def look_for_checkpoints(folder, task):
dirs = [f for f in listdir(folder) if isdir(join(folder, f))]
for d in dirs:
# print(join(folder, d))
if d == task or (d in ['kp', 'ori', 'desc', 'joint']
and task == 'all'):
cp = tf.train.latest_checkpoint(join(folder, d))
if cp is not None:
# Load best validation result
r = loadh5(join(folder, d, 'best_val_loss.h5'))[d]
s = loadh5(join(folder, d, 'step.h5'))[d]
print('{0:s} -> {1:.05f} [{2:d}]'.format(
join(folder, d), r, s))
else:
look_for_checkpoints(join(folder, d), task)
lift_dir = v_des_dir
else:
print('Error: There is something wrong with the scene name: %s'%scene_name)
exit(1)
elif cst.DATA=='hpatches_rot' or cst.DATA=='hpatches_s':
lift_dir = v_des_dir
img0_fn = os.path.join(cst.DATA_DIR, scene_name,'%d.ppm'%1)
print('img_fn: %s'%img0_fn)
img0 = cv2.imread(img0_fn)
old_size0 = (img0.shape[1], img0.shape[0])
if args.resize==1:
img0 = cv2.resize(img0, new_size, interpolation=cv2.INTER_LINEAR)
lift_out0_fn = os.path.join(lift_dir, scene_name, '%d.h5'%1)
lift_out0 = loadh5(lift_out0_fn)
# detection and description
kp0 = []
for line in lift_out0['keypoints'][:args.max_num_feat,:]:
kp = cv2.KeyPoint(x=line[0],y=line[1], _size=2,
_angle=0, _response=0, _octave=0, _class_id=0)
kp0.append(kp)
des0 = lift_out0['descriptors'][:args.max_num_feat,:]
# draw kp on img
kp_on_img0 = cv2.cvtColor(img0, cv2.COLOR_BGR2GRAY)
kp_on_img0 = np.tile(np.expand_dims(kp_on_img0,2), (1,1,3))
for i,kp in enumerate(kp0):
pt = (int(round(kp.pt[0])), int(round(kp.pt[1])))
cv2.circle(kp_on_img0, pt, 1, (0, 255, 0), -1, lineType=16)
img_dir = os.path.join(cst.DATA_DIR, scene_name, 'test/image_color')
img_list = os.listdir(img_dir)
# get 1st img, (resize it), convert to BW
img0_fn = os.path.join(cst.DATA_DIR, scene_name, 'test/image_color/',
img_list[0])
root_fn = img_list[0].split(".")[0]
#print('img_fn: %s'%img0_fn)
img0 = cv2.imread(img0_fn)
old_size0 = (img0.shape[1], img0.shape[0])
if args.resize == 1:
img0 = cv2.resize(img0, new_size, interpolation=cv2.INTER_LINEAR)
# raw sift on img 1
lift_out_fn = os.path.join(lift_dir, scene_name, root_fn + '.h5')
lift_out = loadh5(lift_out_fn)
kp0 = []
for line in lift_out['keypoints'][:args.max_num_feat, :]:
kp = cv2.KeyPoint(x=line[0],
y=line[1],
_size=2,
_angle=0,
_response=0,
_octave=0,
_class_id=0)
kp0.append(kp)
des0 = lift_out['descriptors'][:args.max_num_feat, :]
print(des0.shape)
kp_on_img0 = cv2.cvtColor(img0, cv2.COLOR_BGR2GRAY)
kp_on_img0 = np.tile(np.expand_dims(kp_on_img0, 2), (1, 1, 3))