Metadata-Version: 2.1
Name: auto-face-recognition
Version: 0.0.1
Summary: auto_face_recognition is Tensorflow based python library for fast face recognition
Home-page: https://github.com/Dipeshpal/auto_face_recognition
Author: Dipesh
Author-email: dipeshpal17@gmail.com
License: UNKNOWN
Description: # [auto_face_recognition](https://github.com/Dipeshpal/auto_face_recognition)
        
        ***Last Upadted: 02 September, 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.
        
        ## 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-**
        
        	    obj.predict_faces()
        
        **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'):  
             """  
             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')  
             :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  
        	 """
        
        ## 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
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
