def test_valid_mask():
from utils.utils import pltImshow
batch_size = 1
mat_homographies = [sample_homography(3) for i in range(batch_size)]
mat_H = np.stack(mat_homographies, axis=0)
corner_img = np.array([(-1, -1), (-1, 1), (1, -1), (1, 1)])
# printCorners(corner_img, mat_H)
# points = warp_points_np(corner_img, mat_homographies)
mat_H = torch.tensor(mat_H, dtype=torch.float32)
mat_H_inv = torch.stack([torch.inverse(mat_H[i, :, :]) for i in range(batch_size)])
from utils.utils import compute_valid_mask, labels2Dto3D
device = 'cpu'
shape = torch.tensor([240, 320])
for i in range(1):
r = 3
mask_valid = compute_valid_mask(shape, inv_homography=mat_H_inv, device=device, erosion_radius=r)
pltImshow(mask_valid[0,:,:])
cell_size = 8
mask_valid = labels2Dto3D(mask_valid.view(batch_size, 1, mask_valid.shape[1], mask_valid.shape[2]), cell_size=cell_size)
mask_valid = torch.prod(mask_valid[:,:cell_size*cell_size,:,:], dim=1)
pltImshow(mask_valid[0,:,:].cpu().numpy())
mask = {}
mask.update({'homographies': mat_H, 'masks': mask_valid})
np.savez_compressed('h2.npz', **mask)
print("finish testing valid mask")
def sample_homo(image):
import tensorflow as tf
from utils.homographies import sample_homography
H = sample_homography(tf.shape(image)[:2])
with tf.Session():
H_ = H.eval()
H_ = np.concatenate((H_, np.array([1])[:, np.newaxis]), axis=1)
# warped_im = tf.contrib.image.transform(image, H, interpolation="BILINEAR")
mat = np.reshape(H_, (3, 3))
# for i in range(batch):
# np.stack()
return mat
def test_sample_homography():
batch_size = 30
filename = '../configs/superpoint_coco_train.yaml'
import yaml
with open(filename, 'r') as f:
config = yaml.load(f)
test_tf = False
test_corner_def = True
if test_tf == True:
from utils.homographies import sample_homography as sample_homography
boundary = 1
# from utils.homographies import sample_homography_np as sample_homography
# mat_homographies = matrix[np.newaxis, :,:]
# mat_homographies = [sample_homography(tf.constant([boundary,boundary]),
mat_homographies = [sample_homography(np.array([boundary,boundary]),
**config['data']['warped_pair']['params']) for i in range(batch_size)]
mat_homographies = np.stack(mat_homographies, axis=0)
corner_img = np.array([[0., 0.], [0., boundary], [boundary, boundary], [boundary, 0.]])
printCorners(corner_img, mat_homographies)
if test_corner_def:
corner_img = np.array([(-1, -1), (-1, 1), (1, 1), (1, -1)])
from utils.homographies import sample_homography_np as sample_homography
boundary = 2
mat_homographies = [sample_homography(np.array([boundary,boundary]), shift=-1,
**config['data']['warped_pair']['params']) for i in range(batch_size)]
mat_homographies = np.stack(mat_homographies, axis=0)
printCorners(corner_img, mat_homographies)
else:
from utils.utils import sample_homography
mat_homographies = [sample_homography(1) for i in range(batch_size)]
# sess = tf.Session()
# with sess.as_default():
# m = mat_homographies[0].eval()
print("end")
def sample_homographies(batch_size=1, scale=10, device='cpu'):
## sample homography matrix
# mat_H = [sample_homography(inv_scale=scale) for i in range(batch_size)]
mat_H = [sample_homography(inv_scale=scale) for i in range(batch_size)]
##### debug
# from utils.utils import sample_homo
# mat_H = [sample_homo(image=np.zeros((1,1))) for i in range(batch_size)]
# mat_H = [np.identity(3) for i in range(batch_size)]
mat_H = np.stack(mat_H, axis=0)
mat_H = torch.tensor(mat_H, dtype=torch.float32)
mat_H = mat_H.to(device)
mat_H_inv = torch.stack([torch.inverse(mat_H[i, :, :]) for i in range(batch_size)])
mat_H_inv = torch.tensor(mat_H_inv, dtype=torch.float32)
mat_H_inv = mat_H_inv.to(device)
return mat_H, mat_H_inv