tidy up

src/classify/clipclassifier.py

@@ -182,8 +182,6 @@ def classify_clip(self, clip, model, meta_data, reuse_frames=None):
         predictions.model_load_time = time.time() - start
 
         for i, track in enumerate(clip.tracks):
-            logging.info("Track id is %s", track.get_id())
-
             segment_frames = None
             if reuse_frames:
                 tracks = meta_data.get("tracks")
@@ -247,17 +245,6 @@ def save_metadata(
                 prediction = predictions.prediction_for(track.get_id())
                 if prediction is None:
                     continue
-                # DEBUGGING STUFF REMOVE ME
-                # logging.info("Track predictions %s", track)
-                # for p in prediction.predictions:
-                #     logging.info(
-                #         "Have %s sum %s smoothed %s mass %s",
-                #         p,
-                #         np.sum(p.prediction),
-                #         np.round(p.smoothed_prediction),
-                #         p.mass,
-                #     )
-                # logging.info("smoothed %s", np.round(100 * prediction.class_best_score))
                 prediction_meta = prediction.get_metadata()
                 prediction_meta["model_id"] = model_id
                 prediction_info.append(prediction_meta)

src/ml_tools/hyperparams.py

@@ -90,7 +90,6 @@ def segment_width(self):
 
     @property
     def segment_types(self):
-
         segment_types = self.get("segment_type", [SegmentType.ALL_RANDOM])
         # convert string to enum type
         if isinstance(segment_types[0], str):

src/ml_tools/tools.py

@@ -194,9 +194,17 @@ def saveclassify_image(data, filename):
     # saves image channels side by side, expected data to be values in the range of 0->1
     Path(filename).parent.mkdir(parents=True, exist_ok=True)
     r = Image.fromarray(np.uint8(data[:, :, 0]))
-    g = Image.fromarray(np.uint8(data[:, :, 1]))
-    b = g
-    # b = Image.fromarray(np.uint8(data[:, :, 2]))
+    _, _, channels = data.shape
+
+    if channels == 1:
+        g = r
+    else:
+        g = Image.fromarray(np.uint8(data[:, :, 1]))
+
+    if channels == 2:
+        b = r
+    else:
+        b = Image.fromarray(np.uint8(data[:, :, 2]))
     concat = np.concatenate((r, g, b), axis=1)  # horizontally
     img = Image.fromarray(np.uint8(concat))
     img.save(filename + ".png")

src/rebuildDate.py

@@ -9,7 +9,7 @@
 from dateutil.parser import parse as parse_date
 
 parser = argparse.ArgumentParser()
-parser.add_argument("data_dir", help="Directory of hdf5 files")
+parser.add_argument("data_dir", help="Directory of cptv files")
 args = parser.parse_args()
 args.data_dir = Path(args.data_dir)
 latest_date = None