library make face recognition easy and simple. This library uses Tensorflow 2.0+ for the face recognition and model training.
## 2. Prerequisite-
* To use it only Python (> 3.6) is required.
* Recommended Python < 3.9
## 3. Getting Started (How to use it)-
### Install the latest version-
`pip install auto_face_recognition`
It will install all the required package automatically, including Tensorflow Latest.
### Usage and Features-
After installing the library you can import the module-
1. **Object Creation-**
```
import auto_face_recognition
obj = auto_face_recognition.AutoFaceRecognition()
```
2. **Dataset Creation-**
obj.datasetcreate(haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml')
***Note:*** You need to pass the '[haarcascade_frontalface_default.xml](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml)' and '[haarcascade_eye.xml](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_eye.xml)' path.
3. **Model Training-**
obj.face_recognition_train()
4. **Predict Faces-**
# Real Time
obj.predict_faces()
# Single Face Recofnition
obj.predict_face()
**Parameters You Can Choose-**
datasetcreate
datasetcreate(dataset_path='datasets', class_name='Demo',
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', eye_detect=False,
save_face_only=True, no_of_samples=100,
width=128, height=128, color_mode=False)
""""
Dataset Create by face detection
:param dataset_path: str (example: 'folder_of_dataset')
:param class_name: str (example: 'folder_of_dataset')
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml):param eye_detect: bool (example:True)
:param save_face_only: bool (example:True)
:param no_of_samples: int (example: 100)
:param width: int (example: 128)
:param height: int (example: 128)
:param color_mode: bool (example:False):return: None
"""
face_recognition_train
face_recognition_train(data_dir='datasets', batch_size=32, img_height=128, img_width=128, epochs=10, model_path='model', pretrained=None, base_model_trainable=False):
"""
Train TF Keras model according to dataset path
:param data_dir: str (example: 'folder_of_dataset')
:param batch_size: int (example:32)
:param img_height: int (example:128)
:param img_width: int (example:128)
:param epochs: int (example:10)
:param model_path: str (example: 'model')
:param pretrained: str (example: None, 'VGG16', 'ResNet50' or 'InceptionV3')
:param base_model_trainable: bool (example: False (Enable if you want to train the pretrained model's layer))
:return: None
"""
predict_faces
predict_faces(self, class_name=None, img_height=128, img_width=128,
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',
color_mode=False):
"""
Predict Face
:param class_name: Type-List (example: ['class1', 'class2'] )
:param img_height: int (example:128)
:param img_width: int (example:128)
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml)
:param model_path: str (example: 'model')
:param color_mode: bool (example: False)
:return: None
"""
predict_face
predict_face(self, class_name=None, img_height=128, img_width=128,
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',
color_mode=False, image_path='tmp.png'):
"""
Predict Face
:param class_name: Type-List (example: ['class1', 'class2'] )
:param img_height: int (example:128)
:param img_width: int (example:128)
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml)
:param model_path: str (example: 'model')
:param color_mode: bool (example: False)
:param image_path: str (example: 'src/image_predict.png'
:return: None
"""
## 4. Future?
Finetuning with Resnet and others.
You Suggest.
### Like my work?
Start the project and subscribe me on [YouTube](https://www.youtube.com/dipeshpal17).
https://www.youtube.com/dipeshpal17
%package -n python3-auto-face-recognition
Summary: auto_face_recognition is Tensorflow based python library for fast face recognition
Provides: python-auto-face-recognition
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-auto-face-recognition
# [auto_face_recognition](https://github.com/Dipeshpal/auto_face_recognition)
***Last Upadted: 19 November, 2020***
1. What is auto_face_recognition?
2. Prerequisite
3. Getting Started- How to use it?
4. Future?
## 1. What is auto_face_recognition?
It is a python library for the Face Recognition. This library make face recognition easy and simple. This library uses Tensorflow 2.0+ for the face recognition and model training.
## 2. Prerequisite-
* To use it only Python (> 3.6) is required.
* Recommended Python < 3.9
## 3. Getting Started (How to use it)-
### Install the latest version-
`pip install auto_face_recognition`
It will install all the required package automatically, including Tensorflow Latest.
### Usage and Features-
After installing the library you can import the module-
1. **Object Creation-**
```
import auto_face_recognition
obj = auto_face_recognition.AutoFaceRecognition()
```
2. **Dataset Creation-**
obj.datasetcreate(haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml')
***Note:*** You need to pass the '[haarcascade_frontalface_default.xml](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml)' and '[haarcascade_eye.xml](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_eye.xml)' path.
3. **Model Training-**
obj.face_recognition_train()
4. **Predict Faces-**
# Real Time
obj.predict_faces()
# Single Face Recofnition
obj.predict_face()
**Parameters You Can Choose-**
datasetcreate
datasetcreate(dataset_path='datasets', class_name='Demo',
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', eye_detect=False,
save_face_only=True, no_of_samples=100,
width=128, height=128, color_mode=False)
""""
Dataset Create by face detection
:param dataset_path: str (example: 'folder_of_dataset')
:param class_name: str (example: 'folder_of_dataset')
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml):param eye_detect: bool (example:True)
:param save_face_only: bool (example:True)
:param no_of_samples: int (example: 100)
:param width: int (example: 128)
:param height: int (example: 128)
:param color_mode: bool (example:False):return: None
"""
face_recognition_train
face_recognition_train(data_dir='datasets', batch_size=32, img_height=128, img_width=128, epochs=10, model_path='model', pretrained=None, base_model_trainable=False):
"""
Train TF Keras model according to dataset path
:param data_dir: str (example: 'folder_of_dataset')
:param batch_size: int (example:32)
:param img_height: int (example:128)
:param img_width: int (example:128)
:param epochs: int (example:10)
:param model_path: str (example: 'model')
:param pretrained: str (example: None, 'VGG16', 'ResNet50' or 'InceptionV3')
:param base_model_trainable: bool (example: False (Enable if you want to train the pretrained model's layer))
:return: None
"""
predict_faces
predict_faces(self, class_name=None, img_height=128, img_width=128,
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',
color_mode=False):
"""
Predict Face
:param class_name: Type-List (example: ['class1', 'class2'] )
:param img_height: int (example:128)
:param img_width: int (example:128)
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml)
:param model_path: str (example: 'model')
:param color_mode: bool (example: False)
:return: None
"""
predict_face
predict_face(self, class_name=None, img_height=128, img_width=128,
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',
color_mode=False, image_path='tmp.png'):
"""
Predict Face
:param class_name: Type-List (example: ['class1', 'class2'] )
:param img_height: int (example:128)
:param img_width: int (example:128)
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml)
:param model_path: str (example: 'model')
:param color_mode: bool (example: False)
:param image_path: str (example: 'src/image_predict.png'
:return: None
"""
## 4. Future?
Finetuning with Resnet and others.
You Suggest.
### Like my work?
Start the project and subscribe me on [YouTube](https://www.youtube.com/dipeshpal17).
https://www.youtube.com/dipeshpal17
%package help
Summary: Development documents and examples for auto-face-recognition
Provides: python3-auto-face-recognition-doc
%description help
# [auto_face_recognition](https://github.com/Dipeshpal/auto_face_recognition)
***Last Upadted: 19 November, 2020***
1. What is auto_face_recognition?
2. Prerequisite
3. Getting Started- How to use it?
4. Future?
## 1. What is auto_face_recognition?
It is a python library for the Face Recognition. This library make face recognition easy and simple. This library uses Tensorflow 2.0+ for the face recognition and model training.
## 2. Prerequisite-
* To use it only Python (> 3.6) is required.
* Recommended Python < 3.9
## 3. Getting Started (How to use it)-
### Install the latest version-
`pip install auto_face_recognition`
It will install all the required package automatically, including Tensorflow Latest.
### Usage and Features-
After installing the library you can import the module-
1. **Object Creation-**
```
import auto_face_recognition
obj = auto_face_recognition.AutoFaceRecognition()
```
2. **Dataset Creation-**
obj.datasetcreate(haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml')
***Note:*** You need to pass the '[haarcascade_frontalface_default.xml](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml)' and '[haarcascade_eye.xml](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_eye.xml)' path.
3. **Model Training-**
obj.face_recognition_train()
4. **Predict Faces-**
# Real Time
obj.predict_faces()
# Single Face Recofnition
obj.predict_face()
**Parameters You Can Choose-**
datasetcreate
datasetcreate(dataset_path='datasets', class_name='Demo',
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', eye_detect=False,
save_face_only=True, no_of_samples=100,
width=128, height=128, color_mode=False)
""""
Dataset Create by face detection
:param dataset_path: str (example: 'folder_of_dataset')
:param class_name: str (example: 'folder_of_dataset')
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml):param eye_detect: bool (example:True)
:param save_face_only: bool (example:True)
:param no_of_samples: int (example: 100)
:param width: int (example: 128)
:param height: int (example: 128)
:param color_mode: bool (example:False):return: None
"""
face_recognition_train
face_recognition_train(data_dir='datasets', batch_size=32, img_height=128, img_width=128, epochs=10, model_path='model', pretrained=None, base_model_trainable=False):
"""
Train TF Keras model according to dataset path
:param data_dir: str (example: 'folder_of_dataset')
:param batch_size: int (example:32)
:param img_height: int (example:128)
:param img_width: int (example:128)
:param epochs: int (example:10)
:param model_path: str (example: 'model')
:param pretrained: str (example: None, 'VGG16', 'ResNet50' or 'InceptionV3')
:param base_model_trainable: bool (example: False (Enable if you want to train the pretrained model's layer))
:return: None
"""
predict_faces
predict_faces(self, class_name=None, img_height=128, img_width=128,
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',
color_mode=False):
"""
Predict Face
:param class_name: Type-List (example: ['class1', 'class2'] )
:param img_height: int (example:128)
:param img_width: int (example:128)
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml)
:param model_path: str (example: 'model')
:param color_mode: bool (example: False)
:return: None
"""
predict_face
predict_face(self, class_name=None, img_height=128, img_width=128,
haarcascade_path='haarcascade/haarcascade_frontalface_default.xml',
eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model',
color_mode=False, image_path='tmp.png'):
"""
Predict Face
:param class_name: Type-List (example: ['class1', 'class2'] )
:param img_height: int (example:128)
:param img_width: int (example:128)
:param haarcascade_path: str (example: 'haarcascade_frontalface_default.xml)
:param eyecascade_path: str (example: 'haarcascade_eye.xml)
:param model_path: str (example: 'model')
:param color_mode: bool (example: False)
:param image_path: str (example: 'src/image_predict.png'
:return: None
"""
## 4. Future?
Finetuning with Resnet and others.
You Suggest.
### Like my work?
Start the project and subscribe me on [YouTube](https://www.youtube.com/dipeshpal17).
https://www.youtube.com/dipeshpal17
%prep
%autosetup -n auto_face_recognition-0.0.3
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-auto-face-recognition -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.3-1
- Package Spec generated
|
