def build_db(fn_pattern='.jpg'):
src_img_dir = os.path.join(Pascal3D_root, 'Images')
#
try: os.makedirs(db_path)
except: pass
imdb = ImageData_lmdb(db_path, 'w') # 'a+') #
allIDs = []
for collection in ['train','val']:
for label, cate in enumerate(categories):
_, rcobjs = get_anno(cate, collection=collection, filter='all', img_scale='Org', withCoarseVp=True)
imgIDs = get_imgIDs(rcobjs)
print ('%15s %s %5d' % (cate, collection, len(imgIDs)))
for i, imgID in enumerate(tqdm(imgIDs)):
# if is_py3:
# imgID = imgID.decode('UTF-8')
if imgID[0]=='n':
fo = '%s_imagenet' % cate
else:
fo = '%s_pascal' % cate
image_file = os.path.join(src_img_dir, fo, '%s.%s' % (imgID, fn_pattern.strip('.')))
assert os.path.exists(image_file), image_file
img = cv2.imread(image_file) # , cv2.IMREAD_UNCHANGED)
imdb[imgID] = img
allIDs.append(imgID)
imdb.close()
print ('All Images: %d' % len(allIDs))
print ('All Images: %d (unique)' % len(set(allIDs)))
return db_path
def main(
collection='train',
filter='all',
cates=categories, # cates=['aeroplane','boat','car'], #
):
out_dir = '../anno_db_v2/data.cache/objId2gtbox'
try:
os.makedirs(out_dir)
except:
print('Make dirs skipped!')
# from multiprocessing import Pool
objId2gtbox = dict()
nr_box = 0
for cate in cates:
print(' >>> %10s %5s %20s ' % (collection, filter, cate))
objIDs, rcobjs = get_anno(cate, collection=collection, filter=filter)
for _k, rcobj in enumerate(rcobjs): # tqdm()
gt_box = process(rcobj) # resize_shape cate, _k, len(rcobjs)
objId2gtbox[rcobj.obj_id] = gt_box
nr_box += 1
outpath = os.path.join(
out_dir, 'cate%s_%s.%s.pkl' % (len(cates), collection, filter))
pickle.dump(objId2gtbox, Open(outpath, 'wb'), protocol)
print('[outpath]: ', outpath)
print('nr_box: ', nr_box)
def check(cates=categories):
nr_box = 0
for cate in cates:
print(cate)
for collection in ['train', 'val']:
objIDs, rcobjs = get_anno(cate, collection=collection)
for _k, rcobj in enumerate(rcobjs):
check_one(rcobj) # resize_shape cate, _k, len(rcobjs)
def eval_one(objID2aet_pred,
cate='aeroplane',
theta_levels=[pi / 6.],
nr_worker=20):
# objID2aet_pred = parse_rslt_txt(rslt_txt_file)
keys, rcobjs = get_anno(cate, collection='val', filter='easy')
# print('--->[eval_one] %s '%cate, len(keys))
vps = rcobjs.gt_view
gt_rot_Mats = compute_RotMats(vps.a, vps.e, vps.t)
a_preds, e_preds, t_preds = [], [], []
for rcobj in rcobjs:
_a, _e, _t = objID2aet_pred[rcobj.obj_id]
a_preds.append(_a)
e_preds.append(_e)
t_preds.append(_t)
a_preds = np.array(a_preds, np.float32)
e_preds = np.array(e_preds, np.float32)
t_preds = np.array(t_preds, np.float32)
pred_rot_Mats = compute_RotMats(a_preds, e_preds, t_preds)
# pool = Pool(nr_worker)
# geo_dists = pool.map(_geodesic_dist, zip(pred_rot_Mats,gt_rot_Mats))
geo_dists = []
for pr_R, gt_R in zip(pred_rot_Mats, gt_rot_Mats):
geo_dists.append(geodesic_dist_new(pr_R, gt_R))
#
geo_dists = np.array(geo_dists)
#
MedError = np.median(geo_dists) / pi * 180.
Acc_at_ts = [
sum(geo_dists < theta_level) / float(len(keys))
for theta_level in theta_levels
]
return MedError, Acc_at_ts