Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
1
0
Fork 0
Code Issues 68 Pull Requests Packages Projects Releases Wiki Activity

Add temporarily maxGreen computation

Browse Source 
And got:

```
Denoising images:   0%|                                                                      | 0/100 [00:00<?, ?it/s]maxGreen=2275
Denoising images:   1%|▌                                                             | 1/100 [00:00<01:11,  1.38it/s]maxGreen=2377
Denoising images:   2%|█▏                                                            | 2/100 [00:01<01:18,  1.24it/s]maxGreen=3468
Denoising images:  19%|███████████▌                                                 | 19/100 [00:11<00:46,  1.75it/s]maxGreen=4908
Denoising images: 100%|████████████████████████████████████████████████████████████| 100/100 [00:57<00:00,  1.74it/s]
```
This commit is contained in:
Benjamin Loison 2024-04-16 02:51:27 +02:00
parent 90df8a7cb9
commit 6251c487e2
No known key found for this signature in database
1 changed files with 12 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

29
datasets/raise/extract_noise.py
View File

@ -33,35 +33,30 @@ if requiresRaiseFiltering:
imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{imagesFolderPath}/{imageFileName}').size == (4946, 3278)]
maxGreen = None
def treatImage(imageFileName):
global mean, numberOfImagesInMean
global mean, numberOfImagesInMean, maxGreen
imageFilePath = f'{imagesFolderPath}/{imageFileName}'
if imageFileName.endswith('.NEF'):
imageFileName = 'ra2c888f8t.NEF'
imageFilePath = f'{imagesFolderPath}/{imageFileName}'
print(imageFilePath)
with rawpy.imread(imageFilePath) as raw:
colorDesc = raw.color_desc.decode('ascii')
assert colorDesc == 'RGBG'
assert np.array_equal(raw.raw_pattern, np.array([[3, 2], [0, 1]], dtype = np.uint8))
assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
rawImageVisible = raw.raw_image_visible.copy()
centerY = 2263
centerX = 237
RANGE = 5
rawImageVisible = rawImageVisible.astype(np.float64)
rawImageVisibleShape = rawImageVisible.shape
for y in range(0, rawImageVisibleShape[0], 2):
for x in range(0, rawImageVisibleShape[1], 2):
rawImageVisible[y, x] *= -1
print(rawImageVisible[centerY - RANGE:centerY + RANGE, centerX - RANGE:centerX + RANGE])
#greenRawImageVisible = raw.rawImageVisible
greenRawImageVisible = rawImageVisible[1::2, ::2]
greenRawImageVisibleMax = greenRawImageVisible.max()
if maxGreen is None or greenRawImageVisibleMax > maxGreen:
maxGreen = greenRawImageVisibleMax
print(f'{maxGreen=}')
imageNpArray = greenRawImageVisible
# Pay attention to range of values expected by the denoiser.
exit(1)
#exit(1)
else:
imagePil = Image.open(imageFilePath)
imageNpArray = img_as_float(np.array(imagePil))
# As the arguments differ from a denoiser to the other should use a match instead.
imageDenoisedNpArray = denoise(imageNpArray, channel_axis=-1, convert2ycbcr=True, rescale_sigma=True)
imageDenoisedNpArray = denoise(imageNpArray, rescale_sigma=True)
imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
if mean is None:
mean = imageNoiseNpArray