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FaceRecog.py
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FaceRecog.py
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# May The Force Be With You
# Team FARFASA
# ----------------------------------------------------------------------------
import cv2
import numpy as np
import farfasa as farfasa
import argparse
import imutils
import pickle
import time as t
from datetime import datetime,time,timedelta
import requests
import json
#ap = argparse.ArgumentParser()
#ap.add_argument("-e", "--encodings", required=True, help="path to serialized db of facial encodings")
#args = vars(ap.parse_args())
#input()
def Attendance(args):
timer = t.perf_counter_ns()
key1 = []
val1 = []
video_capture = cv2.VideoCapture(0)
print("loading encodings")
data = pickle.loads(open(args["encodings"], "rb").read())
for key in data:
key1.append(key)
val1.append(data[key])
known_face_encodings = val1[0]
known_face_names = val1[1]
# Initialize variables
face_locations = []
face_names = []
process_this_frame = True
#AttDictList = []
XXX = []
Attendees = []
Times = []
while True:
# change val to change timer
if(t.perf_counter_ns()-timer > .25*(60*(10**9))):
break
TempDict = []
# Grab a single frame of video
ret, frame = video_capture.read()
# 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 = farfasa.faceLocations(rgb_small_frame)
face_encodings = farfasa.faceEncodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = farfasa.compareFaces(known_face_encodings, face_encoding)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = farfasa.faceDist(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)
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), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_ITALIC
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
Attendees.append(name)
Times.append(datetime.now().strftime("%H:%M:%S"))
X = json.dumps({"Roll": name, "Time": datetime.now().strftime("%H:%M:%S"), "Date": datetime.now().strftime("%d-%m-%Y"), "ID" : "PitchWebCam01", "UTC": (datetime.utcnow()-datetime(1970,1,1)).total_seconds()})
#AttDictList.append(X)
if int(round(t.time()*1000))%5 == 0:
XXX.append(X)
SendToServer(X)
#print(X)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
def convertList(a):
dict1 = {a[i]:'a' for i in range(0,len(lst))}
return dict1
lst = known_face_names
AttendanceDict = convertList(lst)
for i in lst:
if i in Attendees:
AttendanceDict[i] = 'p'
a = args["encodings"]
a = a.replace(".pickle", "")
def getPeriod(S=None):
if(8*60+0 <= S < 9*60+20):
return 1
elif(9*60+20 <= S < 10*60+10):
return 2
elif (10*60+30 <= S < 11*60+20):
return 3
elif (11*60+20 <= S < 12*60+10):
return 4
elif (13*60+40 <= S < 14*60+30):
return 5
elif (14*60+30 <= S < 15*60+20):
return 6
elif (15*60+30 <= S < 16*60+20):
return 7
elif (16*60+20 <= S < 17*60+10):
return 8
else:
return 99
times = Times
T = str(timedelta(seconds=sum(map(lambda f: int(f[0])*3600 + int(f[1])*60 + int(f[2]), map(lambda f: f.split(':'), times)))/len(times)))
T1 = T.split(".")
T = T1[0]
def getTimeSec(T):
h,m,s = T.split(":")
return int(h)*60 + int(m)
S = getTimeSec(T)
P = getPeriod(S)
#print(P)
AttendanceDict["Class"] = str(P)+"-"+a
AttendanceDict["Date"] = datetime.now().strftime("%d-%m-%Y")
AttendanceDict["ID"] = "PitchWebCam01"
#print(AttendanceDict)
#print(AttDictList)
SendToServer(AttendanceDict)
#print(XXX)
def SendToServer(Dict1):
print(Dict1)
# API_ENDPOINT = "111.111.111.111"
# API_KEY = "XXXXXXXXXXXXXXXXXXXXX"
# User = "Cam1"
# r = requests.post(url = API_ENDPOINT, json = Dict1, auth =(User,API_KEY))
if __name__ == "__main__":
args = {"encodings":"0"}
#E = input()
E = "encodings.pickle"
args["encodings"] = E
Attendance(args)