update after review

petric.py

@@ -90,38 +90,37 @@ def __call__(self, algo: Algorithm):
 
 class QualityMetrics(ImageQualityCallback):
     """From https://github.com/SyneRBI/PETRIC/wiki#metrics-and-thresholds"""
-    def __init__(self, reference_image, backround_mask, foreground_mask, **kwargs):
+    def __init__(self, reference_image, whole_object_mask, background_mask, **kwargs):
         super().__init__(reference_image, **kwargs)
-        self.background = np.where(backround_mask == 1)
-        self.foreground = np.where(foreground_mask == 1)
+        self.whole_object_indices = np.where(whole_object_mask == 1)
+        self.background_indices = np.where(background_mask == 1)
+        self.ref_im_arr = reference_image.as_array()
 
     def __call__(self, algorithm):
         iteration = algorithm.iteration
         if iteration % algorithm.update_objective_interval != 0 and iteration != algorithm.max_iteration:
             return
-        test_image = algorithm.x # CIL or SIRF ImageData
-
-        test_im_arr, ref_im_arr = test_image.as_array(), self.reference_image.as_array()
 
         for filter_name, filter_func in self.filter.items():
-            if filter_func is not None:
-                test_im, ref_im = map(filter_func, (test_im_arr, ref_im_arr))
+            if filter_func is None:
+                filter_func = lambda x: x
+            test_im, ref_im = (filter_func(img_data).as_array() for img_data in (algorithm.x, self.reference_image))
 
             # (1) global metrics & statistics
-            norm = ref_im[self.background].mean()
-            self.tb_summary_writer.add_scalar(f"RMSE_foreground{filter_name}",
-                                              np.sqrt(mse(test_im[self.foreground], ref_im[self.foreground])) / norm,
-                                              iteration)
-            self.tb_summary_writer.add_scalar(f"RMSE_background{filter_name}",
-                                              np.sqrt(mse(test_im[self.background], ref_im[self.background])) / norm,
-                                              iteration)
+            norm = ref_im[self.background_indices].mean()
+            self.tb_summary_writer.add_scalar(
+                f"RMSE_whole_object{filter_name}",
+                np.sqrt(mse(ref_im[self.whole_object_indices], test_im[self.whole_object_indices])) / norm, iteration)
+            self.tb_summary_writer.add_scalar(
+                f"RMSE_background{filter_name}",
+                np.sqrt(mse(ref_im[self.background_indices], test_im[self.background_indices])) / norm, iteration)
 
             # (2) local metrics & statistics
-            for roi_name, roi_inds in self.roi_indices.items():
+            for voi_name, voi_indices in self.voi_indices.items():
                 # AEM not to be confused with MAE
-                self.tb_summary_writer.add_scalar(f"AEM_VOI_{roi_name}{filter_name}",
-                                                  np.abs(test_im[roi_inds].mean() - ref_im[roi_inds].mean()) / norm,
-                                                  iteration)
+                self.tb_summary_writer.add_scalar(
+                    f"AEM_VOI_{voi_name}{filter_name}",
+                    np.abs(test_im[voi_indices].mean() - ref_im[voi_indices].mean()) / norm, iteration)
 
 
 class MetricsWithTimeout(cbks.Callback):
@@ -172,7 +171,7 @@ def construct_RDP(penalty_strength, initial_image, kappa, max_scaling=1e-3):
 
 Dataset = namedtuple('Dataset', [
     'acquired_data', 'additive_term', 'mult_factors', 'OSEM_image', 'prior', 'kappa', 'reference_image',
-    'background_mask', 'foreground_mask', 'voi_masks'])
+    'background_mask', 'whole_object_mask', 'voi_masks'])
 
 
 def get_data(srcdir=".", outdir=OUTDIR, sirf_verbosity=0):
@@ -197,14 +196,14 @@ def get_data(srcdir=".", outdir=OUTDIR, sirf_verbosity=0):
                                                                              'reference_image.hv').is_file() else None
     background_mask = STIR.ImageData(str(srcdir / 'VOI_background.hv')) if (srcdir /
                                                                             'VOI_background.hv').is_file() else None
-    foreground_mask = STIR.ImageData(str(srcdir / 'VOI_foreground.hv')) if (srcdir /
-                                                                            'VOI_foreground.hv').is_file() else None
+    whole_object_mask = STIR.ImageData(str(srcdir / 'VOI_whole_object.hv')) if (srcdir /
+                                                                            'VOI_whole_object.hv').is_file() else None
     voi_masks = {
         voi.stem: STIR.ImageData(str(voi))
-        for voi in srcdir.glob("VOI_*.hv") if voi.stem[4:] not in ('background', 'foreground')}
+        for voi in srcdir.glob("VOI_*.hv") if voi.stem[4:] not in ('background', 'whole_object')}
 
     return Dataset(acquired_data, additive_term, mult_factors, OSEM_image, prior, kappa, reference_image,
-                   background_mask, foreground_mask, voi_masks)
+                   background_mask, whole_object_mask, voi_masks)
 
 
 if SRCDIR.is_dir():
@@ -233,12 +232,12 @@ def get_data(srcdir=".", outdir=OUTDIR, sirf_verbosity=0):
     assert issubclass(Submission, Algorithm)
     for srcdir, outdir, metrics in data_dirs_metrics:
         data = get_data(srcdir=srcdir, outdir=outdir)
-        metrics_cbk = metrics[0]
+        metrics_with_timeout = metrics[0]
         if data.reference_image is not None:
-            metrics_cbk.callbacks.append(
-                QualityMetrics(data.reference_image, data.background_mask, data.foreground_mask,
-                               tb_summary_writer=metrics_cbk.tb, roi_mask_dict=data.voi_masks))
-        metrics_cbk.reset() # timeout from now
+            metrics_with_timeout.callbacks.append(
+                QualityMetrics(data.reference_image, data.whole_object_mask, data.background_mask,
+                               tb_summary_writer=metrics_with_timeout.tb, roi_mask_dict=data.voi_masks))
+        metrics_with_timeout.reset() # timeout from now
         algo = Submission(data)
         try:
             algo.run(np.inf, callbacks=metrics + submission_callbacks)