identifier.py

from imutils import paths
import face_recognition
import pickle
import cv2
import os
import mysql.connector
from imgarray import save_array_img, load_array_img


###SQL Database
##connection = mysql.connector.connect(
##  
##    host="localhost",
##    user="root",
##    password="nuclesnoor9",
##    database = "smart_camera")
##
##cursor = connection.cursor()
##
##def db_creation():
##
##	gallary = "CREATE TABLE IF NOT EXISTS capture (\
##        pic_id int not null primary key AUTO_INCREMENT,\
##        photo longblob not null,\
##        time_stamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP)"
##	face_ids = "CREATE TABLE IF NOT EXISTS face_ids( \
##        name varchar(200) not null,face_id int not null primary key AUTO_INCREMENT,face_enc longblob not null, \
##        time_stamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP);"
##	
##
##        
##        
##
##
##	faces = "CREATE TABLE IF NOT EXISTS face_id(\
##        name varchar(200) not null,\
##        pic_id int primary key,\
##        foreign key (pic_id) references capture(pic_id))"
##
##	cursor.execute(gallary)
##	connection.commit()
##	cursor.execute(faces)
##	connection.commit()
##	cursor.execute(face_ids)
##	connection.commit()
##
##
##db_creation()


##def save_array_to_binary(numpy_array):
##    
##    save_array_img(numpy_array, 'image.png', img_format='png')
##    with open('image.png', 'rb') as file:
##        binaryData = file.read()
##    return binaryData
##        


##def faceDB(faces_data):
##
##    a = faces_data["encodings"]
##    b = faces_data["names"]
##    
##         
##    # store as blob in DB
##    for enc,name in zip(a, b):
##            
##        
##        encc = save_array_to_binary(enc)	
##        sqlStatment = "INSERT INTO face_ids (name,face_enc) VALUES (%s,%s)"
##        cursor.execute(sqlStatment, (name,encc))
##        connection.commit()




def is_image_file(file_name):

    image_file_extensions = ('.rgb', '.gif', '.pbm', '.pgm', '.ppm', '.tiff', '.rast' '.xbm',
    	'.jpeg', '.jpg', '.JPG', '.bmp', '.png', '.PNG', '.webp', '.exr')

    return file_name.endswith((image_file_extensions))

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
image_dir = os.path.join(BASE_DIR, 'faces')
known_encodings = []
known_names = []

## # testing
##
##test1 = False
##
with open('scanned-faces', 'rt') as f:
	scanned = f.readlines()
	scanned = [i.split('\n')[0] for i in scanned]
print(scanned)


def get_faces_data(mode):
	for root, dirs, files in os.walk(image_dir):

		for file in files:
			path = os.path.join(root, file)
			print(path)
			if is_image_file(file) and path not in scanned:
				# extract the person name from the image path
				name = os.path.basename(root)
                
				# load the input image and convert it from BGR (OpenCV ordering) to dlib ordering (RGB)
				image = cv2.imread(path)
                
				rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
				# Use Face_recognition to locate faces
				boxes = face_recognition.face_locations(rgb, model='hog')
				# compute the facial embedding for the face
				encodings = face_recognition.face_encodings(rgb, boxes)
				# loop over the encodings
				for encoding in encodings:
					known_encodings.append(encoding)
					known_names.append(name)
				#save scanned faces
				with open('scanned-faces', 'at') as f:
					f.write(path+'\n')

	#save encodings along with their names in dictionary faces_data
	faces_data = {"encodings": known_encodings, "names": known_names}
	#print(faces_data['names'])

	#use pickle to save data into a file for later use
	if len(faces_data["encodings"]) != 0:
		with open('faces-data.pickle', mode) as f:
			pickle.dump(faces_data, f)

##	#save encodings along with their names in dictionary faces_data
##	faces_data = {"encodings": known_encodings, "names": known_names}
##	
##	
##        
##
##
##	#use pickle to save data into a file for later use
##	if len(faces_data["encodings"]) != 0:
##		with open('faces-data.pickle', mode) as f:
##			pickle.dump(faces_data, f)
##	faceDB(faces_data)

	

#get_faces_data("wb")
			
#get_faces_data("wb")