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facerec_online.py
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facerec_online.py
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#!/usr/bin/env python
#
# an online / retrainable facerec version using the webcam.
#
# since the face reco code was taken from opencv/samples/python2/face_rec.py
# we won't spare you the licence ;)
#
# Software License Agreement (BSD License)
#
# Copyright (c) 2012, Philipp Wagner <bytefish[at]gmx[dot]de>.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of the author nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# ------------------------------------------------------------------------------------------------
# Note:
# When using the FaceRecognizer interface in combination with Python, please stick to Python 2.
# Some underlying scripts like create_csv will not work in other versions, like Python 3.
# ------------------------------------------------------------------------------------------------
import os
import sys
import cv2
import numpy as np
import random
rand = random.Random()
# added a names list(z)
def read_images(path, sz=None):
"""Reads the images in a given folder, resizes images on the fly if size is given.
Args:
path: Path to a folder with subfolders representing the subjects (persons).
sz: A tuple with the size Resizes
Returns:
A list [X,y,z]
X: The images, which is a Python list of numpy arrays.
y: The corresponding labels (the unique number of the subject, person) in a Python list.
z: A list of person-names, indexed by label
"""
c = 0
X,y,z = [], [], []
for dirname, dirnames, filenames in os.walk(path):
for subdirname in dirnames:
subject_path = os.path.join(dirname, subdirname)
for filename in os.listdir(subject_path):
try:
im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)
if (len(im)==0):
continue # not an image
# resize to given size (if given)
if (sz is not None):
im = cv2.resize(im, sz)
X.append(np.asarray(im, dtype=np.uint8))
y.append(c)
except IOError, (errno, strerror):
print "I/O error({0}): {1}".format(errno, strerror)
except:
print "Unexpected error:", sys.exc_info()[0]
raise
c = c+1
z.append(subdirname)
return [X,y,z]
# reload the images & retrain the model.
# note, that lbp would give you the possibility of
# just updating the model with additonal data instead.
def retrain( imgpath, model,sz ) :
# read in the image data. This must be a valid path!
X,y,names = read_images(imgpath,sz)
if len(X) == 0:
print "image path empty", imgpath
return [[],[],[]]
# Learn the model. Remember our function returns Python lists,
# so we use np.asarray to turn them into NumPy lists to make
# the OpenCV wrapper happy:
# Also convert labels to 32bit integers. This is a workaround for 64bit machines,
model.train(np.asarray(X), np.asarray(y, dtype=np.int32))
return [X,y,names]
if __name__ == "__main__":
# You'll need the path to your image folder, also we need to find
# a haar/lbpcascade for detecting faces, e.g. opencv\data\haarcascades\haarcascade_frontalface_alt2.xml
if len(sys.argv) < 3:
print "USAGE: facerec_online.py </path/to/images> <path/to/cascadefile>"
sys.exit()
print " press 'esc' to quit"
print " press 'a' to append a new face to the database"
print " (you'll be prompted for a name on the console)"
print " press 't' to retrain the model (if you appended faces there)"
# create the img folder, if nessecary
imgdir = sys.argv[1]
try:
os.mkdir(imgdir)
except:
pass # dir already existed
# default face size, all faces in the db need to be the same.
face_size=(90,90)
# open the webcam
cam = cv2.VideoCapture(0)
if ( not cam.isOpened() ):
print "no cam!"
sys.exit()
print "cam: ok."
# load the cascadefile:
cascade = cv2.CascadeClassifier(sys.argv[2])
if ( cascade.empty() ):
print "no cascade!"
sys.exit()
print "cascade:",sys.argv[2]
# Create the model. We are going to use the default
# parameters for this simple example, please read the documentation
#model = cv2.createEigenFaceRecognizer()
#model = cv2.createFisherFaceRecognizer()
model = cv2.createLBPHFaceRecognizer()
# train it from faces in the imgdir:
images,labels,names = retrain(imgdir,model,face_size)
print "trained:",len(images),"images",len(names),"persons"
while True:
ret, img = cam.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
# try to detect a face in the img:
rects = cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv2.CASCADE_SCALE_IMAGE)
# roi will keep the cropped face image ( if there was one )
roi = None
for x, y, w, h in rects:
# crop & resize it
roi = cv2.resize( gray[y:y+h, x:x+h], face_size )
# give some visual feedback for the cascade detection
cv2.rectangle(img, (x,y),(x+w,y+h), (255, 0, 0))
if len(images)>0:
# model.predict is going to return the predicted label and
# the associated confidence:
[p_label, p_confidence] = model.predict(np.asarray(roi))
name = "unknown"
if p_label != -1 : name = names[p_label]
cv2.putText( img, "%s %.2f" % (name, p_confidence),(x+10,y+20), cv2.FONT_HERSHEY_PLAIN,1.3, (0,200,0))
break # use only 1st detected
cv2.imshow('facedetect', img)
k = cv2.waitKey(5) & 0xFF
# bailout on 'esc'
if k == 27: break
# 'a' pressed, add person to the database
if (k == 97) and (roi!=None):
print "please input the name: "
name = sys.stdin.readline().strip('\r').strip('\n')
# make a folder for that person:
dirname = os.path.join(imgdir,name)
try:
os.mkdir(dirname)
except:
pass # dir already existed
# save image
path=os.path.join(dirname,"%d.png" %(rand.uniform(0,10000)))
print "added:",path
cv2.imwrite(path,roi)
# if enough new data was collected, retrain the model
if (k == 116): # 't' pressed
images,labels,names = retrain(imgdir,model,face_size)
print "trained:",len(images),"images",len(names),"persons"