commit 83aff6e8cb51aa447e4775a97f0055bb3e54e22f
author Jim Ostrowski <yimmy13@gmail.com> Sat Oct 30 01:10:15 2021 -0700
committer Jim Ostrowski <yimmy13@gmail.com> Sat Jan 29 22:05:42 2022 -0800
tree b58e793c77dfbadebb53112a4d8efecb49b8caee
parent 18f934c7d81f30a8308c0d1c8d35ff765b8c7697

Write out correct pathname for image used in training

For now, this is very helpful in making sure we're training with the
right images.  Changing debug->log, and stripping off path basename

Also, moving calib info to debug, since this will fail properly in
runtime if hte calibration isn't there.

Change-Id: I307cc430a877a504032c4f4a5cd3e51fc876f1a0
Signed-off-by: Jim Ostrowski <yimmy13@gmail.com>

y2020/vision/tools/python_code/camera_definition.py

diff --git a/y2020/vision/tools/python_code/camera_definition.py b/y2020/vision/tools/python_code/camera_definition.py
index 465f7bd..3e3623d 100644
--- a/y2020/vision/tools/python_code/camera_definition.py
+++ b/y2020/vision/tools/python_code/camera_definition.py
@@ -147,8 +147,8 @@
                 dist_coeffs = np.asarray(calib_dict["dist_coeffs"]).reshape(
                     (1, 5))
 
-            glog.info("Found calib for " + node_name + ", team #" +
-                      str(team_number))
+            glog.debug("Found calib for " + node_name + ", team #" +
+                       str(team_number))
 
             camera_params = CameraParameters()
             camera_params.camera_ext, camera_params.turret_ext = compute_extrinsic_by_pi(

y2020/vision/tools/python_code/target_definition.py

diff --git a/y2020/vision/tools/python_code/target_definition.py b/y2020/vision/tools/python_code/target_definition.py
index 2b403d6..be725c3 100644
--- a/y2020/vision/tools/python_code/target_definition.py
+++ b/y2020/vision/tools/python_code/target_definition.py
@@ -5,6 +5,7 @@
 import json
 import math
 import numpy as np
+import os
 
 import camera_definition
 import define_training_data as dtd
@@ -154,7 +155,10 @@
     # otherwise get them directly from the training targets.
     for target in (ideal_target_list
                    if AUTO_PROJECTION else training_target_list):
-        glog.debug("\nPreparing target for image %s" % target.image_filename)
+        # TODO<Jim>: Save this info to flatbuffer and publish on start
+        # And then, make this debug message again
+        glog.debug("\nPreparing target for image %s" %
+                   os.path.basename(target.image_filename))
         target.extract_features(feature_extractor)
         target.filter_keypoints_by_polygons()
         target.compute_reprojection_maps()