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Detectface.py

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+"""
+@author: Santhosh R
+"""
+import cv2,os
+import shutil
+import csv
+import numpy as np
+from PIL import Image, ImageTk
+import pandas as pd
+
+# This will make sure no duplicates exixts in profile.csv(using Pandas here)
+df = pd.read_csv('Profile.csv')
+df.sort_values('Ids', inplace = True)
+df.drop_duplicates(subset = 'Ids', keep = 'first', inplace = True)
+df.to_csv('Profile.csv', index = False)
+
+# Fuction to detect the face
+def DetectFace():
+    reader = csv.DictReader(open('Profile.csv'))
+    print('Detecting Login Face')
+    for rows in reader:
+        result = dict(rows)
+        #print(result)
+        if result['Ids'] == '1':
+            name1 = result['Name']
+        elif result['Ids'] == '2':
+            name2 = result["Name"]
+    recognizer = cv2.face.LBPHFaceRecognizer_create()  #cv2.createLBPHFaceRecognizer()
+    recognizer.read("TrainData\Trainner.yml")
+    harcascadePath = "hh.xml"
+    faceCascade = cv2.CascadeClassifier(harcascadePath);
+    cam = cv2.VideoCapture(0)
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    Face_Id = ''
+    name2 = ''
+
+    # Camera ON Everytime
+    while True:
+        ret, frame = cam.read()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = faceCascade.detectMultiScale(gray, 1.3, 5)
+        Face_Id = 'Not detected'
+
+        # Drawing a rectagle around the face 
+        for (x, y, w, h) in faces:
+            Face_Id = 'Not detected'
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
+            Id, confidence = recognizer.predict(gray[y:y + h, x:x + w])
+            if (confidence < 80):
+                if (Id == 1):
+                    name = name1
+                    
+                elif (Id == 2):
+                    name = name2
+                    
+                Predicted_name = str(name)
+                Face_Id = Predicted_name
+            else:
+                Predicted_name = 'Unknown'
+                Face_Id = Predicted_name
+                # Here unknown faces detected will be stored
+                noOfFile = len(os.listdir("UnknownFaces")) + 1
+                if int(noOfFile) < 100:
+                    cv2.imwrite("UnknownFaces\Image" + str(noOfFile) + ".jpg", frame[y:y + h, x:x + w])
+                
+                else:
+                    pass
+
+
+            cv2.putText(frame, str(Predicted_name), (x, y + h), font, 1, (255, 255, 255), 2)
+            
+        cv2.imshow('Picture', frame)
+        #print(Face_Id)
+        cv2.waitKey(1)
+
+        # Checking if the face matches for Login
+        if Face_Id == 'Not detected':
+            print("-----Face Not Detected, Try again------")
+            pass
+            
+        elif Face_Id == name1 or name2 and Face_Id != 'Unknown' :
+            print('----------Detected as {}----------'.format(name1))
+            print('-----------login successfull-------')
+            print('***********WELCOME {}**************'.format(name1))
+            break
+        else:
+            print('-----------Login failed please try agian-------')
+        
+        
+        #if (cv2.waitKey(1) == ord('q')):
+        #   break
+DetectFace()
+
+
+

Profile.csv

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+Name,Ids
+Santhu,1

README.md

@@ -1,13 +1,34 @@
----
-title: Login System Using Facial Recognition
-emoji: 🚀
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.36.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Face-Login
+Face login using Open CV Python
+
+This folder contains all the code and folders as well as files required to run this project
+
+## Prerequisites
+Numpy </br>
+OpenCV </br>
+Pillow </br>
+Pandas
+
+### Installation
+
+You can choose to install any environment either system, pipenv, anaconda etc </br>
+I used Visual Code and Python 3.6 and installed these using pip(Google gives more on this)
+
+pip install opencv-python
+pip install opencv-contrib-python
+
+pip install pillow
+
+pip install pandas
+
+pip install numpy
+
+## Running the test for Face login
+
+1. Create the same folders while testing or download entire project
+2. Run Readface.py to take some images of user for login, It asks for Name and Id in console 
+3. Run Train.py, this will train all images taken and put in TrainData Folder
+4. Run Detectface.py to detect the face and Login, Console shows the login messages
+
+Finally Face login is Successful and Unknow faces tried to login are stored in UnkownFaces folder </br>
+✔✔✔✔✔✔✔✔✔

Readface.py

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+"""
+@author: Santhosh R
+"""
+import cv2, os
+import shutil
+import csv
+import numpy as np
+from PIL import Image, ImageTk
+import pandas as pd
+
+
+name , Id = '',''
+dic = {
+    'Name' : name,
+    'Ids' : Id
+}
+def store_data():
+    global name,Id,dic
+    name = str(input("Enter Name  "))
+   
+    Id  = str(input("Enter Id   "))
+   
+    dic = {
+        'Ids' : Id,
+        'Name': name
+    }
+    c = dic
+    return  c
+
+#Fucntion to check if entered ID is number or not
+def is_number(s):
+    try:
+        float(s)
+        return True
+    except ValueError:
+        pass
+ 
+    try:
+        import unicodedata
+        unicodedata.numeric(s)
+        return True
+    except (TypeError, ValueError):
+        pass
+ 
+    return False
+
+def TakeImages():
+    dict1 = store_data()
+    
+    #print(dict1)
+    #name = "Santhu"
+    #Id = '1'
+    if (name.isalpha() and is_number(Id)):
+        #Checking Id if it is 1 we are rewring the profile else just updating csv
+        if Id == '1':
+            fieldnames = ['Name','Ids']
+            with open('Profile.csv','w') as f:
+                writer = csv.DictWriter(f, fieldnames =fieldnames)
+                writer.writeheader()
+                writer.writerow(dict1)
+        else:
+            fieldnames = ['Name','Ids']
+            with open('Profile.csv','a+') as f:
+                writer = csv.DictWriter(f, fieldnames =fieldnames)
+                #writer.writeheader()
+                writer.writerow(dict1)
+        cam = cv2.VideoCapture(0)
+
+        #Haarcascade file for detctionof face
+        harcascadePath = "hh.xml"
+        detector = cv2.CascadeClassifier(harcascadePath)
+        sampleNum = 0
+        while (True):
+            ret, img = cam.read()
+            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            faces = detector.detectMultiScale(gray, 1.3, 5)
+            for (x, y, w, h) in faces:
+                cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3)
+                # Incrementing sample number
+                sampleNum = sampleNum + 1
+                # Saving the captured face in the dataset folder TrainingImage
+                cv2.imwrite("TrainingImage\ " + name + "." + Id + '.' + str(sampleNum) + ".jpg", gray[y:y + h, x:x + w])
+                # display the frame
+            cv2.imshow('Cpaturing Face for Login ', img)
+        
+            # wait for 100 miliseconds
+            if cv2.waitKey(100) & 0xFF == ord('q'):
+                break
+            # break if the sample number is morethan 60
+            elif sampleNum > 60:
+                break
+            
+        
+        cam.release()
+        cv2.destroyAllWindows()
+        res = "Images Saved for Name : " + name + " with ID  " + Id
+        print(res)
+        print(' Images save location is TrainingImage\ ')
+      
+        
+    else:
+        if(name.isalpha()):
+            print('Enter Proper Id')
+        elif(is_number(Id)):
+            print('Enter Proper name')
+        else:
+            print('Enter Proper Id and Name')
+                    
+        
+
+TakeImages()
+

Train.py

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+"""
+@author: Santhosh R
+"""
+import cv2, os
+import shutil
+import csv
+import numpy as np
+from PIL import Image, ImageTk
+import pandas as pd
+
+
+def getImagesAndLabels(path):
+    # Get the path of all the files in the folder
+    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
+
+    # Create empth face list
+    faces = []
+    # Create empty ID list
+    Ids = []
+    # Looping through all the image paths and loading the Ids and the images
+    for imagePath in imagePaths:
+        # Loading the image and converting it to gray scale
+        pilImage = Image.open(imagePath).convert('L')
+        # Now we are converting the PIL image into numpy array
+        imageNp = np.array(pilImage, 'uint8')
+        # getting the Id from the image
+        Id = int(os.path.split(imagePath)[-1].split(".")[1])
+        # extract the face from the training image sample
+        faces.append(imageNp)
+        
+        Ids.append(Id)
+    return faces, Ids
+
+# Train image using LBPHFFace recognizer 
+def TrainImages():
+    recognizer = cv2.face.LBPHFaceRecognizer_create()  # recognizer = cv2.face.LBPHFaceRecognizer_create()#$cv2.createLBPHFaceRecognizer()
+    harcascadePath = "hh.xml"
+    detector = cv2.CascadeClassifier(harcascadePath)
+    faces , Id= getImagesAndLabels("TrainingImage")
+    recognizer.train(faces, np.array(Id))
+    #store data in file 
+    recognizer.save("TrainData\Trainner.yml")
+    res = "Image Trained and data stored in TrainData\Trainner.yml "
+
+    print(res)
+
+   
+
+TrainImages()
+