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Greeting_User_With_Webcam.py
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Greeting_User_With_Webcam.py
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import face_recognition
import cv2
import numpy as np
from gtts import *
import os
import time
import pickle
import numpy as np
import datetime
import requests
##########################################################################################################################
def Send_Push_Notifications(Name , Image_File):
Image_File = 'Webcam_'+str(Image_File)+'.jpg'
r = requests.post("https://api.pushover.net/1/messages.json", data = {
"token": "YOUR TOKEN HERE",
"user": "YOUR USER KEY HERE",
"message": "\""+"Found : "+str(Name)+" With your Laptop.\n"+"Time :"+ Image_File +"\""
},
files = {
"attachment": ("image.jpg", open(Image_File, "rb"), "image/jpeg")
})
print(r.text)
video_capture = cv2.VideoCapture(0)
print("Loading Application..")
with open('dataset_faces.dat', 'rb') as f:
all_face_encodings = pickle.load(f)
# Create arrays of known face encodings and their names
known_face_encodings = np.array(list(all_face_encodings.values()))
known_face_names = list(all_face_encodings.keys())
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
print("Loading App Done.")
print("Getting camera Ready..\nRecognizing Face..Please be steady..");
time.sleep(2)
# Grab a single frame of video
ret, frame = video_capture.read()
print("Frame Captured..");
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
# Loop through each face in this frame of video
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
face_names.append(name)
print(face_names)
face_names_string = " ".join(face_names)
face_names_string = face_names_string.replace("_" ," ")
mytext = 'Sueswagatham'+ str(face_names_string)
# Language in which you want to convert
language = 'te'
# Passing the text and language to the engine,
# here we have marked slow=False. Which tells
# the module that the converted audio should
# have a high speed
myobj = gTTS(text=mytext, lang=language, slow=False)
# Saving the converted audio in a mp3 file named
# welcome
myobj.save("welcome.mp3")
# Playing the converted file
os.system("mpg321 welcome.mp3")
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (255, 0, 0), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (255, 0, 0), cv2.FILLED)
font = cv2.FONT_HERSHEY_PLAIN
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
x = datetime.datetime.now()
cv2.imwrite('Webcam_'+str(x)+'.jpg',frame)
Send_Push_Notifications(str(face_names_string) , x)