updated files

fras/Student.csv

+

fras/app.py

+from flask import Flask, render_template
+
+
+app = Flask(__name__)
+
+@app.route('/')
+def index(name=None):
+    return render_template('index.html',name=name)
+
+@app.route('/exec')
+def parse(name=None):
+    import face_recognize
+    print("done")
+    return render_template('index.html',name=name)
+
+@app.route('/exec2')
+def parse1(name=None):
+	import create_data
+	print("done")
+	return render_template('index.html',name=name)
+
+if __name__ == '__main__':
+    app.run()
+    app.debug = True
+    

fras/app.yaml

+runtime: python27
+api_version: 1
+threadsafe: true
\ No newline at end of file

fras/create_data.py

+#creating database
+import cv2, sys, numpy, os
+haar_file = 'haarcascade_frontalface_default.xml'
+datasets = 'datasets'  #All the faces data will be present this folder
+sub_data = 'new'     #These are sub data sets of folder, for my faces I've used my name
+
+path = os.path.join(datasets, sub_data)
+if not os.path.isdir(path):
+    os.mkdir(path)
+(width, height) = (130, 100)    # defining the size of image
+
+
+face_cascade = cv2.CascadeClassifier(haar_file)
+webcam = cv2.VideoCapture(0) #'0' is use for my webcam, if you've any other camera attached use '1' like this
+
+# The program loops until it has 30 images of the face.
+count = 1
+while count < 100: 
+    (_, im) = webcam.read()
+    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 4)
+    for (x,y,w,h) in faces:
+        cv2.rectangle(im,(x,y),(x+w,y+h),(255,0,0),2)
+        face = gray[y:y + h, x:x + w]
+        face_resize = cv2.resize(face, (width, height))
+        cv2.imwrite('%s/%s.png' % (path,count), face_resize)
+    count += 1
+	
+    cv2.imshow('OpenCV', im)
+    key = cv2.waitKey(10)
+    if key == 27:
+        break