def classify_image(cc, img_idx, C=1.0, gamma=0.0, cls=None):
"""
Input: ClassifierConfig, img_idx
Output: Score for given class; 20list of scores if cls=None
"""
feat_vect = get_feature_vector(cc, img_idx)
if cls == None:
score = {}
for cls_idx, cls in enumerate(cc.d.classes):
print '\tclassify %s for %s on %d'%(cc.d.images[img_idx].name, cls, comm_rank)
path = config.get_classifier_svm_name(cls, C, gamma)
if os.path.exists(path):
clf = load_svm(path)
score[cls_idx] = svm_predict(feat_vect, clf)
else:
clf = load_svm(config.get_classifier_svm_name(cls))
score = svm_predict(feat_vect, clf)
return score
def classify_image(cc, img_idx, C=1.0, gamma=0.0, cls=None):
"""
Input: ClassifierConfig, img_idx
Output: Score for given class; 20list of scores if cls=None
"""
feat_vect = get_feature_vector(cc, img_idx)
if cls == None:
score = {}
for cls_idx, cls in enumerate(cc.d.classes):
print '\tclassify %s for %s on %d' % (cc.d.images[img_idx].name,
cls, comm_rank)
path = config.get_classifier_svm_name(cls, C, gamma)
if os.path.exists(path):
clf = load_svm(path)
score[cls_idx] = svm_predict(feat_vect, clf)
else:
clf = load_svm(config.get_classifier_svm_name(cls))
score = svm_predict(feat_vect, clf)
return score
def train_image_classify_svm(cc, cls, C=1.0, gamma=0.0, force_new=False):
current_fold = cc.d.current_fold
filename = config.get_classifier_svm_name(cls, C, gamma, current_fold)
if not force_new and os.path.exists(filename):
print 'svm for class %s, C=%f, gamma=%f exists already' % (cls, C,
gamma)
return
pyr_feat_size = get_pyr_feat_size(cc.L, cc.dense_codebook.shape[0])
cc.tictocer.tic('overall')
print 'compute classifier(C=%f, gamma=%f) for class %s' % (C, gamma, cls)
pos_images = cc.d.get_pos_samples_for_fold_class(cls)
if pos_images.size == 0:
return
neg_images = cc.d.get_neg_samples_for_fold_class(cls, pos_images.size)
# 1. extract all the pyramids
# ======== POSTIVE IMAGES ===========
print 'compute feature vector for positive images'
num_slices = 3
pos_pyrs = np.zeros(
(len(pos_images),
pyr_feat_size + cc.sparse_codebook.shape[0] * (1 + num_slices)))
for idx, img in enumerate(pos_images):
pos_pyrs[idx, :] = get_feature_vector(cc, img)
# ======== NEGATIVE IMAGES ===========
print 'compute feature vector for negative images'
neg_pyrs = np.zeros(
(len(neg_images),
pyr_feat_size + cc.sparse_codebook.shape[0] * (1 + num_slices)))
for idx, img in enumerate(neg_images):
neg_pyrs[idx, :] = get_feature_vector(cc, img)
# 2. Compute SVM for class
X = np.vstack((pos_pyrs, neg_pyrs))
Y = [1] * pos_pyrs.shape[0] + [-1] * neg_pyrs.shape[0]
if X.shape[0] > 0:
print 'train svm for class %s, C=%f, gamma=%f' % (cls, C, gamma)
cc.tictocer.tic()
clf = train_svm(X, Y, kernel='rbf', gamma=gamma, C=C)
print '\ttook %f seconds' % cc.tictocer.toc(quiet=True)
print 'save as', filename
save_svm(clf, filename)
else:
print 'Don\'t compute SVM, no examples given'
print 'training all classifier SVMs took:', cc.tictocer.toc(
'overall', quiet=True), 'seconds on', comm_rank
def classify_images(cc, images, C, gamma):
res = np.zeros((images.shape[0], len(cc.d.classes)))
for cls_idx, cls in enumerate(cc.d.classes):
filename = config.get_classifier_svm_name(cls, C, gamma, cc.d.current_fold)
if not os.path.exists(filename):
continue
clf = load_svm(filename, probability=False)
for idx2, img_idx in enumerate(images):
x = get_feature_vector(cc, img_idx)
pred = svm_predict(x, clf)
if pred.size > 0:
res[idx2, cls_idx] = 1
return res*2-1
def classify_images(cc, images, C, gamma):
res = np.zeros((images.shape[0], len(cc.d.classes)))
for cls_idx, cls in enumerate(cc.d.classes):
filename = config.get_classifier_svm_name(cls, C, gamma,
cc.d.current_fold)
if not os.path.exists(filename):
continue
clf = load_svm(filename, probability=False)
for idx2, img_idx in enumerate(images):
x = get_feature_vector(cc, img_idx)
pred = svm_predict(x, clf)
if pred.size > 0:
res[idx2, cls_idx] = 1
return res * 2 - 1
def train_image_classify_svm(cc, cls, C=1.0, gamma=0.0, force_new=False):
current_fold = cc.d.current_fold
filename = config.get_classifier_svm_name(cls, C, gamma, current_fold)
if not force_new and os.path.exists(filename):
print 'svm for class %s, C=%f, gamma=%f exists already'%(cls,C,gamma)
return
pyr_feat_size = get_pyr_feat_size(cc.L, cc.dense_codebook.shape[0])
cc.tictocer.tic('overall')
print 'compute classifier(C=%f, gamma=%f) for class %s'%(C, gamma, cls)
pos_images = cc.d.get_pos_samples_for_fold_class(cls)
if pos_images.size == 0:
return
neg_images = cc.d.get_neg_samples_for_fold_class(cls, pos_images.size)
# 1. extract all the pyramids
# ======== POSTIVE IMAGES ===========
print 'compute feature vector for positive images'
num_slices = 3
pos_pyrs = np.zeros((len(pos_images),pyr_feat_size + cc.sparse_codebook.shape[0]*(1+num_slices)))
for idx, img in enumerate(pos_images):
pos_pyrs[idx, :] = get_feature_vector(cc, img)
# ======== NEGATIVE IMAGES ===========
print 'compute feature vector for negative images'
neg_pyrs = np.zeros((len(neg_images),pyr_feat_size + cc.sparse_codebook.shape[0]*(1+num_slices)))
for idx, img in enumerate(neg_images):
neg_pyrs[idx, :] = get_feature_vector(cc, img)
# 2. Compute SVM for class
X = np.vstack((pos_pyrs, neg_pyrs))
Y = [1]*pos_pyrs.shape[0] + [-1]*neg_pyrs.shape[0]
if X.shape[0] > 0:
print 'train svm for class %s, C=%f, gamma=%f'%(cls,C,gamma)
cc.tictocer.tic()
clf = train_svm(X, Y, kernel='rbf', gamma=gamma, C=C)
print '\ttook %f seconds'%cc.tictocer.toc(quiet=True)
print 'save as', filename
save_svm(clf, filename)
else:
print 'Don\'t compute SVM, no examples given'
print 'training all classifier SVMs took:', cc.tictocer.toc('overall', quiet=True), 'seconds on', comm_rank