Add and use getImageCrop

datasets/raise/attribute_source_camera.py

@@ -3,7 +3,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
-from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray
+from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray, getColorMeans, getImageCrop
 import sys
 import os
 import random
@@ -12,7 +12,7 @@ sys.path.insert(0, '../../algorithms/distance/')
 
 from rms_diff import rmsDiffNumpy
 
-DENOISER = 'wavelet'
+DENOISER = 'mean'
 IMAGES_CAMERAS_FOLDER = {
     'RAISE': 'flat-field/nef',
     'Rafael 23/04/24': 'rafael/230424',
@@ -27,7 +27,7 @@ random.seed(0)
 for camera in IMAGES_CAMERAS_FOLDER:
     random.shuffle(imagesCamerasFileNames[camera])
 
-minimumNumberOfImagesCameras = 16#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
+minimumNumberOfImagesCameras = min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
 for camera in IMAGES_CAMERAS_FOLDER:
     imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
     print(camera, imagesCamerasFileNames[camera])
@@ -60,19 +60,22 @@ def getImageFilePath(camera, cameraImageIndex):
 
 def getSingleColorChannelImages(camera, cameraImageIndex):
     imageFilePath = getImageFilePath(camera, cameraImageIndex)
-    singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
+    singleColorChannelImages = {color: rescaleIfNeeded(getImageCrop(getColorChannel(imageFilePath, color), minimalColorChannelCameraResolution), minColor, maxColor) for color in Color}
     return singleColorChannelImages
 
 def getMultipleColorsImage(singleColorChannelImages):
     multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
     return multipleColorsImage
 
-def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage):
-    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
+def getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera):
+    singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) if DENOISER != 'mean' else cameraColorMeans[camera][color] for color in Color}
     multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
     imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
     return imagePrnuEstimateNpArray
 
+imagesCamerasFilePaths = {camera: [f'{IMAGES_CAMERAS_FOLDER[camera]}/{imagesCamerasFileName}' for imagesCamerasFileName in imagesCamerasFileNames[camera]] for camera in imagesCamerasFileNames}
+cameraColorMeans = {camera: getColorMeans(imagesCamerasFilePaths[camera], Color, DENOISER, minimalColorChannelCameraResolution) for camera in imagesCamerasFilePaths}
+
 from utils import silentTqdm
 #tqdm = silentTqdm
 
@@ -90,7 +93,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
                 multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
 
-                imagePrnuEstimateNpArray = getImagePrnuEstimatedNpArray(singleColorChannelImages, multipleColorsImage)
+                imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
                 cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [imagePrnuEstimateNpArray]
     for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
@@ -102,7 +105,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
                 continue
 
-            imagePrnuEstimateNpArray = getImagePrnuEstimatedNpArray(singleColorChannelImages, multipleColorsImage)
+            imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
             cameraIterativeMean = camerasIterativeMean[camera]
             cameraIterativeMean.add(imagePrnuEstimateNpArray)

datasets/raise/utils.py

@@ -132,12 +132,14 @@ def updateExtremes(imageNpArray, minColor, maxColor):
 def print(*toPrint):
     __builtin__.print(datetime.now(), *toPrint)
 
-def getColorMeans(imagesFileNames, colors, denoiser):
+def getColorMeans(imagesFileNames, colors, denoiser, singleColorChannelCropResolution = None):
     colorMeans = {}
     for color in colors:
         colorIterativeMean = iterativeMean()
         for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} colored images'):
             imageNpArray = getImageNpArray(imageFileName, False, color, denoiser)
+            if singleColorChannelCropResolution is not None:
+                imageNpArray = getImageCrop(imageNpArray, singleColorChannelCropResolution)
             imageNpArray = gaussian_filter(imageNpArray, sigma = 5)
             colorIterativeMean.add(imageNpArray)
         colorMeans[color] = colorIterativeMean.mean
@@ -156,3 +158,7 @@ def getImageNpArray(imageFilePath, computeExtremes, color, denoiser):
         # 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
+
+def getImageCrop(image, cropResolution):
+    imageCrop = image[:cropResolution[0], :cropResolution[1]]
+    return imageCrop