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Encode.py
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Encode.py
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import face_recognition as fr
import cv2
import numpy as np
import hashlib
class encode(object):
def __init__(self):
self.state = False
self.camera = None
self.locknum = []
self.label = ""
self.domain = ""
self.continuous_count = 0
self.frame_toggle = False
self.prev_rounded_avg = []
self.continuous_count_threshold = 5
# self.prev_cached_frame = []
def __del__(self):
if self.camera != None:
self.camera.release()
# Generate a continuous feed of images from camera for encoding
def gen_encodefeed(self):
while self.camera != None and self.state == True:
ret, frame = self.camera.read()
if ret:
# Only process every other frame
if self.frame_toggle:
# Resize frame to 1/4 size for faster face recognition
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert small_frame from OpenCV's BGR to RGB
rgb_small_frame = small_frame[:, :, ::-1]
# Encode faces in frame and check rounded_avg for matches
#
# If there is a continuous stream of matches, then the user
# is likely in frame and in a good position to take a picture
face_encoding = fr.face_encodings(rgb_small_frame)
if len(face_encoding) > 0:
rounded_avg = np.around([abs(x*10) for x in face_encoding[0]])
match = True
# If all the locknums match the previous rounded_avg add
# to continous count
if self.prev_rounded_avg != []:
for i in self.locknum:
if rounded_avg[i] != self.prev_rounded_avg[i]:
match = False
break
if (match):
self.continuous_count = self.continuous_count + 1
else:
self.continuous_count = 0
self.prev_rounded_avg = rounded_avg
else: self.prev_rounded_avg = rounded_avg
self.frame_toggle = not self.frame_toggle
# Draw a circle for user to put their face in
center = (round(frame.shape[1] / 2), round(frame.shape[0] / 2))
color = (0, 0, 255)
# print(self.continuous_count)
if (self.continuous_count > self.continuous_count_threshold):
color = (0, 255, 0)
# self.prev_cached_frame = frame
# else:
# self.prev_cached_frame = []
cv2.circle(frame, center, 77, color, 7)
ret, jpeg = cv2.imencode('.jpg', frame)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
# Start "encoding" phase
def start(self, locknum, label, domain):
self.state = True
self.camera = cv2.VideoCapture(0)
self.frame_toggle = True
self.prev_rounded_avg = []
# self.prev_cached_frame = []
self.continuous_count = 0
self.locknum = locknum
self.label = label
self.domain = domain
# Ends the "encoding" process and outputs the password as needed
def end(self):
self.state = False
frames = []
for i in range(5):
ret, frame = self.camera.read()
if ret:
frames.append(frame)
# ret, frame = self.camera.read()
#
# frame = self.prev_cached_frame
# if frame == []:
# print("frame cache empty")
# self.state = True
# return None
# If camera capture failed then don't do anything
if not ret:
print("CAMERA DIDNT RETURN ANYTHING")
self.state = True
return None
print(frame)
# small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert frame from OpenCV's BGR color to RGB color
rgb_frame = [x[:, :, ::-1] for x in frames]
face_encodings = [fr.face_encodings(x) for x in rgb_frame]
# print(face_encoding)
# If the image did not yield a face then don't do anything either
face_encoding = []
for i in face_encodings:
if i != []:
face_encoding = i
break
if len(face_encoding) <= 0:
print("FACE_ENCODING DIDNT RETURN ANYTHING")
self.state = True
return None
# Select all the key values using locknum
rounded_avg = np.around([abs(x*10) for x in face_encoding[0]])
face_values = []
for i in self.locknum:
face_values.append(rounded_avg[i])
# Build the prehash string and then hash it to get password
prehash = self.label + ":" + self.domain + ':'.join([str(round(x)) for x in face_values])
password = hashlib.sha256(prehash.encode('utf-8')).hexdigest()
# Turn off everything and release camera
self.state = False
self.camera.release()
# Return final password
return password
def stop(self):
self.camera.release()
self.state = False