Normalization

snapshots/run/aug_annotations.csv

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 wheat3.jpg,0.87207,0.12207,1.0,0.181641,1
 wheat3.jpg,0.833984,0.948242,0.9375,1.0,1
-wheat3--1.jpg,0.785524349971796,0.659172218980763,0.9179881523154363,0.7621135207140872,1
-wheat3--1.jpg,0.6593555983430807,0.43781417703456355,0.7772515920249531,0.5191518421245941,1
+wheat3--1.jpg,0.785524349971796,0.659172218980763,0.9179881523154363,0.7621135207140873,1
+wheat3--1.jpg,0.6593555983430807,0.4378141770345636,0.7772515920249531,0.5191518421245941,1
 wheat3--1.jpg,0.7538225168778905,0.18230716244653847,0.8665330222478675,0.24739824278418035,1
 wheat3--1.jpg,0.7959133966509837,0.9391888239013733,0.8877445814164957,0.9951284564237372,1
 wheat3--2.jpg,0.859375,0.641602,1.0,0.741211,1

src/discolight/augmentations/normalize.py

@@ -0,0 +1,47 @@
+"""Normalize Images: This is extremely important if one decides to train on imagenet weights.
+The code is with reference from Albumentations."""
+import numpy as np
+from discolight.params.params import Params
+from .augmentation.types import Augmentation
+from .decorators.accepts_probs import accepts_probs
+
+
+@accepts_probs
+class Normalize(Augmentation):
+
+    """Normalize an image. Divide pixel values by 255 = 2**8 - 1, subtract mean per channel and divide by std per channel."""
+
+    def __init__(self, mean=, std=, max_pixel_value=255.0):
+
+        super().__init__()
+
+        self.mean = mean
+        self.std = std
+        self.max_pixel_value = max_pixel_value
+
+    @staticmethod
+    def params():
+        """Return a Params object describing constructor parameters."""
+
+        return (
+            Params()
+            .add("mean", "", NumericalRange(), (0.485, 0.456, 0.406))
+            .add("std", "", NumericalRange(), (0.229, 0.224, 0.225))
+            # Note to user, it will not make sense to tune max_pixel_value to a too small value.
+            .add("max_pixel_value", "", NumericalRange(), (0.0, 255.0))
+        )
+
+    def augment_img(self, img, _bboxes):
+        """Augment an image."""
+        mean = np.array(self.mean, dtype=np.float32)
+        mean *= self.max_pixel_value
+
+        std = np.array(self.std, dtype=np.float32)
+        std *= self.max_pixel_value
+
+        denominator = np.reciprocal(std, dtype=np.float32)
+
+        img = img.astype(np.float32)
+        img -= mean
+        img *= denominator
+        return img

src/discolight/augmentations/randomrotate.py

@@ -28,7 +28,9 @@ def params():
         return Params().add(
             "angle_range",
             "The range from which the random angle will be chosen",
-            NumericalRange(-360.0, 360.0), (-10.0, 10.0))
+            NumericalRange(-360.0, 360.0),
+            (-10.0, 10.0),
+        )
 
     def augment(self, img, bboxes):
         """Augment an image."""

src/discolight/augmentations/randomscale.py

@@ -0,0 +1,38 @@
+"""An augmentation to randomly rotate an image."""
+import random
+from discolight.params.params import Params
+from .augmentation.types import Augmentation, NumericalRange
+from .scale import Scale
+from .decorators.accepts_probs import accepts_probs
+
+
+@accepts_probs
+class RandomScale(Augmentation):
+
+    """Randomly scale the given image."""
+
+    def __init__(self, scale_range):
+        """Construct a RandomScale augmentation.
+
+        You should probably use the augmentation factory or Discolight
+        library interface to construct augmentations. Only invoke
+        this constructor directly if you know what you are doing.
+        """
+        super().__init__()
+
+        self.scale_range = scale_range
+
+    @staticmethod
+    def params():
+        """Return a Params object describing constructor parameters."""
+        return Params().add("scale_range",
+                            "The scale range should be bigger than -1",
+                            NumericalRange(-1), (0.2, 0.2))
+
+    def augment(self, img, bboxes):
+        """Augment an image."""
+        scale_factor_x = random.uniform(*self.scale_range)
+        scale_factor_y = random.uniform(*self.scale_range)
+
+        return Scale(scale_x=scale_factor_x,
+                     scale_y=scale_factor_y).augment(img, bboxes)

src/discolight/augmentations/randomshear.py

@@ -0,0 +1,35 @@
+"""An augmentation to randomly rotate an image."""
+import random
+from discolight.params.params import Params
+from .augmentation.types import Augmentation, NumericalRange
+from .shear import Shear
+from .decorators.accepts_probs import accepts_probs
+
+
+@accepts_probs
+class RandomShear(Augmentation):
+
+    """Randomly shear the given image."""
+
+    def __init__(self, shear_range):
+        """Construct a RandomShear augmentation.
+
+        You should probably use the augmentation factory or Discolight
+        library interface to construct augmentations. Only invoke
+        this constructor directly if you know what you are doing.
+        """
+        super().__init__()
+
+        self.shear_range = shear_range
+
+    @staticmethod
+    def params():
+        """Return a Params object describing constructor parameters."""
+        return Params().add("shear_range", "The shear range has no bounds",
+                            NumericalRange(), (0.2, 0.2))
+
+    def augment(self, img, bboxes):
+        """Augment an image."""
+        shear_factor = random.uniform(*self.shear_range)
+
+        return Shear(shear_factor=shear_factor).augment(img, bboxes)