I’m having trouble executing a file that trains the face recognition system. I’m using the code from pi-facerec-box Github:
#train.py from github link attached above.
import fnmatch
import os
import cv2
import numpy as np
import pickle
from PIL import Image
import test_config
import test_face
MEAN_FILE = 'test_mean.png'
POSITIVE_EIGENFACE_FILE = 'test_positive_eigenface.png'
NEGATIVE_EIGENFACE_FILE = 'test_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 test_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..."
faces = []
names = []
labels = []
pos_count = 0
neg_count = 0
mainpath = 'training'
path1 = []
path1 = [os.path.join(mainpath, f) for f in os.listdir(mainpath)]
for index in range(len(os.listdir(mainpath))):
# Go through every images
for filename in walk_files(path1[index], '*.pgm'):
# Get student id from image name
nbr = int(os.path.split(filename)[1].split(".")[0].replace("subject", ""))
# Get student name from image name
student_name = os.path.split(filename)[1].split(".")[1].replace("subject", "")
# Store resized face images in faces[]
faces.append(prepare_image(filename))
# Store all student names into names[]
names.append(student_name)
# Store all student id into labels[]
labels.append(nbr)
# Train model
print 'Training model...'
model = cv2.face.EigenFaceRecognizer_create()
model.train(np.asarray(faces), np.asarray(labels))
# Save model results
model.save(test_config.TRAINING_FILE)
print 'Training data saved to', test_config.TRAINING_FILE
# Save mean and eignface images which summarize the face recognition model.
mean = model.getMean("mean").reshape(faces[0].shape)
cv2.imwrite(MEAN_FILE, normalize(mean, 0, 255, dtype=np.uint8))
eigenvectors = model.getEigenVectors("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))
I got the following error:
mean = model.getMat("mean").reshape(faces[0].shape)
AttributeError: 'cv2.face_EigenFaceRecognizer' object has no attribute 'getMat'
After googling I found that instead of getMat() I must use getMean() . After using it I following error:
mean = model.getMean("mean").reshape(faces[0].shape)
SystemError: error return without exception set
I couldn’t find any solution for this, So, any idea why I’m getting this.
I’m using OpenCV 4.1.0 with Raspbian Stretch on RaspberryPi 3b.