def get_classifier_crossval(cls=None):
dirname = join(res_dir, 'classify_scores')
makedirs(dirname)
if cls == None:
return join(dirname, 'crossval.txt')
else:
return join(dirname, 'crossval_%s.txt'%cls)
def get_classifier_crossval(cls=None):
dirname = join(res_dir, 'classify_scores')
makedirs(dirname)
if cls == None:
return join(dirname, 'crossval.txt')
else:
return join(dirname, 'crossval_%s.txt' % cls)
def get_classifier_svm_name(cls, C, gamma, current_fold):
dirname = join(res_dir, 'classify_svm')
makedirs(dirname)
if current_fold == -1:
filename = join(dirname, '%s_%f_%f'%(cls, C, gamma))
else:
filename = join(dirname, '%s_%f_%f_%d'%(cls, C, gamma, current_fold))
return filename
def get_classifier_svm_name(cls, C, gamma, current_fold):
dirname = join(res_dir, 'classify_svm')
makedirs(dirname)
if current_fold == -1:
filename = join(dirname, '%s_%f_%f' % (cls, C, gamma))
else:
filename = join(dirname, '%s_%f_%f_%d' % (cls, C, gamma, current_fold))
return filename
def get_gist_fastinf_table_name(dataset, cls):
savedir = makedirs(join(get_gist_svm_dirname(dataset), 'classif'))
if cls == None:
savefile = join(savedir,'cls_gt')
else:
savefile = join(savedir,'cls_gt_%s'%cls)
return savefile
def get_gist_fastinf_table_name(dataset, cls):
savedir = makedirs(join(get_gist_svm_dirname(dataset), 'classif'))
if cls == None:
savefile = join(savedir, 'cls_gt')
else:
savefile = join(savedir, 'cls_gt_%s' % cls)
return savefile
def get_ext_dets_filename(dataset, suffix):
if dataset == None:
# TODO:
"""
Just load eeeevery image
"""
dataset_name = 'full_pascal_full'
else:
dataset_name = dataset.name # NOTE does not depend on # images
dirname = makedirs(join(res_dir,'ext_dets'))
return join(dirname, '%s_%s.npy'%(dataset_name,suffix))
def get_ext_dets_filename(dataset, suffix):
if dataset == None:
# TODO:
"""
Just load eeeevery image
"""
dataset_name = 'full_pascal_full'
else:
dataset_name = dataset.name # NOTE does not depend on # images
dirname = makedirs(join(res_dir, 'ext_dets'))
return join(dirname, '%s_%s.npy' % (dataset_name, suffix))
def get_codebook_path(feature):
dirname = join(data_dir, feature, 'codebooks')
makedirs(dirname)
return join(dirname, 'codebook')
def get_ext_dets_foldname(dataset):
dirname = makedirs(join(res_dir, 'ext_dets', dataset.name))
return dirname
def get_gist_svm_dirname(dataset): return makedirs(join(res_dir, 'gist_svm', dataset.name))
def get_evals_dp_dir(dp, train=False):
dirname = get_evals_dir(dp.dataset.get_name())
if train:
dirname = get_evals_dir(dp.train_dataset.get_name())
return makedirs(join(dirname, dp.get_config_name()))
def get_dp_weights_filename(dp):
dirname = get_evals_dir(dp.weights_dataset_name)
dirname = makedirs(join(dirname, dp.get_config_name()))
return join(dirname, 'weights.txt')
'''
Created on Nov 17, 2011
@author: Tobias Baumgartner
'''
from common_imports import *
from common_mpi import *
import scipy.io
import synthetic.util as ut
folder = config.VOC_dir + 'JPEGImages/gist/'
gist_save = os.path.join(config.res_dir,'gist_features/')
ut.makedirs(gist_save)
files = os.listdir(folder)
mat = {}
for file_idx in range(len(files)):
infile = files[file_idx]
mat[infile[:-4]] = np.transpose(scipy.io.loadmat(os.path.join(folder,infile))['g'])
cPickle.dump(mat, open(os.path.join(gist_save, 'features'),'w'))
'''
Created on Nov 17, 2011
@author: Tobias Baumgartner
'''
from common_imports import *
from common_mpi import *
import scipy.io
import synthetic.util as ut
folder = config.VOC_dir + 'JPEGImages/gist/'
gist_save = os.path.join(config.res_dir, 'gist_features/')
ut.makedirs(gist_save)
files = os.listdir(folder)
mat = {}
for file_idx in range(len(files)):
infile = files[file_idx]
mat[infile[:-4]] = np.transpose(
scipy.io.loadmat(os.path.join(folder, infile))['g'])
cPickle.dump(mat, open(os.path.join(gist_save, 'features'), 'w'))
def get_classifier_score_name(img): dirname = join(res_dir, 'classify_scores') makedirs(dirname) return join(dirname, img.name[:-4])
def get_dp_weights_filename(dp): dirname = get_evals_dir(dp.weights_dataset_name) dirname = makedirs(join(dirname, dp.get_config_name())) return join(dirname,'weights.txt')
def get_classifier_dataset_dirname(classifier, dataset): return makedirs(join(get_classifier_dirname(classifier), dataset.name))
def get_classifier_featvect_name(img): dirname = join(res_dir, 'classify_featvects') makedirs(dirname) return join(dirname, img.name[:-4])
def get_classifier_dirname(classifier):
name = classifier.name+'_svm'
if len(classifier.suffix) >= 1:
name += '_'+classifier.suffix
return makedirs(join(res_dir,name))
def get_cached_dataset_filename(name): assert(name in pascal_paths) dirname = makedirs(join(res_dir,'cached_datasets')) return join(dirname, str(VOCyear)+'_'+name+'.pickle')
def get_ext_dets_foldname(dataset): dirname = makedirs(join(res_dir,'ext_dets',dataset.name)) return dirname
def get_fastinf_data_file(dataset, suffix):
dirname = makedirs(join(fastinf_dir, dataset.name, suffix))
return join(dirname, 'data.txt')
def get_assignments_path(feature, image): dirname = join(data_dir, feature, 'assignments/') makedirs(dirname) return join(dirname, image.name[0:-4])
def get_fastinf_cache_file(dataset, suffix, m='0', r2='', s=0):
dirname = makedirs(join(fastinf_dir, dataset.name, suffix))
filename = append_settings('cache.pickle', m, r2)
return join(dirname, filename + '_Is_' + str(s))
def get_ext_test_support_dir():
return makedirs(join(test_support_dir, 'ext_cls_test'))
def train_with_hard_negatives(d, dtest,cbwords, cbsamps, codebook, cls, pos_table, neg_table,feature_type,\
iterations, kernel='chi2', L=2, \
testsize = 'max',randomize=False):
"""
An iterative training with hard negatives
-input:
d - training Dataset
dtest - test Dataset
codebook - dsift codebook for pyramid (recommended size 3000)
cls - the class to be trained
pos_table - Table with cols [x,y,w,h,img_ind]
neg_table - same
kernel - 'linear', 'rbf', 'chi2'
iterations - number of rounds of training
L - levels for pyramids
testsize - size of initial test
"""
# Featurize and pyramidize the input
e = Extractor()
M = codebook.shape[0]
pyr_size = M*1./3.*(4**(L+1)-1)
print 'get pos train pyrs'
pos_pyrs = get_pyr(d,e,pos_table,pyr_size,L,codebook,feature_type,cls)
print 'get neg train pyrs'
neg_pyrs = get_pyr(d,e,neg_table,pyr_size,L,codebook,feature_type,cls)
print 'built all positive pyramids'
classification = np.asarray([1]*pos_table.arr.shape[0] + [-1]*neg_table.arr.shape[0])
filename = config.data_dir + 'features/' + feature_type + '/svms/' + kernel + \
'/'+ cls
ut.makedirs(config.data_dir + 'features/' + feature_type + '/svms/' + kernel)
# with that we now determined our whole dataset D
#D = np.concatenate((pos_pyrs, neg_pyrs, pos_test_pyr, neg_test_pyr))
#Y = np.concatenate((classification, test_classification))
#idx_train = np.arange(pos_pyrs.shape[0] + neg_pyrs.shape[0])
#idx_test = np.arange(pos_test_pyr.shape[0] + neg_test_pyr.shape[0]) + idx_train.size
train_pyrs = np.concatenate((pos_pyrs,neg_pyrs))
for i in range(iterations):
# train new model - according to hard mining algorithm by Felszenswalb et al.
# "Object Detection with Discriminatively Trained Part Based Modles"
[test_pyrs, test_classification] = get_test_windows(testsize,dtest,e,\
pyr_size,L,codebook,feature_type,cls,\
pos_table.cols, randomize=randomize)
print 'node',comm_rank,'training in round', i, 'with', np.sum(classification==1),'pos and',\
np.sum(classification == -1),'negs'
print 'testing', test_pyrs.shape[0], 'new samples'
print time.strftime('%m-%d %H:%M')
# get new test samples
# ----------1-------------
model = train_svm(train_pyrs, classification, kernel)
testY = svm_predict(np.concatenate((train_pyrs,test_pyrs)), model)
result = testY
print result
res_train = testY[:train_pyrs.shape[0]]
res_test = testY[train_pyrs.shape[0]:]
# ----------3-------------
# remove easy samples from train-set
idx_tr_list = []
for s_ind in range(res_train.shape[0]):
if res_train[s_ind]*classification[s_ind] <= 1:
idx_tr_list.append(s_ind)
indices = np.matrix(idx_tr_list).reshape(1,len(idx_tr_list))
indices = np.array(indices.astype(Int))[0]
train_pyrs = train_pyrs[indices]
classification = classification[indices]
# ----------4-------------
idx_hn_list = []
new_hards = False
for s_ind in range(res_test.shape[0]):
if res_test[s_ind]*test_classification[s_ind] < 1:
new_hards = True
idx_hn_list.append(s_ind)
nu_train_idc = np.matrix(idx_hn_list).reshape(1,len(idx_hn_list))
nu_train_idc = np.array(nu_train_idc.astype(Int))[0]
train_pyrs = np.vstack((train_pyrs, test_pyrs[nu_train_idc]))
classification = np.concatenate((classification, test_classification[nu_train_idc]))
test_result = result[-test_pyrs.shape[0]:]
fp = np.sum(np.multiply(test_result < 0, np.transpose(np.matrix(test_classification == 1))))
tp = np.sum(np.multiply(test_result > 0, np.transpose(np.matrix(test_classification == 1))))
fn = np.sum(np.multiply(test_result > 0, np.transpose(np.matrix(test_classification == -1))))
# save these to the training file
prec = tp/float(tp+fp)
rec = tp/float(tp+fn)
print 'tp, fp:',tp,fp
print 'prec, rec:', prec,rec
with open(filename + '_train', "a") as myfile:
myfile.write(str(prec) + ' ' + str(rec)+'\n')
# ----------2-------------
if not new_hards:
# no new_hards from test set,we want to quit.
break
def get_dp_features_images_dirname(dp): dirname = get_evals_dir(dp.weights_dataset_name) dirname = makedirs(join(dirname, dp.get_config_name())) return makedirs(join(dirname,'features_images'))
def get_evals_dir(dataset_name): return makedirs(join(evals_dir,dataset_name))
def get_classifier_dirname(classifier):
name = classifier.name + '_svm'
if len(classifier.suffix) >= 1:
name += '_' + classifier.suffix
return makedirs(join(res_dir, name))
def get_dp_features_images_dirname(dp):
dirname = get_evals_dir(dp.weights_dataset_name)
dirname = makedirs(join(dirname, dp.get_config_name()))
return makedirs(join(dirname, 'features_images'))
def get_classifier_featvect_name(img):
dirname = join(res_dir, 'classify_featvects')
makedirs(dirname)
return join(dirname, img.name[:-4])
def get_cached_dataset_filename(name):
assert (name in pascal_paths)
dirname = makedirs(join(res_dir, 'cached_datasets'))
return join(dirname, str(VOCyear) + '_' + name + '.pickle')
def get_evals_dp_dir(dp,train=False):
dirname = get_evals_dir(dp.dataset.get_name())
if train:
dirname = get_evals_dir(dp.train_dataset.get_name())
return makedirs(join(dirname, dp.get_config_name()))
def get_codebook_path(feature): dirname = join(data_dir, feature, 'codebooks') makedirs(dirname) return join(dirname, 'codebook')
def get_ext_test_support_dir(): return makedirs(join(test_support_dir, 'ext_cls_test'))
def get_gist_svm_dirname(dataset):
return makedirs(join(res_dir, 'gist_svm', dataset.name))
def get_mrf_bound_filename(dataset, suffix): dirname = makedirs(join(fastinf_dir, dataset.name, suffix)) return join(dirname, 'bounds.txt')
def get_fastinf_data_file(dataset, suffix): dirname = makedirs(join(fastinf_dir, dataset.name, suffix)) return join(dirname, 'data.txt')
def get_fastinf_res_file(dataset, suffix, m='0', r2=''):
dirname = makedirs(join(fastinf_dir, dataset.name, suffix))
return join(dirname, append_settings('res.txt',m,r2))
def get_classifier_dataset_dirname(classifier, dataset):
return makedirs(join(get_classifier_dirname(classifier), dataset.name))
def get_fastinf_cache_file(dataset,suffix, m='0', r2='', s=0):
dirname = makedirs(join(fastinf_dir, dataset.name, suffix))
filename = append_settings('cache.pickle',m,r2)
return join(dirname, filename+'_Is_'+str(s))
def get_classifier_score_name(img):
dirname = join(res_dir, 'classify_scores')
makedirs(dirname)
return join(dirname, img.name[:-4])
def get_dataset_stats_dir(dataset): return makedirs(join(res_dir,'dataset_stats',dataset.name))
def get_assignments_path(feature, image):
dirname = join(data_dir, feature, 'assignments/')
makedirs(dirname)
return join(dirname, image.name[0:-4])
def get_dets_configs_dir(dataset): return makedirs(join(res_dir,'det_configs',dataset.name))
def get_mrf_bound_filename(dataset, suffix):
dirname = makedirs(join(fastinf_dir, dataset.name, suffix))
return join(dirname, 'bounds.txt')
def get_sliding_windows_dir(dataset_name): return makedirs(join(res_dir, 'sliding_windows_%s'%dataset_name))
def get_fastinf_res_file(dataset, suffix, m='0', r2=''):
dirname = makedirs(join(fastinf_dir, dataset.name, suffix))
return join(dirname, append_settings('res.txt', m, r2))
def get_sliding_windows_cached_dir(dataset_name):
sliding_windows_dir = join(temp_res_dir,
'sliding_windows_%s' % dataset_name)
return makedirs(join(sliding_windows_dir, 'cached'))
def get_dataset_stats_dir(dataset):
return makedirs(join(res_dir, 'dataset_stats', dataset.name))
def get_sliding_windows_params_dir(dataset_name):
return makedirs(join(get_sliding_windows_dir(dataset_name), 'params'))
codebook_samples = 15
num_pos = 'max'
testsize = 'max'
kernel = 'chi2'
# num_pos = 3
# testsize = 4
# For my local testings
classes = ['dog']
#classes = ['bicycle','bird','boat','bottle','bus','car','cat']
# testsize = 1
# num_pos = 1
if comm_rank == 0:
ut.makedirs(config.data_dir + 'features/' + feature_type + '/times/')
ut.makedirs(config.data_dir + 'features/' + feature_type + '/codebooks/times/')
ut.makedirs(config.data_dir + 'features/' + feature_type + '/svms/train_times/')
for cls_idx in range(comm_rank, len(classes), comm_size):
#for cls in classes:
cls = classes[cls_idx]
codebook = e.get_codebook(d, feature_type)
pos_arr = d.get_pos_windows(cls)[10:]
neg_arr = d.get_neg_windows(pos_arr.shape[0], cls, max_overlap=0)
if not num_pos == 'max':
if not randomize:
pos_arr = pos_arr[:num_pos]
neg_arr = pos_arr[:num_pos]
def get_jumping_windows_dir(dataset_name):
return makedirs(join(res_dir, 'jumping_windows_%s' % dataset_name))
def main():
parser = argparse.ArgumentParser(
description='Execute different functions of our system')
parser.add_argument('--first_n',
type=int,
help='only take the first N images in the datasets')
parser.add_argument('--name',
help='name for this run',
default='default',
choices=['default', 'nolateral', 'nohal', 'halfsize'])
parser.add_argument('--force',
action='store_true',
default=False,
help='force overwrite')
args = parser.parse_args()
print(args)
#configuration class
class config(object):
pass
cfg = config()
cfg.testname = "../ctfdet/data/finalRL/%s2_test" #object model
cfg.bottomup = False #use complete search
cfg.resize = 1.0 #resize the input image
cfg.hallucinate = True #use HOGs up to 4 pixels
cfg.initr = 1 #initial radious of the CtF search
cfg.ratio = 1 #radious at the next levels
cfg.deform = True #use deformation
cfg.usemrf = True #use lateral constraints
if args.name == 'default':
cfg
# sticking with the default params
elif args.name == 'nolateral':
cfg.usemrf = False
elif args.name == 'nohal':
cfg.hallucinate = False
elif args.name == 'halfsize':
cfg.resize = 0.5
# f**k it, do both
test_datasets = ['val', 'test', 'train']
for test_dataset in test_datasets:
# Load the dataset
dataset = Dataset('full_pascal_' + test_dataset)
if args.first_n:
dataset.images = dataset.images[:args.first_n]
# create directory for storing cached detections
dirname = './temp_data'
if os.path.exists('/u/sergeyk'):
dirname = '/u/vis/x1/sergeyk/object_detection'
dirname = dirname + '/ctfdets/%s' % (args.name)
ut.makedirs(dirname)
num_images = len(dataset.images)
for img_ind in range(comm_rank, num_images, comm_size):
# check for existing det
image = dataset.images[img_ind]
filename = os.path.join(dirname, image.name + '.npy')
if os.path.exists(filename) and not args.force:
#table = np.load(filename)[()]
continue
#read the image
imname = dataset.get_image_filename(img_ind)
img = util2.myimread(imname, resize=cfg.resize)
#compute the hog pyramid
f = pyrHOG2.pyrHOG(img,
interv=10,
savedir="",
notsave=True,
notload=True,
hallucinate=cfg.hallucinate,
cformat=True)
#for each class
all_dets = []
for ccls in dataset.classes:
t = time.time()
cls_ind = dataset.get_ind(ccls)
print "%s Img %d/%d Class: %s" % (test_dataset, img_ind + 1,
num_images, ccls)
#load the class model
m = util2.load("%s%d.model" % (cfg.testname % ccls, 7))
res = []
t1 = time.time()
#for each aspect
for clm, m in enumerate(m):
#scan the image with left and right models
res.append(
pyrHOG2RL.detectflip(f,
m,
None,
hallucinate=cfg.hallucinate,
initr=cfg.initr,
ratio=cfg.ratio,
deform=cfg.deform,
bottomup=cfg.bottomup,
usemrf=cfg.usemrf,
small=False,
cl=clm))
fuse = []
numhog = 0
#fuse the detections
for mix in res:
tr = mix[0]
fuse += mix[1]
numhog += mix[3]
rfuse = tr.rank(fuse, maxnum=300)
nfuse = tr.cluster(rfuse, ovr=0.3, inclusion=False)
#print "Number of computed HOGs:",numhog
time_elapsed = time.time() - t
print "Elapsed time: %.3f s" % time_elapsed
bboxes = [nf['bbox'] for nf in nfuse]
scores = [nf['scr'] for nf in nfuse]
assert (len(bboxes) == len(scores))
if len(bboxes) > 0:
arr = np.zeros((len(bboxes), 7))
arr[:, :4] = BoundingBox.convert_arr_from_corners(
np.array(bboxes))
arr[:, 4] = scores
arr[:, 5] = time_elapsed
arr[:, 6] = cls_ind
all_dets.append(arr)
cols = ['x', 'y', 'w', 'h', 'score', 'time', 'cls_ind']
if len(all_dets) > 0:
all_dets = np.concatenate(all_dets, 0)
else:
all_dets = np.array([])
table = Table(all_dets, cols)
np.save(filename, table)
def get_evals_dir(dataset_name):
return makedirs(join(evals_dir, dataset_name))
def main():
parser = argparse.ArgumentParser(description="Execute different functions of our system")
parser.add_argument("--first_n", type=int, help="only take the first N images in the datasets")
parser.add_argument(
"--name", help="name for this run", default="default", choices=["default", "nolateral", "nohal", "halfsize"]
)
parser.add_argument("--force", action="store_true", default=False, help="force overwrite")
args = parser.parse_args()
print (args)
# configuration class
class config(object):
pass
cfg = config()
cfg.testname = "../ctfdet/data/finalRL/%s2_test" # object model
cfg.bottomup = False # use complete search
cfg.resize = 1.0 # resize the input image
cfg.hallucinate = True # use HOGs up to 4 pixels
cfg.initr = 1 # initial radious of the CtF search
cfg.ratio = 1 # radious at the next levels
cfg.deform = True # use deformation
cfg.usemrf = True # use lateral constraints
if args.name == "default":
cfg
# sticking with the default params
elif args.name == "nolateral":
cfg.usemrf = False
elif args.name == "nohal":
cfg.hallucinate = False
elif args.name == "halfsize":
cfg.resize = 0.5
# f**k it, do both
test_datasets = ["val", "test", "train"]
for test_dataset in test_datasets:
# Load the dataset
dataset = Dataset("full_pascal_" + test_dataset)
if args.first_n:
dataset.images = dataset.images[: args.first_n]
# create directory for storing cached detections
dirname = "./temp_data"
if os.path.exists("/u/sergeyk"):
dirname = "/u/vis/x1/sergeyk/object_detection"
dirname = dirname + "/ctfdets/%s" % (args.name)
ut.makedirs(dirname)
num_images = len(dataset.images)
for img_ind in range(comm_rank, num_images, comm_size):
# check for existing det
image = dataset.images[img_ind]
filename = os.path.join(dirname, image.name + ".npy")
if os.path.exists(filename) and not args.force:
# table = np.load(filename)[()]
continue
# read the image
imname = dataset.get_image_filename(img_ind)
img = util2.myimread(imname, resize=cfg.resize)
# compute the hog pyramid
f = pyrHOG2.pyrHOG(
img, interv=10, savedir="", notsave=True, notload=True, hallucinate=cfg.hallucinate, cformat=True
)
# for each class
all_dets = []
for ccls in dataset.classes:
t = time.time()
cls_ind = dataset.get_ind(ccls)
print "%s Img %d/%d Class: %s" % (test_dataset, img_ind + 1, num_images, ccls)
# load the class model
m = util2.load("%s%d.model" % (cfg.testname % ccls, 7))
res = []
t1 = time.time()
# for each aspect
for clm, m in enumerate(m):
# scan the image with left and right models
res.append(
pyrHOG2RL.detectflip(
f,
m,
None,
hallucinate=cfg.hallucinate,
initr=cfg.initr,
ratio=cfg.ratio,
deform=cfg.deform,
bottomup=cfg.bottomup,
usemrf=cfg.usemrf,
small=False,
cl=clm,
)
)
fuse = []
numhog = 0
# fuse the detections
for mix in res:
tr = mix[0]
fuse += mix[1]
numhog += mix[3]
rfuse = tr.rank(fuse, maxnum=300)
nfuse = tr.cluster(rfuse, ovr=0.3, inclusion=False)
# print "Number of computed HOGs:",numhog
time_elapsed = time.time() - t
print "Elapsed time: %.3f s" % time_elapsed
bboxes = [nf["bbox"] for nf in nfuse]
scores = [nf["scr"] for nf in nfuse]
assert len(bboxes) == len(scores)
if len(bboxes) > 0:
arr = np.zeros((len(bboxes), 7))
arr[:, :4] = BoundingBox.convert_arr_from_corners(np.array(bboxes))
arr[:, 4] = scores
arr[:, 5] = time_elapsed
arr[:, 6] = cls_ind
all_dets.append(arr)
cols = ["x", "y", "w", "h", "score", "time", "cls_ind"]
if len(all_dets) > 0:
all_dets = np.concatenate(all_dets, 0)
else:
all_dets = np.array([])
table = Table(all_dets, cols)
np.save(filename, table)
def get_sliding_windows_params_dir(dataset_name): return makedirs(join(get_sliding_windows_dir(dataset_name), 'params'))
