DataBase Support Added face recognition database isssue

face_recognition/Database-Connection face recognition/Text

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+I have generated a code for upadting values into data base from cmd into
+sqlite while face recognition just first download the sqlite and then form the database file into
+into same folder and then connect all required data connection into it your code.
+
+
+
+And for displaying everthing images from your database on the images recognition go detector.py
+and see the code check it out!!!!!!

face_recognition/Database-Connection face recognition/dataset.py

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+import cv2
+import numpy as np
+import sqlite3
+
+# Load the cascade
+from Tools.scripts.treesync import raw_input
+
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+# To capture video from webcam.
+cap = cv2.VideoCapture(0)
+# To use a video file as input
+# cap = cv2.VideoCapture('filename.mp4')
+
+
+def insertOrupdate(Id, Name):
+    conn=sqlite3.connect("faceBase.db")
+    cmd="SELECT * FROM People WHERE ID="+str(Id)
+    cursor = conn.execute(cmd)
+    isRecordExist=0
+    for row in cursor:
+        isRecordExist=1
+
+    if(isRecordExist==1):
+        cmd="UPDATE People SET NAME "+str(Name)+"WHERE ID"+str(Id)
+    else:
+        cmd="INSERT INTO People(ID,NAME) Values("+str(Id)+","+str(Name)+")"
+
+    conn.execute(cmd)
+    conn.commit()
+    conn.close()
+
+id = raw_input('enter user id')
+name = raw_input('enter your name')
+insertOrupdate(id,name)
+sampleNum = 0
+
+while True:
+    # Read the frame
+    _, img = cap.read()
+
+    # Convert to grayscale
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # Detect the faces
+    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+
+    # Draw the rectangle around each face
+    for (x, y, w, h) in faces:
+        sampleNum=sampleNum+1
+        cv2.imwrite("dataSet/User." +str(id)+"." +str(sampleNum)+".jpg", gray[y:y+h, x:x+w])
+        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
+        cv2.waitKey(100)
+
+    cv2.imshow('img', img)
+    cv2.waitKey(1)
+
+    if (sampleNum>20):
+        break
+
+# Release the VideoCapture object
+cap.release()
+cv2.destroyAllWindows()

face_recognition/Database-Connection face recognition/detector.py

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+from sqlite3.dbapi2 import Cursor
+import cv2
+import numpy as np
+from PIL import Image
+import pickle
+import sqlite3
+
+
+recognizer= cv2.createLBPHFaceRecognizer()
+recognizer.load('trainner/trainner.yml')
+cascadePath="Classifiers/face.xml"
+faceCascade=cv2.CascadeClassifier(cascadePath)
+path='dataset'
+
+
+
+def getProfile(id):
+    conn=sqlite3.connect("faceBase.py")
+    cmd="SELECT * FROM People WHERE ID="+str(id)
+    cursor=conn.execute(cmd)
+    profile=None
+    for row in cursor:
+        profile=row
+    conn.close()
+    return profile
+
+
+cam=cv2.VideoCapture(1)
+font=cv2.cv.InitFont(cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1,0, 1, 1)
+
+while(True):
+    ret,img=cam.read()
+    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    faces=faceCascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5, minSize=(100,100), flags=cv2.CASCADE_SCALE_IMAGE)
+    for (x,y,w,h) in faces:
+        sampleNum=sampleNum+1
+        cv2.imwrite("dataset/user."+id+'.'+str(sampleNum)+"jpg",gray[y:y+h,x:x+w])
+        cv2.rectangle(im , (x-50,y-50), (x+w+50,y+h+50),(225,0,0),2)
+
+    cv2.imshow('im',im)
+    cv2.waitKey(100)
+    if sampleNum>20:
+        cam.realease()
+        cv2.destroyAllWindows()
+        break
+
+
+
+
+
+