Add support for relative depth in point tracking reader.

challenges/point_tracking/dataset.py

@@ -407,7 +407,7 @@ def single_object_reproject(
     # world is convex; can't face away from cam.
     faces_away = tf.zeros([tf.shape(pt)[0], num_frames], dtype=tf.bool)
 
-  return obj_reproj, tf.logical_or(faces_away, obj_occ)
+  return obj_reproj, tf.logical_or(faces_away, obj_occ), depth_proj
 
 
 def get_num_to_sample(counts, max_seg_id, max_sampled_frac, tracks_to_sample):
@@ -522,6 +522,8 @@ def track_points(
   chosen_points = []
   all_reproj = []
   all_occ = []
+  chosen_points_depth = []
+  all_reproj_depth = []
 
   # Convert to metric depth
 
@@ -669,8 +671,18 @@ def get_camera(fr=None):
                      lambda: obj_quat[obj_id, :])
       frame_for_pt = pt_coords[..., 0]
 
+    pt_depth = []
+    for fr in range(num_frames):
+      pt_coords_chunk = tf.boolean_mask(
+          pt_coords, tf.equal(pt_coords[:, 0], fr)
+      )
+      shp = tf.convert_to_tensor(tf.shape(depth_map[fr]))
+      idx = pt_coords_chunk[:, 1] * shp[1] + pt_coords_chunk[:, 2]
+      pt_depth.append(tf.gather(tf.reshape(depth_map[fr], [-1]), idx))
+    chosen_points_depth.append(tf.concat(pt_depth, axis=0))
+
     # Finally, compute the reprojections for this particular object.
-    obj_reproj, obj_occ = tf.cond(
+    obj_reproj, obj_occ, reproj_depth = tf.cond(
         tf.shape(pt)[0] > 0,
         functools.partial(
             single_object_reproject,
@@ -689,11 +701,13 @@ def get_camera(fr=None):
             frame_for_pt=frame_for_pt,
             trust_normals=trust_sn_gather,
         ),
-        lambda:  # pylint: disable=g-long-lambda
-        (tf.zeros([0, num_frames, 2], dtype=tf.float32),
-         tf.zeros([0, num_frames], dtype=tf.bool)))
+        lambda: (  # pylint: disable=g-long-lambda
+            tf.zeros([0, num_frames, 2], dtype=tf.float32),
+            tf.zeros([0, num_frames], dtype=tf.bool),
+            tf.zeros([0, num_frames], dtype=tf.float32)))
     all_reproj.append(obj_reproj)
     all_occ.append(obj_occ)
+    all_reproj_depth.append(reproj_depth)
 
   # Points are currently in pixel coordinates of the original video.  We now
   # convert them to coordinates within the window frame, and rescale to
@@ -712,6 +726,8 @@ def get_camera(fr=None):
   all_reproj = (all_reproj - window_top_left) / window_size
   all_reproj = all_reproj * coord_multiplier[2:0:-1]
   all_occ = tf.concat(all_occ, axis=0)
+  chosen_points_depth = tf.concat(chosen_points_depth, axis=0)
+  all_reproj_depth = tf.concat(all_reproj_depth, axis=0)
 
   # chosen_points is [num_points, (z,y,x)]
   chosen_points = tf.concat(chosen_points, axis=0)
@@ -723,8 +739,10 @@ def get_camera(fr=None):
   chosen_points = chosen_points * coord_multiplier
   # Note: all_reproj is in (x,y) format, but chosen_points is in (z,y,x) format
 
+  all_relative_depth = all_reproj_depth / chosen_points_depth[..., tf.newaxis]
+
   return tf.cast(chosen_points, tf.float32), tf.cast(all_reproj,
-                                                     tf.float32), all_occ
+                                                     tf.float32), all_occ, all_relative_depth
 
 
 def _get_distorted_bounding_box(
@@ -817,7 +835,7 @@ def add_tracks(data,
                               dtype=tf.int32,
                               shape=[4])
 
-  query_points, target_points, occluded = track_points(
+  query_points, target_points, occluded, relative_depth = track_points(
       data['object_coordinates'], data['depth'],
       data['metadata']['depth_range'], data['segmentations'],
       data['normal'],
@@ -831,6 +849,7 @@ def add_tracks(data,
   shp = video.shape.as_list()
   query_points.set_shape([tracks_to_sample, 3])
   target_points.set_shape([tracks_to_sample, num_frames, 2])
+  relative_depth.set_shape([tracks_to_sample, num_frames])
   occluded.set_shape([tracks_to_sample, num_frames])
 
   # Crop the video to the sampled window, in a way which matches the coordinate
@@ -851,6 +870,7 @@ def add_tracks(data,
   res = {
       'query_points': query_points,
       'target_points': target_points,
+      'relative_depth': relative_depth,
       'occluded': occluded,
       'video': video / (255. / 2.) - 1.,
   }