rename lib to opengait

opengait/data/transform.py

@@ -0,0 +1,63 @@
+from data import transform as base_transform
+import numpy as np
+
+from utils import is_list, is_dict, get_valid_args
+
+
+class NoOperation():
+    def __call__(self, x):
+        return x
+
+
+class BaseSilTransform():
+    def __init__(self, disvor=255.0, img_shape=None):
+        self.disvor = disvor
+        self.img_shape = img_shape
+
+    def __call__(self, x):
+        if self.img_shape is not None:
+            s = x.shape[0]
+            _ = [s] + [*self.img_shape]
+            x = x.reshape(*_)
+        return x / self.disvor
+
+
+class BaseSilCuttingTransform():
+    def __init__(self, img_w=64, disvor=255.0, cutting=None):
+        self.img_w = img_w
+        self.disvor = disvor
+        self.cutting = cutting
+
+    def __call__(self, x):
+        if self.cutting is not None:
+            cutting = self.cutting
+        else:
+            cutting = int(self.img_w // 64) * 10
+        x = x[..., cutting:-cutting]
+        return x / self.disvor
+
+
+class BaseRgbTransform():
+    def __init__(self, mean=None, std=None):
+        if mean is None:
+            mean = [0.485*255, 0.456*255, 0.406*255]
+        if std is None:
+            std = [0.229*255, 0.224*255, 0.225*255]
+        self.mean = np.array(mean).reshape((1, 3, 1, 1))
+        self.std = np.array(std).reshape((1, 3, 1, 1))
+
+    def __call__(self, x):
+        return (x - self.mean) / self.std
+
+
+def get_transform(trf_cfg=None):
+    if is_dict(trf_cfg):
+        transform = getattr(base_transform, trf_cfg['type'])
+        valid_trf_arg = get_valid_args(transform, trf_cfg, ['type'])
+        return transform(**valid_trf_arg)
+    if trf_cfg is None:
+        return lambda x: x
+    if is_list(trf_cfg):
+        transform = [get_transform(cfg) for cfg in trf_cfg]
+        return transform
+    raise "Error type for -Transform-Cfg-"