def get_estimate_age(url):
try:
image = url_to_image(url)
except urllib.error.HTTPError:
return False
except urllib.error.URLError:
return False
except:
return False
agender = PyAgender()
faces = agender.detect_genders_ages(image)
if len(faces) > 0:
return round(faces[0]['age'])
else:
return 0
def __init__(self, cascadePath=None, odir=None):
'''
Constructor for PixelCore
'''
self.cascPath = os.path.join(os.getcwd(), 'models', 'haarcascades',
'haarcascade_frontalface_extended.xml')
resnetwts = os.path.join(cascadePath, 'models', 'weights.18-4.06.hdf5')
if cascadePath:
self.cascPath = os.path.join(os.path.abspath(cascadePath),
'models', 'haarcascades',
'haarcascade_frontalface_alt.xml')
print('Loading HAAR-Cascade file: %s' % (self.cascPath))
self.faceCascade = cv2.CascadeClassifier(self.cascPath)
self.face_size = 32
self.agender = PyAgender()
def gender_detect(path):
'''
gender_detect: takes in an image file, uses OpenCV and
PyAgender to detected the gender of the individual in the
live image
Parameters
----------
file: file name + location where file is stored
Returns
-------
Prediction string (i.e. "male")
'''
agender = PyAgender()
face = agender.detect_genders_ages(cv2.imread(path))
gender = face[0]['gender']
if gender >= 0.5:
return 'Female'
else:
return 'Male'
def detect(image):
agenda = PyAgender()
ans = agenda.detect_genders_ages(image)
return ans
def age_estimation(foto):
agender = PyAgender()
faces = agender.detect_genders_ages(cv2.imread(foto))
age = faces[0]['age']
return round(age)
age_before_morph[idx] = age_estimation(i)
# print(age_after_morph)
# print(face_distance_after_morph)
# print(actual_age)
stop = time.time()
print("Time: ", stop-start)
np.savez(save_name, age_after_morph=age_after_morph,age_before_morph=age_before_morph, actual_age=actual_age, face_dist_after = face_distance_after_morph, face_dist_before=face_distance_before_morph)
# Plots_print()
#return age_after_morph, face_distance_after_morph, actual_age, face_distance_before_morph, age_before_morph
def read_npz_after_morph(file):
x = np.load(file, mmap_mode='r')
actual_age = x['actual_age'] # Array with actual ages
age_after_morph = x['age_after_morph'] # Array with detected ages after morphing
age_before_morph = x['age_before_morph'] # Array with detected ages before morphing
face_dist_after = x['face_dist_after'] # Face distance after morphing
face_dist_before = x['face_dist_before'] # Face distance before morphing
return actual_age, age_after_morph, age_before_morph, face_dist_after, face_dist_before
agender = PyAgender()
path = "../SiblingsDB/SiblingsDB_preprocessed/"
#after_morphing("/Users/ellen/Documents/GitHub/Age-Obfuscation-Morphing/averages/average20_30_N3(1).png", path)
#after_morphing("/Users/ellen/Documents/GitHub/Age-Obfuscation-Morphing/averages/average30_40_N3(0).png", path)
# after_morphing("/Users/ellen/Documents/GitHub/Age-Obfuscation-Morphing/averages/average40_50_N3(0).png", path)
# after_morphing("/Users/ellen/Documents/GitHub/Age-Obfuscation-Morphing/averages/average50_60_N3(0).png", path)
after_morphing("../averages/average60_70_handpickedN2.png", path, agender)
def img_clss():
new_file_id=uuid.uuid1()
try:
data_file = request.files['img']
file_name = data_file.name
data_file.save(f'{os.getcwd()}/images/{data_file.filename}')
data_file.close()
img_file = os.listdir(f'{os.getcwd()}/images/')
print('Image ',img_file[0])
agender = PyAgender()
path = os.path.join(f'{os.getcwd()}/images/', img_file[0])
print('Path ', path)
faces = agender.detect_genders_ages(cv2.imread(path))
print('Faces ',faces)
if faces[0]['gender'] < 0.5:
faces[0]['gender'] = 'male'
else:
faces[0]['gender'] = 'female'
faces[0]['age'] = str(faces[0]['age'] ).split('.')[0]
print(faces[0])
resp = make_response(faces[0])
resp.headers.set('Content-Type','multipart/form-data')
print('Response ', resp)
return resp
except Exception as e:
print(e)
try:
# Camera capture
path1 = f'{os.getcwd()}/images/'
image_string = str(request.form.get('file_string'))
image_newstring = image_string.replace("data:image/jpeg;base64,", "")
imgdata = base64.b64decode(image_newstring)
filename = os.path.join(path1 , new_file_id.hex+'.jpg')
with open(filename, 'wb') as f:
f.write(imgdata)
f.close()
img_file = os.listdir(f'{os.getcwd()}/images/')
print('Image ',img_file[0])
agender = PyAgender()
path = os.path.join(f'{os.getcwd()}/images/', img_file[0])
print('Path ',path)
faces = agender.detect_genders_ages(cv2.imread(path))
print('Faces ' ,faces)
if faces[0]['gender'] < 0.5:
faces[0]['gender'] = 'male'
else:
faces[0]['gender'] = 'female'
faces[0]['age'] = str(faces[0]['age'] ).split('.')[0]
print(faces[0])
resp = make_response(faces[0])
resp.headers.set('Content-Type','multipart/form-data')
target_dir=f'{os.getcwd()}/images/'
with os.scandir(target_dir) as entries:
for entry in entries:
time.sleep(1)
if entry.is_file() or entry.is_symlink():
os.remove(entry)
print('Response ', resp)
return resp
except Exception as e:
print(e)
print("Something went wrong with your image. Kindly try with different image")
finally:
target_dir=f'{os.getcwd()}/images/'
with os.scandir(target_dir) as entries:
for entry in entries:
time.sleep(1)
print(entry)
if entry.is_file() or entry.is_symlink():
os.remove(entry)
print("Image Deleted")
import face_recognition
import pickle
import sqlite3
import sys
import os
import cv2
from pyagender import PyAgender
res = PyAgender().detect_genders_ages(cv2.imread(sys.argv[1]))
print('Age: {}'.format(res[0]['age']))
print('Gender: {}'.format(res[0]['gender']))
filename = sys.argv[1]
known_image = face_recognition.load_image_file(filename)
known_encoding = face_recognition.face_encodings(known_image)[0]
con = sqlite3.connect('comparator.db')
cur = con.cursor()
## write faces
# cur.execute('create table if not exists faces (id INTEGER PRIMARY KEY AUTOINCREMENT, name varchar(50), face blob)')
# name_in_db = os.path.splitext(os.path.basename(filename))[0]
# sql = 'insert into faces (name, face) values (?, ?)'
# cur.execute(sql, (name_in_db, pickle.dumps(known_encoding)))
# con.commit()
## recognize faces
cur.execute('select name, face from faces')
face_encodings = cur.fetchall()
for i in range (0, len(face_encodings)):
fe = pickle.loads(face_encodings[i][1])
def __init__(self):
self.agender = PyAgender()
def __init__(self):
self._detector = MTCNN()
self._fa = FaceAligner()
self._image_utils = Image_Utils()
self._agender = PyAgender()
self._gap_db = Gap_Db()
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 1 11:36:51 2020
@author: murie
"""
#import os, fnmatch, facemorpher
from pyagender import PyAgender
import cv2
agender = PyAgender() # see available options in __init__() src
faces = agender.detect_genders_ages(
cv2.imread(
"C:/Users/murie/Documents/school/master/1B/Introduction2Biometrics/project_git/datasets/test"
))
image = agender.detect_genders_ages(cv2.imread("acaro.png"))