Use linear RGB downscaling for most/some downscaling operations.

codes/dataops/augmennt/augmennt/functional.py

@@ -15,6 +15,7 @@
 import collections
 import warnings
 
+from ...colors import linear2srgb, srgb2linear
 from .common import preserve_channel_dim, preserve_shape
 from .common import _cv2_str2pad, _cv2_str2interpolation
 
@@ -168,13 +169,14 @@ def resize(img, size, interpolation='BILINEAR'):
         raise TypeError('img should be numpy image. Got {}'.format(type(img)))
     if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):
         raise TypeError('Got inappropriate size arg: {}'.format(size))
-    
+
     w, h, =  size
     if isinstance(size, int):
         # h, w, c = img.shape #this would defeat the purpose of "size"
-        
+
         if (w <= h and w == size) or (h <= w and h == size):
             return img
+        img = srgb2linear(img)
         if w < h:
             ow = size
             oh = int(size * h / w)
@@ -184,9 +186,10 @@ def resize(img, size, interpolation='BILINEAR'):
             ow = int(size * w / h)
             output = cv2.resize(img, dsize=(ow, oh), interpolation=_cv2_str2interpolation[interpolation])
     else:
+        img = srgb2linear(img)
         output = cv2.resize(img, dsize=(size[1], size[0]), interpolation=_cv2_str2interpolation[interpolation])
-    
-    return output
+
+    return linear2srgb(output)
 
 
 def scale(*args, **kwargs):

codes/dataops/augmentations.py

@@ -5,7 +5,8 @@
 
 import numpy as np
 import dataops.common as util
-from dataops.common import fix_img_channels, get_image_paths, read_img, np2tensor
+from dataops.colors import linear2srgb, srgb2linear
+from dataops.common import fix_img_channels, np2tensor
 from dataops.debug import *
 from dataops.imresize import resize as imresize  # resize # imresize_np
 
@@ -202,20 +203,24 @@ def __call__(self, img:np.ndarray) -> np.ndarray:
             if len(self.out_shape) < 3:
                 self.out_shape = self.out_shape + (image_channels(img),)
 
+        img = srgb2linear(img)
+
         if self.kind == 'transforms':
             if self.out_shape:
-                return resize(
-                    np.copy(img),
+                img = resize(
+                    img,
                     w=self.out_shape[1], h=self.out_shape[0],
                     method=self.interpolation)
-            return scale_(
-                np.copy(img), self.scale, method=self.interpolation)
-        scale = None if self.out_shape else 1/self.scale
-        # return imresize_np(
-        #       np.copy(img), scale=scale, antialiasing=self.antialiasing, interpolation=self.interpolation)
-        return imresize(
-            np.copy(img), scale, out_shape=self.out_shape,
-            antialiasing=self.antialiasing, interpolation=self.interpolation)
+            else:
+                img = scale_(
+                    img, self.scale, method=self.interpolation)
+        else:
+            scale = None if self.out_shape else 1/self.scale
+            img = imresize(
+                img, scale, out_shape=self.out_shape,
+                antialiasing=self.antialiasing, interpolation=self.interpolation)
+
+        return linear2srgb(img)
 
 
 def get_resize(size=None, scale=None, ds_algo=None,

codes/dataops/colors.py

@@ -193,14 +193,65 @@ def yuv_to_rgb(input: torch.Tensor, consts='yuv') -> torch.Tensor:
     b: torch.Tensor = y + Wb * u_shifted
     return torch.stack((r, g, b), -3)
 
-# Not tested:
-def rgb2srgb(imgs):
-    return torch.where(imgs<=0.04045,imgs/12.92,torch.pow((imgs+0.055)/1.055,2.4))
 
-# Not tested:
-def srgb2rgb(imgs):
-    return torch.where(imgs<=0.0031308,imgs*12.92,1.055*torch.pow((imgs),1/2.4)-0.055)
+def srgb2linear(img):
+    """Convert sRGB images to linear RGB color space.
+    Tensors are left as f32 in the range [0, 1].
+    Uint8 numpy arrays are converted from uint8 in the range [0, 255]
+    to f32 in the range [0, 1].
+    F32 numpy arrays are assumed to be already be linear RGB.
+    Always returns a new array.
+    All values are exact as per the sRGB spec.
+    """
+    a = 0.055
+    att = 12.92
+    gamma = 2.4
+    th = 0.04045
+
+    if isinstance(img, torch.Tensor):
+        return torch.where(
+                img <= th, img / att, torch.pow((img + a)/(1 + a), gamma))
+
+    if img.dtype == np.uint8:
+        linear = np.float32(img) / 255.0
+
+        return np.where(
+            linear <= th, linear / att, np.power((linear + a) / (1 + a), gamma))
+
+    return img.copy()
+
+
+def linear2srgb(img):
+    """Convert linear RGB to the sRGB colour space.
+    Tensors are left as f32 in the range [0, 1].
+    F32 numpy arrays are converted back to the expected uint8 format
+    in the range [0, 255].
+    Uint8 numpy arrays are assumed to already be sRGB.
+    Always returns a new array.
+    All values are exact as per the sRGB spec.
+    """
+    a = 0.055
+    att = 12.92
+    gamma = 2.4
+    th = 0.0031308
+
+    if isinstance(img, torch.Tensor):
+        return torch.where(
+                img <= th,
+                img * att, (1 + a) * torch.pow((img), 1 / gamma) - a)
+
+    if img.dtype == np.float32 or img.dtype == np.float64:
+        srgb = np.clip(img, 0.0, 1.0)
+
+        srgb = np.where(
+            srgb <= th, srgb * att, (1 + a) * np.power(srgb, 1.0 / gamma) - a)
+
+        np.clip(srgb * 255, 0.0, 255, out=srgb)
+        np.around(srgb, out=srgb)
+
+        return srgb.astype(np.uint8)
 
+    return img.copy()
 
 
 def color_shift(image: torch.Tensor, mode:str='uniform',