def load_features(
self, image: str
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, Optional[Dict[
str, Optional[np.ndarray]]]]:
if os.path.isfile(self._feature_file_legacy(image)):
return features.load_features(self._feature_file_legacy(image),
self.config)
return features.load_features(self._feature_file(image), self.config)
def display_features(file_path, filename, config, n_fts):
"""
Inputs:
file_path: where the images, features are stored
config: SfM configuration info
n_fts: display every n features
Outputs:
will display the image along with every n_fts on it
"""
fts = features.load_features(file_path + "features/" + filename, config)
points = fts[0]
img_name = filename[:-13]
img = cv2.imread(file_path + "images/" + img_name)
plt.imshow(img)
denorm_pts = features.denormalized_image_coordinates(points, 1920, 1080)
for pt_i, (x, y) in enumerate(denorm_pts):
if (pt_i % n_fts) == 0:
plt.scatter(x, y)
plt.show()
def insert_features(feats, feats_file_path, config):
"""
Input:
feats: Features to add to existing ORB features as a tuple (points, desc, color)
feats_file_path: where the npz file is stored incl. npz file name
config: SfM configuration info
Output:
saves a new npz file with extra features on it
"""
fts = features.load_features(feats_file_path, config)
assert (feats[0].shape[1] == fts[0].shape[1]
) # make sure we have the same dimension
assert (feats[1].shape[1] == fts[1].shape[1]
) # make sure we have the same dimension
assert (feats[2].shape[1] == fts[2].shape[1]
) # make sure we have the same dimension
new_feat_pts = np.vstack((fts[0], feats[0])) # append features together
new_feat_desc = np.vstack((fts[1], feats[1]))
new_feat_col = np.vstack((fts[2], feats[2]))
features.save_features(feats_file_path, new_feat_pts, new_feat_desc,
new_feat_col, config) # save features yeaaaaaah boi
def load_features(self, image):
if os.path.isfile(self._feature_file_legacy(image)):
return features.load_features(self._feature_file_legacy(image), self.config)
return features.load_features(self._feature_file(image), self.config)
def load_features(feature_path, image, config):
if os.path.isfile(_feature_file_legacy(feature_path, image)):
return features.load_features(_feature_file_legacy(feature_path,image), config)
return features.load_features(_feature_file(feature_path,image), config)
def load_features(self, image):
return features.load_features(self._feature_file(image), self.config)
