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recognize_video.py
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# HOW USAGE
'''
python recognize_video.py --detector face_detection_model --embedding-model openface_nn4.small2.v1.t7 --recognizer output/recognizer.pickle --le output/le.pickle
'''
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import pickle
import time
import cv2
import os
import tkinter as tk
#from lib.py import train
from lib import embeding , train
from libData import *
tulisan = "absen"
perintah = "normal"
img_counter = 0
def show_entry_fields():
global tulisan
global perintah
tulisan = "daftar"
part = mystring.get()
print (tulisan)
try:
os.mkdir("dataset//"+part)
except OSError:
print ("gagal")
tulisan = "gagal coba nim lain"
else:
print ("bisa")
img_counter = 0
while True:
frame = vs.read()
frame = imutils.resize(frame, width=600)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, tulisan, (10,450), font, 3, (0, 255, 0), 2, cv2.LINE_AA)
cv2.putText(frame, str(img_counter), (10,300), font, 2, (0, 255, 0), 2, cv2.LINE_AA)
cv2.imshow("Absen", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("r"):
frame = vs.read()
gambar= imutils.resize(frame, width=600)
img_name = "dataset//"+part+"//0000{}.png".format(img_counter)
cv2.imwrite(img_name, gambar)
print("{} written!".format(img_name))
img_counter += 1
if img_counter > 5:
break
master.destroy()
master.quit()
'''
ap = argparse.ArgumentParser()
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())
'''
inputMataKuliah()
args = {}
args["detector"]="face_detection_model"
args["embedding_model"]="openface_nn4.small2.v1.t7"
args["recognizer"]="output/recognizer.pickle"
args["le"]="output/le.pickle"
args["confidence"]=0.5
#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"])
print(args["detector"])
detector = cv2.dnn.readNetFromCaffe(protoPath, modelPath)
#print("[INFO] loading face recognizer...")
embedder = cv2.dnn.readNetFromTorch(args["embedding_model"])
print(args["embedding_model"])
recognizer = pickle.loads(open(args["recognizer"], "rb").read())
print(args["recognizer"])
le = pickle.loads(open(args["le"], "rb").read())
print(args["le"])
#print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
fps = FPS().start()
while True:
frame = vs.read()
frame = imutils.resize(frame, width=600)
(h, w) = frame.shape[:2]
imageBlob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300),(104.0, 177.0, 123.0), swapRB=False, crop=False)
detector.setInput(imageBlob)
detections = detector.forward()
for i in range(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > args["confidence"]:
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
face = frame[startY:endY, startX:endX]
(fH, fW) = face.shape[:2]
if fW < 20 or fH < 20:
continue
faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255,(96, 96), (0, 0, 0), swapRB=True, crop=False)
embedder.setInput(faceBlob)
vec = embedder.forward()
preds = recognizer.predict_proba(vec)[0]
j = np.argmax(preds)
proba = preds[j]
name = le.classes_[j]
simpanData.noId=name
text = "{}: {:.2f}%".format(name, proba * 100)
y = startY - 10 if startY - 10 > 10 else startY + 10
cv2.rectangle(frame, (startX, startY), (endX, endY),(0, 0, 255), 2)
cv2.putText(frame, text, (startX, y),cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
fps.update()
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, tulisan, (10,450), font, 3, (0, 255, 0), 2, cv2.LINE_AA)
cv2.putText(frame, getData("waktu"), (10,350), font, 2, (0, 255, 0), 2, cv2.LINE_AA)
cv2.putText(frame, getData("tangal"), (10,250), font, 2, (0, 255, 0), 2, cv2.LINE_AA)
cv2.putText(frame, dataDataBuff.mataKuliah, (10,50), font, 2, (0, 255, 0), 2, cv2.LINE_AA)
if simpanData.noId != "unknown":
keDb()
cv2.putText(frame, simpanData.getNama(), (10,150), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2, cv2.LINE_AA)
tutupDb()
else:
cv2.putText(frame, "Scan Wajah Anda", (10,150), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2, cv2.LINE_AA)
#print(simpanData.noId)
cv2.imshow("Absen", frame)
key = cv2.waitKey(1) & 0xFF
#print(key)
if key == ord("i"):
keDb()
simpanData.matkul=dataDataBuff.mataKuliah
simpanData.iSql()
tutupDb()
if key == ord("b"):
master = tk.Tk()
mystring = tk.StringVar(master)
tk.Label(master, text="NIM").grid(row=0)
tk.Entry(master,textvariable = mystring).grid(row=0, column=1)
tk.Button(master,text='OK',command=show_entry_fields).grid(row=0, column=2)
master.mainloop()
if key == ord("Q"):
break
if key == ord("q"):
break
if key == ord("z"):
embeding()
train()
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
cv2.destroyAllWindows()
vs.stop()