def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5'):
model = VGGFace.baseModel()
#--------------------------
classes = 2
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
gender_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
if os.path.isfile(home+'/.deepface/weights/gender_model_weights.h5') != True:
print("gender_model_weights.h5 will be downloaded...")
output = home+'/.deepface/weights/gender_model_weights.h5'
gdown.download(url, output, quiet=False)
gender_model.load_weights(home+'/.deepface/weights/gender_model_weights.h5')
return gender_model
def loadModel():
model = VGGFace.baseModel()
#--------------------------
classes = 101
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
age_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
if os.path.isfile(home +
'/.deepface/weights/age_model_weights.h5') != True:
print("age_model_weights.h5 will be downloaded...")
# https://drive.google.com/file/d/1_yy137mGjpPU2rpqT1sSsUEPkfq90jbk/view?usp=sharing
url = 'https://drive.google.com/uc?id=1_yy137mGjpPU2rpqT1sSsUEPkfq90jbk'
output = home + '/.deepface/weights/age_model_weights.h5'
gdown.download(url, output, quiet=False)
age_model.load_weights(home + '/.deepface/weights/age_model_weights.h5')
return age_model
def loadModel():
model = VGGFace.baseModel()
#--------------------------
classes = 101
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
age_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
if os.path.isfile(home +
'/.deepface/weights/age_model_weights.h5') != True:
print("age_model_weights.h5 will be downloaded...")
url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'
output = home + '/.deepface/weights/age_model_weights.h5'
gdown.download(url, output, quiet=False)
age_model.load_weights(home + '/.deepface/weights/age_model_weights.h5')
return age_model
def loadModel():
model = VGGFace.baseModel()
#--------------------------
classes = 6
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
race_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
race_model.load_weights('race_model_single_batch.h5')
return race_model
def loadModel():
model = VGGFace.baseModel()
#--------------------------
classes = 6
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
race_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
# home = str(Path.home())
# if os.path.isfile(home+'/.deepface/weights/race_model_single_batch.h5') != True:
# print("race_model_single_batch.h5 will be downloaded...")
# #zip
# url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
# output = home+'/.deepface/weights/race_model_single_batch.zip'
# gdown.download(url, output, quiet=False)
# #unzip race_model_single_batch.zip
# with zipfile.ZipFile(output, 'r') as zip_ref:
# zip_ref.extractall(home+'/.deepface/weights/')
# race_model.load_weights(home+'/.deepface/weights/race_model_single_batch.h5')
home = str(Path(__file__).parent.absolute())
#print(home)
if os.path.isfile(home + '/weights/race_model_single_batch.h5') != True:
print("race_model_single_batch.h5 will be downloaded...")
#zip
url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
output = home + '/weights/race_model_single_batch.zip'
gdown.download(url, output, quiet=False)
#unzip race_model_single_batch.zip
with zipfile.ZipFile(output, 'r') as zip_ref:
zip_ref.extractall(home + '/weights/')
race_model.load_weights(home + '/weights/race_model_single_batch.h5')
return race_model
def loadModel_age():
model = VGGFace.baseModel()
# --------------------------
classes = 101
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
# --------------------------
age_model = Model(inputs=model.input, outputs=base_model_output)
# --------------------------
# load weights
age_model.load_weights('network/age_model_weights.h5')
return age_model
def loadModel(
url='https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5'
):
model = VGGFace.baseModel()
#--------------------------
classes = 6
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
race_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
if os.path.isfile(home +
'/.deepface/weights/race_model_single_batch.h5') != True:
print("race_model_single_batch.h5 will be downloaded...")
output = home + '/.deepface/weights/race_model_single_batch.h5'
gdown.download(url, output, quiet=False)
"""
#google drive source downloads zip
output = home+'/.deepface/weights/race_model_single_batch.zip'
gdown.download(url, output, quiet=False)
#unzip race_model_single_batch.zip
with zipfile.ZipFile(output, 'r') as zip_ref:
zip_ref.extractall(home+'/.deepface/weights/')
"""
race_model.load_weights(home +
'/.deepface/weights/race_model_single_batch.h5')
return race_model
def loadModel(self):
model = VGGFace.baseModel()
#--------------------------
classes = 2
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
gender_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
if os.path.isfile(home+'/.deepface/weights/gender_model_weights.h5') != True:
print("gender_model_weights.h5 will be downloaded...")
url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'
output = home+'/.deepface/weights/gender_model_weights.h5'
gdown.download(url, output, quiet=False)
gender_model.load_weights(home+'/.deepface/weights/gender_model_weights.h5')
return gender_model
def loadModel():
model = VGGFace.baseModel()
#--------------------------
classes = 6
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
race_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
if os.path.isfile(home +
'/.deepface/weights/race_model_single_batch.h5') != True:
print("race_model_single_batch.h5 will be downloaded...")
#zip https://drive.google.com/file/d/18BRraHOmlmXPYA5PiFIjeFNUkFDO69sn/view?usp=sharing
url = 'https://drive.google.com/uc?id=18BRraHOmlmXPYA5PiFIjeFNUkFDO69sn'
output = home + '/.deepface/weights/race_model_single_batch.zip'
gdown.download(url, output, quiet=False)
#unzip race_model_single_batch.zip
with zipfile.ZipFile(output, 'r') as zip_ref:
zip_ref.extractall(home + '/.deepface/weights/')
race_model.load_weights(home +
'/.deepface/weights/race_model_single_batch.h5')
return race_model
def loadModel():
model = VGGFace.baseModel()
#--------------------------
classes = 2
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
#--------------------------
gender_model = Model(inputs=model.input, outputs=base_model_output)
#--------------------------
#load weights
home = str(Path.home())
if os.path.isfile(home +
'/.deepface/weights/gender_model_weights.h5') != True:
print("gender_model_weights.h5 will be downloaded...")
# https://drive.google.com/file/d/1T5qnV4s-au24MAkjZ53SZtCk8vup8Wi-/view?usp=sharing
url = 'https://drive.google.com/uc?id=1T5qnV4s-au24MAkjZ53SZtCk8vup8Wi-'
output = home + '/.deepface/weights/gender_model_weights.h5'
gdown.download(url, output, quiet=False)
gender_model.load_weights(home +
'/.deepface/weights/gender_model_weights.h5')
return gender_model
#--------------------------
def corridor_enviornment(path=0, gender="man"):
#Import necessary libraries
import numpy as np
import time
import cv2
import requests
import threading
import configparser
from datetime import datetime
from keras.preprocessing.image import img_to_array
from keras.models import load_model
from keras.utils import get_file
import os
import cvlib as cv
from deepface.basemodels import VGGFace
import os
from pathlib import Path
import gdown
import numpy as np
from keras.models import Model, Sequential
from keras.layers import Convolution2D, Flatten, Activation
def telegram():
config = configparser.ConfigParser()
config.read('DATA/Keys/config.ini')
config_viewer = config.items('TOKEN')
token = config_viewer[0][1]
up_url = config_viewer[1][1]
print("start")
try:
time.sleep(2)
token = str(token)
chat_id = str(up_url) # chat id
file = 'Fraud.jpg'
url = f"https://api.telegram.org/bot{token}/sendPhoto"
print(url)
files = {}
files["photo"] = open(file, "rb")
print(requests.get(url, params={"chat_id": chat_id}, files=files))
except:
pass
print("end")
model = VGGFace.baseModel()
# --------------------------
classes = 2
base_model_output = Sequential()
base_model_output = Convolution2D(classes, (1, 1), name='predictions')(
model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
gender_model = Model(inputs=model.input, outputs=base_model_output)
# load model
gender_model.load_weights('Data/Models/gender_model_weights.h5')
net = gender_model
print(gender)
classes = ['woman', 'man']
unauthorised = 0
if gender == "man":
var_male = (0, 255, 0)
var_female = (0, 0, 255)
elif gender == "woman":
var_male = (0, 0, 255)
var_female = (0, 255, 0)
video_capture = cv2.VideoCapture(path)
font = cv2.FONT_HERSHEY_SIMPLEX
man = 0
woman = 0
telegx = 0
iter = 0
while (True):
#Read each frame and flip it, and convert to grayscale
ret, frame = video_capture.read()
frame = cv2.flip(frame, 1)
(H, W) = frame.shape[:2]
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.putText(frame, "INTELEGIX (Washroom Corridor Monitoring System)",
(400, 40), font, 0.7, (0, 0, 0), 2)
cv2.rectangle(frame, (20, 50), (W - 20, 15), (0, 0, 0), 2)
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
tot_str = "Washroom Corridor for : " + str(gender).capitalize()
high_str = "Male Detected : " + str(man)
low_str = "Female Detected : " + str(woman)
safe_str = "Total Persons: " + str(man + woman)
man = 0
woman = 0
unauthorised = 0
sub_img = frame[H - 100:H, 0:260]
black_rect = np.ones(sub_img.shape, dtype=np.uint8) * 0
res = cv2.addWeighted(sub_img, 0.8, black_rect, 0.2, 1.0)
frame[H - 100:H, 0:260] = res
cv2.putText(frame, tot_str, (10, H - 80), font, 0.5, (255, 255, 255),
1)
cv2.putText(frame, high_str, (10, H - 55), font, 0.5, (0, 255, 0), 1)
cv2.putText(frame, low_str, (10, H - 30), font, 0.5, (0, 120, 255), 1)
cv2.putText(frame, safe_str, (10, H - 5), font, 0.5, (0, 0, 150), 1)
now = datetime.now()
# cv2.imwrite(str("Data/Saved_Images/CLASS_ENVIRONMENT/") + str(now.strftime("%Y%m%d%H%M%S") + str(".jpg")), img)
# cv2.putText(img, str(now.strftime("%d-%m-%Y% %H:%M:%S")), (W-10, H - 5),
# font, 0.5, (0, 0, 150), 1)
timex = str(now.strftime("%d/%m/%Y %H:%M:%S"))
cv2.putText(frame, timex, (W - 200, H - 10), font, 0.5,
(255, 255, 255), 1)
# apply face detection
face, confidence = cv.detect_face(frame)
print(face)
print(confidence)
# loop through detected faces
for idx, f in enumerate(face):
# get corner points of face rectangle
(startX, startY) = f[0], f[1]
(endX, endY) = f[2], f[3]
# # draw rectangle over face
# cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 0), 2)
# crop the detected face region
face_crop = np.copy(frame[startY:endY, startX:endX])
if (face_crop.shape[0]) < 10 or (face_crop.shape[1]) < 10:
continue
# preprocessing for gender detection model
face_crop = cv2.resize(face_crop, (224, 224))
face_crop = face_crop.astype("float") / 255.0
face_crop = img_to_array(face_crop)
face_crop = np.expand_dims(face_crop, axis=0)
# apply gender detection on face
conf = net.predict(face_crop)[0]
print(conf)
print(classes)
# get label with max accuracy
idx = np.argmax(conf)
label = classes[idx]
print(label)
print(label, gender)
if str(label) != str(gender):
unauthorised += 1
if label == "man":
# draw rectangle over face
cv2.rectangle(frame, (startX, startY), (endX, endY), var_male,
2)
label = "{}: {:.2f}%".format(label, conf[idx] * 100)
Y = startY - 10 if startY - 10 > 10 else startY + 10
# write label and confidence above face rectangle
cv2.putText(frame, label, (startX, Y), font, 0.7, var_male, 2)
man += 1
if label == "woman":
# draw rectangle over face
cv2.rectangle(frame, (startX, startY), (endX, endY),
var_female, 2)
label = "{}: {:.2f}%".format(label, conf[idx] * 100)
Y = startY - 10 if startY - 10 > 10 else startY + 10
# write label and confidence above face rectangle
cv2.putText(frame, label, (startX, Y), font, 0.7, var_female,
2)
woman += 1
if unauthorised == 0:
#image = draw_outputs(img, (boxes, scores, classes, nums), class_names, color=(0, 255, 0))
cv2.circle(frame, (25, 80), 10, (0, 255, 0), -1)
cv2.putText(frame, "All Ok", (50, 85), font, 0.5, (0, 255, 0), 2)
else:
if unauthorised > 0:
#image = draw_outputs(img, (boxes, scores, classes, nums), class_names, color=(0, 0, 255))
cv2.circle(frame, (25, 80), 10, (0, 0, 255), -1)
cv2.putText(
frame,
"Unrestricted Access at " + str(gender) + " washroom",
(50, 85), font, 0.5, (0, 0, 255), 2)
telegx += 1
print(telegx)
if telegx > 3:
cv2.imwrite("Fraud.jpg", frame)
threading.Thread(target=telegram).start()
telegx = 0
now = datetime.now()
cv2.imwrite(
str("Data/Saved_Images/CORRIDOR_ENVIRONMENT/") +
str(now.strftime("%Y%m%d%H%M%S") + str(".jpg")), frame)
cv2.namedWindow("Output", cv2.WINDOW_NORMAL)
cv2.setWindowProperty("Output", cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
cv2.imshow("Output", frame)
if (cv2.waitKey(1) & 0xFF == ord('q')):
break
#Finally when video capture is over, release the video capture and destroyAllWindows
video_capture.release()
cv2.destroyAllWindows()
# if __name__=="__main__":
# corridor_enviornment(path=0,gender="man")
