run_script.py

from flask import Flask, render_template
import cv2
from align_custom import AlignCustom
from face_feature import FaceFeature
from mtcnn_detect import MTCNNDetect
from tf_graph import FaceRecGraph
import argparse
import sys
import json
import time
import numpy as np

app = Flask(__name__)


@app.route('/')
def home():
    return render_template('home.html')
    
@app.route('/input')
def create_manual_data():
    
    FRGraph = FaceRecGraph();
    MTCNNGraph = FaceRecGraph();
    aligner = AlignCustom();
    extract_feature = FaceFeature(FRGraph)
    face_detect = MTCNNDetect(MTCNNGraph, scale_factor=2); #scale_factor, rescales image for faster detection
    vs = cv2.VideoCapture(0); #get input from webcam
#     print("Please input new user ID:")
    new_name = request.form['input_name']; #ez python input()
    f = open('./facerec_128D.txt','r');
    data_set = json.loads(f.read());
    person_imgs = {"Left" : [], "Right": [], "Center": []};
    person_features = {"Left" : [], "Right": [], "Center": []};
    print("Please start turning slowly. Press 'q' to save and add this new user to the dataset");
    while True:
        _, frame = vs.read();
        rects, landmarks = face_detect.detect_face(frame, 80);  # min face size is set to 80x80
        for (i, rect) in enumerate(rects):
            aligned_frame, pos = aligner.align(160,frame,landmarks[:,i]);
            if len(aligned_frame) == 160 and len(aligned_frame[0]) == 160:
                person_imgs[pos].append(aligned_frame)
                cv2.imshow("Captured face", aligned_frame)
        key = cv2.waitKey(1) & 0xFF
        if key == ord("q"):
            break

    for pos in person_imgs: #there r some exceptions here, but I'll just leave it as this to keep it simple
        person_features[pos] = [np.mean(extract_feature.get_features(person_imgs[pos]),axis=0).tolist()]
    data_set[new_name] = person_features;
    f = open('./facerec_128D.txt', 'w');
    f.write(json.dumps(data_set))

    return render_template('home.html')
    

@app.route('/verify')
def camera_recog():
    FRGraph = FaceRecGraph();
    MTCNNGraph = FaceRecGraph();
    aligner = AlignCustom();
    extract_feature = FaceFeature(FRGraph)
    face_detect = MTCNNDetect(MTCNNGraph, scale_factor=2); #scale_factor, rescales image for faster detection
    print("[INFO] camera sensor warming up...")
    vs = cv2.VideoCapture(0); #get input from webcam
    detect_time = time.time()
    while True:
        _,frame = vs.read();
        #u can certainly add a roi here but for the sake of a demo i'll just leave it as simple as this
        rects, landmarks = face_detect.detect_face(frame,80);#min face size is set to 80x80
        aligns = []
        positions = []

        for (i, rect) in enumerate(rects):
            aligned_face, face_pos = aligner.align(160,frame,landmarks[:,i])
            if len(aligned_face) == 160 and len(aligned_face[0]) == 160:
                aligns.append(aligned_face)
                positions.append(face_pos)
            else: 
                print("Align face failed") #log        
        if(len(aligns) > 0):
            features_arr = extract_feature.get_features(aligns)
            recog_data = findPeople(features_arr,positions)
            for (i,rect) in enumerate(rects):
                cv2.rectangle(frame,(rect[0],rect[1]),(rect[2],rect[3]),(255,0,0)) #draw bounding box for the face
                cv2.putText(frame,recog_data[i][0]+" - "+str(recog_data[i][1])+"%",(rect[0],rect[1]),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),1,cv2.LINE_AA)


        cv2.imshow("Frame",frame)
        key = cv2.waitKey(1) & 0xFF
        if key == ord("q"):
            break
            
    return render_template('home.html')

            
            
def findPeople(features_arr, positions, thres = 0.6, percent_thres = 70):
    '''
    :param features_arr: a list of 128d Features of all faces on screen
    :param positions: a list of face position types of all faces on screen
    :param thres: distance threshold
    :return: person name and percentage
    '''
    TIMEOUT = 10 #10 seconds

    f = open('./facerec_128D.txt','r')
    data_set = json.loads(f.read());
    returnRes = [];
    for (i,features_128D) in enumerate(features_arr):
        result = "Unknown";
        smallest = sys.maxsize
        for person in data_set.keys():
            person_data = data_set[person][positions[i]];
            for data in person_data:
                distance = np.sqrt(np.sum(np.square(data-features_128D)))
                if(distance < smallest):
                    smallest = distance;
                    result = person;
        percentage =  min(100, 100 * thres / smallest)
        if percentage <= percent_thres :
            result = "Unknown"
        returnRes.append((result,percentage))
    return returnRes    




if __name__ == '__main__':
    app.run()