Remove unused transforms

dan/transforms.py

@@ -8,7 +8,7 @@ import cv2
 import numpy as np
 from cv2 import dilate, erode, normalize
 from numpy import random
-from PIL import Image, ImageOps
+from PIL import Image
 from torchvision.transforms import (
     ColorJitter,
     GaussianBlur,
@@ -20,24 +20,12 @@ from torchvision.transforms.functional import InterpolationMode
 from dan.utils import rand, rand_uniform, randint
 
 
-class SignFlipping:
-    """
-    Color inversion
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, x):
-        return ImageOps.invert(x)
-
-
 class DPIAdjusting:
     """
     Resolution modification
     """
 
-    def __init__(self, factor, preserve_ratio):
+    def __init__(self, factor):
         self.factor = factor
 
     def __call__(self, x):
@@ -179,31 +167,6 @@ class ElasticDistortion:
         return Image.fromarray(dst.astype(np.uint8))
 
 
-class Tightening:
-    """
-    Reduce interline spacing
-    """
-
-    def __init__(self, color=255, remove_proba=0.75):
-        self.color = color
-        self.remove_proba = remove_proba
-
-    def __call__(self, x):
-        x_np = np.array(x)
-        interline_indices = [np.all(line == 255) for line in x_np]
-        indices_to_removed = np.logical_and(
-            np.random.choice(
-                [True, False],
-                size=len(x_np),
-                replace=True,
-                p=[self.remove_proba, 1 - self.remove_proba],
-            ),
-            interline_indices,
-        )
-        new_x = x_np[np.logical_not(indices_to_removed)]
-        return Image.fromarray(new_x.astype(np.uint8))
-
-
 def get_list_augmenters(img, aug_configs, fill_value):
     """
     Randomly select a list of data augmentation techniques to used based on aug_configs
@@ -236,9 +199,7 @@ def get_list_augmenters(img, aug_configs, fill_value):
                         and factor * img.size[1] < aug_config["min_height"]
                     )
                 )
-            augmenters.append(
-                DPIAdjusting(factor, preserve_ratio=aug_config["preserve_ratio"])
-            )
+            augmenters.append(DPIAdjusting(factor))
 
         elif aug_config["type"] == "zoom_ratio":
             ratio_h = rand_uniform(aug_config["min_ratio_h"], aug_config["max_ratio_h"])
@@ -351,94 +312,6 @@ def apply_data_augmentation(img, da_config):
     return img
 
 
-def apply_transform(img, transform):
-    """
-    Apply data augmentation technique on input image
-    """
-    img = img[:, :, 0] if img.shape[2] == 1 else img
-    img = Image.fromarray(img)
-    img = transform(img)
-    img = np.array(img)
-    return np.expand_dims(img, axis=2) if len(img.shape) == 2 else img
-
-
-def line_aug_config(proba_use_da, p):
-    return {
-        "order": "random",
-        "proba": proba_use_da,
-        "augmentations": [
-            {
-                "type": "dpi",
-                "proba": p,
-                "min_factor": 0.5,
-                "max_factor": 1.5,
-                "preserve_ratio": True,
-            },
-            {
-                "type": "perspective",
-                "proba": p,
-                "min_factor": 0,
-                "max_factor": 0.4,
-            },
-            {
-                "type": "elastic_distortion",
-                "proba": p,
-                "min_alpha": 0.5,
-                "max_alpha": 1,
-                "min_sigma": 1,
-                "max_sigma": 10,
-                "min_kernel_size": 3,
-                "max_kernel_size": 9,
-            },
-            {
-                "type": "dilation_erosion",
-                "proba": p,
-                "min_kernel": 1,
-                "max_kernel": 3,
-                "iterations": 1,
-            },
-            {
-                "type": "color_jittering",
-                "proba": p,
-                "factor_hue": 0.2,
-                "factor_brightness": 0.4,
-                "factor_contrast": 0.4,
-                "factor_saturation": 0.4,
-            },
-            {
-                "type": "gaussian_blur",
-                "proba": p,
-                "min_kernel": 3,
-                "max_kernel": 5,
-                "min_sigma": 3,
-                "max_sigma": 5,
-            },
-            {
-                "type": "gaussian_noise",
-                "proba": p,
-                "std": 0.5,
-            },
-            {
-                "type": "sharpen",
-                "proba": p,
-                "min_alpha": 0,
-                "max_alpha": 1,
-                "min_strength": 0,
-                "max_strength": 1,
-            },
-            {
-                "type": "zoom_ratio",
-                "proba": p,
-                "min_ratio_h": 0.8,
-                "max_ratio_h": 1,
-                "min_ratio_w": 0.99,
-                "max_ratio_w": 1,
-                "keep_dim": True,
-            },
-        ],
-    }
-
-
 def aug_config(proba_use_da, p):
     return {
         "order": "random",
@@ -449,7 +322,6 @@ def aug_config(proba_use_da, p):
                 "proba": p,
                 "min_factor": 0.75,
                 "max_factor": 1,
-                "preserve_ratio": True,
             },
             {
                 "type": "perspective",