try random section

src/classify/clipclassifier.py

@@ -182,6 +182,8 @@ 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")

src/ml_tools/datasetstructures.py

@@ -987,6 +987,7 @@ def get_segments(
     frame_min_mass=None,
     fp_frames=None,
     repeat_frame_indices=True,
+    min_segments=None,
 ):
     if min_frames is None:
         min_frames = segment_width / 4.0
@@ -1059,7 +1060,9 @@ def get_segments(
             segments.extend(new_segments)
             filtered_stats.merge(filtered)
             continue
-        if len(frame_indices) < min_frames:
+        if len(frame_indices) < min_frames and (
+            min_segments == 0 or min_segments is None
+        ):
             filtered_stats["too short"] += 1
             continue
 
@@ -1069,7 +1072,6 @@ def get_segments(
         # probably only counts for all random
         if max_segments is not None and segment_type not in [SegmentType.ALL_SECTIONS]:
             segment_count = min(max_segments, segment_count)
-
         # take any segment_width frames, this could be done each epoch
         whole_indices = frame_indices
         random_frames = segment_type in [
@@ -1079,17 +1081,31 @@ def get_segments(
             SegmentType.TOP_RANDOM,
             None,
         ]
+        random_mask = True
         for _ in range(repeats):
-            frame_indices = whole_indices.copy()
-            if random_frames:
-                # random_frames and not random_sections:
-                np.random.shuffle(frame_indices)
+            used_indices = []
+            if not random_mask:
+                frame_indices = whole_indices.copy()
+
+                if random_frames:
+                    # random_frames and not random_sections:
+                    np.random.shuffle(frame_indices)
+
             for i in range(segment_count):
+                if random_mask:
+                    mask_start = i * 25
+                    frame_indices = list(whole_indices[0:mask_start].copy())
+                    frame_indices.extend(whole_indices[mask_start + 25 :].copy())
+                    frame_indices = [f for f in frame_indices if f not in used_indices]
+                    frame_indices = np.uint32(frame_indices)
+                    np.random.shuffle(frame_indices)
+
                 # always get atleast one segment, not doing annymore
-                if (
-                    len(frame_indices) < segment_width / 2.0 and len(segments) > 1
-                ) or len(frame_indices) < segment_width / 4:
-                    break
+                if len(frame_indices) == 0 or len(segments) >= min_segments:
+                    if (
+                        len(frame_indices) < segment_width / 2.0 and len(segments) > 1
+                    ) or len(frame_indices) < segment_width / 4:
+                        break
 
                 if segment_type == SegmentType.ALL_SECTIONS:
                     # random frames from section 2.2 * segment_width
@@ -1106,6 +1122,7 @@ def get_segments(
                 elif random_frames:
                     # frame indices already randomized so just need to grab some
                     frames = frame_indices[:segment_width]
+                    used_indices.extend(frames)
                     frame_indices = frame_indices[segment_width:]
                 else:
                     segment_start = i * segment_frame_spacing

src/ml_tools/interpreter.py

@@ -304,6 +304,7 @@ def preprocess_segments(
             max_segments=max_segments,
             dont_filter=dont_filter,
             filter_by_fp=False,
+            min_segments=1,
         )
         frame_indices = set()
         for segment in segments:

src/track/track.py

@@ -445,6 +445,7 @@ def get_segments(
         ffc_frames=None,
         dont_filter=False,
         filter_by_fp=False,
+        min_segments=1,
     ):
         if from_last is not None:
             if from_last == 0:
@@ -490,6 +491,7 @@ def get_segments(
                 segment_types=segment_types,
                 max_segments=max_segments,
                 dont_filter=dont_filter,
+                min_segments=min_segments,
             )
 
         return segments