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web.py
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web.py
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from flask import Flask, render_template, Response
import cv2
import numpy as np
import os
app = Flask(__name__)
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);
font = cv2.FONT_HERSHEY_SIMPLEX
# Initialize and start realtime video capture
camera = cv2.VideoCapture(0) # use 0 for web camera
camera.set(3, 640) # set video widht
camera.set(4, 480) # set video height
# Define min window size to be recognized as a face
minW = 0.1*camera.get(3)
minH = 0.1*camera.get(4)
#iniciate id counter
id = 0
def gen_frames(): # generate frame by frame from camera
while True:
# Capture frame-by-frame
success, img = camera.read() # read the camera frame
if not success:
break
else:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor = 1.2,
minNeighbors = 5,
minSize = (int(minW), int(minH)),
)
for(x,y,w,h) in faces:
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
# Check if confidence is less them 100 ==> "0" is perfect match
if (round(100 - confidence) < 60):
id = "unknown"
confidence = " {0}%".format(round(100 - confidence))
# confidence = " {0}%".format(round(100 - confidence))
cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)
ret, buffer = cv2.imencode('.jpg', img)
frame = buffer.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') # concat frame one by one and show result
@app.route('/video_feed')
def video_feed():
#Video streaming route. Put this in the src attribute of an img tag
return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
@app.route('/')
def index():
"""Video streaming home page."""
return render_template('index.html')
if __name__ == '__main__':
app.run(debug=True)