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face_recognition.py
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import numpy as np
import cv2
import os
#face_cascade_Haar=cv2.CascadeClassifier('C:\\Users\\nic 005\\Downloads\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_default.xml')
#face_cascade_LBP=cv2.CascadeClassifier('C:\\Users\\nic 005\\Downloads\\opencv\\sources\\data\\lbpcascades\\lbpcascade_frontalface.xml')
subjects=["","amitabh","modi","parmendera sir","virat"]
def face_detection(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade_Haar=cv2.CascadeClassifier('C:\\Users\\nic 005\\Downloads\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_default.xml')
faces = face_cascade_Haar.detectMultiScale(gray,scaleFactor=1.3,minNeighbors=5)
if(len(faces)==0):
return None, None
(x,y,w,h)=faces[0]
return gray[y:y+w, x:x+h], faces[0]
'''
def prep_training_data(path):
dirs=os.listdir(path)
faces=[]
labels=[]
k=0
for dir_names in dirs:
label=k+1
subject_dir_path= path + "/" + dir_names
subject_images_names= os.listdir(subject_dir_path)
for image_name in subject_images_names:
if image_name.startswith("."):
continue
image=cv2.imread(subject_dir_path+ "/" + image_name)
cv2.imshow("Training on image...", cv2.resize(image, (400, 500)))
cv2.waitKey(100)
face, rect = face_detection(image)
if face is not None:
faces.append(face)
labels.append(int(label))
k=k+1
return faces,labels
'''
def prep_training_data(path):
dirs=os.listdir(path)
faces=[]
labels=[]
k=0
for dir_names in dirs:
label=k+1
subject_dir_path= path + "/" + dir_names
if subject_dir_path.startswith("."):
continue
image=cv2.imread(subject_dir_path)
cv2.imshow("Training on image...", cv2.resize(image, (400, 500)))
cv2.waitKey(100)
face, rect = face_detection(image)
if face is not None:
faces.append(face)
labels.append(int(label))
k=k+1
return faces,labels
def draw_rectangle(img, rect):
(x, y, w, h) = rect
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
def draw_text(img, text, x, y):
cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 255, 0), 2)
def predict(test_img):
img=test_img.copy()
face, rect=face_detection(img)
label, confidence=face_recognizer.predict(face)
print('confidence of prediction for '+subjects[label]+' is ',confidence)
label_name=subjects[label]
draw_rectangle(test_img,rect)
draw_text(test_img, label_name, rect[0], rect[1]-5)
##the predicted image
name=subjects[label]
cv2.imshow("name",cv2.resize(test_img,(400,500)))
cv2.waitKey(0)
cv2.destroyAllWindows()
#return test_img
def test_prediction(path):
test_images=os.listdir(path)
for image in test_images:
img=cv2.imread(path+'/'+image)
predict(img)
faces, labels = prep_training_data('testing\\training\\')
print('total images', len(faces))
print('total labels',len(labels))
face_recognizer= cv2.face.LBPHFaceRecognizer_create()
face_recognizer.train(faces, np.array(labels))
test_prediction('testing\\testing\\')
'''
test_img=cv2.imread('C:\\Users\\nic 005\\Desktop\\face_recognition\\db\\testing\\doraj.20.jpg')
predicted_img=predict(test_img)
cv2.imshow("img",predicted_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''