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putAttendance.py
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putAttendance.py
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# USAGE
# python putAttendance.py --detector face_detection_model --embedding-model openface_nn4.small2.v1.t7 --recognizer output/recognizer.pickle --le output/le.pickle --image received/
# import the necessary packages
import numpy as np
import argparse
import imutils
import pickle
import cv2
import os
import time
import datetime
from pymongo import MongoClient
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to input image")
ap.add_argument("-d", "--detector", required=True,
help="path to OpenCV's deep learning face detector")
ap.add_argument("-m", "--embedding-model", required=True,
help="path to OpenCV's deep learning face embedding model")
ap.add_argument("-r", "--recognizer", required=True,
help="path to model trained to recognize faces")
ap.add_argument("-l", "--le", required=True,
help="path to label encoder")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# load our serialized face detector from disk
#print("[INFO] loading face detector...")
protoPath = os.path.sep.join([args["detector"], "deploy.prototxt"])
modelPath = os.path.sep.join([args["detector"],
"res10_300x300_ssd_iter_140000.caffemodel"])
detector = cv2.dnn.readNetFromCaffe(protoPath, modelPath)
client = MongoClient(port=27017)
db = client.record
# load our serialized face embedding model from disk
#print("[INFO] loading face recognizer...")
embedder = cv2.dnn.readNetFromTorch(args["embedding_model"])
# load the actual face recognition model along with the label encoder
recognizer = pickle.loads(open(args["recognizer"], "rb").read())
le = pickle.loads(open(args["le"], "rb").read())
# load the image, resize it to have a width of 600 pixels (while
# maintaining the aspect ratio), and then grab the image dimensions
#image = cv2.imread(args["image"])
days = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
d = datetime.date.today().timetuple()
date = str(d[2]) + ":" + str(d[1]) + ":" + str(d[0])
day = days[d[6]]
directory = args["image"]
def getPeriod() :
now = datetime.datetime.now()
p = now.replace(hour = 9, minute = 25)
if now < p :
return 1
p = now.replace(hour = 10, minute = 15)
if now < p :
return 2
p = now.replace(hour = 11, minute = 25)
if now < p :
return 3
p = now.replace(hour = 12, minute = 15)
if now < p :
return 4
p = now.replace(hour = 14, minute = 0)
if now < p :
return 5
p = now.replace(hour = 14, minute = 50)
if now < p :
return 6
p = now.replace(hour = 15, minute = 50)
if now < p :
return 7
p = now.replace(hour = 16, minute = 45)
if now < p :
return 8
period = getPeriod()
attendees = {}
for per in le.classes_ :
attendees[per] = []
# loop over the detections
for i in range(0, 100):
time.sleep(.4)
try:
image = cv2.imread(directory +str(i) + ".png")
except:
continue
image = imutils.resize(image, width=600)
(h, w) = image.shape[:2]
# construct a blob from the image
imageBlob = cv2.dnn.blobFromImage(
cv2.resize(image, (300, 300)), 1.0, (300, 300),
(104.0, 177.0, 123.0), swapRB=False, crop=False)
# apply OpenCV's deep learning-based face detector to localize
# faces in the input image
detector.setInput(imageBlob)
detections = detector.forward()
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections
if confidence > args["confidence"]:
# compute the (x, y)-coordinates of the bounding box for the
# face
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# extract the face ROI
face = image[startY:endY, startX:endX]
(fH, fW) = face.shape[:2]
# ensure the face width and height are sufficiently large
if fW < 20 or fH < 20:
continue
# construct a blob for the face ROI, then pass the blob
# through our face embedding model to obtain the 128-d
# quantification of the face
faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255, (96, 96),
(0, 0, 0), swapRB=True, crop=False)
embedder.setInput(faceBlob)
vec = embedder.forward()
# perform classification to recognize the face
preds = recognizer.predict_proba(vec)[0]
j = np.argmax(preds)
proba = preds[j]
name = le.classes_[j]
#print(name)
timestamp = time.strftime('%H:%M:%S')
attendees[name].append([timestamp])
#print(result)
# draw the bounding box of the face along with the associated
# probability
#text = "{}: {:.2f}%".format(name, proba * 100)
#y = startY - 10 if startY - 10 > 10 else startY + 10
#cv2.rectangle(image, (startX, startY), (endX, endY),
# (0, 0, 255), 2)
#cv2.putText(image, text, (startX, y),
# cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
# show the output image
#cv2.imshow("Image", image)
#cv2.waitKey(0)
for key, value in attendees.iteritems():
#print key, value
if len(value) > 5 :
record = { 'name': key, 'time' : value[0][0], 'date' : date, 'day' : day, 'period' : period}
result = db.attendance.insert_one(record)
print("\nAttendance Updated");