update the code to work with segmentation + adding strategies to bin …

…file

signwriting_transcription/pose_to_signwriting/bin.py

@@ -21,7 +21,9 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--pose', required=True, type=str, help='path to input pose file')
     parser.add_argument('--elan', required=True, type=str, help='path to elan file')
-    parser.add_argument('--model', type=str, default='359a544.ckpt', help='model to use')
+    parser.add_argument('--model', type=str, default='bc2de71.ckpt', help='model to use')
+    parser.add_argument('--strategies', required=False, type=str, default='tight',
+                        options=['tight', 'wide'], help='strategy to use')
     return parser.parse_args()
 
 
@@ -66,12 +68,18 @@ def main():
 
     print('Preprocessing pose.....')
     temp_pose_path = temp_dir / 'pose.pose'
-    preprocess_single_file(args.pose, temp_pose_path, normalization=False)
+    preprocess_single_file(args.pose, temp_pose_path, normalization=True)
 
     print('Predicting signs...')
     temp_files = []
+    start = 0
     for index, segment in tqdm(enumerate(sign_annotations)):
-        np_pose = pose_to_matrix(temp_pose_path, segment[0], segment[1]).filled(fill_value=0)
+        if args.strategies == 'wide':
+            end = (sign_annotations[index + 1][0] + segment[1]) // 2 if index + 1 < len(sign_annotations) else None
+            np_pose = pose_to_matrix(temp_pose_path, start, end).filled(fill_value=0)
+            start = end
+        else:
+            np_pose = pose_to_matrix(temp_pose_path, segment[0], segment[1]).filled(fill_value=0)
         pose_path = temp_dir / f'{index}.npy'
         np.save(pose_path, np_pose)
         temp_files.append(pose_path)

signwriting_transcription/pose_to_signwriting/data/datasets_pose.py

@@ -17,23 +17,24 @@ def ms2frame(ms, frame_rate) -> int:
     return int(ms / 1000 * frame_rate)
 
 
-def pose_to_matrix(file_path, start_ms, end_ms):
+def pose_to_matrix(file_path, start_ms=0, end_ms=None):
     with open(file_path, "rb") as file:
         pose = Pose.read(file.read())
     frame_rate = 29.97003 if file_path == '19097be0e2094c4aa6b2fdc208c8231e.pose' else pose.body.fps
     pose = pose.body.data
     pose = pose.reshape(len(pose), -1)
-    pose = pose[ms2frame(start_ms, frame_rate):ms2frame(end_ms, frame_rate)]
+    pose = pose[ms2frame(start_ms, frame_rate):ms2frame(end_ms, frame_rate)] if (
+            end_ms is not None) else pose[ms2frame(start_ms, frame_rate):]
     return pose
 
 
-def load_dataset(folder_name):
-    with open(f'{folder_name}/target.csv', 'r', encoding='utf-8') as csvfile:
+def load_dataset(target_folder, data_folder):
+    with open(f'{target_folder}/target.csv', 'r', encoding='utf-8') as csvfile:
         reader = csv.DictReader(csvfile)
         dataset = []
         for line in reader:
             try:
-                pose = pose_to_matrix(f"{folder_name}/{line['pose']}", line['start'], line['end'])
+                pose = pose_to_matrix(f"{data_folder}/{line['pose']}", line['start'], line['end'])
             except FileNotFoundError:
                 continue
             pose = pose.filled(fill_value=0)

signwriting_transcription/pose_to_signwriting/data/prepare_poses.py

@@ -83,8 +83,8 @@ def get_split_data(dataset, feature_root, pumping):
 
 def process(args):
     # pylint: disable=too-many-locals
-    dataset_root, data_root, name, tokenizer_type, pumping = (
-        args.dataset_root, args.data_root, args.dataset_name, args.tokenizer_type, args.pumping)
+    dataset_root, data_root, name, tokenizer_type, data_segment, pumping = (
+        args.dataset_root, args.data_root, args.dataset_name, args.tokenizer_type, args.data_segment, args.pumping)
     cur_root = Path(data_root).absolute()
     cur_root = cur_root / name
 
@@ -96,7 +96,17 @@ def process(args):
     print(f"Create pose {name} dataset.")
 
     print("Fetching train split ...")
-    dataset = load_dataset(dataset_root)
+    dataset = load_dataset(dataset_root, dataset_root)
+    if data_segment:
+        segment_dataset = load_dataset(data_segment, dataset_root)
+        modified_segment_dataset = []
+        for instance in segment_dataset:
+            instance = list(instance)
+            instance[0] = f"seg_{instance[0]}"
+            instance[3] = 'train' if instance[3] == 'test' else instance[3]
+            dataset.extend(segment_dataset)
+            modified_segment_dataset.append(tuple(instance))
+        dataset.extend(modified_segment_dataset)
 
     print("Extracting pose features ...")
     for instance in dataset:
@@ -141,6 +151,7 @@ def main():
     parser.add_argument("--dataset-root", required=True, type=str)
     parser.add_argument("--dataset-name", required=True, type=str)
     parser.add_argument("--tokenizer-type", required=True, type=str)
+    parser.add_argument("--data-segment", required=False, type=str, default=None)
     parser.add_argument("--pumping", required=False, type=str, default=True)
     args = parser.parse_args()
     process(args)