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emotionclassification.py
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emotionclassification.py
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import os
import sys
import pickle
#import Image
import numpy
import shutil
from sklearn import svm
from sklearn import cross_validation
from decaf.scripts.imagenet import DecafNet
import cv2
mapping = "0=neutral, 1=anger, 2=contempt, 3=disgust, 4=fear, 5=happy, 6=sadness, 7=surprise"
mapping = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
labelnumbers = [45.0,18.0,59.0,25.0,69.0,28.0,83.0]
data_dir = "dataset location"
image_dir = "cohn-kanade-images"
label_dir = "Emotion"
number_sequences = 327
#feature_length = 4096
feature_length = 9216
"""
pool5_cudanet_out: the last convolutional layer output, of size 6x6x256.
fc6_cudanet_out: the 4096 dimensional feature after the first fully connected layer.
fc6_neuron_cudanet_out: similar to the above feature, but after ReLU so the negative part is cropped out.
fc7_cudanet_out: the 4096 dimensional feature after the second fully connected layer.
fc7_neuron_cudanet_out: after ReLU
"""
#feature_level = "fc6_neuron_cudanet_out"
#feature_level = "fc6_cudanet_out"
feature_level = "pool5_cudanet_out"
def getMoreFeatures():
net = DecafNet()
features = []
labels = []
counter = 0
for participant in os.listdir(os.path.join(data_dir,image_dir)):
for sequence in os.listdir(os.path.join(data_dir,image_dir, participant)):
if sequence != ".DS_Store":
image_files = sorted(os.listdir(os.path.join(data_dir,image_dir, participant,sequence)))
cutoff = len(image_files)/2
image_files = image_files[cutoff::]
label_file = open(os.path.join(data_dir,label_dir, participant,sequence,image_files[-1][:-4]+"_emotion.txt"))
label = eval(label_file.read())
label_file.close()
for image_file in image_files:
print counter, image_file
imarray = numpy.asarray(Image.open(os.path.join(data_dir,image_dir, participant,sequence,image_file)))
scores = net.classify(imarray, center_only=True)
features.append(net.feature(feature_level))
labels.append(label)
counter += 1
numpy.save("featuresMore",numpy.array(features))
numpy.save("labelsMore",numpy.array(labels))
def getPeakFaceFeatures():
net = DecafNet()
cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')
features = numpy.zeros((number_sequences,feature_length))
labels = numpy.zeros((number_sequences,1))
counter = 0
# Maybe sort them
for participant in os.listdir(os.path.join(data_dir,image_dir)):
for sequence in os.listdir(os.path.join(data_dir,image_dir, participant)):
if sequence != ".DS_Store":
image_files = sorted(os.listdir(os.path.join(data_dir,image_dir, participant,sequence)))
image_file = image_files[-1]
print counter, image_file
imarray = cv2.imread(os.path.join(data_dir,image_dir, participant,sequence,image_file))
imarray = cv2.cvtColor(imarray,cv2.COLOR_BGR2GRAY)
rects = cascade.detectMultiScale(imarray, 1.3, 3, cv2.cv.CV_HAAR_SCALE_IMAGE, (150,150))
if len(rects) > 0:
facerect=rects[0]
imarray = imarray[facerect[1]:facerect[1]+facerect[3], facerect[0]:facerect[0]+facerect[2]]
scores = net.classify(imarray, center_only=True)
features[counter] = net.feature(feature_level).flatten()
label_file = open(os.path.join(data_dir,label_dir, participant,sequence,image_file[:-4]+"_emotion.txt"))
labels[counter] = eval(label_file.read())
label_file.close()
counter += 1
numpy.save("featuresPeakFace5",features)
numpy.save("labelsPeakFace5",labels)
def getPeakFeatures():
net = DecafNet()
features = numpy.zeros((number_sequences,feature_length))
labels = numpy.zeros((number_sequences,1))
counter = 0
# Maybe sort them
for participant in os.listdir(os.path.join(data_dir,image_dir)):
for sequence in os.listdir(os.path.join(data_dir,image_dir, participant)):
if sequence != ".DS_Store":
image_files = sorted(os.listdir(os.path.join(data_dir,image_dir, participant,sequence)))
image_file = image_files[-1]
print counter, image_file
imarray = cv2.imread(os.path.join(data_dir,image_dir, participant,sequence,image_file))
imarray = cv2.cvtColor(imarray,cv2.COLOR_BGR2GRAY)
scores = net.classify(imarray, center_only=True)
features[counter] = net.feature(feature_level)#.flatten()
label_file = open(os.path.join(data_dir,label_dir, participant,sequence,image_file[:-4]+"_emotion.txt"))
labels[counter] = eval(label_file.read())
label_file.close()
counter += 1
numpy.save("featuresPeak5",features)
numpy.save("labelsPeak5",labels)
def testClassifier():
images = numpy.load("featuresPeakFace6.npy")
labels = numpy.load("labelsPeakFace6.npy").flatten()
#images = images.reshape((3018,4096)) # For featuresMore.npy
if False: # If featureMores is used
labels = labels.reshape((3018,1))
imla = numpy.hstack((images,labels))
numpy.random.shuffle(imla)
images = imla[:,0:feature_length]
labels = imla[:,-1]
with open("oneversusone6.sav","w") as opened_file:
for value in [1e1,1.0,1e-1,1e-2,1e-3,1e-4,1e-5,1e-6]:
#classifier = svm.LinearSVC(C=1e-4,class_weight="auto")
#classifier = svm.SVC(C=value, kernel="poly", degree=6, class_weight="auto")
classifier = svm.SVC(C=value, kernel="linear", class_weight="auto")
if True:
# Leave-one-subject-out
subjects = []
subject_index = 0
for participant in os.listdir(os.path.join(data_dir,image_dir)):
subjects.append(subject_index)
#print participant, subject_index
for sequence in os.listdir(os.path.join(data_dir,image_dir, participant)):
if sequence != ".DS_Store":
subject_index += 1
print subjects, len(subjects)
loso_results = []
confusion_matrix = numpy.zeros((7,7))
for i,subject in enumerate(subjects):
if i == len(subjects)-1:
trainimages = images[0:subject]
trainlabels = labels[0:subject]
testimages = images[subject:]
testlabels = labels[subject:]
else:
length = subjects[i+1]-subjects[i]
trainimages = numpy.vstack((images[0:subject],images[subject+length:]))
trainlabels = numpy.hstack((labels[0:subject],labels[subject+length:]))
testimages = images[subject:subject+length]
testlabels = labels[subject:subject+length]
classifier.fit(trainimages, trainlabels)
predictions = classifier.predict(testimages)
#print predictions,testlabels
for pre,lab in zip(predictions,testlabels):
confusion_matrix[int(lab)-1,int(pre)-1] += 1/labelnumbers[int(lab)-1]
loso_results.append(classifier.score(testimages,testlabels))
print i, loso_results[-1],
final_score = sum(loso_results)/len(loso_results)
print final_score
total=0.0
for i in xrange(7):
total+=confusion_matrix[i,i]
total = total/7.0
print total
opened_file.write(str(total))
with open("oneversusall6.sav","w") as opened_file:
for value in [1e1,1.0,1e-1,1e-2,1e-3,1e-4,1e-5,1e-6]:
classifier = svm.LinearSVC(C=value,class_weight="auto")
#classifier = svm.SVC(C=value, kernel="linear", class_weight="auto")
if True:
# Leave-one-subject-out
subjects = []
subject_index = 0
for participant in os.listdir(os.path.join(data_dir,image_dir)):
subjects.append(subject_index)
#print participant, subject_index
for sequence in os.listdir(os.path.join(data_dir,image_dir, participant)):
if sequence != ".DS_Store":
subject_index += 1
print subjects, len(subjects)
loso_results = []
confusion_matrix = numpy.zeros((7,7))
for i,subject in enumerate(subjects):
if i == len(subjects)-1:
trainimages = images[0:subject]
trainlabels = labels[0:subject]
testimages = images[subject:]
testlabels = labels[subject:]
else:
length = subjects[i+1]-subjects[i]
trainimages = numpy.vstack((images[0:subject],images[subject+length:]))
trainlabels = numpy.hstack((labels[0:subject],labels[subject+length:]))
testimages = images[subject:subject+length]
testlabels = labels[subject:subject+length]
classifier.fit(trainimages, trainlabels)
predictions = classifier.predict(testimages)
#print predictions,testlabels
for pre,lab in zip(predictions,testlabels):
confusion_matrix[int(lab)-1,int(pre)-1] += 1/labelnumbers[int(lab)-1]
loso_results.append(classifier.score(testimages,testlabels))
print i, loso_results[-1],
final_score = sum(loso_results)/len(loso_results)
print final_score
total=0.0
for i in xrange(7):
total+=confusion_matrix[i,i]
total = total/7.0
print total
opened_file.write(str(total))
def getClassifier():
images = numpy.load("featuresPeakFace5.npy")
labels = numpy.load("labelsPeakFace5.npy").flatten()
#images = images.reshape((3018,4096)) # For featuresMore.npy
if False: # If featureMores is used
labels = labels.reshape((3018,1))
imla = numpy.hstack((images,labels))
numpy.random.shuffle(imla)
images = imla[:,0:feature_length]
labels = imla[:,-1]
# loso - 85.4 - 85.4 - 1e-4: 85.6\% - 85.5 - 83.4
#classifier = svm.LinearSVC(C=1e-4,class_weight="auto")
#classifier = svm.SVC(C=1e-6, kernel="poly", degree=2,class_weight="auto")
classifier = svm.SVC(C=1e-6, kernel="linear", class_weight="auto")
if True:
# Leave-one-subject-out
subjects = []
subject_index = 0
for participant in os.listdir(os.path.join(data_dir,image_dir)):
subjects.append(subject_index)
print participant, subject_index
for sequence in os.listdir(os.path.join(data_dir,image_dir, participant)):
if sequence != ".DS_Store":
subject_index += 1
print subjects, len(subjects)
confusion_matrix = numpy.zeros((7,7))
loso_results = []
for i,subject in enumerate(subjects):
if i == len(subjects)-1:
trainimages = images[0:subject]
trainlabels = labels[0:subject]
testimages = images[subject:]
testlabels = labels[subject:]
else:
length = subjects[i+1]-subjects[i]
trainimages = numpy.vstack((images[0:subject],images[subject+length:]))
trainlabels = numpy.hstack((labels[0:subject],labels[subject+length:]))
testimages = images[subject:subject+length]
testlabels = labels[subject:subject+length]
classifier.fit(trainimages, trainlabels)
predictions = classifier.predict(testimages)
#print predictions,testlabels
for pre,lab in zip(predictions,testlabels):
confusion_matrix[int(lab)-1,int(pre)-1] += 1/labelnumbers[int(lab)-1]
print numpy.round(confusion_matrix,3)*100
loso_results.append(classifier.score(testimages,testlabels))
print i, loso_results[-1]
#with open("performance.sav","wb") as pickle_file:
# pickle.dump(loso_results, pickle_file)
#print numpy.round(confusion_matrix,3)*100
total=0.0
for i in xrange(7):
total+=confusion_matrix[i,i]
print "Prediction:",total/7.0
print sum(loso_results)/len(loso_results)
#numpy.save("confusion_matrix.sav",confusion_matrix)
#sets = cross_validation.LeaveOneOut(327)
#results = cross_validation.cross_val_score(classifier, images, labels, cv=10)
#print results, results.mean()
else:
classifier.fit(images,labels)
with open("classifierBestFace.sav","wb") as pickle_file:
pickle.dump(classifier, pickle_file)
def remove():
a = 0
for participant in os.listdir(os.path.join(data_dir,label_dir)):
labelled = os.listdir(os.path.join(data_dir,label_dir,participant))
images = os.listdir(os.path.join(data_dir,image_dir,participant))
good = []
for sequence in labelled:
if len(os.listdir(os.path.join(data_dir,label_dir,participant,sequence))) != 0:
good.append(sequence)
for sequence in images:
if not sequence in good:
if sequence != ".DS_Store":
print "deleting image", participant, sequence
shutil.rmtree(os.path.join(data_dir,image_dir,participant,sequence))
for sequence in labelled:
if not sequence in good:
if sequence != ".DS_Store":
print "deleting label", participant, sequence
shutil.rmtree(os.path.join(data_dir,label_dir,participant,sequence))
if __name__ == "__main__":
#getPeakFeatures()
#getMoreFeatures()
getPeakFaceFeatures()
getClassifier()
#testClassifier()