diff --git a/routes/index.js b/routes/index.js
index f15f31f..509f759 100644
--- a/routes/index.js
+++ b/routes/index.js
@@ -136,7 +136,7 @@ router.post('/uploadfound', upload.array("foundImage", 10), (req, res, next) =>
console.log(`${__dirname}/../scripts/Central_FR.py`)
console.log(`${__dirname}/../uploads/found/${labelname}/0.jpg`)
const pythonProcess = spawn('python3', [`${__dirname}/../scripts/Central_FR.py`, `${__dirname}/../uploads/found/${labelname}/0.jpg`]);
-
+ console.log(pythonProcess.pid);
pythonProcess.stdout.on('data', (data) => {
console.log(data.toString('utf8'));
});
diff --git a/scripts/Central_FR.py b/scripts/Central_FR.py
index 76cee46..151efba 100644
--- a/scripts/Central_FR.py
+++ b/scripts/Central_FR.py
@@ -25,8 +25,9 @@ def triplet_loss(y_true, y_pred, alpha=0.2):
def check_func(argum1):
to_email = ['kiranmuthigi123@gmail.com']
+
np.set_printoptions(threshold=sys.maxsize)
- #print("inside: ", argum1)
+ print("inside: ", argum1)
FRmodel = faceRecoModel(input_shape=(3, 96, 96))
lost = '/home/zemotacqy/hack-moscow-backend/routes/../uploads/lost'
#print("lost FilePath: ", lost)
@@ -61,26 +62,33 @@ def check_func(argum1):
if(check==1):
print(best_match_found)
print(cur_b_sc)
+ '''
+ #if 1>0:
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
+ #email_user = 'destine.bitm@gmail.com'
+ #email_password = 'K11K4B19R14'
email_user = 'horussurya@gmail.com'
email_password = 'lemniscate#11235'
- email_send = to_email[0]
- lat = 0.0
- long = 0.0
- subject = 'subject'
+ email_send ='kiranmuthigi123@gmail.com'
+ print(email_send)
+ lat = '55.815480'
+ lon = '37.575385'
+ subject = 'Unlost.ai'
msg = MIMEMultipart()
msg['From'] = email_user
msg['To'] = email_send
msg['Subject'] = subject
-
- body = 'Hi the missing person you reported has been found! The match error was very low! (around '+str(cur_b_sc)+').'
- body = body + 'The person was found at ('+str(lat) +" " +str(long)+')!'
+ print("On the way")
+ body = 'Hi, there.'
+
+
+ #body = 'Hi the missing person you reported has been found! The match error was very low! (around 0.2).The person was found at ('+lat +' ' +lon+')!'
msg.attach(MIMEText(body, 'plain'))
-
+
# filename='filename'
# attachment =open(filename,'rb')
@@ -89,15 +97,38 @@ def check_func(argum1):
# encoders.encode_base64(part)
# part.add_header('Content-Disposition',"attachment; filename= "+filename)
- # msg.attach(part)
+
+ # msg.attach(part)
text = msg.as_string()
+ print("text:", text)
server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login(email_user, email_password)
-
+ print("Loggedin")
server.sendmail(email_user, email_send, text)
+ print("sent mail")
server.quit()
-
+
+ from sendgrid import SendGridAPIClient
+ from sendgrid.helpers.mail import Mail
+ message = Mail(from_email="horussurya@gmail.com", to_emails="mranjan1398@gmail.com", subject="hello", html_content="helllo
")
+ try:
+ sg = SendGridAPIClient("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")
+ response = sg.send(message)
+ except Exception as e:
+ print(e.message)
+ '''
+ lat = "55.815480"
+ lon = "37.575385"
+ from sendgrid import SendGridAPIClient
+ from sendgrid.helpers.mail import Mail
+ html_msg = 'Hi the missing person you reported has been found! The match error was very low! (around 0.2).The person was found at ('+lat +' ' +lon+')!
The distance to the home location is 6508 km. The nearest authority is Police Station, Moskva, 125047
'
+ message = Mail( from_email='shivampkumar@gmail.com', to_emails='bhaskar.aayush.brc@gmail.com', subject='Unlost.ai', html_content=html_msg)
+ try:
+ sg = SendGridAPIClient('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')
+ response = sg.send(message)
+ except Exception as e:
+ print(e.message)
sys.stdout.flush()
else:
print("NULL")
@@ -105,5 +136,7 @@ def check_func(argum1):
sys.stdout.flush()
if __name__=="__main__":
+ print("dsfsdf")
+ sys.stdout.flush()
argument1 = sys.argv[1]
check_func(argument1)
diff --git a/scripts/Central_FR.py.save b/scripts/Central_FR.py.save
new file mode 100644
index 0000000..e474ffc
--- /dev/null
+++ b/scripts/Central_FR.py.save
@@ -0,0 +1,135 @@
+from keras import backend as K
+K.set_image_data_format('channels_first')
+import sys
+from fr_utils import *
+from inception_blocks_v2 import *
+import os
+from os.path import join
+import numpy as np
+import pickle
+
+def triplet_loss(y_true, y_pred, alpha=0.2):
+ anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
+
+ # Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
+ pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
+ # Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
+ neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
+ # Step 3: subtract the two previous distances and add alpha.
+ basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
+ # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
+ loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))
+
+ return loss
+
+def check_func(argum1):
+
+ to_email = ['kiranmuthigi123@gmail.com']
+ '''
+ np.set_printoptions(threshold=sys.maxsize)
+ print("inside: ", argum1)
+ FRmodel = faceRecoModel(input_shape=(3, 96, 96))
+ lost = '/home/zemotacqy/hack-moscow-backend/routes/../uploads/lost'
+ #print("lost FilePath: ", lost)
+ files= os.listdir(lost)
+ #print(files)
+ #sys.stdout.flush()
+ FRmodel.compile(optimizer='adam', loss=triplet_loss, metrics=['accuracy'])
+ #print("Loading weitghts:")
+ load_weights_from_FaceNet(FRmodel)
+ cur_encoding = img_to_encoding(argum1, FRmodel)
+ #print(cur_encoding)
+ #sys.stdout.flush()
+ check = 0
+ best_match_found = ""
+ cur_b_sc= 0.7
+ for i in files:
+ fadd = join(lost,i)
+ imgs2 = os.listdir(fadd)
+ imgs = []
+ for file in imgs2:
+ if file.endswith(".pkl"):
+ imgs.append(file)
+ #horussurya needforspeed
+ for j in imgs:
+ with open(join(fadd,j), 'rb') as f:
+ val = pickle.load(f)
+ dist = np.linalg.norm(np.subtract(cur_encoding, val))
+ if dist0:
+ import smtplib
+ from email.mime.text import MIMEText
+ from email.mime.multipart import MIMEMultipart
+
+ #email_user = 'destine.bitm@gmail.com'
+ #email_password = 'K11K4B19R14'
+ email_user = 'horussurya@gmail.com'
+ email_password = 'lemniscate#11235'
+ email_send ='kiranmuthigi123@gmail.com'
+ print(email_send)
+ lat = '55.815480'
+ lon = '37.575385'
+ subject = 'Unlost.ai'
+
+ msg = MIMEMultipart()
+ msg['From'] = email_user
+ msg['To'] = email_send
+ msg['Subject'] = subject
+ print("On the way")
+ body = 'Hi, there.'
+
+
+ #body = 'Hi the missing person you reported has been found! The match error was very low! (around 0.2).The person was found at ('+lat +' ' +lon+')!'
+ msg.attach(MIMEText(body, 'plain'))
+
+ # filename='filename'
+ # attachment =open(filename,'rb')
+
+ # part = MIMEBase('application','octet-stream')
+ # part.set_payload((attachment).read())
+ # encoders.encode_base64(part)
+ # part.add_header('Content-Disposition',"attachment; filename= "+filename)
+
+
+ # msg.attach(part)
+ text = msg.as_string()
+ print("text:", text)
+ server = smtplib.SMTP('smtp.gmail.com', 587)
+ server.starttls()
+ server.login(email_user, email_password)
+ print("Loggedin")
+ server.sendmail(email_user, email_send, text)
+ print("sent mail")
+ server.quit()
+
+ if 1>0:
+
+ import sendgrid
+ client = sendgrid.SendGridClient("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")
+ message = sendgrid.Mail()
+ message.add_to("test@sendgrid.com")
+ message.set_from("horussurya@gmail.com")
+ message.set_subject("Unlost.ai")
+ message.set_html("sdjf")
+ client.send(message)
+ '''
+ if 1>0:
+ from sendgrid import SendGridAPIClient
+ from sendgrid.helpers.mail import Mail message = Mail(
+ from_email='from_email@example.com', sys.stdout.flush()
+ to_emails='to@example.com', else:
+ subject='Sending with Twilio SendGrid is Fun', print("NULL")
+ html_content='and easy to do anywhere, even with Python') print("NULL") try: sys.stdout.flush()
+ sg = SendGridAPIClient(os.environ.get('SENDGRID_API_KEY')) if __name__=="__main__":
+ response = sg.send(message) print("dsfsdf")
+ print(response.status_code) sys.stdout.flush()
+ print(response.body) argument1 = sys.argv[1]
+ print(response.headers) check_func(argument1) except Exception as e:
+ print(e.message)
diff --git a/scripts/Central_FR_x1.py b/scripts/Central_FR_x1.py
new file mode 100644
index 0000000..c81fbb8
--- /dev/null
+++ b/scripts/Central_FR_x1.py
@@ -0,0 +1,105 @@
+from keras import backend as K
+K.set_image_data_format('channels_first')
+import sys
+from fr_utils import *
+from inception_blocks_v2 import *
+import os
+from os.path import join
+import numpy as np
+import pickle
+
+def triplet_loss(y_true, y_pred, alpha=0.2):
+ anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
+
+ # Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
+ pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
+ # Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
+ neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
+ # Step 3: subtract the two previous distances and add alpha.
+ basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
+ # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
+ loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))
+
+ return loss
+
+def check_func(argum1):
+
+ to_email = ['kiranmuthigi123@gmail.com']
+ np.set_printoptions(threshold=sys.maxsize)
+ #print("inside: ", argum1)
+ FRmodel = faceRecoModel(input_shape=(3, 96, 96))
+ lost = '/home/zemotacqy/hack-moscow-backend/routes/../uploads/lost'
+ #print("lost FilePath: ", lost)
+ files= os.listdir(lost)
+ #print(files)
+ #sys.stdout.flush()
+ FRmodel.compile(optimizer='adam', loss=triplet_loss, metrics=['accuracy'])
+ #print("Loading weitghts:")
+ load_weights_from_FaceNet(FRmodel)
+ cur_encoding = img_to_encoding(argum1, FRmodel)
+ #print(cur_encoding)
+ #sys.stdout.flush()
+ check = 0
+ best_match_found = ""
+ cur_b_sc= 0.7
+ for i in files:
+ fadd = join(lost,i)
+ imgs2 = os.listdir(fadd)
+ imgs = []
+ for file in imgs2:
+ if file.endswith(".pkl"):
+ imgs.append(file)
+ #horussurya needforspeed
+ for j in imgs:
+ with open(join(fadd,j), 'rb') as f:
+ val = pickle.load(f)
+ dist = np.linalg.norm(np.subtract(cur_encoding, val))
+ if dist