<reponame>sequery/Face-Recognition-Project
import cv2
import os
import numpy as np
import faceRecognition as fr

# This module takes images  stored in diskand performs face recognition
test_img = cv2.imread('trainImages/Aga.jpg')  # test_img path
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)

# Comment below lines when running this program second time. Since it saves training.yml file in directory
faces, faceID = fr.labels_for_training_data('trainingImages')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('trainingData.yml')

# Uncomment below line for subsequent runs
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.read('trainingData.yml')  # use this to load training data for subsequent runs

name = {1: "<NAME>"}  # creating dictionary containing names for each label

for face in faces_detected:
    (x, y, w, h) = face
    roi_gray = gray_img[y:y + h, x:x + h]
    label, confidence = face_recognizer.predict(roi_gray)  # predicting the label of given image
    print("confidence:", confidence)
    print("label:", label)
    fr.draw_rect(test_img, face)
    predicted_name = name[label]
    if confidence > 37:  # If confidence more than 37 then don't print predicted face text on screen
        continue
    fr.put_text(test_img, predicted_name, x, y)

resized_img = cv2.resize(test_img, (1000, 1000))
cv2.imshow("Face Recognition Project", resized_img)
cv2.waitKey(0)  # Waits indefinitely until a key is pressed
cv2.destroyAllWindows()

import cv2
import os
import numpy as np
import faceRecognition as fr

# This module takes images  stored in diskand performs face recognition
test_img = cv2.imread('trainImages/Aga.jpg')  # test_img path
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)

# Comment below lines when running this program second time. Since it saves training.yml file in directory
faces, faceID = fr.labels_for_training_data('trainingImages')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('trainingData.yml')

# Uncomment below line for subsequent runs
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.read('trainingData.yml')  # use this to load training data for subsequent runs

name = {1: "<NAME>"}  # creating dictionary containing names for each label

for face in faces_detected:
    (x, y, w, h) = face
    roi_gray = gray_img[y:y + h, x:x + h]
    label, confidence = face_recognizer.predict(roi_gray)  # predicting the label of given image
    print("confidence:", confidence)
    print("label:", label)
    fr.draw_rect(test_img, face)
    predicted_name = name[label]
    if confidence > 37:  # If confidence more than 37 then don't print predicted face text on screen
        continue
    fr.put_text(test_img, predicted_name, x, y)

resized_img = cv2.resize(test_img, (1000, 1000))
cv2.imshow("Face Recognition Project", resized_img)
cv2.waitKey(0)  # Waits indefinitely until a key is pressed
cv2.destroyAllWindows()
# This module takes images  stored in diskand performs face recognition # test_img path # Comment below lines when running this program second time. Since it saves training.yml file in directory # Uncomment below line for subsequent runs # use this to load training data for subsequent runs # creating dictionary containing names for each label # predicting the label of given image # If confidence more than 37 then don't print predicted face text on screen # Waits indefinitely until a key is pressed