Update README.md

README.md

+Face recognition Based home alarm system
+
+
+
+The Face Recognition process in this tutorial is divided into three steps.
+
+#Prepare training data: In this step we will read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs to.
+
+#Train Face Recognizer: In this step we will train OpenCV's LBPH face recognizer by feeding it the data we prepared in step 1.
+
+#Testing: In this step we will pass some test images to face recognizer and see if it predicts them correctly.
+
+
+*****************************************
+
+Requirements
+
+*****************************************
+
+You need Python 3.6 64bit  to run facerecognition Dlib libraries as these are not available in version latest then 3.6. 
+
+
+******************************
+
+
+Installations Quick start
+
+******************************
+
+Import Required Modules
+
+import OpenCV module
+
+import cv2
+
+import os module for reading training data directories and paths
+
+import os
+
+import numpy to convert python lists to numpy arrays as 
+
+it is needed by OpenCV face recognizers
+
+import numpy as np
+
+matplotlib for display our images
+
+import matplotlib.pyplot as plt
+
+%matplotlib inline 
+py -m pip install numpy
+py -m pip install opencv-python
+py -m pip install matplotib
+py -m pip install pytesseract
+*******************************************************
+For Alarm use
+*******************************************************
+pip install py-notifier
+
+For example
+
+from pynotifier import Notification
+
+Notification(
+    title='Notification Title',
+    description='Notification Description',
+    icon_path='path/to/image/file/icon.png', # On Windows .ico is required, on Linux - .png
+    duration=5,                              # Duration in seconds
+    urgency=Notification.URGENCY_CRITICAL
+).send()
+
+**************************************
+#Detector
+***************************************
+Loading Recognizer Lets start the main loop and do the following basic steps Starts capturing frames from the camera object Convert it to Gray Scale Detect and extract faces from the images Use the recognizer to recognize the Id of the user Put predicted Id/Name and Rectangle on detected face.
+
+#function to detect face using OpenCV
+
+def detect_face(img):
+
+    #convert the test image to gray image as opencv face detector expects gray images
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#return only the face part of the image
+
+return gray[y:y+w, x:x+h], faces[0]
+
+
+********************************************************************************************
+
+Main complete code 
+
+********************************************************************************************
+
+{
+import os
+import cv2
+import face_recognition as fr
+import numpy as np
+from pynotifier import Notification
+import win10toast
+
+def get_encoded_faces(folder="./faces"):
+    """
+    looks through the faces folder and encodes all
+    the faces
+
+    :return: dict of (name, image encoded)
+    """
+    encoded = {}
+
+    for dirpath, dnames, fnames in os.walk(folder):
+        for f in fnames:
+            if f.endswith(".jpg") or f.endswith(".png"):
+                face = fr.load_image_file("faces/" + f)
+                encoding = fr.face_encodings(face)[0]
+                encoded[f.split(".")[0]] = encoding
+                # normally face_encodings check if face is on image - and it can get empty result
+
+                
+
+    return encoded
+
+
+def classify_face(im):
+    """
+    will find all of the faces in a given image and label
+    them if it knows what they are
+
+    :param im: str of file path
+    :return: list of face names
+    """
+    faces = get_encoded_faces()
+    faces_encoded = list(faces.values())
+    known_face_names = list(faces.keys())
+
+    cap = cv2.VideoCapture(0)
+
+    while True:
+
+        status, img = cap.read()
+        #print('status:', status)
+
+        face_locations = fr.face_locations(img)
+        unknown_face_encodings = fr.face_encodings(img, face_locations)
+
+        face_names = []
+        for location, face_encoding in zip(face_locations, unknown_face_encodings): # I moved `zip()` in this place
+
+            # See if the face is a match for the known face(s)
+            matches = fr.compare_faces(faces_encoded, face_encoding)
+            name = "Unknown"
+
+            # use the known face with the smallest distance to the new face
+            face_distances = fr.face_distance(faces_encoded, face_encoding)
+
+            best_match_index = np.argmin(face_distances)
+            if matches[best_match_index]:
+                name = known_face_names[best_match_index]
+
+            face_names.append(name)
+
+            top, right, bottom, left = location
+            # Draw a box around the face
+
+            cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)
+
+            # Draw a label with a name below the face
+            cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
+            font = cv2.FONT_HERSHEY_DUPLEX
+            cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)
+
+        if name == "Swami" or "shan":
+            Notification(
+                title = 'Known Person',
+                description = 'Door can be accessed',
+                duration = 10,
+                urgency = Notification.URGENCY_CRITICAL
+                ).send()
+            print("Door open")
+        else:
+            Notification(
+                title = 'Unknown Person',
+                description = 'Access Denied',
+                duration = 10,
+                urgency = Notification.URGENCY_CRITICAL
+            ).send()
+            print("Door access denied")
+        cv2.imshow('Video', img)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            return face_names 
+
+# --- main ---
+
+print(classify_face("test-image"))
+
+cv2.destroyAllWindows()
+
+
+}