datasets/raise/utils.py

@@ -3,15 +3,12 @@ import skimage.restoration
 import numpy as np
 import rawpy
 from tqdm import tqdm
-from PIL import Image, ImageDraw
+from PIL import Image
 from skimage import img_as_float
 from datetime import datetime
 import builtins as __builtin__
 from scipy.ndimage import gaussian_filter
 import os
-from skimage.restoration import estimate_sigma
-from scipy import fftpack
-import imageio
 
 class Color(Enum):
     RED = auto()
@@ -27,23 +24,16 @@ class Denoiser(Enum):
     BILATERAL = auto()
     TV_CHAMBOLLE = auto()
     MEAN = auto()
-    NL_MEANS = auto()
-    LOW_PASS = auto()
 
     def __str__(self):
         return self.name.lower()
 
-# Source: https://stackoverflow.com/a/43346070
-def getGaussianKernel(size, sigma):
-    axis = np.linspace(-(size - 1) / 2., (size - 1) / 2., size)
-    gauss = np.exp(-0.5 * np.square(axis) / np.square(sigma))
-    kernel = np.outer(gauss, gauss)
-    return kernel / np.sum(kernel)
-
-# `denoiser` can be all values of `Denoiser` except `MEAN`.
+# Among:
+# - `wavelet`
+# - `bilateral`
+# - `tv_chambolle`
 def denoise(imageNpArray, denoiser):
-    if denoiser != Denoiser.LOW_PASS:
-        skImageRestorationDenoise = getattr(skimage.restoration, f'denoise_{denoiser}')
+    skImageRestorationDenoise = getattr(skimage.restoration, f'denoise_{denoiser}')
 
     match denoiser:
         case Denoiser.WAVELET:
@@ -52,36 +42,6 @@ def denoise(imageNpArray, denoiser):
             imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, sigma_color=0.05, sigma_spatial=15)
         case Denoiser.TV_CHAMBOLLE:
             imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, weight=0.2)
-        case Denoiser.NL_MEANS:
-            sigmaEst = np.mean(estimate_sigma(imageNpArray, channel_axis = -1))
-            imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, patch_size = 7, patch_distance = 4, h = 0.8 * sigmaEst, fast_mode = True)
-        case Denoiser.LOW_PASS:
-            # Based on [the Stack Overflow answer 54662336](https://stackoverflow.com/a/54662336).
-            # fft of image
-            fft1 = fftpack.fftshift(fftpack.fft2(imageNpArray))
-
-            # Padding as follows may not be perfectly correct but seems fine enough.
-            GAUSSIAN_SIGMA = 50
-
-            imageNpArrayShape = imageNpArray.shape
-            lowPassNpArraySize = min(imageNpArrayShape)
-            lowPassNpArray = getGaussianKernel(lowPassNpArraySize, GAUSSIAN_SIGMA)
-
-            wantedHeight, wantedWidth = imageNpArrayShape
-            verticalPadSize, horizontalPadSize = [(wantedDimension - lowPassNpArraySize) // 2 for wantedDimension in [wantedHeight, wantedWidth]]
-            rectangleLowPassNpArray = np.pad(lowPassNpArray, [(verticalPadSize, verticalPadSize), (horizontalPadSize, horizontalPadSize)])
-
-            # Unsure that doing such *normalization* is appropriate.
-            rectangleLowPassNpArray /= rectangleLowPassNpArray.max()
-
-            # multiply both the images
-            filtered = np.multiply(fft1, rectangleLowPassNpArray)
-
-            # inverse fft
-            ifft2 = np.real(fftpack.ifft2(fftpack.ifftshift(filtered)))
-            ifft2 = np.maximum(0, np.minimum(ifft2, 255))
-
-            imageDenoisedNpArray = ifft2.astype(np.uint8)
     return imageDenoisedNpArray
 
 class iterativeMean:
@@ -131,7 +91,7 @@ def isARawImage(imageFilePath):
 def getColorChannel(imageFilePath, color):
     if isARawImage(imageFilePath):
         numpyFilePath = f'{imageFilePath}.{color}.npy'
-        if False and os.path.isfile(numpyFilePath):
+        if os.path.isfile(numpyFilePath):
             imageNpArray = np.load(numpyFilePath)
         else:
             with rawpy.imread(imageFilePath) as raw:
@@ -184,8 +144,7 @@ def updateExtremes(imageNpArray, minColor, maxColor):
     return minColor, maxColor
 
 def print(*toPrint):
-    #__builtin__.print(datetime.now(), *toPrint)
-    tqdm.write(f'{datetime.now()} {str(toPrint)}')
+    __builtin__.print(datetime.now(), *toPrint)
 
 def getColorMeans(imagesFileNames, colors, singleColorChannelCropResolution = None):
     colorMeans = {}
@@ -200,18 +159,19 @@ def getColorMeans(imagesFileNames, colors, singleColorChannelCropResolution = No
         colorMeans[color] = colorIterativeMean.mean
     return colorMeans
 
-def getImageNpArray(imageFilePath, computeExtremes, color, denoiser, minColor, maxColor):
+def getImageNpArray(imageFilePath, computeExtremes, color, denoiser):
+    global minColor, maxColor
     imageNpArray = getColorChannel(imageFilePath, color)
 
     if computeExtremes:
         minColor, maxColor = updateExtremes(imageNpArray, minColor, maxColor)
-        return None, minColor, maxColor
+        return
 
     if isARawImage(imageFilePath) and denoiser != Denoiser.MEAN:
         imageNpArray = rescaleRawImageForDenoiser(imageNpArray, minColor, maxColor)
         # Pay attention to range of values expected by the denoiser.
         # Indeed if provide the thousands valued raw image, then the denoiser only returns values between 0 and 1 and making the difference between both look pointless.
-    return imageNpArray, minColor, maxColor
+    return imageNpArray
 
 def getImageCrop(image, cropResolution):
     imageCrop = image[:cropResolution[0], :cropResolution[1]]