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facial.py
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facial.py
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import threading
import time
import shutil
from itertools import groupby
from multiprocessing import Pool,cpu_count
import numpy as np
import face_recognition
import subprocess
from flask import Flask, request, redirect, jsonify
from PIL import Image
from flask_cors import CORS, cross_origin
import math
import os
import os.path
import glob
from util import train, predict, insertPerson, ALLOWED_EXTENSIONS, carpeta, carpeta_standby, carpeta_fotos, personas, \
getEncode, formatingFile, moveToFotos, insertarasistencia, carpeta_reconocidos, carpeta_sin_rostro
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
app = Flask(__name__)
cors = CORS(app, resources={r"/": {"origins": "*"}})
app.config['CORS_HEADERS'] = 'Content-Type'
servidor = '192.168.33.72'
servidorlocal = servidor
codigoequipo = 6666
lineacomando = 'curl -F "file=@1.jpg" http://192.168.33.72:5001'
app = Flask(__name__)
def puertolibre(numerouno):
for puerto in range(int(numerouno)):
try:
socket.connect((host, puerto))
return True
socket.close()
except:
print("Puerto "+str(puerto)+" cerrado.")
return False
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
def leerBandera():
archivo = open('band.txt', 'r')
band = archivo.read(1)
archivo.close()
return str(band) == 'T'
def cambiarBandera(val):
archivo = open('band.txt', 'w')
archivo.write(val)
archivo.close()
def hayImagenesParaProcesar():
imagenes = os.listdir(carpeta)
imagenes_a_procesar = []
if len(imagenes) > 0:
imagenes_a_procesar = filterImagenesSinProcesar(imagenes)
return len(imagenes_a_procesar) > 0
def getImagenesParaProcesar():
nro_imagenes = 5
imagenes = os.listdir(carpeta)
imagenes = sorted(imagenes)
imagenes_a_procesar = filterImagenesSinProcesar(imagenes)[:nro_imagenes]
return imagenes_a_procesar
def filterImagenesSinProcesar(path_imagenes):
return list(filter(lambda img: '.jpg' in img and '+P.jpg' not in img, path_imagenes))
def procesarImagenes():
while hayImagenesParaProcesar():
imagenes = getImagenesParaProcesar()
try:
for img in imagenes:
result = imagenRecognition(carpeta + img)
except Exception as e:
print('ERROR_RECONOCIMIENTO', e, imagenes)
cambiarBandera('F')
print()
cambiarBandera('F')
def eliminarImagen(imagen):
os.remove(imagen)
def moveStandBy(filename, cod_cliente):
ruta = carpeta_standby+cod_cliente+'/'
try:
os.stat(carpeta_standby)
except:
os.mkdir(carpeta_standby)
try:
fileName = filename.split('/')[-1]
try:
os.stat(ruta)
except:
os.mkdir(ruta)
shutil.move(filename, ruta)
except:
return False
return ruta, fileName
def moveToReconocidos(filename, cod_cliente):
ruta = carpeta_reconocidos+cod_cliente+'/'
try:
os.stat(carpeta_reconocidos)
except:
os.mkdir(carpeta_reconocidos)
try:
fileName = filename.split('/')[-1]
try:
os.stat(ruta)
except:
os.mkdir(ruta)
shutil.move(filename, ruta)
except:
return False
return ruta+fileName
def copyToReconocidos(filename, cod_cliente):
ruta = carpeta_reconocidos+cod_cliente+'/'
try:
os.stat(carpeta_reconocidos)
except:
os.mkdir(carpeta_reconocidos)
try:
fileName = filename.split('/')[-1]
try:
os.stat(ruta)
except:
os.mkdir(ruta)
shutil.copy(filename, ruta)
except:
return False
return ruta+fileName
def moveToSinRostro(filename, cod_cliente):
ruta = carpeta_sin_rostro+cod_cliente+'/'
try:
os.stat(carpeta_sin_rostro)
except:
os.mkdir(carpeta_sin_rostro)
try:
fileName = filename.split('/')[-1]
try:
os.stat(ruta)
except:
os.mkdir(ruta)
shutil.move(filename, ruta)
except:
return False
return ruta+fileName
def imagenRecognition(imgD):
time1 = time.time()
history = open('./history.txt','a')
new_nombre = imgD.replace('.jpg', '+P.jpg')
os.rename(imgD, new_nombre)
imgD = new_nombre
datos = formatingFile(imgD)
result = None
if datos:
datos['url'] = imgD
template = getEncode(imgD)
personas = searchPersons()
if len(personas['doc_ids']) > 0:
count = int(cpu_count())
print(count)
p = Pool(count)
# newReconociomiento = partial(reconocimiento,templete=template,datos=datos)
result = p.apply(reconocimiento,(personas,template,datos))
print(p)
# result = reconocimiento(personas, template, datos)
else:
datos['url'] = moveStandBy(datos['url'], datos['cliente'])
print('movido a standby No Ids', datos['url'], datos['cliente'])
result = {'descripcion': 'movido a standby No Ids',
'url': datos['url'], 'cliente': datos['cliente']}
time2 = time.time()
history.write('\n'+imgD+" Tiempo fue de "+str(time2-time1)+str(result)+"\n")
history.close()
return result
def searchPersons():
retornar = {
"templates": [],
"doc_ids": []
}
templates = list(personas.find(
{}, {'doc_id', 'template_recognition'}))
for elementos in templates:
for (index, elemento) in enumerate(elementos['template_recognition']):
retornar["templates"].append([float(i) for i in elemento])
retornar["doc_ids"].append(elementos['doc_id'])
return (retornar)
def face_multiple(matches, count):
faces = []
for (index, face) in enumerate(matches):
if face:
count += 1
faces.append(index)
return count, faces
def reconocimiento(search, templete, datos):
if (len(templete) > 0 and len(templete[0]) > 0):
templete = [float(i) for i in templete[0]]
templete = np.asarray(templete)
searchs = np.asarray(search['templates'])
matches = face_recognition.compare_faces(
searchs, templete, tolerance=0.40)
face_distances = face_recognition.face_distance(searchs, templete)
count = 0
prueba, prueba1 = face_multiple(matches, count)
# for pos in prueba1:
# print('reconocido', prueba, pos, search['doc_ids'][pos], face_distances[pos])
documentos = []
promedios = []
for doc, data in groupby(list(search['doc_ids'])):
m = [face_distances[i] for i, x in enumerate(
list(search['doc_ids'])) if x == str(doc)]
if len(m) > 0:
documentos.append(doc)
promedios.append((sum(m)/len(m)))
first_match_index = np.where(
min(list(face_distances)) == face_distances)[0][0]
doc_id = search['doc_ids'][first_match_index]
distance = face_distances[first_match_index]
promedios_min = []
doc_id_min_prom = documentos[promedios.index(
min(promedios))] if documentos else 'N/A'
distance_min_prom = min(promedios) if promedios else 'N/A'
print([face_distances[i] for i, x in enumerate(
list(search['doc_ids'])) if x == str(doc_id_min_prom)])
if os.path.exists('modeloknn.clf'):
predictions, distance_knn = predict(
datos['url'], model_path="modeloknn.clf")
promedio = (distance+distance_knn)/2
for name, (top, right, bottom, left) in predictions:
if str(name) == str(doc_id_min_prom) and str(name) == doc_id and promedio < 0.43:
datos["doc_id"] = str(name)
if promedio < 0.25:
resp, template_recognition_lentgh = insertPerson(
getEncode(datos['url']),
str(name),
None,
None,
datos['cliente']
)
ruta_foto = datos['url']
new_nombre = ruta_foto.replace(
'.jpg', '-'+str(template_recognition_lentgh)+'.jpg')
os.rename(ruta_foto, new_nombre)
new_nombre = moveToFotos(new_nombre, str(name))
datos['url'] = copyToReconocidos(
new_nombre, datos['cliente'])
else:
datos['url'] = moveToReconocidos(
datos['url'], datos['cliente'])
print('Reconocido', datos['url'], doc_id, distance, name, distance_knn, promedio,
((distance+distance_knn) * promedio), ' - ', doc_id_min_prom, distance_min_prom)
#insertarasistencia(datos['dispositivo'], str(name), datos)
return {
'descripcion': 'reconocido por knn', 'url': datos['url'], 'cliente': datos['cliente'],
'distance': distance, 'distance_knn': distance_knn, 'doc_id_knn': str(name),
'doc_id': doc_id, 'distance_min_prom': distance_min_prom, 'doc_id_min_prom': doc_id_min_prom
}
else:
datos['url'] = moveStandBy(datos['url'], datos['cliente'])
print('Standby', datos['url'], doc_id, distance, name, distance_knn, promedio,
((distance+distance_knn) * promedio), ' - ', doc_id_min_prom, distance_min_prom)
return {
'descripcion': 'movido a standby', 'url': datos['url'], 'cliente': datos['cliente'],
'distance': distance, 'distance_knn': distance_knn, 'doc_id': doc_id,
'doc_id_knn': str(name)
}
else:
datos['url'] = moveToSinRostro(datos['url'], datos['cliente'])
print('movido a Sin Rostro', datos['url'], datos['cliente'])
return {'descripcion': 'movido a sin Rostro', 'url': datos['url'], 'cliente': datos['cliente']}
def face_distance_to_conf(face_distance, face_match_threshold=0.3):
if face_distance > face_match_threshold:
range = (1.0 - face_match_threshold)
linear_val = (1.0 - face_distance) / (range * 2.0)
return linear_val
else:
range = face_match_threshold
linear_val = 1.0 - (face_distance / (range * 2.0))
return linear_val + ((1.0 - linear_val) * math.pow((linear_val - 0.5) * 2, 0.2))
def face_distance_to_conf_1(face_distance, face_match_threshold, name):
if face_distance > face_match_threshold:
range = (1.0 - face_match_threshold)
linear_val = (1.0 - face_distance) / (range * 2.0)
linear_val_f = linear_val + face_distance
return linear_val
else:
range = face_match_threshold
linear_val = 1.0 - (face_distance / (range * 2.0))
linear_val_f = linear_val + face_distance
return linear_val + ((1.0 - linear_val) * math.pow((linear_val - 0.5) * 2, 0.2))
@app.route('/', methods=['GET', 'POST'])
@cross_origin(origin='*', headers=['Content-Type', 'Authorization'])
def upload_image():
print(request)
# Chequea la imagen que llego
if request.method == 'POST':
if 'file' not in request.files:
return redirect(request.url)
file = request.files['file'] # file almacena la imagen
if file.filename == '':
return redirect(request.url)
if allowed_file(file.filename):
# valida la imagen y la envia a reconocimiento facial y la devuelve en result
file.filename = str(file.filename).replace('&', ':')
try:
image = Image.open(file)
except IOError:
print("ERROR processing image", file.filename)
return 'F'
if not os.path.exists(carpeta+str(file.filename)) and \
not os.path.exists(carpeta+str(file.filename).replace('.jpg', '+P.jpg')):
# cv2.imwrite(carpeta + str(file.filename), image) #Guardar imagen en './cloud/nombreDeImagen'
image.save(carpeta + str(file.filename))
band = leerBandera()
if not band or len(threading.enumerate()) < 6:
cambiarBandera('T')
hilo1 = threading.Thread(name='hilo1', target=procesarImagenes)
hilo1.start()
# return jsonify(result)
return 'T'
if __name__ == "__main__":
print("Scaneando Puerto....")
print("******Cargando Datos de Servidor y Puerto***************")
configuracion = []
with open("ibartir.txt") as f:
for linea in f:
configuracion.append(linea)
f.close()
mi_puerto = int(configuracion[0])
mi_server = configuracion[1]
codigoimagenl = configuracion[2]
# try:
# #verificar si el puerto esta abierto
# resultado =subprocess.check_output(lineacomando, shell=True)
# print(resultado)
# except subprocess.CalledProcessError as e:
# print(e.output)
app.run(host='192.168.33.72', port=mi_puerto, debug=True)
cambiarBandera('T')