Merge branch 'master' of https://github.com/pupil-labs/pupil

pupil_src/shared_modules/background_helper.py

@@ -47,8 +47,9 @@ def _wrapper(self, pipe, _should_terminate_flag, generator, *args, **kwargs):
                 pipe.send(datum)
         except Exception as e:
             pipe.send(e)
-            import traceback
-            logger.warning(traceback.format_exc())
+            if not isinstance(e, EarlyCancellationError):
+                import traceback
+                print(traceback.format_exc())
         else:
             pipe.send(StopIteration())
         finally:

pupil_src/shared_modules/calibration_routines/calibrate.py

@@ -313,53 +313,34 @@ def find_nearest_idx(array,value):
 
 
 def preprocess_2d_data_monocular(matched_data):
-    cal_data = []
-    for pair in matched_data:
-        ref,pupil = pair['ref'],pair['pupil']
-        cal_data.append( (pupil["norm_pos"][0], pupil["norm_pos"][1],ref['norm_pos'][0],ref['norm_pos'][1]) )
+    cal_data = [(*pair['pupil']["norm_pos"],
+                 *pair['ref']["norm_pos"])
+                for pair in matched_data]
     return cal_data
 
+
 def preprocess_2d_data_binocular(matched_data):
-    cal_data = []
-    for triplet in matched_data:
-        ref,p0,p1 = triplet['ref'],triplet['pupil'],triplet['pupil1']
-        data_pt = p0["norm_pos"][0], p0["norm_pos"][1],p1["norm_pos"][0], p1["norm_pos"][1],ref['norm_pos'][0],ref['norm_pos'][1]
-        cal_data.append( data_pt )
+    cal_data = [(*triplet['pupil']["norm_pos"],
+                 *triplet['pupil1']["norm_pos"],
+                 *triplet['ref']["norm_pos"])
+                for triplet in matched_data]
     return cal_data
 
-def preprocess_3d_data(matched_data, g_pool):
-    ref_processed = []
-    pupil0_processed = []
-    pupil1_processed = []
 
-    is_binocular = len(matched_data[0] ) == 3
-    for data_point in matched_data:
-        try:
-            # taking the pupil normal as line of sight vector
-            pupil0 = data_point['pupil']
-            gaze_vector0 = np.array(pupil0['circle_3d']['normal'])
-            pupil0_processed.append( gaze_vector0 )
-
-            if is_binocular: # we have binocular data
-                pupil1 = data_point['pupil1']
-                gaze_vector1 = np.array(pupil1['circle_3d']['normal'])
-                pupil1_processed.append( gaze_vector1 )
+def preprocess_3d_data(matched_data, g_pool):
+    pupil0_processed = [dp['pupil']['circle_3d']['normal'] for dp in matched_data if 'circle_3d' in dp['pupil']]
 
-            # projected point uv to normal ray vector of camera
-            ref = data_point['ref']['screen_pos']
-            ref_vector = np.array(ref).reshape(-1,1,2)
-            ref_vector = g_pool.capture.intrinsics.unprojectPoints(ref_vector, normalize=True)
-            # assuming a fixed (assumed) distance we get a 3d point in world camera 3d coords.
-            ref_processed.append(ref_vector)
+    pupil1_processed = [dp['pupil1']['circle_3d']['normal'] for dp in matched_data if 'pupil1' in dp and 'circle_3d' in dp['pupil1']]
 
-        except KeyError as e:
-            # this pupil data point did not have 3d detected data.
-            pass
+    ref = np.array([dp['ref']['screen_pos'] for dp in matched_data])
+    ref_processed = g_pool.capture.intrinsics.unprojectPoints(ref, normalize=True)
 
-    return ref_processed,pupil0_processed,pupil1_processed
+    return ref_processed, pupil0_processed, pupil1_processed
 
 
 def find_rigid_transform(A, B):
+    # we expect the shape to be of length 2
+    assert len(A.shape) == len(B.shape) == 2
     assert A.shape[0] == B.shape[0]
 
     centroid_A = np.mean(A, axis=0)

pupil_src/shared_modules/camera_models.py

@@ -165,10 +165,9 @@ def unprojectPoints(self, pts_2d, use_distortion=True, normalize=False):
         Undistorts points according to the camera model.
         cv2.fisheye.undistortPoints does *NOT* perform the same unprojection step the original cv2.unprojectPoints does.
         Thus we implement this function ourselves.
-        :param pts_2d: Distorted points. Can be a list of points or a single point.
-        :return: Array of unprojected 3d points
+        :param pts_2d, shape: Nx2
+        :return: Array of unprojected 3d points, shape: Nx3
         """
-        input_shape = pts_2d.shape
 
         pts_2d = pts_2d.reshape((-1, 2))
         eps = np.finfo(np.float32).eps
@@ -303,15 +302,10 @@ def undistort(self, img):
     def unprojectPoints(self, pts_2d, use_distortion=True, normalize=False):
         """
         Undistorts points according to the camera model.
-        :param pts_2d: 2d points. Can be a list of points or a single point.
-        :return: Array of unprojected 3d points
+        :param pts_2d, shape: Nx2
+        :return: Array of unprojected 3d points, shape: Nx3
         """
         pts_2d = np.array(pts_2d, dtype=np.float32)
-        input_shape = pts_2d.shape
-
-        # If given a single point expand to a 1-point list
-        if len(pts_2d.shape) == 1:
-            pts_2d = pts_2d.reshape((1, 2))
 
         # Delete any posibly wrong 3rd dimension
         if pts_2d.ndim == 3:
@@ -336,7 +330,7 @@ def unprojectPoints(self, pts_2d, use_distortion=True, normalize=False):
         pts_3d.shape = -1, 3
 
         if normalize:
-            pts_3d /= np.linalg.norm(pts_3d, axis=1)
+            pts_3d /= np.linalg.norm(pts_3d, axis=1)[:, np.newaxis]
 
         return pts_3d