camera.py
import cv2 import os import time from flask import Response from pathlib import Path import uuid from contextlib import contextmanager from typing import Callable
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") ds_factor = 0.6 font = cv2.FONT_HERSHEY_SIMPLEX
datasets = "datasets"
class VideoCamera: def __new__(cls, args, *kwargs): if getattr(cls, '_instance', False): return cls._instance
cls._instance = super().__new__(cls, *args, **kwargs)
return cls._instance
def __init__(self):
if not hasattr(self, 'video'):
self.video = cv2.VideoCapture(0)
def get_frame(self) -> bytes:
success, image = self.video.read()
if not success:
return b''
output = image.copy()
cv2.rectangle(image, (400, 300), (800, 600), (255, 255, 255), 2)
cv2.addWeighted(image, 0.5, output, 1 - .5, 0, output)
cv2.resize(output, None, fx=ds_factor, fy=ds_factor, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
roi = gray[300:600, 400:800] # ymin,ymax, xmin,xmax !
faces = face_cascade.detectMultiScale(
roi, scaleFactor=1.1,
minNeighbors=5,
minSize=(200, 200),
flags=cv2.CASCADE_SCALE_IMAGE)
for (x, y, w, h) in faces:
cv2.rectangle(output, (x + 400, y + 300), (x + 400 + w, y + 300 + h), (255, 0, 0), 3)
cv2.putText(output, 'Number of Faces : ' + str(len(faces)), (40, 40), font, 1, (255, 0, 0), 2)
break
ret, jpeg = cv2.imencode('.jpg', output)
return jpeg.tobytes()
def save_to_dataset(self) -> str:
data_set_size: int = 20
sub_folder = 'swetha'
(width, height) = (130, 100)
dst_dir = Path(__file__).parent / Path(f'{datasets}/{sub_folder}')
dst_dir.mkdir(parents=True, exist_ok=True)
num_of_files = len([_ for _ in dst_dir.glob('*.*')])
if num_of_files >= data_set_size:
return ""
for _ in range(data_set_size - num_of_files):
success, image = self.video.read()
output = image.copy()
cv2.rectangle(image, (400, 300), (800, 600), (255, 255, 255), 2)
cv2.addWeighted(image, 0.5, output, 1 - .5, 0, output)
cv2.resize(output, None, fx=ds_factor, fy=ds_factor, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
roi = gray[300:600, 400:800] # ymin,ymax, xmin,xmax !
faces = face_cascade.detectMultiScale(
roi, scaleFactor=1.1,
minNeighbors=5,
minSize=(200, 200),
flags=cv2.CASCADE_SCALE_IMAGE)
for (x, y, w, h) in faces:
cv2.rectangle(output, (x + 400, y + 300), (x + 400 + w, y + 300 + h), (255, 0, 0), 3)
cv2.putText(output, 'Number of Faces : ' + str(len(faces)), (40, 40), font, 1, (255, 0, 0), 2)
face = gray[y + 300:y + 300 + h, x + 400:x + 400 + w]
face_resize = cv2.resize(face, (width, height))
cv2.imwrite(f'{dst_dir / Path(str(uuid.uuid4()))}.png', face_resize)
return f'{data_set_size} image captured.'
app.py
from flask import Flask, render_template, Response, request, jsonify from werkzeug.utils import secure_filename from camera import VideoCamera from facerecog import VideoCamera import numpy as np from os import path, getcwd import pathlib import json import time import cv2 import os
app = Flask(__name__)
@app.route('/') def index(): timeNow = time.asctime(time.localtime(time.time())) # temp, hum = getDHTdata()
templateData = {
'time': timeNow,
# 'temp': temp,
# 'hum': hum
}
return render_template('index.html', **templateData)
@app.route('/camera') def cam(): timeNow = time.asctime(time.localtime(time.time())) templateData = { 'time': timeNow } return render_template('camera.html', **templateData)
def gen(camera): while True: frame = camera.get_frame() yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
@app.route('/video_feed') def video_feed(): return Response(gen(VideoCamera()), mimetype='multipart/x-mixed-replace; boundary=frame')
@app.route('/exec2') def parse1(): response_data_collection = VideoCamera().save_to_dataset() response_data_collection = "Done with Collecting Data" if response_data_collection else "you are already exist" return render_template('camera.html', alert=response_data_collection)
@app.route('/facerecog') def clock(): timeNow = time.asctime(time.localtime(time.time())) templateData = { 'time': timeNow } return render_template('recognize.html', **templateData)
def gen(facerecog): while True: frame = facerecog.get_frame() yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
@app.route('/feed') def feed(): return Response(gen(VideoCamera()), mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__': app.run(host='0.0.0.0', debug=True)
index.html
 }})
Face Recognition System
Go To The Face Recognition!
camera.html
<html> <head> <title>Add Face</title> <link rel="stylesheet" href="../static/style.css"/> <style> body { background-color: white; text-align: center; color: black; } </style> </head> <body>
Keep your face inside the box
{{ time }}
RETURN
</body> </html>
