i am doing face recognition in python using openCV and fisherface algorithm. the predict function
prediction = model.predict(image) gives results like this distance : (2, 483.88206400085585) i do not know what actually is this distance': (2, 483.88206400085585). In this result483.8806400085585 is the distance but i do not have any idea about the 2this value changes between 1 to 4. that is my code:
# facerec.py
import cv2, sys, numpy, os
import json
size = 3
fn_dir2 = 'unknown'
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'
path2='/home/irum/Desktop/Face-Recognition/thakarrecog/unknown/UNKNOWNS'
path='/home/irum/Desktop/Face-Recognition/thakarrecog/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
model = cv2.createFisherFaceRecognizer()
model.train(images, lables)
# Part 2: Use fisherRecognizer on camera stream
haar_cascade = cv2.CascadeClassifier(fn_haar)
# Capturing camera feed
webcam = cv2.VideoCapture(0)
webcam.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 1920)
webcam.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 1080)
while True:
# Reading Frames from live stream
(rval, frame) = webcam.read()
frame=cv2.flip(frame,1,0)
#Convert frame into gray
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#gray = cv2.GaussianBlur(gray, (21, 21), 0)
# Resize the gary
mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
# Detecting the face
faces = haar_cascade.detectMultiScale(mini,1.1, 5)
for i in range(len(faces)):
face_i = faces[i]
(x, y, w, h) = [v * size for v in face_i]
# Croping face
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (im_width, im_height))
# Eualize Histogram
eq = cv2.equalizeHist(face_resize)
# Try to recognize the face
prediction = model.predict(eq)
print "Recognition Prediction" ,prediction
# Draw rectangle around the face
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# Write the name of recognized face
result = {
'face': {
'distance': prediction,
'coords': {
'x': str(faces[0][0]),
'y': str(faces[0][1]),
'width': str(faces[0][2]),
'height': str(faces[0][3])
}
}
}
print "1 Result of Over all Prediction" ,result
if prediction[0]>0 and prediction[0]<4 and prediction[1]<=500:
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])
}
}
}
dist = result['face']['distance']
print "Known Face DISTANCE" , dist
cv2.putText(frame,
'%s - %.0f' % (names[prediction[0]],prediction[1]),
(x-10, y-10), cv2.FONT_HERSHEY_DUPLEX,1,(255, 255, 0))
break
else:
#if prediction[0]<=0 and prediction[0]>=4 and prediction[1]>600:
print "for prediction more than 600"
print "prediction", prediction
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])
}
}
}
dist = result['face']['distance']
print "UNKNOWN FACE" , dist
cv2.putText(frame,
'Unknown',
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(255, 255, 0))
pin=sorted([int(n[:n.find('.')]) for n in os.listdir(path2)
if n[0]!='.' ]+[0])[-1] + 1
cv2.imwrite('%s/%s.png' % (path2, pin), eq)
cv2.imshow('OpenCV', frame)
key = cv2.waitKey(10)
if key == 27:
break
