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204 Commits
prnu_writt ... a8725e5e88

Author SHA1 Message Date
Benjamin Loison
a8725e5e88 Move denoising testing images to work even if provide minColor and maxColor (#63) 2024-04-30 05:36:28 +02:00
Benjamin Loison
3c403dbed3 WIP (#63) 2024-04-30 05:31:14 +02:00
Benjamin Loison
b6425b426e #63: WIP 2024-04-30 05:01:05 +02:00
Benjamin Loison
319ca8fb60 Add attribute_source_camera.py 
It is a modified copy of `split_and_compare_prnus_of_subgroups.py`.
 2024-04-30 04:48:02 +02:00
Benjamin Loison
aa9f73e8a0 Fix random seed to improve reproducibility 2024-04-30 03:49:45 +02:00
Benjamin Loison
22fc4ce46b Remove rescale for plt.imsave as do not seem necessary anymore 2024-04-30 03:36:37 +02:00
Benjamin Loison
20fcb247c6 Simplify loops 
The idea was initially in the context of #62 to optimize execution
speed.
 2024-04-30 03:36:04 +02:00
Benjamin Loison
ad5aa22ae6 Add missing import for saveNpArray and add a missing f-string 2024-04-30 03:35:30 +02:00
Benjamin Loison
c2a2dea2c7 Add a missing argument to getColorChannel in extract_noise.py 2024-04-30 03:22:03 +02:00
Benjamin Loison
31b979edb3 Override print to also print current time 2024-04-30 03:21:32 +02:00
Benjamin Loison
c9f75286bf Make merge_single_color_channel_images_according_to_bayer_filter.py 
virtual environment friendly
 2024-04-30 03:20:44 +02:00
Benjamin Loison
ab9466c738 #62: Optimize significantly mergeSingleColorChannelImagesAccordingToBayerFilter 2024-04-30 03:17:19 +02:00
Benjamin Loison
2d67c2bca1 Add and use rescaleRawImageForDenoiser and updateExtremes (#59) 2024-04-30 02:04:17 +02:00
Benjamin Loison
671a692114 #59: Save rmss for futher investigations 
For instance to plot a fair comparison, see [issues/59#issuecomment-1732](#59 (comment)).
 2024-04-30 01:04:33 +02:00
Benjamin Loison
5647ab4019 #59: Should make split_and_compare_prnus_of_subgroups.py compatible with RAW images 2024-04-27 23:13:48 +02:00
Benjamin Loison
9b2f2281e4 Make merge_single_color_channel_images_according_to_bayer_filter.py more general 2024-04-27 22:57:29 +02:00
Benjamin Loison
5ea0a862c0 Add and use getColorChannel 2024-04-27 21:49:32 +02:00
Benjamin Loison
561ec9d1e0 Add and use escapeFilePath 2024-04-27 21:48:07 +02:00
Benjamin Loison
b50f013234 Add and use isARawImage 2024-04-27 21:46:40 +02:00
Benjamin Loison
a0fc38d023 Add and use getRawColorChannel to utils.py 2024-04-27 20:47:33 +02:00
Benjamin Loison
80a33428d6 Make split_and_compare_prnus_of_subgroups.py execution compatible with chaining shell commands 
Otherwise it is stuck on `plt.show` until it is closed but we want to
chain with a notification command, so the notification is not
interesting in this case.
 2024-04-27 19:36:45 +02:00
Benjamin Loison
9d8e5ddbec Make split_and_compare_prnus_of_subgroups.py only use 2 images of RAM 
quantity
 2024-04-27 19:24:12 +02:00
Benjamin Loison
4ac75448a6 Correct in theory the range of color values 2024-04-27 17:54:16 +02:00
Benjamin Loison
643fee9f4a Make split_and_compare_prnus_of_subgroups.py executable from terminal 
Used to be only executable from Pyzo.
 2024-04-27 16:55:03 +02:00
Benjamin Loison
c8fe71444f #59: Leverage iterativeMean to not suffer of RAM quantity issue 2024-04-27 16:49:06 +02:00
Benjamin Loison
6f6721da8d Make merge_single_color_channel_images_according_to_bayer_filter.py executable from terminal 
Used to only run in Pyzo.
 2024-04-27 16:20:02 +02:00
Benjamin Loison
b0fa01c6c4 Remove potential chronological bias 2024-04-27 16:10:20 +02:00
Benjamin Loison
e9dc28dc2a Simplify and clean split_and_compare_prnus_of_subgroups.py 2024-04-27 16:06:47 +02:00
Benjamin Loison
ee7353cfde Correct iterativeMean implementation for images 
Otherwise get:

```
Traceback (most recent call last):
  File "/home/benjamin/robust_image_source_identification_on_modern_smartphones/datasets/raise/./extract_noise.py", line 148, in <module>
    treatImage(imageFileName, color = color)
  File "/home/benjamin/robust_image_source_identification_on_modern_smartphones/datasets/raise/./extract_noise.py", line 114, in treatImage
    estimatedPrnuIterativeMean.add(imageNoiseNpArray)
  File "/home/benjamin/robust_image_source_identification_on_modern_smartphones/datasets/raise/utils.py", line 34, in add
    self.mean = ((self.mean * self.numberOfElementsInMean) + element) / (self.numberOfElementsInMean + 1)
                  ~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
TypeError: unsupported operand type(s) for *: 'NoneType' and 'int'
```
 2024-04-27 15:58:29 +02:00
Benjamin Loison
bd0b9aca94 Correct support for JPG images 2024-04-27 15:55:25 +02:00
Benjamin Loison
e9038bdb76 Hide inappropriate figure title 2024-04-27 15:21:57 +02:00
Benjamin Loison
af947dbf65 Add articles/Euclidean distance - Wikipedia/ 2024-04-27 15:08:56 +02:00
Benjamin Loison
5baabae99d Make mean initialization more general in iterativeMean 2024-04-26 15:56:10 +02:00
Benjamin Loison
1ea1bde910 Leverage iterativeMean in extract_noise.py 2024-04-26 14:25:08 +02:00
Benjamin Loison
674f87d480 Remove unnecessary iterativeMean parentheses and rename addElement to add 2024-04-26 14:22:59 +02:00
Benjamin Loison
7af96d6bf6 Add iterativeMean to utils 
Verified with:

```py
myIterativeMean = iterativeMean()
print(myIterativeMean.mean)
myIterativeMean.addElement(2)
print(myIterativeMean.mean)
myIterativeMean.addElement(3)
print(myIterativeMean.mean)
```

Note that cannot simplify as follows in the general case of using images as
`element`s for instance:

```diff
diff --git a/datasets/raise/utils.py b/datasets/raise/utils.py
index fba5523..a116edb 100644
--- a/datasets/raise/utils.py
+++ b/datasets/raise/utils.py
@@ -27,12 +27,9 @@ def denoise(imageNpArray, denoiserName):
     return imageDenoisedNpArray

 class iterativeMean():
-    mean = None
+    mean = 0
     numberOfElementsInMean = 0

     def addElement(self, element):
-        if self.mean is None:
-            self.mean = element
-        else:
-            self.mean = ((self.mean * self.numberOfElementsInMean) + element) / (self.numberOfElementsInMean + 1)
+        self.mean = ((self.mean * self.numberOfElementsInMean) + element) / (self.numberOfElementsInMean + 1)
         self.numberOfElementsInMean += 1
\ No newline at end of file
```
 2024-04-26 14:16:35 +02:00
Benjamin Loison
7684e1408d Remove unused imports in split_and_compare_prnus_of_subgroups.py 2024-04-26 13:58:19 +02:00
Benjamin Loison
9c469cf67a Leverage utils.py denoise in split_and_compare_prnus_of_subgroups.py 2024-04-26 13:58:00 +02:00
Benjamin Loison
9d2903af60 Move denoise from extract_noise.py to utils.py 
See next commit leveraging it in `split_and_compare_prnus_of_subgroups.py`
 2024-04-26 13:57:18 +02:00
Benjamin Loison
6a93b04686 Remove already used previous temporary analysis commit code 2024-04-26 13:49:31 +02:00
Benjamin Loison
b52fff9deb Add commented code to enforce merging Numpy arrays with a greyscale 
colormap.
 2024-04-26 13:48:44 +02:00
Benjamin Loison
3ffcd33efe Add Gaussian filter to blur images when using mean as denoiser 
See
https://docs.scipy.org/doc/scipy-1.13.0/reference/generated/scipy.ndimage.gaussian_filter.html#ba1e4df0-d4be-47c2-81e5-059286837a2b.
 2024-04-26 04:39:06 +02:00
Benjamin Loison
9641641b61 Remove commented code from analyze_bayer_filter_mean_denoiser.py 2024-04-26 04:13:08 +02:00
Benjamin Loison
3e8b607b97 Add analyze_bayer_filter_mean_denoiser.py 2024-04-26 04:12:13 +02:00
Benjamin Loison
f1cafc1eb1 Improve yticks of boxplot in flat-field_pointer.py 2024-04-26 04:10:41 +02:00
Benjamin Loison
25b3b30634 Remove a commented print in plot_dates.py 2024-04-26 04:10:02 +02:00
Benjamin Loison
3ada720db9 Add execution results, plot of locations and distance analysis 2024-04-26 03:05:17 +02:00
Benjamin Loison
56037b058b Add Zoom to rectangle by default 2024-04-26 01:49:57 +02:00
Benjamin Loison
f5c235c3cd Add support to TIF for RAISE flat-field 2024-04-26 01:49:13 +02:00
Benjamin Loison
ab72da470e Ready for pointing 2024-04-26 01:34:55 +02:00
Benjamin Loison
981e2a48ae Add flat-field_pointer.py 
https://matplotlib.org/3.8.4/gallery/event_handling/coords_demo.html
 2024-04-26 01:21:20 +02:00
Benjamin Loison
1e8d9d844c Make plot_dates.py leverage photo exifs 2024-04-26 00:52:52 +02:00
Benjamin Loison
7cfe566290 Add plot_dates.py as it is as a Matplotlib example 
https://matplotlib.org/3.8.0/gallery/lines_bars_and_markers/timeline.html
 2024-04-26 00:51:12 +02:00
Benjamin Loison
719bb487dd Restore other than raw images support 2024-04-25 18:12:36 +02:00
Benjamin Loison
f88b6ec1a9 Save mean of each color channel to merge them into a single image being 
the mean of images

Otherwise for each color channel could use:

```py
plt.imsave(f'{fileName}.png', colorMean)
```
 2024-04-25 16:00:23 +02:00
Benjamin Loison
4907d0c0fb Add and use saveNpArray 2024-04-25 15:59:26 +02:00
Benjamin Loison
65708e4977 Correctly implement mean denoiser 
Used to have:

```py
imageDenoisedNpArray = imageNpArray - means[color]
imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
```

so:

```py
imageNoiseNpArray = means[color]
```

then we mean the mean...

While should have:

```py
imageDenoisedNpArray = means[color]
```
 2024-04-25 15:58:24 +02:00
Benjamin Loison
60a4f78fa6 Ease providing {min,max}Color 2024-04-25 15:30:23 +02:00
Benjamin Loison
c642fa430e Add commented code to crop image 
It is commented as it does not seem to be finally what we are looking
for.
 2024-04-25 14:37:08 +02:00
Benjamin Loison
674ce8dabe Use relative path for os.chdir in generate_histogram.py 2024-04-25 14:36:38 +02:00
Benjamin Loison
807f586841 Remove no more relevant print in show_mean_noise.py 2024-04-25 14:28:36 +02:00
Benjamin Loison
5c1f346f2e Clean single image histogram 
Source: https://www.codespeedy.com/plot-a-histogram-for-an-image-in-pil-python/
 2024-04-25 14:16:58 +02:00
Benjamin Loison
e2602347d4 Use a single figure to generate histogram 2024-04-25 12:39:24 +02:00
Benjamin Loison
ec3ddd9c94 Add generate_histogram.py 2024-04-25 12:37:21 +02:00
Benjamin Loison
a78dcd6f99 Clean extract_noise.py 2024-04-24 14:10:22 +02:00
Benjamin Loison
c65b3642d8 Precise multiple_colors.png file name 2024-04-23 05:01:22 +02:00
Benjamin Loison
be9d4f8f4b Add denoiser mean possibility 2024-04-23 04:39:28 +02:00
Benjamin Loison
a15b356e16 Add and use getImageNpArray 2024-04-23 04:38:40 +02:00
Benjamin Loison
809aa7fb43 Correct Rafael images filtering 2024-04-23 04:37:46 +02:00
Benjamin Loison
e3bbb2c63d Precise possibilities for denoiser 2024-04-23 03:57:43 +02:00
Benjamin Loison
fc0cc8808a Add the missing import numpy as np in merge_single_color_channel_images_according_to_bayer_filter.py 2024-04-23 03:51:56 +02:00
Benjamin Loison
9f2334ceea Precise imagesFolderPathFileName depending on sky or wall 2024-04-21 19:54:04 +02:00
Benjamin Loison
e2406071d4 Simplify specifying colors 2024-04-21 19:53:21 +02:00
Benjamin Loison
29f3acc479 Enable computing specific colors 
For instance if have tested on one and do not want to waste its
computation.
 2024-04-21 19:52:52 +02:00
Benjamin Loison
9365b58d1d #49: Remove intermediary bias by not working on images but numpy arrays instead 2024-04-19 17:21:54 +02:00
Benjamin Loison
675b01271a #49: Remove shift per color channel 2024-04-19 17:19:47 +02:00
Benjamin Loison
77330e24d2 Make datasets/raise/merge_single_color_channel_images_according_to_bayer_filter.py production ready 2024-04-18 01:21:31 +02:00
Benjamin Loison
0b52e781e3 First try to merge multiple single color channel images 2024-04-18 01:00:41 +02:00
Benjamin Loison
6aca1126a1 Complete dict assignment 2024-04-18 00:53:15 +02:00
Benjamin Loison
3636335b63 Add match statement 2024-04-18 00:52:08 +02:00
Benjamin Loison
be91a07dd5 Add and use getImageFileNameByColor 2024-04-18 00:43:14 +02:00
Benjamin Loison
fddb89c64a Add datasets/raise/merge_single_color_channel_images_according_to_bayer_filter.py 2024-04-18 00:41:43 +02:00
Benjamin Loison
faaf3eb263 Ease support for single or multiple colors 2024-04-18 00:35:34 +02:00
Benjamin Loison
cca81927dd Move Color Enum to utils.py 2024-04-18 00:19:01 +02:00
Benjamin Loison
eac2cf2174 Add a missing parameter 2024-04-18 00:18:20 +02:00
Benjamin Loison
fb58d78fed Name output file the same way as the input one 2024-04-18 00:13:21 +02:00
Benjamin Loison
8c9dfc2e41 Output an estimated PRNU per color channel 2024-04-17 20:25:14 +02:00
Benjamin Loison
f15af68bba Add and use Color enumeration 2024-04-17 20:15:33 +02:00
Benjamin Loison
f4d8c028b2 Remove unnecessary spaces 
Thanks to:

```bash
sed -i 's/^ *$//g' extract_noise.py
```
 2024-04-17 14:32:55 +02:00
Benjamin Loison
3760164622 Add and use colorRawImageVisible 2024-04-17 14:27:03 +02:00
Benjamin Loison
83a12f4e0a Add and use SKY and WALL 2024-04-17 14:26:13 +02:00
Benjamin Loison
e080e841f5 Add and use raiseNotFlatFields 2024-04-17 14:10:11 +02:00
Benjamin Loison
a6ef3977dd Add comment concerning printing {min,max}Color 2024-04-16 16:48:29 +02:00
Benjamin Loison
8ca1972ddb Correct raise not flat-fields processing 2024-04-16 16:48:00 +02:00
Benjamin Loison
817c016815 Give a try to get image size with rawpy but it seems far slower 
As got:

```
Filtering images:   0%|▏                                                                               | 21/8156 [00:11<1:17:28,  1.75it/s]
```
 2024-04-16 04:06:49 +02:00
Benjamin Loison
04e0de4d33 Try making not flat-images compatible 2024-04-16 03:59:26 +02:00
Benjamin Loison
3a4f774e28 Make denoise arguments depend on denoiser 2024-04-16 03:27:26 +02:00
Benjamin Loison
caf7025504 Correct typos in previous commit 2024-04-16 03:15:53 +02:00
Benjamin Loison
d4e13ed123 Compute automatically extreme values 2024-04-16 03:11:38 +02:00
Benjamin Loison
a8fa053687 #49: Get PRNU by using RAW images 2024-04-16 03:02:58 +02:00
Benjamin Loison
6251c487e2 Add temporarily maxGreen computation 
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]
```
 2024-04-16 02:51:27 +02:00
Benjamin Loison
90df8a7cb9 Verify raw.raw_pattern as raw.color_desc does not seem enough 
As got:

```bash
python3 extract_noise.py
```
```
Denoising images:   0%|                                                                                           | 0/8156 [00:00<?, ?it/s]/mnt/HDD0/raise/nef/ra2c888f8t.NEF
Denoising images:   0%|                                                                                           | 0/8156 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/benjamin/robust_image_source_identification_on_modern_smartphones/datasets/raise/extract_noise.py", line 73, in <module>
    treatImage(imageFileName)
  File "/home/benjamin/robust_image_source_identification_on_modern_smartphones/datasets/raise/extract_noise.py", line 46, in treatImage
    assert np.array_equal(raw.raw_pattern, np.array([[3, 2], [0, 1]], dtype = np.uint8))
AssertionError
```
 2024-04-16 01:06:59 +02:00
Benjamin Loison
120eaf563f WIP: Add .nef support 2024-04-15 23:39:47 +02:00
Benjamin Loison
a0b366e98a Use shebang compatible with venv 
See [Benjamin-Loison/cpython/issues/10](https://github.com/Benjamin-Loison/cpython/issues/10).
 2024-04-15 23:17:04 +02:00
Benjamin Loison
bddfa4c8d5 Clean datasets/raise/extract_noise.py 2024-04-15 16:16:29 +02:00
Benjamin Loison
a09f2ba1c0 #29: Add Nikon D7000 4946x3278 PRNU estimation from NEF to PNG images 2024-04-15 16:06:05 +02:00
Benjamin Loison
847d4bb42f Update annotations 2024-04-11 23:52:38 +02:00
Benjamin Loison
78b54bf169 Add articles/Mathématiques appliquées — Wikipédia/ 2024-04-11 23:47:20 +02:00
Benjamin Loison
322e201048 Update annotations 2024-04-09 04:06:49 +02:00
Benjamin Loison
fed5698e84 Update annotations 2024-04-09 03:43:17 +02:00
Benjamin Loison
283fa781e5 Add articles/Bayer filter - Wikipedia/ 2024-04-09 03:16:11 +02:00
Benjamin Loison
bc06612501 Make extract_noise.py easier to configure by providing denoiser 2024-04-08 04:52:29 +02:00
Benjamin Loison
38d29e3c8f Add articles/Binomial distribution - Wikipedia/ 2024-04-07 22:59:09 +02:00
Benjamin Loison
502ac5d14e Update annotations 2024-04-07 22:58:50 +02:00
Benjamin Loison
6f3623fb44 Add articles/Luminance — Wikipédia/ 2024-04-07 22:33:57 +02:00
Benjamin Loison
1c264ff405 Update annotations 2024-04-07 22:31:28 +02:00
Benjamin Loison
9ed68af7e1 Add articles/Luminance - Wikipedia/ 2024-04-07 22:22:23 +02:00
Benjamin Loison
9468bda3df Update annotations 2024-04-07 19:58:31 +02:00
Benjamin Loison
3f47afb165 Add articles/Résumé de cours Variables aléatoires finies/ 2024-04-07 18:11:39 +02:00
Benjamin Loison
68797b1470 Update annotations 2024-04-07 17:33:52 +02:00
Benjamin Loison
dfe5b6ac38 Add articles/Random variable - Wikipedia/ 2024-04-07 17:24:05 +02:00
Benjamin Loison
8651242c2b Update annotations 2024-04-07 17:21:29 +02:00
Benjamin Loison
08d9cdc900 Add articles/Bernoulli distribution - Wikipedia/ 2024-04-07 17:17:36 +02:00
Benjamin Loison
75f674cd95 Update annotations 2024-04-07 17:11:41 +02:00
Benjamin Loison
ca1fa73c49 Add articles/Inflection point - Wikipedia/ 2024-04-07 17:09:30 +02:00
Benjamin Loison
3d366eb426 Update annotations 2024-04-07 16:59:19 +02:00
Benjamin Loison
70e5ef902d Add articles/Central limit theorem - Wikipedia/ 2024-04-07 16:52:25 +02:00
Benjamin Loison
7301e2fe35 Update annotations 2024-04-07 16:50:40 +02:00
Benjamin Loison
93663e1044 Add articles/Additive white Gaussian noise - Wikipedia/ 2024-04-07 16:42:10 +02:00
Benjamin Loison
2d3830848b Update annotations 2024-04-07 16:40:49 +02:00
Benjamin Loison
9d1527c5c4 Add articles/Gaussian noise - Wikipedia/ 2024-04-07 16:30:58 +02:00
Benjamin Loison
8748e648b5 Remove no more relevant commented code for a test 2024-04-06 00:06:13 +02:00
Benjamin Loison
15c41c0463 Add and use imageDenoisedNpArray to ease using other denoisers 2024-04-06 00:03:50 +02:00
Benjamin Loison
b18080e9f5 #33: Switch to mono-thread 2024-04-06 00:00:24 +02:00
Benjamin Loison
a83e8bb1e0 Replace tabs with spaces in datasets/raise/extract_noise.py 2024-04-05 23:57:37 +02:00
Benjamin Loison
ba452096d5 #24: WebArchive articles/Positional Learning Quentin Bammey 
As there is no WebArchive error anymore.
 2024-04-05 19:01:31 +02:00
Benjamin Loison
d271b502fa Add articles/Pixel binning - Wikipedia/ 2024-04-05 14:12:21 +02:00
Benjamin Loison
658aa34600 Add articles/Self-supervised learning - Wikipedia 2024-04-05 11:37:33 +02:00
Benjamin Loison
4d267bf540 Add articles/Positional Learning Quentin Bammey/ 2024-04-05 11:32:20 +02:00
Benjamin Loison
606821d45e Add show_mean_noise.py 2024-04-03 22:18:18 +02:00
Benjamin Loison
b354f59ac5 Make parallel production ready 2024-04-03 19:15:21 +02:00
Benjamin Loison
0f394faa61 Multicores prototype 2024-04-03 18:20:31 +02:00
Benjamin Loison
faed1e33e8 #33: Add datasets/raise/extract_noise.py 2024-04-03 18:20:08 +02:00
Benjamin Loison
4dd52aae90 Use plt.imsave instead of toPilImage 2024-04-03 16:57:49 +02:00
Benjamin Loison
d3af12ce3e Try to have clear figure for thesis audition 2024-04-03 15:20:24 +02:00
Benjamin Loison
aafb7ebc92 Plot an exhaustive curve to make sure that results are not just by chance 2024-04-02 23:58:41 +02:00
Benjamin Loison
631ed6de34 #31: Establish expected wanted results about finding an identical PRNU for both subgroups 2024-04-02 21:05:15 +02:00
Benjamin Loison
f09665f856 Make first rendering with both subgroups 2024-04-02 19:58:59 +02:00
Benjamin Loison
ea00f42c58 Add datasets/raise/split_and_compare_prnus_of_subgroups.py 2024-04-02 18:45:40 +02:00
Benjamin Loison
90a806a7e2 Move webpage articles to datasets/raise/website/ 2024-04-02 18:31:27 +02:00
Benjamin Loison
c69221a336 Update annotations 2024-04-02 11:54:54 +02:00
Benjamin Loison
4df8a4f16e Add articles/RAISE - Dataset Download/ 2024-04-02 11:51:54 +02:00
Benjamin Loison
dfb384a8be Update annotations 2024-04-02 11:49:39 +02:00
Benjamin Loison
491e7c906b Update annotations 2024-04-02 11:47:51 +02:00
Benjamin Loison
1c7fc33e06 Add articles/Darktable - Wikipedia/ 2024-04-02 11:47:45 +02:00
Benjamin Loison
57298e2e2e Update annotations 2024-04-02 11:43:57 +02:00
Benjamin Loison
adc224334e Add articles/XnView - Wikipedia/ 2024-04-02 11:43:13 +02:00
Benjamin Loison
34d922901f Add articles/IrfanView - Wikipedia/ 2024-04-02 11:42:29 +02:00
Benjamin Loison
bd18868bc7 Add articles/Nikon Electronic File — Wikipédia/ 2024-04-02 11:34:38 +02:00
Benjamin Loison
fe7d2f0cb0 Add articles/Raster graphics - Wikipedia/ 2024-04-02 11:33:39 +02:00
Benjamin Loison
3351c909af Update annotations 2024-04-02 11:32:35 +02:00
Benjamin Loison
2f33ecf02a Add articles/TIFF - Wikipedia/ 2024-04-02 11:30:42 +02:00
Benjamin Loison
f5e99fe4d3 Update annotations 2024-04-02 11:24:22 +02:00
Benjamin Loison
e0b41b3682 Add articles/RAISE - Guide/ 2024-04-02 11:16:05 +02:00
Benjamin Loison
5165c5a638 Update annotations 2024-04-02 11:12:01 +02:00
Benjamin Loison
7f2ab40b70 Add articles/RAISE - The Raw Images Dataset/ 2024-04-02 11:03:54 +02:00
Benjamin Loison
0d6a956db8 Shorten figure titles 2024-04-02 11:03:47 +02:00
Benjamin Loison
f0db61efe2 Make single figure to compare multiple splitNXN values 2024-03-29 13:47:27 +01:00
Benjamin Loison
81852d5dc6 Make Gaussian noise in getPrnuShownAsSuch only on text 2024-03-29 13:22:36 +01:00
Benjamin Loison
ec7c5c6688 Add gaussianNoise to getPrnuShownAsSuch but applies on the whole PRNU image 2024-03-29 13:16:50 +01:00
Benjamin Loison
2d96bdc225 Not correct generalized SPLIT_N_X_N due to imageWithoutPrnuNpArrayTile 2024-03-29 13:11:24 +01:00
Benjamin Loison
96bbd50a3b Generalize with a partial image SPLIT_N_X_N 2024-03-29 12:57:56 +01:00
Benjamin Loison
4bf9ef8206 Add splitNxN compatible with 1 and 4 2024-03-29 12:44:42 +01:00
Benjamin Loison
60eceb575e Simplify grayscale rendering 2024-03-29 12:16:13 +01:00
Benjamin Loison
b375acbb3a Use v{min,max} for enforcing matplotlib colormap 
If it proceeds linearly to *covers the complete value range of the supplied data* then doing so is unnecessary and even if it is another not very different scale transformation then it is still fine for my goal.
 2024-03-29 12:11:41 +01:00
Benjamin Loison
5fa61f7ff8 Make PRNU compatible with 4x4 split 2024-03-29 11:23:48 +01:00
Benjamin Loison
8351e46437 Comparison of an image without and with Gaussian noise and PRNU 2024-03-29 01:55:46 +01:00
Benjamin Loison
2f66e82f22 Show both RMS and RMS normalized 2024-03-29 01:47:01 +01:00
Benjamin Loison
3ccec5bbd0 Add RMS computation 2024-03-29 01:35:48 +01:00
Benjamin Loison
f297060f42 Split images in 4x4 to increase PRNU estimation accuracy 2024-03-29 01:21:26 +01:00
Benjamin Loison
9b57d3441c Render PRNU estimate taking into account all images 2024-03-29 01:10:31 +01:00
Benjamin Loison
d59a251b1f Revert to some extent previous commit 2024-03-29 01:07:48 +01:00
Benjamin Loison
4382b3d649 Add PRNU_FACTOR 0.1 and 0.01 view on a single figure 2024-03-29 01:05:28 +01:00
Benjamin Loison
dfe2540c02 Apply Context-Adaptive Interpolator 2024-03-29 00:06:13 +01:00
Benjamin Loison
9a3cfd7ba1 Add PRNU showing such 2024-03-28 22:24:16 +01:00
Benjamin Loison
ba5a1b742b #24: Make executions reproducible 2024-03-28 22:20:40 +01:00
Benjamin Loison
a99e942d3a Add datasets/noise_free_test_images/estimate_prnu.py 2024-03-28 17:06:58 +01:00
Benjamin Loison
0953fb7475 Add issue reference to datasets/fake/generate_dataset.py 2024-03-28 17:06:36 +01:00
Benjamin Loison
82e7026264 Add algorithms/image_utils/image_utils.py and move there randomGaussianImage and showImageWithMatplotlib 2024-03-28 16:32:38 +01:00
Benjamin Loison
70ccb094d5 Rename randomImage to randomGaussianImage 2024-03-28 16:18:32 +01:00
Benjamin Loison
64eaeddf98 Update annotations 2024-03-28 15:26:57 +01:00
Benjamin Loison
18d34a3101 Add datasets/noise_free_test_images/no_noise_images.zip.txt 2024-03-28 15:25:21 +01:00
Benjamin Loison
cbc778c4dc Move datasets/noise_free_test_images/noise_free_test_images.{pdf.txt,xopp.xml} to datasets/noise_free_test_images/webpage/ 2024-03-28 15:22:28 +01:00
Benjamin Loison
5af7afaf75 Add datasets/noise_free_test_images/ 2024-03-28 15:03:57 +01:00
Benjamin Loison
77d6c038fe Update annotations 2024-03-28 14:50:45 +01:00
Benjamin Loison
69368a9057 Add articles/Law of large numbers - Wikipedia/ 2024-03-28 14:50:41 +01:00
Benjamin Loison
abfc9772e8 Update annotations 2024-03-28 14:47:13 +01:00
Benjamin Loison
d716574973 Update annotations 2024-03-26 18:23:30 +01:00
Benjamin Loison
2093f2e613 Update annotations 2024-03-26 18:21:12 +01:00
Benjamin Loison
9028d4d12c Add __pycache__ to .gitignore 
Note that I do not precisely know if Pyzo or Python script shell execution generate `__pycache__`.
 2024-03-26 18:21:01 +01:00
Benjamin Loison
811b355989 Add articles/Shot noise - Wikipedia/ 2024-03-26 18:20:28 +01:00
Benjamin Loison
cfe718da7d Make x-axis logarithmic 2024-03-26 02:32:03 +01:00
Benjamin Loison
2bc13c5949 #21: Make a RMS curve depending on the number of images considered for the mean 2024-03-26 01:59:22 +01:00
Benjamin Loison
c2862eaf43 #21: Incorrect mean, as it is a RMS mean, not a mean of images 2024-03-26 01:55:08 +01:00
75 changed files with 2682 additions and 36 deletions
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1
.gitignore vendored
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@ -1,2 +1,3 @@
*.pdf
*.xopp
__pycache__

1
algorithms/context_adaptive_interpolator/context_adaptive_interpolator.py
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@ -6,6 +6,7 @@ from wiener_filter import wienerFilter
# Assume greyscale PIL image passed.
# What about other color channels? See #11.
# `THRESHOLD` seems to have been designed to assume 256 range based images.
def contextAdaptiveInterpolator(I, IImage, showProgress = False):
rImage = Image.new('L', (IImage.size[0] - 2, IImage.size[1] - 2))
r = rImage.load()

10
algorithms/distance/rms_diff.py
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@ -14,5 +14,13 @@ def rmsDiffPil(im1, im2):
map(lambda h, i: h*(i**2), h, range(256))
) / (float(im1.size[0]) * im1.size[1]))
def rmsDiffNumpy(image0, image1):
def normalizeImage(image):
image = image - image.min()
image = image / image.max()
return image
def rmsDiffNumpy(image0, image1, normalize = False):
if normalize:
image0 = normalizeImage(image0)
image1 = normalizeImage(image1)
return np.sqrt(np.mean(np.square(image0 - image1)))

41
algorithms/image_utils/image_utils.py Normal file
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from PIL import Image, ImageFont, ImageDraw
import numpy as np
from matplotlib import pyplot as plt
import os
def randomGaussianImage(scale, size):
return np.random.normal(loc = 0, scale = scale, size = size)
# `cmap` source: https://matplotlib.org/3.8.0/api/_as_gen/matplotlib.pyplot.imshow.html
def showImageWithMatplotlib(npArray, title = None, cmap = 'viridis'):
if title is not None:
plt.title(title)
plt.imshow(npArray, cmap = cmap)
plt.show()
def toPilImage(npArray):
return Image.fromarray(npArray)
def getPrnuShownAsSuch(size, gaussianNoise = 0):
# Supports `WIDTH` > `HEIGHT` and conversely.
WIDTH, HEIGHT = size
TEXT = 'PRNU'
imagePil = Image.new('L', size)
draw = ImageDraw.Draw(imagePil)
fontPath = os.path.expanduser('~/.local/share/fonts/impact.ttf')
for fontSize in range(1, HEIGHT + 1):
font = ImageFont.truetype(fontPath, fontSize)
if font.getlength(TEXT) > WIDTH:
break
# Center vertically, especially in the case `HEIGHT` > `WIDTH`.
draw.text((0, HEIGHT // 2 - fontSize // 2), TEXT, 255, font = font)
imageNpArray = np.array(imagePil)
gaussianNoiseNpArray = randomGaussianImage(gaussianNoise, size[::-1])
#prnuShownAsSuch = imageNpArray + gaussianNoiseNpArray
prnuShownAsSuch = imageNpArray
for y in range(HEIGHT):
for x in range(WIDTH):
if prnuShownAsSuch[y, x] != 0:
prnuShownAsSuch[y, x] += gaussianNoiseNpArray[y, x]
return prnuShownAsSuch

1
articles/Additive white Gaussian noise - Wikipedia/Additive white Gaussian noise - Wikipedia.pdf.txt Normal file
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https://en.wikipedia.org/w/index.php?title=Additive_white_Gaussian_noise&oldid=1181981955

71
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datasets/fake/generate_dataset.py
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# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/21
import numpy as np
from matplotlib import pyplot as plt
from PIL import Image
@ -10,6 +12,10 @@ from rms_diff import rmsDiffPil, rmsDiffNumpy
sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
from context_adaptive_interpolator import contextAdaptiveInterpolator
sys.path.insert(0, '../../algorithms/image_utils/')
from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage
from tqdm import tqdm
IMAGE_SIZE = 64
@ -18,30 +24,38 @@ NUMBER_OF_IMAGES_PER_PHONE = 10_000
# Compared to images being 1.
PRNU_FACTOR = 0.1
IMAGE_SIZE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE)
np.random.seed(0)
# Generate PRNUs and images of phones.
# Is such `np.maximum` probabilistically correct with our theoretical method? See #19.
def randomImage(scale):
return np.random.normal(loc = 0, scale = scale, size = (IMAGE_SIZE, IMAGE_SIZE))
imagesWithoutPrnu = [[randomGaussianImage(scale = 1, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
imagesWithoutPrnu = [[randomImage(scale = 1) for _ in range(NUMBER_OF_IMAGES_PER_PHONE)] for phoneIndex in range(NUMBER_OF_PHONES)]
prnus = [np.array(Image.open('prnu.png').convert('F')) * PRNU_FACTOR / 255]
prnus = [randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE) for _ in range(NUMBER_OF_PHONES)]
imagesWithPrnu = [[imageWithoutPrnu + prnus[phoneIndex] for imageWithoutPrnu in imagesWithoutPrnu[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
allImages = np.max([np.max(imagesWithoutPrnu) + np.max(prnus) + np.max(imagesWithPrnu)])
def toPilImage(npArray):
return Image.fromarray(npArray)
def showImageWithPil(npArray):
npArray -= npArray.min()
npArray = (npArray / npArray.max()) * 255
Image.fromarray(npArray).show()
def showImageWithMatplotlib(npArray):
plt.imshow(npArray)
plt.show()
plt.title('RMS between actual PRNU and the mean of the first $N$ images with PRNU (i.e. estimated PRNU)')
plt.xlabel('$N$ first images with PRNU')
plt.ylabel('RMS')
plt.xscale('log')
rmss = []
mean = np.zeros(IMAGE_SIZE_SHAPE)
for imageIndex in range(NUMBER_OF_IMAGES_PER_PHONE):
mean = (mean * imageIndex + imagesWithPrnu[0][imageIndex]) / (imageIndex + 1)
rms = rmsDiffNumpy(mean, prnus[0])
rmss += [rms]
plt.plot(rmss)
plt.show()
##
NUMBER_OF_ROWS = 5
NUMBER_OF_COLUMNS = 3
@ -75,23 +89,6 @@ plt.show()
##
def toFileName(title):
return title.lower().replace(' ', '_').replace(',', '_')
for title, image in zip(['Actual PRNU', 'First image without PRNU'], [prnus[0], imagesWithoutPrnu[0][0]]):
plt.title(title)
plt.imshow(image)
plt.savefig(title.lower().replace(' ', '_') + '.svg')
for numberOfImages in [10 ** power for power in range(NUMBER_OF_ROWS)]:
title = 'First image with PRNU' if numberOfImages == 1 else f'Mean of first {numberOfImages:,} images with PRNU'
image = np.array(imagesWithPrnu[0][:numberOfImages]).mean(axis = 0)
plt.title(f'{title}\ni.e. estimated PRNU\nRMS with actual PRNU = {round(rmsDiffNumpy(image, prnus[0]), 4)}')
plt.imshow(image)
plt.savefig(f'{toFileName(title)}.svg')
##
# Compute CAI of phone images.
caiImages = [[contextAdaptiveInterpolator(image.load(), image) for image in imagesWithPrnuPil[phoneIndex]] for phoneIndex in tqdm(range(NUMBER_OF_PHONES))]
#caiImages[0][0].show()

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datasets/noise_free_test_images/estimate_prnu.py Normal file
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# Notes: https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/25
import os
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.insert(0, '../../algorithms/image_utils/')
from image_utils import showImageWithMatplotlib, randomGaussianImage, toPilImage, getPrnuShownAsSuch
sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
from context_adaptive_interpolator import contextAdaptiveInterpolator
sys.path.insert(0, '../../algorithms/distance/')
from rms_diff import rmsDiffNumpy
from skimage.restoration import denoise_tv_chambolle
datasetPath = 'no_noise_images'
# Note that contrarily to `datasets/fake/`, here we do not have images being Gaussian with `scale` `1` but actual images with pixel values between 0 and 255.
# In addition to the range difference, note that the distribution in the first set of images was a Gaussian and here is very different and specific.
PRNU_FACTOR = 0.01
NOISE_FACTOR = 0.1
np.random.seed(0)
SPLIT_N_X_N_S = [1, 2, 4]
# len(SPLIT_N_X_N_S)
fig, axes = plt.subplots(2, 4)
fig.suptitle('PRNU estimation with different number of images having Gaussian noise and Gaussian noised PRNU')
for splitNXNIndex, splitNXN in enumerate(SPLIT_N_X_N_S):
IMAGE_SIZE_SHAPE = [dimension // splitNXN for dimension in (704, 469)]
#prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
prnuNpArray = getPrnuShownAsSuch(IMAGE_SIZE_SHAPE, 255) * PRNU_FACTOR
def isIn256Range(x):
return 0 <= x and x <= 255
imagesPrnuEstimateNpArray = []
isFirstImage = True
for imageName in os.listdir(datasetPath):
if imageName.endswith('.png'):
imagePath = f'{datasetPath}/{imageName}'
imageWithoutPrnuPil = Image.open(imagePath).convert('F')
imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
m = IMAGE_SIZE_SHAPE[1]
n = IMAGE_SIZE_SHAPE[0]
imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x : x + m, y : y + n] for x in range(0, imageWithoutPrnuNpArray.shape[0], m) for y in range(0, imageWithoutPrnuNpArray.shape[1], n)]
for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles:
#print(imageWithoutPrnuNpArrayTile.shape, tuple(IMAGE_SIZE_SHAPE[::-1]))
#if imageWithoutPrnuNpArrayTile.shape != tuple(IMAGE_SIZE_SHAPE[::-1]):
# continue
imageNoise = randomGaussianImage(scale = 255 * NOISE_FACTOR, size = imageWithoutPrnuNpArrayTile.shape)
imageWithPrnuNpArray = imageWithoutPrnuNpArrayTile + prnuNpArray + imageNoise
if splitNXNIndex == 0 and isFirstImage:
axis = axes[0]
axis[0].set_title('First image without noise')
axis[0].imshow(imageWithoutPrnuNpArrayTile)
axis[1].set_title('Actual Gaussian noised PRNU')
axis[1].imshow(prnuNpArray)
axis[2].set_title('F. i. with G. n.')
axis[2].imshow(imageWithoutPrnuNpArray + imageNoise)
axis[3].set_title('F. i. with G. n. and PRNU')
axis[3].imshow(imageWithoutPrnuNpArray + prnuNpArray + imageNoise)
isFirstImage = False
#assert all([isIn256Range(extreme) for extreme in [imageWithPrnuNpArray.max(), imageWithPrnuNpArray.min()]]), 'Adding the PRNU resulted in out of 256 bounds image'
imageWithPrnuPil = toPilImage(imageWithPrnuNpArray)
#imagePrnuEstimatePil = contextAdaptiveInterpolator(imageWithPrnuPil.load(), imageWithPrnuPil)
#imagePrnuEstimateNpArray = np.array(imagePrnuEstimatePil)
imagePrnuEstimateNpArray = imageWithPrnuNpArray - denoise_tv_chambolle(imageWithPrnuNpArray, weight=0.2, channel_axis=-1)
imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
cameraPrnuEstimateNpArray = np.array(imagesPrnuEstimateNpArray).mean(axis = 0)
rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
title = f'RMS with actual PRNU: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f}\n(normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})'
axis = axes[1]
axis[splitNXNIndex].set_title(f'Number of images: {len(imagesPrnuEstimateNpArray)}\n{title}')
axis[splitNXNIndex].imshow(cameraPrnuEstimateNpArray)
axes[1][3].axis('off')
plt.tight_layout()
plt.show()

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<stroke tool="highlighter" color="#00c0ff7f" width="8.50000000" fill="128">44.88887314 272.09091817 331.36022445 272.09091817</stroke>
<stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">350.89020532 268.80336793 450.39163443 268.80336793</stroke>
<stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">44.93306058 287.15898498 351.10131254 287.15898498</stroke>
<stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">45.27054464 307.01382219 263.17418553 307.01382219</stroke>
<stroke tool="highlighter" color="#ffff007f" width="8.50000000" fill="128">93.80213209 345.70257013 454.50227983 345.70257013</stroke>
<text font="Sans" size="11.00000000" x="45.82731792" y="353.90876455" color="#00c0ffff" ts="0" fn="">legends not matching actual webpage web-browser rendering</text>
<text font="Sans" size="11.00000000" x="48.63067627" y="32.63059998" color="#00c0ffff" ts="0" fn="">Almost identical content as `no_noise_images.zip/readme.txt`.</text>
</layer>
</page>
<page width="596.00000000" height="842.00000000">
<background type="pdf" pageno="2"/>
<layer/>
</page>
<page width="596.00000000" height="842.00000000">
<background type="pdf" pageno="3"/>
<layer/>
</page>
</xournal>

65
datasets/rafael/240424/plot_dates.py Normal file
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from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
from PIL import Image
from PIL.ExifTags import TAGS
import os
path = 'photos'
os.chdir(path)
names = []
dates = []
for fileName in sorted(os.listdir()):
try:
image = Image.open(fileName)
except:
# Skip raw images.
continue
imageExif = image.getexif()
dateTimeKey = list(TAGS.keys())[list(TAGS.values()).index('DateTime')]
dateTime = imageExif[dateTimeKey]
names += [fileName.replace('DSC0', '').replace('.JPG', '')]
dates += [dateTime[:(-1 if fileName.endswith('.tif') else len(dateTime))]]
# Convert date strings to datetime
dates = [datetime.strptime(d, "%Y:%m:%d %H:%M:%S") for d in dates]
# Choose some nice levels
NUMBER_OF_LEVELS = 10
actualLevels = range(-NUMBER_OF_LEVELS * 2 + 1, NUMBER_OF_LEVELS * 2 , 2)
levels = np.tile(actualLevels,
int(np.ceil(len(dates)/len(actualLevels))))[:len(dates)]
# Create figure and plot a stem plot with the date
fig, ax = plt.subplots(figsize=(8.8, 4), layout="constrained")
#ax.set(title="Matplotlib release dates")
ax.vlines(dates, 0, levels, color="tab:red") # The vertical stems.
ax.plot(dates, np.zeros_like(dates), "-o",
color="k", markerfacecolor="w") # Baseline and markers on it.
# annotate lines
for d, l, r in zip(dates, levels, names):
ax.annotate(r, xy=(d, l),
xytext=(-3, np.sign(l)*3), textcoords="offset points",
horizontalalignment="right",
verticalalignment="bottom" if l > 0 else "top")
# format x-axis with 4-month intervals
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y:%m:%d %H:%M:%S"))
plt.setp(ax.get_xticklabels(), rotation=30, ha="right")
# remove y-axis and spines
ax.yaxis.set_visible(False)
ax.spines[["left", "top", "right"]].set_visible(False)
ax.margins(y=0.1)
plt.show()

35
datasets/raise/analyze_bayer_filter_mean_denoiser.py Normal file
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import numpy as np
from utils import Color
import os
from tqdm import tqdm
import rawpy
import matplotlib.pyplot as plt
os.chdir('flat-field/NEF')
firstBayerFilterOccurrenceImages = []
for fileName in tqdm(os.listdir()):
with rawpy.imread(fileName) as raw:
colorDesc = raw.color_desc.decode('ascii')
assert colorDesc == 'RGBG'
assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
# RG
# GB
firstBayerFilterOccurrenceImage = raw.raw_image_visible.copy()[:2, :2]
firstBayerFilterOccurrenceImages += [firstBayerFilterOccurrenceImage]
firstBayerFilterOccurrenceImages = np.array(firstBayerFilterOccurrenceImages)
print(rawImageVisible)
NUMBER_OF_COLORS = 3
HEX_COLOR = '#' + '%02x' * NUMBER_OF_COLORS
COLOR_BASE = 256
def getColor(colorIndex):
return HEX_COLOR % tuple((255 if colorIndex == colorIndexTmp else 0) for colorIndexTmp in range(NUMBER_OF_COLORS))
for colorIndex, (colorY, colorX) in enumerate([(0, 0), (0, 1), (1, 1)]):
X = firstBayerFilterOccurrenceImages[:, colorY, colorX] - np.mean(firstBayerFilterOccurrenceImages, axis = 0)[colorY, colorX]
plt.hist(X, bins = len(set(X)), color = getColor(colorIndex), alpha = 0.3)
plt.show()

122
datasets/raise/attribute_source_camera.py Executable file
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#!/usr/bin/env python
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
import sys
import os
import random
sys.path.insert(0, '../../algorithms/distance/')
from rms_diff import rmsDiffNumpy
DENOISER = 'wavelet'
IMAGES_CAMERAS_FOLDER = {
'RAISE': 'flat-field/nef',
'Rafael 23/04/24': 'rafael/230424',
}
TRAINING_PORTION = 0.5
setting = ','.join([escapeFilePath(imageCameraFolder) for imageCameraFolder in IMAGES_CAMERAS_FOLDER]) + f'_{DENOISER}'
imagesCamerasFileNames = {camera: os.listdir(imageCameraFolder) for camera, imageCameraFolder in IMAGES_CAMERAS_FOLDER.items()}
random.seed(0)
# To not have a bias (chronological for instance) when split to make training and testing sets.
for camera in IMAGES_CAMERAS_FOLDER:
random.shuffle(imagesCamerasFileNames[camera])
minimumNumberOfImagesCameras = 4#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
for camera in IMAGES_CAMERAS_FOLDER:
imagesCamerasFileNames[camera] = imagesCamerasFileNames[camera][:minimumNumberOfImagesCameras]
numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
camerasIterativeMean = {camera: iterativeMean() for camera in IMAGES_CAMERAS_FOLDER}
minColor = None
maxColor = None
# Assume that for each camera, its images have the same resolution.
# The following consider a given color channel resolution, assuming they all have the same resolution.
minimalColorChannelCameraResolution = None
for camera in IMAGES_CAMERAS_FOLDER:
imageFileName = imagesCamerasFileNames[camera][0]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
singleColorChannelImagesShape = getColorChannel(imageFilePath, Color.RED).shape
if minimalColorChannelCameraResolution is None or singleColorChannelImagesShape < minimalColorChannelCameraResolution:
minimalColorChannelCameraResolution = singleColorChannelImagesShape
minColor = 13#None
maxColor = 7497#None
accuracy = []
numberOfTrainingImages = int(minimumNumberOfImagesCameras * TRAINING_PORTION)
numberOfTestingImages = minimumNumberOfImagesCameras - int(minimumNumberOfImagesCameras * TRAINING_PORTION)
cameraTestingImagesNoise = {}
returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
if not computeExtremes:
print(f'{minColor=} {maxColor=}')
print('Extracting noise of testing images')
for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
# Should make a function
imageFileName = imagesCamerasFileNames[camera][numberOfTrainingImages + cameraTestingImageIndex]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
# Should make a function
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
cameraTestingImagesNoise[camera] = cameraTestingImagesNoise.get(camera, []) + [multipleColorsDenoisedImage]
for cameraTrainingImageIndex in tqdm(range(minimumNumberOfImagesCameras if computeExtremes else numberOfTrainingImages), 'Camera training image index'):
for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
imageFileName = imagesCamerasFileNames[camera][cameraTrainingImageIndex]
imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color)[:minimalColorChannelCameraResolution[0],:minimalColorChannelCameraResolution[1]], minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
if computeExtremes:
minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
continue
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
cameraIterativeMean = camerasIterativeMean[camera]
cameraIterativeMean.add(imagePrnuEstimateNpArray)
if cameraIndex == numberOfCameras - 1:
numberOfTrainingImagesAccuracy = 0
# Loop over each camera testing image folder.
for actualCamera in IMAGES_CAMERAS_FOLDER:
for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
cameraPredicted = None
minimalDistance = None
# Loop over each camera to compute closeness between the considered testing image noise and the estimated PRNUs of the various cameras.
for camera in IMAGES_CAMERAS_FOLDER:
distance = rmsDiffNumpy(cameraTestingImagesNoise[camera][cameraTestingImageIndex], camerasIterativeMean[camera].mean)
print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
if minimalDistance is None or distance < minimalDistance:
minimalDistance = distance
cameraPredicted = camera
if cameraPredicted == actualCamera:
numberOfTrainingImagesAccuracy += 1
accuracy += [numberOfTrainingImagesAccuracy / (numberOfTestingImages * numberOfCameras)]
for camera in IMAGES_CAMERAS_FOLDER:
plt.imsave(f'{setting}_estimated_prnu_subgroup_{escapeFilePath(camera)}.png', (camerasIterativeMean[camera].mean))
plt.title(f'Accuracy of camera source attribution thanks to a given number of images to estimate PRNUs with {DENOISER} denoiser')
plt.xlabel('Number of images to estimate PRNU')
plt.ylabel('Accuracy of camera source attribution')
plt.plot(accuracy)
internalTitle = f'{setting}_accuracy_of_camera_source_attribution'
saveNpArray(internalTitle, accuracy)
plt.savefig(f'{internalTitle}.svg')

113
datasets/raise/extract_noise.py Executable file
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#!/usr/bin/env python
import numpy as np
import os
from tqdm import tqdm
import csv
from utils import Color, denoise, iterativeMean, isARawImage, escapeFilePath, getColorChannel, saveNpArray, rescaleRawImageForDenoiser, updateExtremes
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter
imagesFolderPath = 'rafael/arw'
imagesFolderPathFileName = escapeFilePath(imagesFolderPath)
# Among:
# `denoise` possible denoisers and `mean`.
denoiser = 'mean'
raiseNotFlatFields = False
# `[Color.RED, Color.GREEN_RIGHT, ...]` or `Color` or `[None]` for not raw images.
colors = [None]
imagesFileNames = os.listdir(imagesFolderPath + ('/png' if raiseNotFlatFields else ''))
if raiseNotFlatFields:
files = {}
with open('RAISE_all.csv') as csvfile:
reader = csv.DictReader(csvfile)
for row in tqdm(list(reader), 'CSV parsing'):
file = row['File'] + '.png'
files[file] = row
imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{imagesFolderPath}/png/{imageFileName}').size == (4946, 3278)]
# Among:
# - `None`
# - `'sky'`
# - `'wall'`
type_ = None
if type_ is not None:
ranges = {
'sky': range(2_699, 2_807),
'wall': range(2_807, 2_912),
}
imagesFileNames = [f'DSC0{imageIndex}.ARW' for imageIndex in ranges[type_]]
imagesFolderPathFileName += f'_{type_}'
minColor = None
maxColor = None
def getImageNpArray(imageFileName, computeExtremes, color):
global minColor, maxColor
if raiseNotFlatFields:
imageFileName = imageFileName.replace('.png', '.NEF')
imageFilePath = f'{imagesFolderPath}/nef/{imageFileName}'
else:
imageFilePath = f'{imagesFolderPath}/{imageFileName}'
imageNpArray = getColorChannel(imageFilePath, color)
if computeExtremes:
minColor, maxColor = updateExtremes(imageNpArray, minColor, maxColor)
return
if isARawImage(imageFileName) and 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
# `color` is the actual color to estimate PRNU with.
def treatImage(imageFileName, computeExtremes = False, color = None):
global estimatedPrnuIterativeMean
imageNpArray = getImageNpArray(imageFileName, computeExtremes, color)
if imageNpArray is None:
return
if denoiser != 'mean':
imageDenoisedNpArray = denoise(imageNpArray, denoiser)
else:
imageDenoisedNpArray = means[color]
imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
estimatedPrnuIterativeMean.add(imageNoiseNpArray)
if (minColor is None or maxColor is None) and denoiser != 'mean':
# Assuming same intensity scale across color channels.
for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
for color in colors:
treatImage(imageFileName, computeExtremes = True, color = color)
# To skip this step next time.
# Maybe thanks to `rawpy.RawPy` fields, possibly stating device maximal value, can avoid doing so to some extent.
print(f'{minColor=}')
print(f'{maxColor=}')
if denoiser == 'mean':
means = {}
for color in colors:
colorIterativeMean = iterativeMean()
for imageFileName in tqdm(imagesFileNames, f'Computing mean of {color} colored images'):
imageNpArray = getImageNpArray(imageFileName, False, color)
imageNpArray = gaussian_filter(imageNpArray, sigma = 5)
colorIterativeMean.add(imageNpArray)
means[color] = colorIterativeMean.mean
fileName = f'mean_{imagesFolderPathFileName}_{color}'
# Then use `merge_single_color_channel_images_according_to_bayer_filter.py` to consider all color channels, instead of saving this single color channel as an image.
saveNpArray(fileName, colorIterativeMean.mean)
for color in colors:
estimatedPrnuIterativeMean = iterativeMean()
for imageFileName in tqdm(imagesFileNames, f'Denoising images for color {color}'):
treatImage(imageFileName, color = color)
npArrayFilePath = f'mean_{imagesFolderPathFileName}_{denoiser}_{color}'
saveNpArray(npArrayFilePath, estimatedPrnuIterativeMean.mean)

92
datasets/raise/flat-field_pointer.py Normal file
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import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backend_bases import MouseButton
import matplotlib.image as mpimg
import os
from tqdm import tqdm
# `Zoom to rectangle` shortcut is `o`.
os.chdir('flat-field/TIF')
fileNames = sorted(os.listdir())[41:]
fileNameIndex = 0
xys = []
progressBar = tqdm(total = len(fileNames))
def displayImage():
global fileNameIndex
if len(fileNames) > fileNameIndex:
fileName = fileNames[fileNameIndex]
image = mpimg.imread(fileName)
fig, ax = plt.subplots()
ax.imshow(image)
fileNameIndex += 1
plt.connect('button_press_event', onClick)
mng = plt.get_current_fig_manager()
mng.full_screen_toggle()
fig.canvas.toolbar.zoom()
fig.show()
def onClick(event):
global xys
button = event.button
if button is MouseButton.RIGHT:
xy = [event.xdata, event.ydata]
xys += [xy]
if button is MouseButton.MIDDLE or button is MouseButton.RIGHT:
if button is MouseButton.MIDDLE:
print(f'Skipped {fileName}')
plt.close()
displayImage()
progressBar.update(1)
displayImage()
##
xys = [[3061.83568617998, 842.2814890347822], [3048.9553647053262, 891.7951806771933], [3109.6923734795832, 878.8472318972372], [3095.1992301129835, 859.646383417094], [3044.950680354029, 830.531447782855], [3034.039239345914, 775.7097977790371], [3034.0562409262707, 753.1780473232427], [2991.7499740162116, 742.1417475753151], [3021.5541233968024, 737.7119154247914], [3022.412107608028, 703.0028101114289], [3094.4565209481857, 680.7339885543926], [3101.840188177543, 692.6886985125186], [3150.09819940581, 686.3913997808281], [3148.4955568040136, 694.3747788064451], [3165.156239230552, 703.9346483213995], [3158.7235775979357, 710.9522942666542], [3156.5302112965014, 697.5993396871294], [3106.8119050930513, 698.4561327048264], [3151.4728367372877, 694.3380683877142], [3137.283014559081, 662.9543422436452], [3008.1987322850605, 692.0180565561739], [3036.647953172549, 705.1188029786949], [2988.9166660643627, 684.0851408200217], [3001.5070402052334, 684.1944057536488], [2967.282534077184, 680.6072888791263], [2983.5140619626286, 693.469904886339], [2979.972210442175, 702.9033995969894], [2974.7535915953795, 709.6086279669086], [2972.55952140686, 700.4689248964266], [2967.1769510144627, 703.4861098128929], [2968.4714535085923, 708.5924315970815], [2978.725084963369, 725.0064286729727], [2992.7472737250696, 718.9682686788141], [2999.106406229902, 713.0463041988097], [2981.5746364633296, 692.5739107725338], [3014.22885383495, 675.1638989177214], [3004.351989366563, 635.6738446427469], [2964.9109157636794, 580.2416938434527], [2976.672441933737, 599.3922555819147], [2972.9623072548534, 578.6218471929612], [2694.5865522760287, 972.5526873692775], [2924.1211630176217, 1115.9816839025543], [2910.2861978522596, 1095.5172128924614], [2917.4530137143342, 1067.9680470058072], [2919.3469031337613, 1068.44539073963], [2959.4757277956496, 1105.6227950624348], [2936.370033263817, 1157.9142039933472], [2965.3568614486203, 1145.3186319170795], [2951.1991473505136, 1070.8989245366433], [2946.108094501549, 1045.891549058405], [2947.19213475732, 1074.4303801863052], [2943.690837961984, 1071.4536958497956], [3021.083385372544, 1116.881810271317], [3051.7205580454297, 1103.0992679894152], [3107.0873956690143, 972.8919609441568], [2861.932349690137, 1131.4847600231471], [2789.583044951935, 964.6512841164608]]
x, y = [[xy[dimensionIndex] for xy in xys] for dimensionIndex in range(2)]
plt.title('Center wall marker locations')
plt.scatter(x, y, c = range(len(x)))
for xyIndex, xy in enumerate(xys):
plt.text(xy[0], xy[1] + 10, str(xyIndex), horizontalalignment = 'center')
plt.show()
##
greatestDistances = []
#for xy in xys:
for xy, otherXy in zip(xys, xys[1:]):
greatestDistance = None
#for otherXy in xys:
if xy != otherXy:
distance = sum([(xy[dimensionIndex] - otherXy[dimensionIndex]) ** 2 for dimensionIndex in range(2)]) ** 0.5
if greatestDistance is None or distance > greatestDistance:
greatestDistance = distance
greatestDistances += [greatestDistance]
fig, ax = plt.subplots()
plt.title('Distance between a wall marker location and the next one')
ax.boxplot(greatestDistances)
ys = []
for percentile in [25, 50, 75]:
y = np.percentile(greatestDistances, percentile)
ys += [y]
ax.axhline(y = y)
ax.set_yticks(list(ax.get_yticks())[1:-1] + ys)
ax.set_xticks([], [])
fig.show()

41
datasets/raise/generate_histogram.py Normal file
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import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
os.chdir('flat-field/TIF')
NUMBER_OF_COLORS = 3
HEX_COLOR = '#' + '%02x' * NUMBER_OF_COLORS
COLOR_BASE = 256
def getColor(colorIntensity, colorIndex):
return HEX_COLOR % tuple((colorIntensity if colorIndex == colorIndexTmp else 0) for colorIndexTmp in range(NUMBER_OF_COLORS))
def getHistogram(fileName):
image = Image.open(fileName)
#image = image.crop((1, 1, 2, 2))
#print(image.size)
histogram = image.histogram()
colors = [histogram[COLOR_BASE * colorIndex:COLOR_BASE * (colorIndex + 1)] for colorIndex in range(NUMBER_OF_COLORS)]
return colors
def plotHistogram(colors):
for colorIndex, color in enumerate(colors):
for colorIntensity in range(COLOR_BASE):
plt.bar(colorIntensity, color[colorIntensity], color = getColor(colorIntensity, colorIndex), alpha = 0.3)
fileNameColors = []
for fileName in tqdm(os.listdir()):
colors = getHistogram(fileName)
fileNameColors += [colors]
meanFileNameColors = np.mean(fileNameColors, axis = 0)
plotHistogram(meanFileNameColors)
plt.show()

18
datasets/raise/merge_single_color_channel_images_according_to_bayer_filter.py Executable file
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#!/usr/bin/env python
from PIL import Image
from utils import Color, mergeSingleColorChannelImagesAccordingToBayerFilter
import matplotlib.pyplot as plt
import numpy as np
PREFIX = 'mean_rafael_arw_sky_mean_'
def getImageByColor(color):
filePath = PREFIX + f'{color}.npy'
image = np.load(filePath)
return image
singleColorChannelImages = {color: getImageByColor(color) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages, progress = True)
plt.imsave(PREFIX + 'multiple_colors.png', multipleColorsImage)

10
datasets/raise/show_mean_noise.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
fileName = 'mean_flat-field_nef_wavelet_blue'
npArray = np.load(f'{fileName}.npy')
# For other than raw images:
#npArray = (npArray - npArray.min()) / (npArray.max() - npArray.min())
plt.imsave(f'{fileName}.png', npArray)
#plt.imshow(npArray)
#plt.show()

70
datasets/raise/split_and_compare_prnus_of_subgroups.py Executable file
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#!/usr/bin/env python
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
import sys
import os
import random
sys.path.insert(0, '../../algorithms/distance/')
from rms_diff import rmsDiffNumpy
NUMBER_OF_SUBGROUPS = 2
DENOISER = 'wavelet'
IMAGES_FOLDER = 'flat-field/NEF'
setting = escapeFilePath(IMAGES_FOLDER) + f'_{DENOISER}'
imagesFileNames = os.listdir(IMAGES_FOLDER)
random.seed(0)
# To not have a bias (chronological for instance) when split to make subgroups.
random.shuffle(imagesFileNames)
numberOfImagesPerSubgroup = len(imagesFileNames) // NUMBER_OF_SUBGROUPS
subgroupsIterativeMean = [iterativeMean() for _ in range(NUMBER_OF_SUBGROUPS)]
rmss = []
minColor = None
maxColor = None
returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
for subgroupImageIndex in tqdm(range(numberOfImagesPerSubgroup), 'Subgroup image index'):
for subgroupIndex in tqdm(range(NUMBER_OF_SUBGROUPS), 'Subgroup'):
imageIndex = (subgroupIndex * NUMBER_OF_SUBGROUPS) + subgroupImageIndex
imageFileName = imagesFileNames[imageIndex]
imageFilePath = f'{IMAGES_FOLDER}/{imageFileName}'
singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
if computeExtremes:
minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
continue
singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
subgroupIterativeMean = subgroupsIterativeMean[subgroupIndex]
subgroupIterativeMean.add(imagePrnuEstimateNpArray)
if subgroupIndex == NUMBER_OF_SUBGROUPS - 1:
assert NUMBER_OF_SUBGROUPS == 2
rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - subgroupIndex].mean)
rmss += [rms]
for subgroupIndex in range(NUMBER_OF_SUBGROUPS):
plt.imsave(f'{setting}_estimated_prnu_subgroup_{subgroupIndex}.png', (subgroupsIterativeMean[subgroupIndex].mean))
plt.title(f'RMS between both subgroups estimated PRNUs with {DENOISER} denoiser for a given number of images among them')
plt.xlabel('Number of images of each subgroup')
plt.ylabel('RMS between both subgroups estimated PRNUs')
plt.plot(rmss)
saveNpArray(f'{setting}_rmss', rmss)
plt.savefig(f'{setting}_rms_between_estimated_prnu_of_2_subgroups.svg')
#plt.show()

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from enum import Enum, auto
import skimage.restoration
import numpy as np
import rawpy
from tqdm import tqdm
from PIL import Image
from skimage import img_as_float
from datetime import datetime
import builtins as __builtin__
class Color(Enum):
RED = auto()
GREEN_RIGHT = auto()
GREEN_BOTTOM = auto()
BLUE = auto()
def __str__(self):
return self.name.lower()
# Among:
# - `wavelet`
# - `bilateral`
# - `tv_chambolle`
def denoise(imageNpArray, denoiserName):
skImageRestorationDenoise = getattr(skimage.restoration, f'denoise_{denoiserName}')
match denoiserName:
case 'wavelet':
imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, rescale_sigma=True)
case 'bilateral':
imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, sigma_color=0.05, sigma_spatial=15)
case 'tv_chambolle':
imageDenoisedNpArray = skImageRestorationDenoise(imageNpArray, weight=0.2)
return imageDenoisedNpArray
class iterativeMean:
mean = None
numberOfElementsInMean = 0
def add(self, element):
if self.mean is None:
self.mean = element
else:
self.mean = ((self.mean * self.numberOfElementsInMean) + element) / (self.numberOfElementsInMean + 1)
self.numberOfElementsInMean += 1
RAW_IMAGE_FILE_EXTENSIONS = [
'arw',
'nef',
]
def getRawColorChannel(raw, color):
colorDesc = raw.color_desc.decode('ascii')
assert colorDesc == 'RGBG'
assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
# RG
# GB
rawImageVisible = raw.raw_image_visible.copy()
redRawImageVisible = rawImageVisible[::2, ::2]
greenRightRawImageVisible = rawImageVisible[::2, 1::2]
greenBottomRawImageVisible = rawImageVisible[1::2, ::2]
blueRawImageVisible = rawImageVisible[1::2, 1::2]
match color:
case Color.RED:
imageNpArray = redRawImageVisible
case Color.GREEN_RIGHT:
imageNpArray = greenRightRawImageVisible
case Color.GREEN_BOTTOM:
imageNpArray = greenBottomRawImageVisible
case Color.BLUE:
imageNpArray = blueRawImageVisible
return imageNpArray
def isARawImage(imageFilePath):
return any([imageFilePath.lower().endswith(f'.{rawImageFileExtension}') for rawImageFileExtension in RAW_IMAGE_FILE_EXTENSIONS])
def getColorChannel(imageFilePath, color):
if isARawImage(imageFilePath):
with rawpy.imread(imageFilePath) as raw:
imageNpArray = getRawColorChannel(raw, color)
else:
imagePil = Image.open(imageFilePath)
imageNpArray = img_as_float(np.array(imagePil))
return imageNpArray
def escapeFilePath(filePath):
return filePath.replace('/', '_')
def getNewIndex(index, offset):
newIndex = (index - offset) * 2 + offset
return newIndex
def silentTqdm(data, desc = None):
return data
def mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages, progress = False):
colorImage = singleColorChannelImages[list(Color)[0]]
multipleColorsImage = np.empty([dimension * 2 for dimension in colorImage.shape], dtype = np.float64)
'''
Assume Bayer Filter:
RG
GB
'''
multipleColorsImage[::2,::2] = singleColorChannelImages[Color.RED]
multipleColorsImage[1::2,::2] = singleColorChannelImages[Color.GREEN_RIGHT]
multipleColorsImage[::2,1::2] = singleColorChannelImages[Color.GREEN_BOTTOM]
multipleColorsImage[1::2,1::2] = singleColorChannelImages[Color.BLUE]
return multipleColorsImage
def saveNpArray(fileName, npArray):
with open(f'{fileName}.npy', 'wb') as f:
np.save(f, npArray)
def rescaleRawImageForDenoiser(imageNpArray, minColor, maxColor):
imageNpArray = (imageNpArray - minColor) / (maxColor - minColor)
return imageNpArray
def updateExtremes(imageNpArray, minColor, maxColor):
colorRawImageVisibleMin = imageNpArray.min()
colorRawImageVisibleMax = imageNpArray.max()
if minColor is None or colorRawImageVisibleMin < minColor:
minColor = colorRawImageVisibleMin
if maxColor is None or colorRawImageVisibleMax > maxColor:
maxColor = colorRawImageVisibleMax
return minColor, maxColor
def print(*toPrint):
__builtin__.print(datetime.now(), *toPrint)

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