fix animal calibration, adjust calibration parameters

anipose/calibrate.py

@@ -63,29 +63,32 @@ def load_2d_data(config, calibration_path):
         points_raw = out['points']
         scores = out['scores']
 
+        print(points_raw.shape)
+        print(scores.shape)
+
         all_points.append(points_raw)
         all_scores.append(scores)
 
-    all_points = np.vstack(all_points)
-    all_scores = np.vstack(all_scores)
+    all_points = np.hstack(all_points)
+    all_scores = np.hstack(all_scores)
 
     return all_points, all_scores
 
 def process_points_for_calibration(all_points, all_scores):
-    """Takes in an array all_points of shape FxCxJx2 and all_scores of shape FxCxJ, where
-    F: number of frames
+    """Takes in an array all_points of shape CxFxJx2 and all_scores of shape CxFxJ, where
     C: number of cameras
+    F: number of frames
     J: number of joints"""
 
     assert all_points.shape[3] == 2, \
         "points are not 2 dimensional, or shape of points is wrong: {}".format(all_points.shape)
 
-    n_frames, n_cams, n_joints, _ = all_points.shape
+    n_cams, n_frames, n_joints, _ = all_points.shape
 
-    points = np.copy(all_points).swapaxes(0, 1).reshape(n_cams, -1, 2)
-    scores = all_scores.swapaxes(0, 1).reshape(n_cams, -1)
+    points = np.copy(all_points).reshape(n_cams, -1, 2)
+    scores = all_scores.reshape(n_cams, -1)
 
-    points[scores < 0.98] = np.nan
+    points[scores < 0.95] = np.nan
 
     num_good = np.sum(~np.isnan(points[:, :, 0]), axis=0)
     good = num_good >= 2
@@ -173,13 +176,18 @@ def process_session(config, session_path):
         cgroup.set_camera_sizes_videos(video_list)
         error = cgroup.calibrate_rows(all_rows, board,
                                       init_intrinsics=init_stuff,
-                                      init_extrinsics=init_stuff)
+                                      init_extrinsics=init_stuff,
+                                      n_iters=10, error_threshold=3, end_mu=1,
+                                      n_samp_iter=100, n_samp_full=2000)
 
     if config['calibration']['animal_calibration']:
         all_points, all_scores = load_2d_data(config, calibration_path)
         imgp = process_points_for_calibration(all_points, all_scores)
-        error = cgroup.bundle_adjust(imgp, threshold=10,
-                                     ftol=1e-4, loss='huber')
+        # error = cgroup.bundle_adjust(imgp, threshold=10, ftol=1e-4, loss='huber')
+        error = cgroup.bundle_adjust_iter(imgp, ftol=1e-4, n_iters=5,
+                                          n_samp_iter=100, n_samp_full=1000,
+                                          max_nfev=500,
+                                          verbose=True)
         cgroup.metadata['adjusted'] = True
     else:
         cgroup.metadata['adjusted'] = False