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
class Agender():
def __init__(self):
self.agender = PyAgender()
def detect(self, img_path):
# img_path = '/Users/woffee/www/language_design/couples-posing-800x450.jpg'
faces = self.agender.detect_genders_ages(cv2.imread(img_path))
return faces
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 __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()
pictures = []
for i in onder_70:
print(i)
path = find(i,x)
print(path)
if str(path) != "None":
z = find2('*.jpg', path)
#pictures = pictures + " " + '"' + str(z[0]) + '"'
#print(z)
pictures.append(str(z[0]))
print(pictures)
#facemorpher.morpher(pictures, plot=True)
facemorpher.averager(pictures, plot=True)
agender = PyAgender()
# see available options in __init__() src
faces = agender.detect_genders_ages(cv2.imread("/Users/ellen/Documents/Twente/Q2/Introduction to biometrics/Final/result.png"))
print(faces)
# In[3]:
# Vergelijk de gemiddelde leeftijd met de detected leeftijd van de morph van deze leeftijd groep
from pyagender import PyAgender
import cv2
import matplotlib.pyplot as plt
agender = PyAgender()
from pyagender import PyAgender
import numpy as np
import matplotlib.pyplot as plt
import cv2
from urllib.request import urlopen
from datetime import datetime
print("Importation Complete!")
def list_format(phrase):
return phrase[1:-1].replace("'", "").replace(" ", "").split(',')
df = pd.read_csv("with_source.csv", encoding='utf-8')
agender = PyAgender()
print("csv read, agender created")
df['urls'] = df['urls'].apply(list_format)
img_sample = df.iloc[2]['urls'][1]
req = urlopen(img_sample)
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
img = cv2.imdecode(arr, -1)
no_profile = img
print("no profile announced")
data = {
'name': [],
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()
import imutils
import cv2
import numpy as np
import sys
from utils import dirfiles, num2fixstr, imread
from pyagender import PyAgender
import dlib
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
# parameters for loading data and images
expression_model_path = '_mini_XCEPTION.106-0.65.hdf5'
expression_classifier = load_model(expression_model_path, compile=False)
EXPRESSIONS = ["angry","disgust","scared", "happy", "sad", "surprised","neutral"]
agender = PyAgender()
dirpath = '../faces/'
img_names = dirfiles(dirpath,'*.jpg')
n = len(img_names)
print(n)
print(img_names)
gender_age = np.zeros((n,2))
landmarks = np.zeros((n,136))
for i in range(n):
img = img_names[i]
def age_estimation(foto):
agender = PyAgender()
faces = agender.detect_genders_ages(cv2.imread(foto))
age = faces[0]['age']
return round(age)
# define store path
store_root_dir = r"D:\Datasets\Korean"
store_image_dir = os.path.join(store_root_dir, dataset_name + postfix)
if os.path.exists(store_image_dir) is False:
os.makedirs(store_image_dir)
if os.path.exists(os.path.join(root_data_dir,
dataset_name + '_F')) is False:
os.makedirs(os.path.join(root_data_dir, dataset_name + '_F'))
if os.path.exists(os.path.join(root_data_dir,
dataset_name + '_E')) is False:
os.makedirs(os.path.join(root_data_dir, dataset_name + '_E'))
for filename in tqdm(os.listdir(image_root_dir)):
filepath = os.path.join(image_root_dir, filename)
if os.path.isdir(filepath):
continue
width = -1
height = -1
with Image.open(filepath) as img:
width, height = img.size
agender = PyAgender()
# see available options in __init__() src
faces = agender.detect_genders_ages(cv2.imread(MY_IMAGE))
# print(width, ", ", height)
# if width <= 128 or height <= 128: #F
# shutil.move(filepath, os.path.join(os.path.join(root_data_dir, dataset_name + '_F'), filename))
# elif width <= 256 or height <= 256: #E
# shutil.move(filepath, os.path.join(os.path.join(root_data_dir, dataset_name + '_E'), filename))
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")
srcs.append(img)
for i in srcs:
if not is_absolute(i):
# if img has relative URL, make it absolute by joining
i = urljoin(url, i)
if i[0:2] == "//":
i = "http:" + i
if is_valid(i):
urls.append(i)
return urls
## commence scraping
agender = PyAgender()
for i in get_all_images(url):
response = urllib.request.Request(i, headers=headers)
try:
with urllib.request.urlopen(response) as url:
with open('temp.jpg', 'wb') as f:
f.write(url.read())
except:
print("Error: image not read")
pass
try:
faces = agender.detect_genders_ages(cv2.imread("temp.jpg"))
except:
import random
def GetPhoto():
url = 'https://www.thispersondoesnotexist.com/image'
user_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
request = urllib.request.Request(url, headers={'User-Agent': user_agent})
response = urllib.request.urlopen(request)
html = response.read()
f = open('img.jpg', 'wb')
f.write(html)
f.close()
GetPhoto()
agender = PyAgender()
faces = agender.detect_genders_ages(cv2.imread('img.jpg'))
age = int(faces[0]['age'])
print("age: ", age)
print("date of birth: " + str(int(str(datetime.now())[:4]) - age) + '.' +
str(random.randint(1, 12)) + '.' + str(random.randint(1, 31)))
if faces[0]['gender'] < 0.5:
print('gender: male')
gender = 'male'
elif faces[0]['gender'] > 0.5:
print('gender: female')
gender = 'female'
else:
print('gender: idk')
if random.randint(0, 1):
gender = 'male'
import cv2
import numpy as np
import pyrebase
from pyagender import PyAgender
agender = PyAgender()
capture = cv2.VideoCapture(0)
config = {
"apiKey": "AIzaSyDT8zvCg7Ax3h_IYtpwaawKe_80LmoN0aY",
"authDomain": "craeye-fa4f0.firebaseapp.com",
"databaseURL": "https://craeye-fa4f0.firebaseio.com",
"storageBucket": "craeye-fa4f0.appspot.com"
}
firebase = pyrebase.initialize_app(config)
fb_database = firebase.database()
while (True):
ret, frame = capture.read()
faces = agender.detect_genders_ages(frame)
age_values = []
gender_values = []
input_data = {}
if faces:
for int_index in range(0, len(faces)):
for value in faces[int_index]:
def __init__(self):
self._detector = MTCNN()
self._fa = FaceAligner()
self._image_utils = Image_Utils()
self._agender = PyAgender()
self._gap_db = Gap_Db()
class Age_Gender_Detect():
def __init__(self):
self._detector = MTCNN()
self._fa = FaceAligner()
self._image_utils = Image_Utils()
self._agender = PyAgender()
self._gap_db = Gap_Db()
def detect_face_age_gender(self, fn, debug_mode=False):
print("\nprocessing file %s..." % fn)
# check if faces already detected for the family
family_image_name = os.path.basename(fn).split('.')[0]
family_id = self._gap_db.get_family_id(family_image_name)
if self._gap_db.is_face_done(family_id):
print("Faces already detected for the family! Moving on...\n")
return
else:
# if faces not detected for the family yet, remove previous faces for
# the family
os.system("rm -rf %s" %
(os.path.join(FACE_IMG_DIR, '%s*' % family_image_name)))
img_original = cv2.imread(fn)
if img_original.shape[0] >= img_original.shape[1]:
img = imutils.resize(img_original, height=2000)
else:
img = imutils.resize(img_original, width=2000)
faces = self._detector.detect_faces(img)
self._image_utils.squarify_box(faces)
if debug_mode:
self._image_utils.show_key_points(img, faces)
face_patches = self._image_utils.show_face_patches(img,
faces,
show=False)
# align faces
faces_aligned = self._fa.align(faces, img)
assert len(faces_aligned) == len(face_patches)
# detect age and gender
ages = []
genders = []
for face in face_patches:
g, a = self._agender.gender_age(face)
gender = ['female' if g > 0.5 else 'male'][0]
age = int(a)
ages.append(age)
genders.append(gender)
print("gender: %s, age: %i" % (gender, age))
# save faces
face_images = []
face_no = 0
for face in faces_aligned:
face_fn = family_image_name + '_' + str(face_no) + '.jpg'
face_images.append(face_fn[:-4]) # remove 'extension'
face_f_fn = os.path.join(FACE_IMG_DIR, face_fn)
cv2.imwrite(face_f_fn, face)
face_no += 1
## update database
for image, age, gender in zip(face_images, ages, genders):
self._gap_db.update_faces(family_id, image, age, gender)
"#climatechangescam", "#globalwarmingalarmism", "#globalwarmingcult",
"#climatechangefrenzy", "#globalwarmingfraud", "#globalwarmingscam",
"#globalwarmingnonsense", "#globalwarmingbullshit", "#climatefraud",
"#climatescam", "#climatecult", "#climatenonsense", "#climatebullshit",
"#climatechangebullshit", "#climatechangenonsense", "#climatechangecult",
"#climatehysteria"
]
#raw_data = [json.loads(line) for line in open(path_raw_data, 'r')]
#raw_data = []
_d = 0
_b = 0
r = []
agender = PyAgender()
#with open(path_out_data, 'w') as fp:
# json.dump(r, fp)
#for i, tweet in enumerate(raw_data):
tweets = []
with open(path_raw_data, 'r') as infile:
with open(path_out_data, 'w') as outfile:
for i, line in enumerate(infile):
tweet_in = json.loads(line)
if i % 1000 == 0:
print(f"{i}/?")
#if i >= 100:
# break
tweet = {}
# -*- coding: utf-8 -*-
"""Untitled0.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1yA-cb7JozrTN3GaMLwhWENbCnqeB2YC_
"""
from fer import FER
import cv2
img = cv2.imread("/content/pic22.jpg")
detector = FER()
detector.detect_emotions(img)
emotion, score = detector.top_emotion(img)
score
emotion
from pyagender import PyAgender
agender = PyAgender()
faces = agender.detect_genders_ages(cv2.imread("/content/pic22.jpg"))
faces
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)
from pyagender import PyAgender
import cv2
import math
from sklearn import neighbors
import os
import os.path
import pickle
from PIL import Image, ImageDraw
import face_recognition
from face_recognition.face_recognition_cli import image_files_in_folder
import pandas as pd
import numpy as np
import datetime
agender = PyAgender()
df = pd.DataFrame(columns= ['name','age', 'gender','time_in','time_out'])
def predict( imgMe,path,knn_clf=None, model_path=None, distance_threshold=0.6):
if knn_clf is None and model_path is None:
raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")
# Load a trained KNN model (if one was passed in)
if knn_clf is None:
with open(model_path, 'rb') as f:
knn_clf = pickle.load(f)
# Load image file and find face locations
X_img = imgMe
X_face_locations = face_recognition.face_locations(X_img)
def detect(image):
agenda = PyAgender()
ans = agenda.detect_genders_ages(image)
return ans
# -*- 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"))
