def vr_make_meta_gt_visual_phrase():
m = h5py.File('data/sg_vrd_meta.h5','r',driver='core')
h5f = h5py.File('data/sg_vrd_vp_meta.h5')
triplets = {}
cnt = 0
zl.tick()
for k in m['gt/train'].keys():
if cnt %1000==0:
print cnt,zl.tock()
zl.tick()
cnt+=1
gt_boxes = []
gt_labels = []
sub_boxes = m['gt/train/%s/sub_boxes'%k][...]
obj_boxes = m['gt/train/%s/obj_boxes'%k][...]
rlp_labels = m['gt/train/%s/rlp_labels'%k][...]
for i in xrange(rlp_labels.shape[0]):
sub_box = sub_boxes[i]
obj_box = obj_boxes[i]
rlp_label = rlp_labels[i]
joint_box = [min(sub_box[0],obj_box[0]), min(sub_box[1],obj_box[1]),max(sub_box[2],obj_box[2]),max(sub_box[3],obj_box[3])]
s_lbl = zl.idx2name_cls(m,rlp_label[0])
o_lbl = zl.idx2name_cls(m,rlp_label[2])
p_lbl = zl.idx2name_pre(m,rlp_label[1])
spo = '%s_%s_%s'%(s_lbl,p_lbl,o_lbl)
lbl = zl.name2idx_tri(h5f,spo)
gt_boxes.append(joint_box)
gt_labels.append(lbl)
h5f.create_dataset('gt/train/%s/labels'%k,data = np.array(gt_labels).astype(np.int16))
h5f.create_dataset('gt/train/%s/boxes'%k,data = np.array(gt_boxes).astype(np.int16))
def run_retrieval_vp():
h5f = h5py.File('output/precalc/vg1_2_vp2016_test_new.hdf5')
# print h5f[h5f.keys()[0]].keys()
# exit(0)
#h5f = h5py.file('output/results/lu_method_results.hdf5')
data_root = '/home/zawlin/g/py-faster-rcnn/data/vg1_2_2016/Data/test/'
m = h5py.File('/home/zawlin/Dropbox/proj/vg1_2_meta.h5', 'r', 'core')
m_vp = h5py.File('/home/zawlin/Dropbox/proj/vg1_2_vp_meta.h5', 'r', 'core')
thresh = 0.0
filters = []
rois = []
images = {}
imids = h5f.keys()
results = {}
cnt = 0
pre = 'teddy bear_sit on_blanket'
# pre = 'paw_in front of_cat'
# pre = 'racket_hold by_player'
r_idx = zl.name2idx_tri(m_vp, pre)
# sub_lbl = pre.split('_')[0]
# pre_lbl = pre.split('_')[1]
# obj_lbl = pre.split('_')[2]
# sub_idx = zl.name2idx_cls(m,sub_lbl)
# pre_idx = zl.name2idx_pre(m,pre_lbl)
# obj_idx = zl.name2idx_cls(m,obj_lbl)
# rlp_label = np.array([sub_idx,pre_idx,obj_idx]).astype(np.int16)
results = {}
for imid in imids:
if cnt % 100 == 0:
print cnt, zl.tock()
zl.tick()
cnt += 1
# rlp_labels = h5f[imid+'/labels'][...]
rlp_confs = h5f[imid + '/confs'][:, r_idx]
ind = np.argsort(rlp_confs)[::-1]
rlp_confs = rlp_confs[ind[:5]]
results[imid] = np.average(rlp_confs)
if rlp_confs.shape[0] == 0:
results[imid] = 0.0
continue
zl.tick()
indexor = np.arange(rlp_labels.shape[0])
# ind = indexor[np.all(rlp_labels==rlp_label,axis=1)]
ind = np.where(rlp_labels == r_idx)[0]
if ind.shape[0] == 0:
results[imid] = 0.0
continue
confs = rlp_confs[ind]
results[imid] = np.average(confs)
results_sorted = zl.sort_dict_by_val(results)
example_folder = 'output/examples_retr_vg_vp/%s/' % pre
zl.make_dirs(example_folder)
cnt = 0
for imid, v in results_sorted[:200]:
boxes = h5f[imid + '/boxes'][...]
rlp_labels = h5f[imid + '/labels'][...]
rlp_confs = h5f[imid + '/confs'][...]
# indexor = np.arange(rlp_labels.shape[0])
# ind = indexor[np.all(rlp_labels==rlp_label,axis=1)]
ind = np.where(rlp_labels == r_idx)[0]
if ind.shape[0] != 0:
boxes = boxes[ind]
sb = boxes[0]
# ob = obj_boxes[0]
im = cv2.imread(data_root + zl.imid2path(m, imid))
cv2.rectangle(im, (sb[0], sb[1]), (sb[2], sb[3]), (0, 200, 0), 1)
# cv2.rectangle(im,(ob[0],ob[1]),(ob[2],ob[3]),(0,0,200),1)
cv2.imwrite(example_folder + str(cnt) + '_%s.jpg' % imid, im)
#cv2.imshow('im',im)
cnt += 1
def run_retrieval_vp_all():
h5f = h5py.File('output/precalc/vg1_2_vp2016_test_new.hdf5', 'r')
# print h5f[h5f.keys()[0]].keys()
# exit(0)
#h5f = h5py.file('output/results/lu_method_results.hdf5')
img_set_file = 'data/vg1_2_2016/ImageSets/test.txt'
imlist = {
line.strip().split(' ')[1]: line.strip().split(' ')[0]
for line in open(img_set_file)
}
data_root = '/home/zawlin/g/py-faster-rcnn/data/vg1_2_2016/Data/test/'
m = h5py.File('/home/zawlin/Dropbox/proj/vg1_2_meta.h5', 'r', 'core')
m_vp = h5py.File('/home/zawlin/Dropbox/proj/vg1_2_vp_meta.h5', 'r', 'core')
thresh = 0.0
filters = []
rois = []
images = {}
imids = h5f.keys()
results = {}
cnt = 0
# pre = 'teddy bear_sit on_blanket'
pre = 'paw_in front of_cat'
# pre = 'racket_hold by_player'
r_idx = zl.name2idx_tri(m_vp, pre)
# sub_lbl = pre.split('_')[0]
# pre_lbl = pre.split('_')[1]
# obj_lbl = pre.split('_')[2]
# sub_idx = zl.name2idx_cls(m,sub_lbl)
# pre_idx = zl.name2idx_pre(m,pre_lbl)
# obj_idx = zl.name2idx_cls(m,obj_lbl)
# rlp_label = np.array([sub_idx,pre_idx,obj_idx]).astype(np.int16)
results = {}
for imid in imids:
# if cnt>2000:
# break
if cnt % 100 == 0:
print cnt, zl.tock()
zl.tick()
cnt += 1
# rlp_labels = h5f[imid+'/labels'][...]
if imid + '/scores' not in h5f:
print imid
continue
# exit(0)
rlp_confs = h5f[imid + '/scores'][:, r_idx]
# print np.argmax(rlp_confs)
# exit(0)
# ind = np.argsort(rlp_confs)[::-1]
rlp_confs = rlp_confs[np.argmax(rlp_confs)]
# print rlp_confs
results[imid] = np.average(rlp_confs)
# print np.average(rlp_confs)
results_sorted = zl.sort_dict_by_val(results)
example_folder = 'output/examples_retr_vg_vp/%s/' % pre
zl.make_dirs(example_folder)
cnt = 0
for imid, v in results_sorted[:200]:
if imid + '/scores' not in h5f:
continue
#rlp_labels = h5f[imid+'/labels'][...]
impath = imlist[imid]
imid_orig = impath.split('/')[1].replace('.jpg', '')
rlp_confs = h5f[imid + '/scores'][:, r_idx]
ind = np.argsort(rlp_confs)[::-1]
rlp_confs = rlp_confs[ind[:1]]
boxes = h5f[imid + '/boxes'][...][ind, :]
# indexor = np.arange(rlp_labels.shape[0])
# ind = indexor[np.all(rlp_labels==rlp_label,axis=1)]
#boxes = boxes[ind]
sb = boxes[0]
# ob = obj_boxes[0]
im = cv2.imread(data_root + zl.imid2path(m, imid_orig))
cv2.rectangle(im, (sb[0], sb[1]), (sb[2], sb[3]), (0, 200, 0), 1)
# cv2.rectangle(im,(ob[0],ob[1]),(ob[2],ob[3]),(0,0,200),1)
cv2.imwrite(example_folder + str(cnt) + '_%s.jpg' % imid, im)
#cv2.imshow('im',im)
cnt += 1