python for Face Recognition.For your review only
Published: 2019-04-20

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os
import numpy as np
import cv2 as cv
import sklearn.preprocessing as sp
fd = cv.CascadeClassifier('face.xml')

def search_faces(directory):
directory = os.path.normpath(directory)
if not os.path.isdir(directory):
raise IOError("The directory '" + directory +
"' doesn't exist!")
faces = {}
for curdir, subdirs, files in os.walk(directory):
for jpeg in (file for file in files
if file.endswith('.jpg')):
path = os.path.join(curdir, jpeg)
label = path.split(os.path.sep)[-2]
if label not in faces:
faces[label] = []
faces[label].append(path)
return faces

train _ faces = search _ faces ('') # put the training set folder here
codec = sp.LabelEncoder()
codec.fit(list(train_faces.keys()))
train_x, train_y = [], []
for label, filenames in train_faces.items():
for filename in filenames:
image = cv.imread(filename)
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
faces = fd.detectMultiScale(
gray, 1.1, 2, minSize=(100, 200))
for l, t, w, h in faces:
train_x.append(gray[t:t + h, l:l + w])
train_y.append(int(
codec.transform([label])[0]))
train_y = np.array(train_y)
# Local Binary Pattern Histogram Face Recognizer
model = cv.face.LBPHFaceRecognizer_create()
model.train(train_x, train_y)
Test_faces = search_faces('')# Place the test set folder here
test_x, test_y, test_z = [], [], []
for label, filenames in test_faces.items():
for filename in filenames:
image = cv.imread(filename)
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
faces = fd.detectMultiScale(
gray, 1.1, 2, minSize=(100, 200))
for l, t, w, h in faces:
test_x.append(gray[t:t + h, l:l + w])
test_y.append(int(
codec.transform([label])[0]))
a, b = int(w / 2), int(h / 2)
cv.ellipse(image, (l + a, t + b), (a, b), 0,
0, 360, (255, 0, 255), 2)
test_z.append(image)
test_y = np.array(test_y)
pred_test_y = []
for face in test_x:
pred_code = model.predict(face)[0]
pred_test_y.append(pred_code)
escape = False
while not escape:
for code, pred_code, image in zip(
test_y, pred_test_y, test_z):
label = codec.inverse_transform(
[code])[0]
pred_label = codec.inverse_transform(
[pred_code])[0]
text = '{} {} {}'.format(
label, '==' if label == pred_label else '!=',
pred_label)
cv.putText(image, text, (10, 60),
cv.FONT_HERSHEY_SIMPLEX, 2,
(255, 255, 255), 6)
cv.imshow('Recognizing...', image)
if cv.waitKey(1000) == 27:
escape = True
Break