i am using fisher-face to recognize faces and i want to show a percentage prediction on a rectangle with the matching name. secondly, i want to set a value e.g if the matching percentage is 80% it should display the name of a person otherwise it should display unknown. third, i do not want to run recognition all the time. once the face is recognized by the algorithm as a known or unknown person it should not do it again and again. in short it should run recognition once on every face.

if someone can help i will be very grateful.

facerec.py

import cv2, sys, numpy, os size = 4 fn_haar = 'haarcascade_frontalface_default.xml' fn_dir = 'att_faces'

Part 1: Create fisherRecognizer

print('Training...')

Create a list of images and a list of corresponding names

(images, lables, names, id) = ([], [], {}, 0) for (subdirs, dirs, files) in os.walk(fn_dir): for subdir in dirs: names[id] = subdir subjectpath = os.path.join(fn_dir, subdir) for filename in os.listdir(subjectpath): path = subjectpath + '/' + filename lable = id images.append(cv2.imread(path, 0)) lables.append(int(lable)) id += 1 (im_width, im_height) = (112, 92)

Create a Numpy array from the two lists above

(images, lables) = [numpy.array(lis) for lis in [images, lables]]

OpenCV trains a model from the images

NOTE FOR OpenCV2: remove '.face'

model = cv2.createFisherFaceRecognizer() model.train(images, lables)

Part 2: Use fisherRecognizer on camera stream

haar_cascade = cv2.CascadeClassifier(fn_haar) webcam = cv2.VideoCapture(0) while True: (rval, frame) = webcam.read() frame=cv2.flip(frame,1,0) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size)) faces = haar_cascade.detectMultiScale(mini) for i in range(len(faces)): face_i = faces[i] (x, y, w, h) = [v * size for v in face_i] face = gray[y:y + h, x:x + w] face_resize = cv2.resize(face, (im_width, im_height))

    # Try to recognize the face
    prediction  = model.predict(face_resize)
result = {
      'face': {

    'distance': prediction[1],
    'coords': {
      'x': str(faces[0][0]),
      'y': str(faces[0][1]),
      'width': str(faces[0][2]),
      'height': str(faces[0][3])
      }
       }
}
print result
print "prediction",prediction 
    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)

    # Write the name of recognized face
    # [1]

    if prediction[1]<700:
  cv2.putText(frame,
          '%s - %.0f' % (names[prediction[0]],prediction[1]),
          (x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
else:
  cv2.putText(frame,
           'Unknown',
           (x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
cv2.imshow('OpenCV', frame)
key = cv2.waitKey(10)
if key == 27:
    break

i am using fisher-face to recognize faces and i want to show a percentage prediction on a rectangle with the matching name. secondly, i want to set a value e.g if the matching percentage is 80% it should display the name of a person otherwise it should display unknown. third, i do not want to run recognition all the time. once the face is recognized by the algorithm as a known or unknown person it should not do it again and again. in short it should run recognition once on every face.

if someone can help i will be very grateful.

# facerec.py
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'
 
'att_faces'

# Part 1: Create fisherRecognizer
 
print('Training...')
 
fisherRecognizer
print('Training...')
# Create a list of images and a list of corresponding names
 
names
(images, lables, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(fn_dir):
    for subdir in dirs:
        names[id] = subdir
        subjectpath = os.path.join(fn_dir, subdir)
        for filename in os.listdir(subjectpath):
            path = subjectpath + '/' + filename
            lable = id
            images.append(cv2.imread(path, 0))
            lables.append(int(lable))
        id += 1
(im_width, im_height) = (112, 92)
 
92)

# Create a Numpy array from the two lists above
 
above
(images, lables) = [numpy.array(lis) for lis in [images, lables]]
 
lables]]

# OpenCV trains a model from the images
 
images
# NOTE FOR OpenCV2: remove '.face'
 
'.face'
model = cv2.createFisherFaceRecognizer()
model.train(images, lables)
 
lables)

# Part 2: Use fisherRecognizer on camera stream
 
stream
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
while True:
    (rval, frame) = webcam.read()
    frame=cv2.flip(frame,1,0)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
    faces = haar_cascade.detectMultiScale(mini)
    for i in range(len(faces)):
        face_i = faces[i]
        (x, y, w, h) = [v * size for v in face_i]
        face = gray[y:y + h, x:x + w]
        face_resize = cv2.resize(face, (im_width, im_height))
 
im_height))

        # Try to recognize the face
     prediction  = model.predict(face_resize)
 result = {
       'face': {

     'distance': prediction[1],
     'coords': {
       'x': str(faces[0][0]),
       'y': str(faces[0][1]),
       'width': str(faces[0][2]),
       'height': str(faces[0][3])
       }
        }
 }
 print result
 print "prediction",prediction 
     cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)

     # Write the name of recognized face
     # [1]

     if prediction[1]<700:
   cv2.putText(frame,
           '%s - %.0f' % (names[prediction[0]],prediction[1]),
           (x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
 else:
   cv2.putText(frame,
            'Unknown',
            (x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
 cv2.imshow('OpenCV', frame)
 key = cv2.waitKey(10)
 if key == 27:
     break