import face as fr
test_img=cv2.imread(r'Test_Image.jpg') #Give path to the image which you want to test
faces_detected,gray_img=fr.faceDetection(test_img)
#print("face Detected: ",faces_detected)
face_recognizer=cv2.face.LBPHFaceRecognizer_create()
face_recognizer.read(r'trainingData.json') #Give path of where trainingData.yml is saved
name={0:"Sujit",1:"Roshan",2:"Neue"} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
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)
print ("Confidence :",confidence)
print("label :",label)
fr.draw_rect(test_img,face)
predicted_name=name[label]
fr.put_text(test_img,predicted_name,x,y)
resized_img=cv2.resize(test_img,(1000,700))
cv2.imshow("face detection ", resized_img)
cv2.waitKey(0)
cv2.destroyAllWindows
0: "Sujit",
1: "Roshan",
2: "Neue"
} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
while True:
ret, test_img = cap.read()
faces_detected, gray_img = fr.faceDetection(test_img)
#print("face Detected: ",faces_detected)
for (x, y, w, h) in faces_detected:
cv2.rectangle(test_img, (x, y), (x + w, y + h), (0, 255, 0),
thickness=5)
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)
print("Confidence :", confidence)
#print("label :",label)
fr.draw_rect(test_img, face)
predicted_name = name[label]
if confidence > 55:
fr.put_text(test_img, "unknown", x, y)
continue
fr.put_text(test_img, predicted_name, x, y)
resized_img = cv2.resize(test_img, (1000, 700))
cv2.imshow("face detection ", resized_img)
if cv2.waitKey(10) == ord('q'):
break