refactor: unify sift GPU feature with current ones.Merged normalization/rooting functions.Removed seperate storage

Author: YanNoun

opensfm/commands/detect_features.py

@@ -69,17 +69,10 @@ def detect(args):
         data.feature_type().upper(), image))
 
     start = timer()
-    if data.config["feature_type"] == "SIFT_GPU":
-        unmasked, keypoints = features.extract_features(
-            data.load_image(image), data.config)
-        p_unmasked, f_unmasked, c_unmasked = unmasked
-    else:
-        p_unmasked, f_unmasked, c_unmasked = features.extract_features(
-            data.load_image(image), data.config)
+    p_unmasked, f_unmasked, c_unmasked = features.extract_features(
+        data.load_image(image), data.config)
 
     fmask = data.load_features_mask(image, p_unmasked)
-    if data.config["feature_type"] == "SIFT_GPU":
-        keypoints = keypoints[fmask]
     p_unsorted = p_unmasked[fmask]
     f_unsorted = f_unmasked[fmask]
     c_unsorted = c_unmasked[fmask]
@@ -93,9 +86,6 @@ def detect(args):
     p_sorted = p_unsorted[order, :]
     f_sorted = f_unsorted[order, :]
     c_sorted = c_unsorted[order, :]
-    if data.config["feature_type"] == "SIFT_GPU":
-        keypoints_sorted = keypoints[order]
-        data.save_gpu_features(image, keypoints_sorted)
     data.save_features(image, p_sorted, f_sorted, c_sorted)
 
     if need_words:

opensfm/dataset.py

@@ -328,21 +328,6 @@ def load_features(self, image):
     def save_features(self, image, points, descriptors, colors):
         self._save_features(self._feature_file(image), points, descriptors, colors)
 
-    def save_gpu_features(self, image, keypoints):
-        io.mkdir_p(self._feature_path())
-        path = "./"+self._gpu_feature_file(image)
-        # Store data (serialize)
-        with open(path, 'wb+') as handle:
-            pickle.dump(keypoints, handle, protocol=pickle.HIGHEST_PROTOCOL)
-
-    def load_gpu_features(self, image):
-        path = self._gpu_feature_file(image)
-        if os.path.isfile(path):
-            with open(path, 'rb') as handle:
-                keypoints = pickle.load(handle)
-                return keypoints
-        return None
-
     def _words_file(self, image):
         return os.path.join(self._feature_path(), image + '.words.npz')
 

opensfm/features.py

@@ -35,21 +35,12 @@ def resized_image(image, config):
 
 
 def root_feature(desc, l2_normalization=False):
-    if l2_normalization:
-        s2 = np.linalg.norm(desc, axis=1)
-        desc = (desc.T / s2).T
-    s = np.sum(desc, 1)
-    desc = np.sqrt(desc.T / s).T
-    return desc
-
-
-def root_feature_sift_gpu(desc, l2_normalization=False):
     if l2_normalization:
         s2 = np.linalg.norm(desc, axis=1)
         idx = np.where(s2 == 0)
         s2[idx] = 1
         desc = (desc.T / s2).T
-    s = np.max(desc, 1)
+    s = np.sum(desc, 1)
     idx = np.where(s == 0)
     s[idx] = 1
     desc = np.sqrt(desc.T / s).T
@@ -150,7 +141,7 @@ def extract_features_sift(image, config):
 
 def extract_features_sift_gpu(image, config):
     check_gpu_initialization(config, image)
-    keypoints = sift_gpu.detect_image(image)
+    keypoints = sift_gpu.detect_image(image, config)
     idx = np.where(np.sum(keypoints.desc, 1) != 0)
     keypoints = keypoints[idx]
 
@@ -160,8 +151,8 @@ def extract_features_sift_gpu(image, config):
                              np.expand_dims(keypoints[:].angle, axis=1)], axis=1)
     desc = np.array(keypoints[:].desc, dtype=np.float32)
     if config['feature_root']:
-        desc = root_feature_sift_gpu(desc)
-    return points, desc, keypoints
+        desc = root_feature(desc)
+    return points, desc
 
 
 def extract_features_surf(image, config):
@@ -317,17 +308,14 @@ def extract_features(color_image, config):
     elif feature_type == 'ORB':
         points, desc = extract_features_orb(image, config)
     elif feature_type == 'SIFT_GPU':
-        points, desc, keypoints = extract_features_sift_gpu(image, config)
+        points, desc = extract_features_sift_gpu(image, config)
     else:
         raise ValueError('Unknown feature type '
                          '(must be SURF, SIFT, AKAZE, HAHOG, SIFT_GPU or ORB)')
 
     xs = points[:, 0].round().astype(int)
     ys = points[:, 1].round().astype(int)
     colors = color_image[ys, xs]
-    if keypoints is not None:
-        return normalize_features(points, desc, colors,
-                                  image.shape[1], image.shape[0]), keypoints
     return normalize_features(points, desc, colors,
                               image.shape[1], image.shape[0])
 

opensfm/matching.py

@@ -203,12 +203,9 @@ def match(im1, im2, camera1, camera2, data):
             matches = match_brute_force(f1, f2, config)
     elif matcher_type == 'SIFT_GPU':
         check_gpu_initialization(config, im1, data)
-        k1 = data.load_gpu_features(im1)
-        k2 = data.load_gpu_features(im2)
-        if k1 is None or k2 is None:
-            k1 = feature_loader.instance.create_gpu_keypoints_from_features(p1, f1)
-            k2 = feature_loader.instance.create_gpu_keypoints_from_features(p2, f2)
-        matches = sift_gpu.match_images(k1, k2)
+        k1 = feature_loader.instance.create_gpu_keypoints_from_features(p1, f1)
+        k2 = feature_loader.instance.create_gpu_keypoints_from_features(p2, f2)
+        matches = sift_gpu.match_images(k1, k2, config)
     else:
         raise ValueError("Invalid matcher_type: {}".format(matcher_type))