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facedetector.py
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facedetector.py
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import face_recognition
from maskdetect import detect_and_predict_mask
import numpy as np
import cv2
import db
dbconnect = db.connect()
knownencodings = []
allmails = []
def faceencodingvalues(img):
imgload = face_recognition.load_image_file(img)
imgload = cv2.cvtColor(imgload,cv2.COLOR_BGR2RGB)
try:
faceloc = face_recognition.face_locations(imgload)[0] # (260, 825, 528, 557)
except:
return [],[]
encodeimg = face_recognition.face_encodings(imgload)[0]
return (encodeimg,faceloc)
def predata():
encodinglist = []
emaillist = []
q = "select email,encodings from tarpusers"
result = db.select(q)
for i in result:
emaillist.append(i[0])
encodinglist.append(np.array(eval(i[1])))
global knownencodings,allmails
knownencodings = encodinglist
allmails = emaillist
def detectmask(img):
(locs,preds) = detect_and_predict_mask(img)
for (box,pred) in zip(locs,preds):
(mask,withoutmask) = pred
if mask > withoutmask:return ("mask",box,pred)
else:return ("withoutmask",box,pred)
def detectface(img):
imgS = cv2.resize(img,(0,0),None,0.25,0.25)
imgS = cv2.cvtColor(imgS,cv2.COLOR_BGR2RGB)
facesS = face_recognition.face_locations(imgS)
encodeS = face_recognition.face_encodings(imgS,facesS)
for encodeFace,faceLoc in zip(encodeS,facesS):
matches = face_recognition.compare_faces(knownencodings,encodeFace)
faceDis = face_recognition.face_distance(knownencodings,encodeFace)
matchindex = np.argmin(faceDis)
if faceDis[matchindex]<0.6:
return allmails[matchindex]