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| #! /usr/bin/env python
import fnmatch
import os
import MySQLdb as mdb
import cv2
import numpy as np
import config
import face
MEAN_FILE = 'mean.png'
POSITIVE_EIGENFACE_FILE = 'positive_eigenface.png'
NEGATIVE_EIGENFACE_FILE = 'negative_eigenface.png'
def walk_files(directory, match='*'):
"""Generator function to iterate through all files in a directory recursively
which match the given filename match parameter.
"""
for root, dirs, files in os.walk(directory):
for filename in fnmatch.filter(files, match):
yield os.path.join(root, filename)
def prepare_image(filename):
"""Read an image as grayscale and resize it to the appropriate size for
training the face recognition model.
"""
return face.resize(cv2.imread(filename, cv2.IMREAD_GRAYSCALE))
def normalize(X, low, high, dtype=None):
"""Normalizes a given array in X to a value between low and high.
Adapted from python OpenCV face recognition example at:
https://github.com/Itseez/opencv/blob/2.4/samples/python2/facerec_demo.py
"""
X = np.asarray(X)
minX, maxX = np.min(X), np.max(X)
# normalize to [0...1].
X = X - float(minX)
X = X / float((maxX - minX))
# scale to [low...high].
X = X * (high-low)
X = X + low
if dtype is None:
return np.asarray(X)
return np.asarray(X, dtype=dtype)
if __name__ == '__main__':
#print "Reading training images..."
conn = mdb.connect('localhost','xxxx','xxxxxx','data')
cur = conn.cursor()
query = ((" UPDATE rpi3portail SET resultattrain = 'Reading training images...' WHERE id= 2 "))
cur.execute(query)
conn.commit()
cur.close()
conn.close()
faces = []
labels = []
pos_count = 0
neg_count = 0
# Read all positive images
for filename in walk_files(config.POSITIVE_DIR, '*.pgm'):
faces.append(prepare_image(filename))
labels.append(config.POSITIVE_LABEL)
pos_count += 1
# Read all negative images
for filename in walk_files(config.NEGATIVE_DIR, '*.pgm'):
faces.append(prepare_image(filename))
labels.append(config.NEGATIVE_LABEL)
neg_count += 1
#print 'Read', pos_count, 'positive images and', neg_count, 'negative images.'
# Train model
#print 'Training model...'
conn = mdb.connect('localhost','xxxxx','xxxxxx','data')
cur = conn.cursor()
query = (" UPDATE rpi3portail SET resultattrain = 'Training model...' WHERE id= 2 ")
cur.execute(query)
conn.commit()
cur.close()
conn.close()
model = cv2.createEigenFaceRecognizer()
model.train(np.asarray(faces), np.asarray(labels))
# Save model results
model.save(config.TRAINING_FILE)
#print 'Training data saved to', config.TRAINING_FILE
conn = mdb.connect('localhost','root','uargea2h','data')
cur = conn.cursor()
query = ((" UPDATE rpi3portail SET resultattrain = 'Termine' WHERE id= 2 "))
cur.execute(query)
conn.commit()
cur.close()
conn.close()
# Save mean and eignface images which summarize the face recognition model.
mean = model.getMat("mean").reshape(faces[0].shape)
cv2.imwrite(MEAN_FILE, normalize(mean, 0, 255, dtype=np.uint8))
eigenvectors = model.getMat("eigenvectors")
pos_eigenvector = eigenvectors[:,0].reshape(faces[0].shape)
cv2.imwrite(POSITIVE_EIGENFACE_FILE, normalize(pos_eigenvector, 0, 255, dtype=np.uint8))
neg_eigenvector = eigenvectors[:,1].reshape(faces[0].shape)
cv2.imwrite(NEGATIVE_EIGENFACE_FILE, normalize(neg_eigenvector, 0, 255, dtype=np.uint8)) |
