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facefrontend.py
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facefrontend.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jan 15 14:49:19 2019
@author: krish.naik
"""
# Face Recognition
# Importing the libraries
from PIL import Image
from keras.applications.vgg16 import preprocess_input
import base64
from io import BytesIO
import json
import random
import cv2
from keras.models import load_model
import numpy as np
from keras.preprocessing import image
model = load_model('facefeatures_new_model_final.h5')
# Loading the cascades
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
def face_extractor(img):
# Function detects faces and returns the cropped face
# If no face detected, it returns the input image
#gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(img, 1.3, 5)
if faces is ():
return None
# Crop all faces found
for (x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,255),2)
cropped_face = img[y:y+h, x:x+w]
return cropped_face
# Doing some Face Recognition with the webcam
video_capture = cv2.VideoCapture(0)
while True:
_, frame = video_capture.read()
#canvas = detect(gray, frame)
#image, face =face_detector(frame)
face=face_extractor(frame)
if type(face) is np.ndarray:
face = cv2.resize(face, (224, 224))
im = Image.fromarray(face, 'RGB')
#Resizing into 128x128 because we trained the model with this image size.
img_array = np.array(im)
#Our keras model used a 4D tensor, (images x height x width x channel)
#So changing dimension 128x128x3 into 1x128x128x3
img_array = np.expand_dims(img_array, axis=0)
pred = model.predict(img_array)
print(pred)
name="None matching"
if(pred[0][3]>0.5):
name='Krish'
cv2.putText(frame,name, (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
else:
cv2.putText(frame,"No face found", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()