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main2.py
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#image size should be less than 150 kb and jpg format
import face_recognition #library for face recognition
import cv2
import numpy as np
import time
import pandas as pd
import os
import tkinter as tk # for defining mouse click functions
from datetime import datetime,timezone
from multiprocessing import Process
import requests
import json
video_capture = cv2.VideoCapture(0)
# data0=[]
logging_file = open('log.txt',"a")
global window
global flag
flag=0
width = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
counter = 1 # initialize counter outside the function
def mouse_click(event,x,y,flags,params):
global time_type, counter
time_type=""
if event == cv2.EVENT_LBUTTONDOWN:
if x<320:
time_type="in_time"
else:
time_type="out_time"
if counter % 2 == 0: # write to log file only on even clicks
# Process(target=insert_data,
# args=(time_type, name, datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"))).start()
logging_file.write(name + " " + time_type + " " + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + '\n')
if name != 'Prof_Samanta':
insert_data( time_type, name, datetime.now().strftime("%Y-%m-%d %H:%M:%S") )
counter += 1 # increment the counter on every click
flag=1
window.destroy()
folderPath = 'Images'
known_face_encodings = []
known_face_names = []
for file in os.listdir(folderPath):
# Load image and get face encoding
img_path = os.path.join(folderPath, file)
image = face_recognition.load_image_file(img_path)
face_encoding = face_recognition.face_encodings(image,model='large')[0]
# Extract name from file name
name = file.split('.')[0]
# Append face encoding and name to lists
known_face_encodings.append(face_encoding)
known_face_names.append(name)
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
# names =set()s
# prev_time = time.time()
# store_interval = 10
def insert_data(time_type, name, time):
logging_file = open('log.txt',"a")
if name=="Unknown or try again....":
logging_file.write("skipped for " + time_type + " for " + name + " at " + time + "\n")
return
logging_file.write("hitting " + time_type + " for " + name + " at " + time + "\n")
if time_type == "in_time":
resp = requests.get("http://13.232.181.16/items/attendance?filter[roll][_eq]="+ str(name) +"&filter[day(in_time)][_eq]=" + str(
datetime.now().day) + "&filter[month(in_time)][_eq]=" + str(datetime.now().month) +
"&filter[year(in_time)][_eq]=" + str(datetime.now().year) +
"&filter[out_time][_null]=null").text
res = json.loads(resp)
print(res, len(res["data"]))
if len(res["data"]): #there is in time but no time for same entry
inTime = res["data"][0]["in_time"]
timeDiff = ( datetime.strptime(time, "%Y-%m-%d %H:%M:%S") - datetime.strptime(inTime, "%Y-%m-%dT%H:%M:%S.%fZ") )
if timeDiff.total_seconds() < 15*60: #the in time is within 15 minutes then skip
logging_file.write("skipped for " + time_type + " for " + name + " at " + time + "\n")
return
in_data = {'roll': name, time_type: time}
data = requests.post('http://13.232.181.16/items/attendance', json=in_data)
else:
resp = requests.get("http://13.232.181.16/items/attendance?filter[roll][_eq]=" + str(
name) + "&filter[out_time][_null]=null")
res = json.loads(resp.text)
if len(res["data"]) > 0:
id = res["data"][0]["id"]
res["data"][0]["out_time"] = time
resp2 = requests.patch('http://13.232.181.16/items/attendance/' + id, json=res["data"][0])
else:
logging_file.write("Skipped for " + name + " " + time_type + "\n")
while True:
if(flag==1):
time.sleep(2)
flag=0
ret, frame = video_capture.read()
cv2.namedWindow('Video')
cv2.setMouseCallback('Video',mouse_click)
# 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)
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,model='large')
face_names = []
for face_encoding in face_encodings:
matches = face_recognition.compare_faces(known_face_encodings, face_encoding,tolerance=0.5)
name = "Unknown or try again...."
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)
# prev=len(names)
# names.add(name)
# if len(names) > prev:
# t = time.localtime()
# current_time = time.strftime("%H:%M:%S", t)
# data.append([name,current_time])
# df = pd.DataFrame(data, columns=['Name', 'Time'])
# df.to_excel('name_time.xlsx', index=False)
# prev_time = time.time()
process_this_frame = not process_this_frame
# Display the results
# cv2.namedWindow('Video')
# cv2.setMouseCallback('Video',mouse_click)
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_HERSHEY_DUPLEX
frame=cv2.putText(frame, name, (left + 14, bottom - 6), font, 0.5, (255, 255, 255), 1)
if counter % 2 == 0:
frame = cv2.putText(frame, "IN", (int(width / 4), 50), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 255), 5)
frame = cv2.putText(frame, "OUT", (int(3 * width / 4), 50), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 255), 5)
frame = cv2.line(frame, (int(width / 2), 0), (int(width / 2), int(height)), (255, 255, 255), 2)
else:
frame = cv2.putText(frame,"FACE DETECTED", (int(width / 4), 440), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 255), 5)
frame = cv2.putText(frame, "CLICK TO CONTINUE", (int(width / 5), 50), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 255), 5)
cv2.imshow('Video', frame)
window = tk.Tk()
window.withdraw()
window.geometry("6x6")
window.mainloop()
# 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()
logging_file.close()