face_recognition.py

from tkinter import *
from tkinter import ttk
from PIL import Image, ImageTk
from tkinter import messagebox
from time import strftime
from datetime import datetime
import mysql.connector
import cv2
import os
import numpy as np

class Face_Recognition:
    def __init__(self, root):
        self.root = root
        self.root.geometry("1530x790+0+0")  
        self.root.title("Face Recognition System")

        title_lbl = Label(self.root, text="FACE RECOGNITION", font=("times to roman", 34, "bold"), bg="White", fg="green")
        title_lbl.place(x=0, y=0, width=1530, height=40)

        # 1st image
        img_top = Image.open(r"C:\Users\Rohit Kumar Bansal\OneDrive\Face Recognised\college_images\face1.jpg")
        img_top = img_top.resize((650, 700), Image.LANCZOS)
        self.photoimg_top = ImageTk.PhotoImage(img_top)

        f_lbl = Label(self.root, image=self.photoimg_top)
        f_lbl.place(x=0, y=55, width=650, height=700)

        # 2nd image
        img_bottom = Image.open(r"C:\Users\Rohit Kumar Bansal\OneDrive\Face Recognised\college_images\facer.webp")
        img_bottom = img_bottom.resize((950, 700), Image.LANCZOS)
        self.photoimg_bottom = ImageTk.PhotoImage(img_bottom)

        f_lbl = Label(self.root, image=self.photoimg_bottom)
        f_lbl.place(x=650, y=55, width=950, height=700)

        # button
        b1_1 = Button(f_lbl, text="Face Recognition", cursor="hand2", command=self.face_recog, font=("times to roman", 15, "bold"), bg="red", fg="black")
        b1_1.place(x=365, y=620, width=200, height=40)

    # ==========attendance===========

    def mark_attendance(self, i, r, n, d):
        with open("rohit.csv", "r+", newline="\n") as f:
            myDataList = f.readlines()
            name_list = []
            for line in myDataList:
                entry = line.split(",")
                name_list.append(entry[0])
            if ((i not in name_list) and (r not in name_list) and (n not in name_list) and (d not in name_list)):
                now = datetime.now()
                d1 = now.strftime("%d/%m/%Y")
                dtString = now.strftime("%H:%M:%S")
                f.writelines(f"\n{i},{r},{n},{d},{dtString},{d1},Present")

    # ======face recognition=========

    def face_recog(self):
        def draw_boundary(img, classifier, scaleFactor, minNeighbors, color, text, clf):
            gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            features = classifier.detectMultiScale(gray_image, scaleFactor, minNeighbors)

            coord = []

            for (x, y, w, h) in features:
                cv2.rectangle(img, (x, y), (x + w, y + h), color, 3)
                id, predict = clf.predict(gray_image[y:y + h, x:x + w])
                confidence = int((100 * (1 - predict / 300)))

                print(f"Detected ID: {id} with confidence: {confidence}")

                conn = mysql.connector.connect(host="localhost", user="root", password="Rkb@2005", database="face_recognised")
                my_cursor = conn.cursor()

                my_cursor.execute("Select Name from student where StudentID=" + str(id))
                n = my_cursor.fetchone()
                n = "+".join(n) if n else "Unknown"

                my_cursor.execute("Select Roll from student where StudentID=" + str(id))
                r = my_cursor.fetchone()
                r = "+".join(r) if r else "Unknown"

                my_cursor.execute("Select Department from student where StudentID=" + str(id))
                d = my_cursor.fetchone()
                d = "+".join(d) if d else "Unknown"

                my_cursor.execute("Select StudentID from student where StudentID=" + str(id))
                i = my_cursor.fetchone()
                i = "+".join(i) if i else "Unknown"

                print(f"Name: {n}, Roll: {r}, Department: {d}, ID: {i}")

                if confidence > 60:  # Lowered the confidence threshold
                    cv2.putText(img, f"ID: {i}", (x, y - 75), cv2.FONT_HERSHEY_COMPLEX, 0.8, (255, 255, 255), 3)
                    cv2.putText(img, f"Roll: {r}", (x, y - 55), cv2.FONT_HERSHEY_COMPLEX, 0.8, (255, 255, 255), 3)
                    cv2.putText(img, f"Name: {n}", (x, y - 30), cv2.FONT_HERSHEY_COMPLEX, 0.8, (255, 255, 255), 3)
                    cv2.putText(img, f"Department: {d}", (x, y - 5), cv2.FONT_HERSHEY_COMPLEX, 0.8, (255, 255, 255), 3)
                    self.mark_attendance(i, r, n, d)
                else:
                    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)
                    cv2.putText(img, "Unknown Face", (x, y - 5), cv2.FONT_HERSHEY_COMPLEX, 0.8, (255, 255, 255), 3)

                coord = [x, y, w, h]

            return coord

        def recognize(img, clf, faceCascade):
            coord = draw_boundary(img, faceCascade, 1.1, 10, (255, 25, 255), "Face", clf)
            return img

        faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
        clf = cv2.face.LBPHFaceRecognizer_create()
        clf.read("classifier.xml")

        video_cap = cv2.VideoCapture(0)

        while True:
            ret, img = video_cap.read()
            img = recognize(img, clf, faceCascade)
            cv2.imshow("Welcome To Face Recognition", img)

            if cv2.waitKey(1) == 13:
                break

        video_cap.release()
        cv2.destroyAllWindows()

def capture_samples(person_id, person_name):
    # Create a directory for the person if it doesn't exist
    sample_dir = f"dataset/{person_name}_{person_id}"
    if not os.path.exists(sample_dir):
        os.makedirs(sample_dir)

    # Initialize the webcam
    video_cap = cv2.VideoCapture(0)
    count = 0

    while True:
        ret, img = video_cap.read()
        if not ret:
            break

        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
        faces = faceCascade.detectMultiScale(gray, 1.1, 4)

        for (x, y, w, h) in faces:
            count += 1
            # Save the captured image in the dataset folder
            cv2.imwrite(f"{sample_dir}/{person_name}_{count}.jpg", gray[y:y+h, x:x+w])
            cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
            cv2.putText(img, f"Sample {count}", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)

        cv2.imshow("Capturing Samples", img)

        # Break the loop if 'q' is pressed or 50 samples are captured
        if cv2.waitKey(1) & 0xFF == ord('q') or count >= 50:
            break

    video_cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    root = Tk()
    obj = Face_Recognition(root)
    root.mainloop()

    person_id = input("Enter person ID: ")
    person_name = input("Enter person name: ")
    capture_samples(person_id, person_name)