from PIL import Image
import face_recognition
from app.config import Config
config = Config()
img_path = config.get_common_param('IMG_STORE_PATH')
# Load the jpg file into a numpy array
image = face_recognition.load_image_file(
"/home/suren/Projects/TNEB/RPi/ServerModule/images/IMG_27Dec20181201.png")
# Find all the faces in the image using the default HOG-based model.
# This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
# See also: find_faces_in_picture_cnn.py
face_locations = face_recognition.face_locations(image)
print("I found {} face(s) in this photograph.".format(len(face_locations)))
for face_location in face_locations:
# Print the location of each face in this image
top, right, bottom, left = face_location
print(
"A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}"
.format(top, left, bottom, right))
# You can access the actual face itself like this:
face_image = image[top:bottom, left:right]
pil_image = Image.fromarray(face_image)
pil_image.show()
class Face_Recog:
def __init__(self, image_name):
self.config = Config()
self.image_name = image_name
self.known_face_encds = []
self.fr_tolerance_value = float(self.config.get_common_param('FR_TOLERANCE_VALUE') )
def get_face_encodings(self,image_name):
image = face_recognition.load_image_file(image_name)
cv2.imshow("detected face",image)
small_frame = cv2.resize(image, (0, 0), fx=0.50, fy=0.50)
rgb_small_frame = small_frame[:, :, ::-1]
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encoding = face_recognition.face_encodings(rgb_small_frame, face_locations)
# print(face_encoding)
return face_encoding
def get_known_face_encodings(self,image_name):
image = face_recognition.load_image_file(image_name)
# cv2.imshow("KNown face",image)
# face_locations = face_recognition.face_locations(image)
face_encoding = face_recognition.face_encodings(image)[0]
return face_encoding
def get_known_faces_encodings_list(self,known_images_path):
fe = FaceEncodings()
data = fe.load_known_face_encodings()
self.known_face_encds = data["encodings"]
self.known_face_names = data["names"]
# print(self.known_face_encds)
# def get_known_faces_encodings_list(self,known_images_path):
# self.known_face_encds = []
# self.known_face_names = []
# for root, dirs, files in os.walk(known_images_path):
# for name in files:
# if name.endswith((".jpeg",".png", ".JPG")):
# # print (name)
# # print(known_images_path+name)
# face_enc = self.get_known_face_encodings(known_images_path+name)
# self.known_face_encds.append(face_enc)
# self.known_face_names.append(name)
def g_emp_name(self, in_str):
return in_str.split("_")[0]
# def main_method(self):
# self.get_known_faces_encodings_list(self.config.get_common_param('KNOWN_IMG_STORE_PATH'))
# # print("********************************** KNOWN FACES ENC **************************************")
# # print(self.known_face_encds)
# # print("********************************** Names *******************")
# print(self.known_face_names)
# detected_face_encodings = self.get_face_encodings(self.image_name)
# # print(detected_face_encodings)
# # print("*****************************************************************************************")
# # matches = face_recognition.compare_faces(known_face_encodings=self.known_face_encds,face_encoding_to_check=detected_face_encodings[0])
# # print(matches)
# localtime = time.localtime(time.time())
# timestamp = time.strftime('%d-%b-%Y %H:%M:%S',localtime)
# emp_name = 'Unknown'
# status = False
# for face_encoding in detected_face_encodings:
# # print("****************************** DETECTED FACES ENC ***********************************")
# # print(face_encoding)
# # print(self.known_face_encds)
# print("***********************************")
# # print(face_encoding)
# matches = face_recognition.compare_faces(known_face_encodings=self.known_face_encds, face_encoding_to_check=face_encoding)
# dist = face_recognition.face_distance(face_encodings=self.known_face_encds,face_to_compare=face_encoding)
# print(dist)
# print(matches)
#
# if True in matches:
# emp_name = self.g_emp_name(self.known_face_names[matches.index(True)])
# status= True
# else:
# print("NO Match")
# status = False
# status_dict = {'Name': emp_name, 'Status': status, 'Time' : timestamp}
# print(status_dict)
# return status_dict
def main_method(self):
self.get_known_faces_encodings_list(self.config.get_common_param('KNOWN_IMG_STORE_PATH'))
# print("********************************** KNOWN FACES ENC **************************************")
# print(self.known_face_encds)
# print("********************************** Names *******************")
print(self.known_face_names)
detected_face_encodings = self.get_face_encodings(self.image_name)
# print(detected_face_encodings)
# print("*****************************************************************************************")
# matches = face_recognition.compare_faces(known_face_encodings=self.known_face_encds,face_encoding_to_check=detected_face_encodings[0])
# print(matches)
localtime = time.localtime(time.time())
timestamp = time.strftime('%d-%b-%Y %H:%M:%S',localtime)
emp_name = 'Unknown'
status = False
for face_encoding in detected_face_encodings:
# print("****************************** DETECTED FACES ENC ***********************************")
# print(face_encoding)
# print(self.known_face_encds)
print("***********************************")
# print(face_encoding)
matches = face_recognition.compare_faces(known_face_encodings=self.known_face_encds, face_encoding_to_check=face_encoding)
face_distances = face_recognition.face_distance(face_encodings=self.known_face_encds,face_to_compare=face_encoding)
matches = list(face_distances<=self.fr_tolerance_value) # Using a appropriate tolerance value .Lower is more strict. 0.6 is typical best performance.
print(face_distances)
print(matches)
if True in matches:
match_index = np.argmin(face_distances)
emp_name = self.g_emp_name(self.known_face_names[match_index])
status= True
else:
print("NO Match")
status = False
status_dict = {'Name': emp_name, 'Status': status, 'Time' : timestamp}
print(status_dict)
return status_dict
