I am trying to run a validation test of facerecognition algorithm provided by opencv such as eigenface, fisherface and LBPH on a particular database. However I am following the steps provided by this website: https://bytefish.de/blog/validating_algorithms/. I converted the code in python 3 version and also change some sklearn module which will be depreciated in future. But still I am unable to perform the validation test. Here is the converted and slightly modified code:
import os
import sys
import cv2
import numpy as np
from sklearn import cross_validation as cval
from sklearn.base import BaseEstimator
from sklearn.metrics import precision_score
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]
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.
"""
c = 0
X,y = [], []
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)
# 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 as xxx_todo_changeme:
(errno, strerror) = xxx_todo_changeme.args
print("I/O error({0}): {1}".format(errno, strerror))
except:
print("Unexpected error:", sys.exc_info()[0])
raise
c = c+1
return [X,y]
class FaceRecognizerModel(BaseEstimator):
def __init__(self):
self.model = cv2.face.FisherFaceRecognizer_create()
def fit(self, X, y):
self.model.train(X,y)
def predict(self, T):
return [self.model.predict(T[i]) for i in range(0, T.shape[0])]
if __name__ == "__main__":
# You'll need at least some images to perform the validation on:
if len(sys.argv) < 2:
print("USAGE: facerec_demo.py </path/to/images> [</path/to/store/images/at>]")
sys.exit()
# Read the images and corresponding labels into X and y.
[X,y] = read_images(sys.argv[1])
# Convert labels to 32bit integers. This is a workaround for 64bit machines,
# because the labels will truncated else. This is fixed in recent OpenCV
# revisions already, I just leave it here for people on older revisions.
#
# Thanks to Leo Dirac for reporting:
y = np.asarray(y, dtype=np.int32)
# Then we create a 10-fold cross validation iterator:
cv = cval.StratifiedKFold(y, 10)
# Now we'll create a classifier, note we wrap it up in the
# FaceRecognizerModel we have defined in this file. This is
# done, so we can use it in the awesome scikit-learn library:
estimator = FaceRecognizerModel()
# And getting the precision_scores is then as easy as writing:
precision_scores = cval.cross_val_score(estimator, X, y, score_func=precision_score, cv=cv)
# Let's print them:
print(precision_scores)
When I run this code. I got the following error:
Traceback (most recent call last):
File "yale.py", line 70, in <module>
cv = cval.StratifiedKFold(y, 10)
File "C:\Users\mahsan2\PycharmProjects\untitled1\venv\lib\site-
packages\sklearn\cross_validation.py", line 538, in __init__
len(y), n_folds, shuffle, random_state)
File "C:\Users\mahsan2\PycharmProjects\untitled1\venv\lib\site-
packages\sklearn\cross_validation.py", line 262, in __init__
" than the number of samples: {1}.").format(n_folds, n))
ValueError: Cannot have number of folds n_folds=10 greater than the number
of samples: 0.
I am not sure what should I do. I will be grateful if someone provide a valuable solutions. *** I tried to change some validation api of sklearn but it shows different kind of error. To make the post short I did not put all those trial and error. I am just providing the code. Thanks in advance.
