Find frames per track

src/kabr_tools/miniscene2behavior.py

@@ -19,12 +19,15 @@ def get_input_clip(cap: cv2.VideoCapture, cfg: CfgNode, keyframe_idx: int) -> li
     # https://github.com/facebookresearch/SlowFast/blob/bac7b672f40d44166a84e8c51d1a5ba367ace816/slowfast/visualization/ava_demo_precomputed_boxes.py
     seq_length = cfg.DATA.NUM_FRAMES * cfg.DATA.SAMPLING_RATE
     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    assert keyframe_idx < total_frames, f"keyframe_idx: {keyframe_idx}" \
+        f" >= total_frames: {total_frames}"
     seq = get_sequence(
         keyframe_idx,
         seq_length // 2,
         cfg.DATA.SAMPLING_RATE,
         total_frames,
     )
+
     clip = []
     for frame_idx in seq:
         cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
@@ -124,29 +127,34 @@ def annotate_miniscene(cfg: CfgNode, model: torch.nn.Module,
 
     # find all tracks
     tracks = []
+    frames = {}
     for track in root.iterfind("track"):
         track_id = track.attrib["id"]
         tracks.append(track_id)
+        frames[track_id] = []
 
-    # find all frames
-    # TODO: rewrite - some tracks may have different frames
-    assert len(tracks) > 0, "No tracks found in track file"
-    frames = []
-    for box in track.iterfind("box"):
-        frames.append(int(box.attrib["frame"]))
+        # find all frames
+        for box in track.iterfind("box"):
+            frames[track_id].append(int(box.attrib["frame"]))
 
     # run model on miniscene
     for track in tracks:
         video_file = f"{miniscene_path}/{track}.mp4"
         cap = cv2.VideoCapture(video_file)
-        for frame in tqdm(frames, desc=f"{track} frames"):
-            inputs = get_input_clip(cap, cfg, frame)
+        index = 0
+        for frame in tqdm(frames[track], desc=f"{track} frames"):
+            try:
+                inputs = get_input_clip(cap, cfg, index)
+            except AssertionError as e:
+                print(e)
+                break
+            index += 1
 
             if cfg.NUM_GPUS:
                 # transfer the data to the current GPU device.
                 if isinstance(inputs, (list,)):
-                    for i in range(len(inputs)):
-                        inputs[i] = inputs[i].cuda(non_blocking=True)
+                    for i, input_clip in enumerate(inputs):
+                        inputs[i] = input_clip.cuda(non_blocking=True)
                 else:
                     inputs = inputs.cuda(non_blocking=True)
 
@@ -163,6 +171,7 @@ def annotate_miniscene(cfg: CfgNode, model: torch.nn.Module,
             if frame % 20 == 0:
                 pd.DataFrame(label_data).to_csv(
                     output_path, sep=" ", index=False)
+        cap.release()
     pd.DataFrame(label_data).to_csv(output_path, sep=" ", index=False)
 
 

tests/test_miniscene2behavior.py

@@ -5,6 +5,7 @@
 import requests
 from unittest.mock import Mock, patch
 import torch
+from lxml import etree
 import numpy as np
 import pandas as pd
 from kabr_tools import (
@@ -97,12 +98,13 @@ def test_run(self):
     @patch('kabr_tools.miniscene2behavior.process_cv2_inputs')
     @patch('kabr_tools.miniscene2behavior.cv2.VideoCapture')
     def test_matching_tracks(self, video_capture, process_cv2_inputs):
-
-        # Create fake model that always returns a prediction of 1
+        # create fake model that weights class 98
         mock_model = Mock()
-        mock_model.return_value = torch.tensor([1])
+        prob = torch.zeros(99)
+        prob[-1] = 1
+        mock_model.return_value = prob
 
-        # Create fake cfg
+        # create fake cfg
         mock_config = Mock(
             DATA=Mock(NUM_FRAMES=16,
                       SAMPLING_RATE=5,
@@ -111,25 +113,36 @@ def test_matching_tracks(self, video_capture, process_cv2_inputs):
             OUTPUT_DIR=''
         )
 
-        # Create fake video capture
+        # create fake video capture
         vc = video_capture.return_value
         vc.read.return_value = True, np.zeros((8, 8, 3), np.uint8)
-        vc.get.return_value = 1
+        vc.get.return_value = 21
 
         self.output = '/tmp/annotation_data.csv'
+        miniscene_dir = os.path.join(EXAMPLESDIR, "MINISCENE1")
+        video_name = "DJI"
 
         annotate_miniscene(cfg=mock_config,
                            model=mock_model,
-                           miniscene_path=os.path.join(
-                               EXAMPLESDIR, "MINISCENE1"),
-                           video='DJI',
+                           miniscene_path=miniscene_dir,
+                           video=video_name,
                            output_path=self.output)
 
-        # Read in output CSV and make sure we have the expected columns and at least one row
+        # check output CSV
         df = pd.read_csv(self.output, sep=' ')
         self.assertEqual(list(df.columns), [
                          "video", "track", "frame", "label"])
-        self.assertGreater(len(df.index), 0)
+        row_ct = 0
+
+        root = etree.parse(
+            f"{miniscene_dir}/metadata/DJI_tracks.xml").getroot()
+        for track in root.iterfind("track"):
+            track_id = int(track.get("id"))
+            for box in track.iterfind("box"):
+                row_val = [video_name, track_id, int(box.get("frame")), 98]
+                self.assertEqual(list(df.loc[row_ct]), row_val)
+                row_ct += 1
+        self.assertEqual(len(df.index), row_ct)
 
     @patch('kabr_tools.miniscene2behavior.process_cv2_inputs')
     @patch('kabr_tools.miniscene2behavior.cv2.VideoCapture')
@@ -151,9 +164,11 @@ def test_nonmatching_tracks(self, video_capture, process_cv2_inputs):
         # Create fake video capture
         vc = video_capture.return_value
         vc.read.return_value = True, np.zeros((8, 8, 3), np.uint8)
-        vc.get.return_value = 1
+        vc.get.return_value = 21
 
         self.output = '/tmp/annotation_data.csv'
+        miniscene_dir = os.path.join(EXAMPLESDIR, "MINISCENE2")
+        video_name = "DJI"
 
         annotate_miniscene(cfg=mock_config,
                            model=mock_model,
@@ -162,11 +177,22 @@ def test_nonmatching_tracks(self, video_capture, process_cv2_inputs):
                            video='DJI',
                            output_path=self.output)
 
-        # Read in output CSV and make sure we have the expected columns and at least one row
+        # check output CSV
         df = pd.read_csv(self.output, sep=' ')
         self.assertEqual(list(df.columns), [
                          "video", "track", "frame", "label"])
-        self.assertGreater(len(df.index), 0)
+        row_ct = 0
+
+        root = etree.parse(
+            f"{miniscene_dir}/metadata/DJI_tracks.xml").getroot()
+        for track in root.iterfind("track"):
+            track_id = int(track.get("id"))
+            for box in track.iterfind("box"):
+                row_val = [video_name, track_id, int(box.get("frame")), 0]
+                self.assertEqual(list(df.loc[row_ct]), row_val)
+                row_ct += 1
+        self.assertEqual(len(df.index), row_ct)
+
 
     def test_parse_arg_min(self):
         # parse arguments