utils: raspberrypi: ctt: Fix NaNs in chromatic aberration tables

NaNs can appear if no black dots can be found and analysed in a
particular region of the calibration image. There needs to be at least
one such dot in every 8x8 cell covering the image.

This is now detected, and an error message issued. No CAC tables are
generated, so CAC is disabled.

Signed-off-by: David Plowman <[email protected]>

Author: davidplowman

utils/raspberrypi/ctt/ctt.py

@@ -198,9 +198,12 @@ def cac_cal(self, do_alsc_colour):
         """
         Write output to json
         """
-        self.json['rpi.cac']['cac'] = cacs
-        self.log += '\nCAC calibration written to json file'
-        print('Finished CAC calibration')
+        if cacs:
+            self.json['rpi.cac']['cac'] = cacs
+            self.log += '\nCAC calibration written to json file'
+            print('Finished CAC calibration')
+        else:
+            self.log += "\nCAC calibration failed"
 
 
     """

utils/raspberrypi/ctt/ctt_cac.py

@@ -108,12 +108,29 @@ def shifts_to_yaml(red_shift, blue_shift, image_dimensions, output_grid_size=9):
         ybsgrid[xgridloc][ygridloc].append(blue_shift[3])
 
     # Now calculate the average pixel shift for each square in the grid
+    grid_incomplete = False
     for x in range(output_grid_size - 1):
         for y in range(output_grid_size - 1):
-            xrgrid[x, y] = np.mean(xrsgrid[x][y])
-            yrgrid[x, y] = np.mean(yrsgrid[x][y])
-            xbgrid[x, y] = np.mean(xbsgrid[x][y])
-            ybgrid[x, y] = np.mean(ybsgrid[x][y])
+            if xrsgrid[x][y]:
+                xrgrid[x, y] = np.mean(xrsgrid[x][y])
+            else:
+                grid_incomplete = True
+            if yrsgrid[x][y]:
+                yrgrid[x, y] = np.mean(yrsgrid[x][y])
+            else:
+                grid_incomplete = True
+            if xbsgrid[x][y]:
+                xbgrid[x, y] = np.mean(xbsgrid[x][y])
+            else:
+                grid_incomplete = True
+            if ybsgrid[x][y]:
+                ybgrid[x, y] = np.mean(ybsgrid[x][y])
+            else:
+                grid_incomplete = True
+
+    if grid_incomplete:
+        raise RuntimeError("\nERROR: CAC measurements do not span the image!"
+                           "\nConsider using improved CAC images, or remove them entirely.\n")
 
     # Next, we start to interpolate the central points of the grid that gets passed to the tuning file
     input_grids = np.array([xrgrid, yrgrid, xbgrid, ybgrid])
@@ -219,7 +236,12 @@ def cac(Cam):
     # tuning file
     print("\nCreating output grid")
     Cam.log += '\nCreating output grid'
-    rx, ry, bx, by = shifts_to_yaml(red_shift, blue_shift, image_size)
+    try:
+        rx, ry, bx, by = shifts_to_yaml(red_shift, blue_shift, image_size)
+    except RuntimeError as e:
+        print(str(e))
+        Cam.log += "\nCAC correction failed! CAC will not be enabled."
+        return {}
 
     print("CAC correction complete!")
     Cam.log += '\nCAC correction complete!'