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FacialRecognition-Demo
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verification and identification works

This commit is contained in:
Askill 2020-04-27 19:44:55 +02:00
parent 33a49a25d1
commit 8e1f765c52
3 changed files with 40 additions and 64 deletions
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8
application/endpoints.py
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@ -6,7 +6,7 @@ import json
import cv2
import base64
from application.db import Session, Person, Fingerprint
import application.face_rec as fr
lastImage = ""
class PersonList(Resource):
@ -58,17 +58,19 @@ class PersonList(Resource):
if id is not None:
# validate
data = list(session.query(Person).filter_by(person_id=id))[0].serialize()
data["matching_score"] = 0.95
results = fr.identifyFace(lastImage)
data["matching_score"] = 1 - results[int(id)]
# return identified person object + matching score
return flask.make_response(flask.jsonify({'data': data}), 200)
else:
# replace by Biometric function
# identify
# return identified person object + matching score
results = fr.identifyFace(lastImage)
data = []
for x in list(session.query(Person).all()):
ser = x.serialize()
ser["matching_score"] = 0.95
ser["matching_score"] = 1 - results[x.person_id]
data.append(ser)
return flask.make_response(flask.jsonify({'data': data}), 200)

92
application/face_rec.py
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@ -1,7 +1,7 @@
import face_recognition
import os
import cv2
from db import Session, Person
from application.db import Session, Person
import base64
import numpy as np
from base64 import decodestring
@ -34,77 +34,51 @@ def readb64(base64_string):
print('Loading known faces...')
known_faces = []
known_names = []
session = Session()
# We oranize known faces as subfolders of KNOWN_FACES_DIR
# Each subfolder's name becomes our label (name)
for face, name in session.query(Person.face, Person.lname).all():
# Load an image
nparr = np.fromstring(base64.b64decode(face), np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# Get 128-dimension face encoding
# Always returns a list of found faces, for this purpose we take first face only (assuming one face per image as you can't be twice on one image)
encoding = face_recognition.face_encodings(image)[0]
# Append encodings and name
known_faces.append(encoding)
known_names.append(name)
print('Processing unknown faces...')
# Now let's loop over a folder of faces we want to label
image = face_recognition.load_image_file('C:/Users/ofjok/Desktop/1.png')
def initFaceRec():
session = Session()
# We oranize known faces as subfolders of KNOWN_FACES_DIR
# Each subfolder's name becomes our label (name)
for face, name in session.query(Person.face, Person.person_id).all():
# Load an image
nparr = np.fromstring(base64.b64decode(face), np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# This time we first grab face locations - we'll need them to draw boxes
locations = face_recognition.face_locations(image, model=MODEL)
# Get 128-dimension face encoding
# Always returns a list of found faces, for this purpose we take first face only (assuming one face per image as you can't be twice on one image)
encoding = face_recognition.face_encodings(image)[0]
# Now since we know loctions, we can pass them to face_encodings as second argument
# Without that it will search for faces once again slowing down whole process
encodings = face_recognition.face_encodings(image, locations)
# Append encodings and name
known_faces.append(encoding)
known_names.append(name)
# We passed our image through face_locations and face_encodings, so we can modify it
# First we need to convert it from RGB to BGR as we are going to work with cv2
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
def identifyFace(imgage):
print('Processing unknown faces...')
image = face_recognition.load_image_file('C:/Users/ofjok/Desktop/1.png')
# But this time we assume that there might be more faces in an image - we can find faces of dirrerent people
locations = face_recognition.face_locations(image, model=MODEL)
encodings = face_recognition.face_encodings(image, locations)
for face_encoding, face_location in zip(encodings, locations):
# We use compare_faces (but might use face_distance as well)
# Returns array of True/False values in order of passed known_faces
results = face_recognition.compare_faces(known_faces, face_encoding, TOLERANCE)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Since order is being preserved, we check if any face was found then grab index
# then label (name) of first matching known face withing a tolerance
match = None
if True in results: # If at least one is true, get a name of first of found labels
match = known_names[results.index(True)]
print(f' - {match} from {results}')
res = {}
# Each location contains positions in order: top, right, bottom, left
top_left = (face_location[3], face_location[0])
bottom_right = (face_location[1], face_location[2])
for face_encoding, face_location in zip(encodings, locations):
# Get color by name using our fancy function
color = name_to_color(match)
# We use compare_faces (but might use face_distance as well)
# Returns array of True/False values in order of passed known_faces
results = face_recognition.face_distance(known_faces, face_encoding)
# Paint frame
cv2.rectangle(image, top_left, bottom_right, color, FRAME_THICKNESS)
# Since order is being preserved, we check if any face was found then grab index
# then label (name) of first matching known face withing a tolerance
# If at least one is true, get a name of first of found labels
res = {known_names[i]: results[i] for i in range(0, len(results)) }
# Now we need smaller, filled grame below for a name
# This time we use bottom in both corners - to start from bottom and move 50 pixels down
top_left = (face_location[3], face_location[2])
bottom_right = (face_location[1], face_location[2] + 22)
return res
# Paint frame
cv2.rectangle(image, top_left, bottom_right, color, cv2.FILLED)
# Wite a name
cv2.putText(image, match, (face_location[3] + 10, face_location[2] + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 200), FONT_THICKNESS)
# Show image
cv2.imshow("1", image)
cv2.waitKey(0)
cv2.destroyWindow("1")
initFaceRec()
identifyFace("")

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test.sqlite
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