def projSceneRecBoW(feature='placeholder', classifier='placeholder'):
'''
For this project, you will need to report performance for three
combinations of features / classifiers. We recommend that you code them in
this order:
1) Tiny image features and nearest neighbor classifier
2) Bag of word features and nearest neighbor classifier
3) Bag of word features and linear SVM classifier
The starter code is initialized to 'placeholder' just so that the starter
code does not crash when run unmodified and you can get a preview of how
results are presented.
Interpreting your performance with 100 training examples per category:
accuracy = 0 -> Something is broken.
accuracy ~= .07 -> Your performance is equal to chance.
Something is broken or you ran the starter code unchanged.
accuracy ~= .20 -> Rough performance with tiny images and nearest
neighbor classifier. Performance goes up a few
percentage points with K-NN instead of 1-NN.
accuracy ~= .20 -> Rough performance with tiny images and linear SVM
classifier. Although the accuracy is about the same as
nearest neighbor, the confusion matrix is very different.
accuracy ~= .40 -> Rough performance with bag of word and nearest
neighbor classifier. Can reach .60 with K-NN and
different distance metrics.
accuracy ~= .50 -> You've gotten things roughly correct with bag of
word and a linear SVM classifier.
accuracy >= .70 -> You've also tuned your parameters well. E.g. number
of clusters, SVM regularization, number of patches
sampled when building vocabulary, size and step for
dense features.
accuracy >= .80 -> You've added in spatial information somehow or you've
added additional, complementary image features. This
represents state of the art in Lazebnik et al 2006.
accuracy >= .85 -> You've done extremely well. This is the state of the
art in the 2010 SUN database paper from fusing many
features. Don't trust this number unless you actually
measure many random splits.
accuracy >= .90 -> You used modern deep features trained on much larger
image databases.
accuracy >= .96 -> You can beat a human at this task. This isn't a
realistic number. Some accuracy calculation is broken
or your classifier is cheating and seeing the test
labels.
'''
# Step 0: Set up parameters, category list, and image paths.
FEATURE = feature
CLASSIFIER = classifier
# This is the path the script will look at to load images from.
data_path = '../data/'
# This is the list of categories / directories to use. The categories are
# somewhat sorted by similarity so that the confusion matrix looks more
# structured (indoor and then urban and then rural).
categories = [
'Kitchen', 'Store', 'Bedroom', 'LivingRoom', 'Office', 'Industrial',
'Suburb', 'InsideCity', 'TallBuilding', 'Street', 'Highway',
'OpenCountry', 'Coast', 'Mountain', 'Forest'
]
# This list of shortened category names is used later for visualization.
abbr_categories = [
'Kit', 'Sto', 'Bed', 'Liv', 'Off', 'Ind', 'Sub', 'Cty', 'Bld', 'St',
'HW', 'OC', 'Cst', 'Mnt', 'For'
]
# Number of training examples per category to use. Max is 100. For
# simplicity, we assume this is the number of test cases per category as
# well.
num_train_per_cat = 100
# This function returns string arrays containing the file path for each train
# and test image, as well as string arrays with the label of each train and
# test image. By default all four of these arrays will be 1500x1 where each
# entry is a string.
print('Getting paths and labels for all train and test data.')
train_image_paths, test_image_paths, train_labels, test_labels = \
get_image_paths(data_path, categories, num_train_per_cat)
# train_image_paths 1500x1 list
# test_image_paths 1500x1 list
# train_labels 1500x1 list
# test_labels 1500x1 list
############################################################################
## Step 1: Represent each image with the appropriate feature
# Each function to construct features should return an N x d matrix, where
# N is the number of paths passed to the function and d is the
# dimensionality of each image representation. See the starter code for
# each function for more details.
############################################################################
print('Using %s representation for images.' % FEATURE)
if FEATURE.lower() == 'tiny_image':
print('Loading tiny images...')
# YOU CODE get_tiny_images (see student.py)
train_image_feats = get_tiny_images(train_image_paths)
test_image_feats = get_tiny_images(test_image_paths)
print('Tiny images loaded.')
elif FEATURE.lower() == 'bag_of_words':
# Because building the vocabulary takes a long time, we save the generated
# vocab to a file and re-load it each time to make testing faster. If
# you need to re-generate the vocab (for example if you change its size
# or the length of your feature vectors), simply delete the vocab.npy
# file and re-run main.py
if not os.path.isfile('vocab.npy'):
print(
'No existing visual word vocabulary found. Computing one from training images.'
)
# Larger values will work better (to a point), but are slower to compute
vocab_size = 200
# YOU CODE build_vocabulary (see student.py)
vocab = build_vocabulary(train_image_paths, vocab_size)
np.save('vocab.npy', vocab)
# YOU CODE get_bags_of_words.m (see student.py)
train_image_feats = get_bags_of_words(train_image_paths)
# You may want to write out train_image_features here as a *.npy and
# load it up later if you want to just test your classifiers without
# re-computing features
test_image_feats = get_bags_of_words(test_image_paths)
# Same goes here for test image features.
elif FEATURE.lower() == 'placeholder':
train_image_feats = []
test_image_feats = []
else:
raise ValueError('Unknown feature type!')
############################################################################
## Step 2: Classify each test image by training and using the appropriate classifier
# Each function to classify test features will return an N x 1 string array,
# where N is the number of test cases and each entry is a string indicating
# the predicted category for each test image. Each entry in
# 'predicted_categories' must be one of the 15 strings in 'categories',
# 'train_labels', and 'test_labels'. See the starter code for each function
# for more details.
############################################################################
print('Using %s classifier to predict test set categories.' % CLASSIFIER)
if CLASSIFIER.lower() == 'nearest_neighbor':
# YOU CODE nearest_neighbor_classify (see student.py)
predicted_categories = nearest_neighbor_classify(
train_image_feats, train_labels, test_image_feats)
elif CLASSIFIER.lower() == 'support_vector_machine':
# YOU CODE svm_classify (see student.py)
predicted_categories = svm_classify(train_image_feats, train_labels,
test_image_feats)
elif CLASSIFIER.lower() == 'placeholder':
# The placeholder classifier simply predicts a random category for every test case
random_permutation = np.random.permutation(len(test_labels))
predicted_categories = [test_labels[i] for i in random_permutation]
else:
raise ValueError('Unknown classifier type')
############################################################################
## Step 3: Build a confusion matrix and score the recognition system
# You do not need to code anything in this section.
# If we wanted to evaluate our recognition method properly we would train
# and test on many random splits of the data. You are not required to do so
# for this project.
# This function will recreate results_webpage/index.html and various image
# thumbnails each time it is called. View the webpage to help interpret
# your classifier performance. Where is it making mistakes? Are the
# confusions reasonable?
############################################################################
create_results_webpage(train_image_paths, test_image_paths, train_labels,
test_labels, categories, abbr_categories,
predicted_categories)
def projSceneRecBoW():
'''
For this project, you will need to report performance for three
combinations of features / classifiers. We recommend that you code them in
this order:
- Bag of word features and linear SVM classifier
The starter code is initialized to 'placeholder' just so that the starter
code does not crash when run unmodified and you can get a preview of how
results are presented.
Interpreting your performance with 100 training examples per category:
accuracy = 0 -> Something is broken.
accuracy ~= .07 -> Your performance is equal to chance.
Something is broken or you ran the starter code unchanged.
accuracy ~= .20 -> Rough performance with tiny images and nearest
neighbor classifier. Performance goes up a few
percentage points with K-NN instead of 1-NN.
accuracy ~= .20 -> Rough performance with tiny images and linear SVM
classifier. Although the accuracy is about the same as
nearest neighbor, the confusion matrix is very different.
accuracy ~= .40 -> Rough performance with bag of word and nearest
neighbor classifier. Can reach .60 with K-NN and
different distance metrics.
accuracy ~= .50 -> You've gotten things roughly correct with bag of
word and a linear SVM classifier.
accuracy >= .70 -> You've also tuned your parameters well. E.g. number
of clusters, SVM regularization, number of patches
sampled when building vocabulary, size and step for
dense features.
accuracy >= .80 -> You've added in spatial information somehow or you've
added additional, complementary image features. This
represents state of the art in Lazebnik et al 2006.
accuracy >= .85 -> You've done extremely well. This is the state of the
art in the 2010 SUN database paper from fusing many
features. Don't trust this number unless you actually
measure many random splits.
accuracy >= .90 -> You used modern deep features trained on much larger
image databases.
accuracy >= .96 -> You can beat a human at this task. This isn't a
realistic number. Some accuracy calculation is broken
or your classifier is cheating and seeing the test
labels.
'''
#FEATURE = 'placeholder'
FEATURE = 'bag of words'
#FEATURE = 'SIFT'
#FEATURE = 'GMM'
#FEATURE = 'LBP'
#FEATURE = 'SURF'
CLASSIFIER = 'support vector machine'
#CLASSIFIER = 'placeholder'
data_path = './data/'
categories = ['Kitchen', 'Store', 'Bedroom', 'LivingRoom', 'Office',
'Industrial', 'Suburb', 'InsideCity', 'TallBuilding', 'Street',
'Highway', 'OpenCountry', 'Coast', 'Mountain', 'Forest']
abbr_categories = ['Kit', 'Sto', 'Bed', 'Liv', 'Off', 'Ind', 'Sub',
'Cty', 'Bld', 'St', 'HW', 'OC', 'Cst', 'Mnt', 'For']
num_train_per_cat = 100
print('Getting paths and labels for all train and test data.')
train_image_paths, test_image_paths, train_labels, test_labels = \
get_image_paths(data_path, categories, num_train_per_cat)
print('Using %s representation for images.' % FEATURE)
if FEATURE.lower() == 'bag of words' or FEATURE.lower() == 'sift'\
or FEATURE.lower() == 'lbp' or FEATURE.lower() == 'gmm'\
or FEATURE.lower() == 'surf':
if not os.path.isfile('vocab.npy'):
print('No existing visual word vocabulary found. Computing one from training images.')
vocab_size = 200
vocab = build_vocabulary(train_image_paths, vocab_size, FEATURE.lower())
np.save('vocab.npy', vocab)
print("success vocabulary generation")
print("find vocabulary file")
# 해당 코드에서는 임시로 image_feature를 뽑아내는 함수를 get_bags_of_words 라고 정의한다.
train_image_feats = get_bags_of_words(train_image_paths, FEATURE.lower())
test_image_feats = get_bags_of_words(test_image_paths, FEATURE.lower())
elif FEATURE.lower() == 'placeholder':
train_image_feats = []
test_image_feats = []
else:
raise ValueError('Unknown feature type!')
print('Using %s classifier to predict test set categories.' % CLASSIFIER)
if CLASSIFIER.lower() == 'support vector machine':
predicted_categories = svm_classify(train_image_feats, train_labels, test_image_feats)
elif CLASSIFIER.lower() == 'placeholder':
random_permutation = np.random.permutation(len(test_labels))
predicted_categories = [test_labels[i] for i in random_permutation]
else:
raise ValueError('Unknown classifier type')
create_results_webpage(train_image_paths, \
test_image_paths, \
train_labels, \
test_labels, \
categories, \
abbr_categories, \
predicted_categories)