import cv2
 
cv2

import os
 
os

import numpy as np
 
np

subjects = ["", "Ramiz Raja", "Elvis Presley"]
 
Presley"]

# function to detect face using OpenCV
 
OpenCV
def detect_face(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
cv2.COLOR_BGR2GRAY)

    face_cascade = cv2.CascadeClassifier('C:\\Users\\darsh\\PycharmProjects\\helloworld\\opencv-files\\lbpcascade_frontalface.xml')

 faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);

 # if no faces are detected then return original img
 if (len(faces) == 0):
     return None, None

 # under the assumption that there will be only one face,
 # extract the face area
 (x, y, w, h) = faces[0]

 # return only the face part of the image
 return gray[y:y + w, x:x + h], faces[0]
 
 def prepare_training_data(data_folder_path):
    # ------STEP-1--------
    # get the directories (one directory for each subject) in data folder
    dirs = os.listdir(data_folder_path)
 
os.listdir(data_folder_path)

    # list to hold all subject faces
 faces = []
 # list to hold labels for all subjects
 labels = []

 # let's go through each directory and read images within it
 for dir_name in dirs:

     # our subject directories start with letter 's' so
     # ignore any non-relevant directories if any
     if not dir_name.startswith("s"):
         continue;

     label = int(dir_name.replace("s", ""))

     subject_dir_path = data_folder_path + "\\" + dir_name

     # get the images names that are inside the given subject directory
     subject_images_names = os.listdir(subject_dir_path)

     for image_name in subject_images_names:

         # ignore system files like .DS_Store
         if image_name.startswith("."):
             continue;

         # build image path
         # sample image path = training-data/s1/1.pgm
         image_path = subject_dir_path + "\\" + image_name

         # read image
         image = cv2.imread(image_path)

         # display an image window to show the image
         cv2.imshow("Training on image...", cv2.resize(image, (400, 500)))
         cv2.waitKey(100)

         # detect face
         face, rect = detect_face(image)

         # ------STEP-4--------
         # for the purpose of this tutorial
         # we will ignore faces that are not detected
         if face is not None:
             # add face to list of faces
             faces.append(face)
             # add label for this face
             labels.append(label)

 cv2.destroyAllWindows()
 cv2.waitKey(1)
 cv2.destroyAllWindows()

 return faces, labels
 
 print("Preparing data...")
faces, labels = prepare_training_data("C:\Users\darsh\PycharmProjects\helloworld\training-data")
prepare_training_data("C:\\Users\\darsh\\PycharmProjects\\helloworld\\training-data")
print("Data prepared")
 
prepared")

# print total faces and labels
 
labels
print("Total faces: ", len(faces))
print("Total labels: ", len(labels))
 
len(labels))

# create our LBPH face recognizer
 
recognizer
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
 
cv2.face.LBPHFaceRecognizer_create()

# train our face recognizer of our training faces
 
faces
face_recognizer.train(faces, np.array(labels))
 
np.array(labels))

def draw_rectangle(img, rect):
    (x, y, w, h) = rect
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
2)

def draw_text(img, text, x, y):
    cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
 
2)

def predict(test_img):
    # make a copy of the image as we don't want to chang original image
    img = test_img.copy()
    # detect face from the image
    face, rect = detect_face(img)
 
detect_face(img)

    # predict the image using our face recognizer
 label, confidence = face_recognizer.predict(face)
 # get name of respective label returned by face recognizer
 label_text = subjects[label[0]]

 # draw a rectangle around face detected
 draw_rectangle(img, rect)
 # draw name of predicted person
 draw_text(img, label_text, rect[0], rect[1] - 5)

 return img
 
 print("Predicting images...")
 
images...")

# load test images
 
images
test_img1 = cv2.imread("C:\Users\darsh\PycharmProjects\helloworld\test-data\test1.jpg")
cv2.imread("C:\\Users\\darsh\\PycharmProjects\\helloworld\\test-data\\test1.jpg")
test_img2 = cv2.imread("C:\Users\darsh\PycharmProjects\helloworld\test-data\test2.jpg")
 
cv2.imread("C:\\Users\\darsh\\PycharmProjects\\helloworld\\test-data\\test2.jpg")

# perform a prediction
 
prediction
predicted_img1 = predict(test_img1)
predicted_img2 = predict(test_img2)
print("Prediction complete")
 
complete")

# display both images
 
images
cv2.imshow(subjects[1], cv2.resize(predicted_img1, (400, 500)))
cv2.imshow(subjects[2], cv2.resize(predicted_img2, (400, 500)))
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()cv2.destroyAllWindows()