app.py from flask import Flask, render_template, request, jsonify import os import cv2 import numpy as np import pandas as pd from datetime import datetime import base64 app = Flask(__name__) # Setup face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml") dataset_path = "./face_dataset/" os.makedirs(dataset_path, exist_ok=True) # KNN distance def distance(v1, v2): return np.sqrt(((v1 - v2) ** 2).sum()) def knn(train, test, k=5): dist = [] for i in range(train.shape[0]): ix = train[i, :-1] iy = train[i, -1] d = distance(test, ix) dist.append([d, iy]) dk = sorted(dist, key=lambda x: x[0])[:k] labels = np.array(dk)[:, -1] return np.unique(labels, return_counts=True)[0][0] # Attendance system class AttendanceSystem: def __init__(self): self.file = "attendance.csv" self.columns = ["Name", "Date", "Time"] if not os.path.exists(self.file): pd.DataFrame(columns=self.columns).to_csv(self.file, index=False) def mark(self, name): today = datetime.now().strftime("%Y-%m-%d") now = datetime.now().strftime("%H:%M:%S") df = pd.read_csv(self.file) existing = df[(df["Name"] == name) & (df["Date"] == today)] if existing.empty: new_entry = pd.DataFrame([[name, today, now]], columns=self.columns) df = pd.concat([df, new_entry], ignore_index=True) df.to_csv(self.file, index=False) return True return False attendance = AttendanceSystem() # Home page @app.route('/') def index(): return render_template('index.html') @app.route('/register') def register(): return render_template('register.html') @app.route('/mark') def mark(): return render_template('mark.html') # API to save face during registration # Modified register_face endpoint with preprocessing @app.route('/api/register_face', methods=['POST']) def register_face(): data = request.json name = data['name'] images = data['images'] # List of base64 images face_data = [] for img_data in images: img_bytes = base64.b64decode(img_data.split(",")[1]) np_arr = np.frombuffer(img_bytes, np.uint8) img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) gray = cv2.equalizeHist(gray) # Histogram equalization faces = face_cascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces[:1]: face = img[y:y+h, x:x+w] face = cv2.resize(face, (100, 100)) # Original face face_data.append(face.flatten()) # Data augmentation: horizontal flip flipped_face = cv2.flip(face, 1) face_data.append(flipped_face.flatten()) if face_data: face_data = np.array(face_data) np.save(os.path.join(dataset_path, f"{name}.npy"), face_data) return jsonify({"status": "success", "message": f"{len(face_data)} faces saved"}) else: return jsonify({"status": "fail", "message": "No faces detected"}) # API to mark attendance @app.route('/api/mark_attendance', methods=['POST']) def mark_attendance(): img_data = request.json['image'] img_bytes = base64.b64decode(img_data.split(",")[1]) np_arr = np.frombuffer(img_bytes, np.uint8) img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) # Load training data face_data = [] labels = [] names = {} class_id = 0 for file in os.listdir(dataset_path): if file.endswith('.npy'): data = np.load(os.path.join(dataset_path, file)) face_data.append(data) names[class_id] = file[:-4] labels.extend([class_id] * data.shape[0]) class_id += 1 if not face_data: return jsonify({"status": "fail", "message": "No trained data found"}) X_train = np.concatenate(face_data, axis=0) y_train = np.array(labels).reshape(-1, 1) trainset = np.hstack((X_train, y_train)) for (x, y, w, h) in faces[:1]: face = img[y:y+h, x:x+w] face = cv2.resize(face, (100, 100)).flatten() pred_id = knn(trainset, face) name = names.get(pred_id, "Unknown") if name != "Unknown": marked = attendance.mark(name) msg = "Attendance marked" if marked else "Already marked today" return jsonify({"status": "success", "name": name, "message": msg}) return jsonify({"status": "fail", "message": "No known face detected"}) if __name__ == '__main__': app.run(host='0.0.0.0', port=5000, debug=True) index.html Face Attendance

Welcome to Face Attendance System

Register Face Mark Attendance
register.html Register Face
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Register Face

Enter your name and click "Start Capture"

mark.html Mark Attendance
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Mark Attendance

Position your face in the camera and click the button

webcam.js function initWebcam(videoId) { const video = document.getElementById(videoId); navigator.mediaDevices.getUserMedia({ video: true }).then(stream => { video.srcObject = stream; }); } function captureFrame(frameArray, single = false) { return new Promise((resolve) => { const video = document.getElementById("video"); const canvas = document.createElement("canvas"); canvas.width = video.videoWidth; canvas.height = video.videoHeight; const ctx = canvas.getContext("2d"); ctx.drawImage(video, 0, 0); // Check if frame contains content const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height); const isEmpty = !imageData.data.some(channel => channel !== 0); if (!isEmpty) { const image = canvas.toDataURL("image/jpeg"); if (!single) { frameArray.push(image); } resolve(image); } else { resolve(null); } }); }