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204 lines
7.7 KiB
204 lines
7.7 KiB
High Dynamic Range Imaging {#tutorial_hdr_imaging} |
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========================== |
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@tableofcontents |
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@next_tutorial{tutorial_stitcher} |
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| Original author | Fedor Morozov | |
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| Compatibility | OpenCV >= 3.0 | |
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Introduction |
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------------ |
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Today most digital images and imaging devices use 8 bits per channel thus limiting the dynamic range |
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of the device to two orders of magnitude (actually 256 levels), while human eye can adapt to |
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lighting conditions varying by ten orders of magnitude. When we take photographs of a real world |
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scene bright regions may be overexposed, while the dark ones may be underexposed, so we can’t |
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capture all details using a single exposure. HDR imaging works with images that use more that 8 bits |
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per channel (usually 32-bit float values), allowing much wider dynamic range. |
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There are different ways to obtain HDR images, but the most common one is to use photographs of the |
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scene taken with different exposure values. To combine this exposures it is useful to know your |
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camera’s response function and there are algorithms to estimate it. After the HDR image has been |
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blended it has to be converted back to 8-bit to view it on usual displays. This process is called |
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tonemapping. Additional complexities arise when objects of the scene or camera move between shots, |
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since images with different exposures should be registered and aligned. |
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In this tutorial we show how to generate and display HDR image from an exposure sequence. In our |
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case images are already aligned and there are no moving objects. We also demonstrate an alternative |
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approach called exposure fusion that produces low dynamic range image. Each step of HDR pipeline can |
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be implemented using different algorithms so take a look at the reference manual to see them all. |
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Exposure sequence |
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----------------- |
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Source Code |
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----------- |
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@add_toggle_cpp |
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This tutorial code's is shown lines below. You can also download it from |
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[here](https://github.com/opencv/opencv/tree/4.x/samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp) |
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@include samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp |
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@end_toggle |
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@add_toggle_java |
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This tutorial code's is shown lines below. You can also download it from |
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[here](https://github.com/opencv/opencv/tree/4.x/samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java) |
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@include samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java |
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@end_toggle |
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@add_toggle_python |
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This tutorial code's is shown lines below. You can also download it from |
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[here](https://github.com/opencv/opencv/tree/4.x/samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py) |
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@include samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py |
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@end_toggle |
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Sample images |
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------------- |
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Data directory that contains images, exposure times and `list.txt` file can be downloaded from |
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[here](https://github.com/opencv/opencv_extra/tree/4.x/testdata/cv/hdr/exposures). |
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Explanation |
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----------- |
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- **Load images and exposure times** |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Load images and exposure times |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Load images and exposure times |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Load images and exposure times |
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@end_toggle |
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Firstly we load input images and exposure times from user-defined folder. The folder should |
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contain images and *list.txt* - file that contains file names and inverse exposure times. |
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For our image sequence the list is following: |
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@code{.none} |
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memorial00.png 0.03125 |
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memorial01.png 0.0625 |
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... |
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memorial15.png 1024 |
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@endcode |
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- **Estimate camera response** |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Estimate camera response |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Estimate camera response |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Estimate camera response |
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@end_toggle |
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It is necessary to know camera response function (CRF) for a lot of HDR construction algorithms. |
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We use one of the calibration algorithms to estimate inverse CRF for all 256 pixel values. |
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- **Make HDR image** |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Make HDR image |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Make HDR image |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Make HDR image |
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@end_toggle |
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We use Debevec's weighting scheme to construct HDR image using response calculated in the previous |
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item. |
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- **Tonemap HDR image** |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Tonemap HDR image |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Tonemap HDR image |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Tonemap HDR image |
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@end_toggle |
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Since we want to see our results on common LDR display we have to map our HDR image to 8-bit range |
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preserving most details. It is the main goal of tonemapping methods. We use tonemapper with |
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bilateral filtering and set 2.2 as the value for gamma correction. |
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- **Perform exposure fusion** |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Perform exposure fusion |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Perform exposure fusion |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Perform exposure fusion |
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@end_toggle |
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There is an alternative way to merge our exposures in case when we don't need HDR image. This |
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process is called exposure fusion and produces LDR image that doesn't require gamma correction. It |
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also doesn't use exposure values of the photographs. |
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- **Write results** |
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@add_toggle_cpp |
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@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Write results |
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@end_toggle |
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@add_toggle_java |
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@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Write results |
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@end_toggle |
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@add_toggle_python |
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@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Write results |
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@end_toggle |
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Now it's time to look at the results. Note that HDR image can't be stored in one of common image |
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formats, so we save it to Radiance image (.hdr). Also all HDR imaging functions return results in |
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[0, 1] range so we should multiply result by 255. |
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You can try other tonemap algorithms: cv::TonemapDrago, cv::TonemapMantiuk and cv::TonemapReinhard |
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You can also adjust the parameters in the HDR calibration and tonemap methods for your own photos. |
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Results |
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------- |
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### Tonemapped image |
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### Exposure fusion |
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Additional Resources |
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-------------------- |
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1. Paul E Debevec and Jitendra Malik. Recovering high dynamic range radiance maps from photographs. In ACM SIGGRAPH 2008 classes, page 31. ACM, 2008. @cite DM97 |
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2. Mark A Robertson, Sean Borman, and Robert L Stevenson. Dynamic range improvement through multiple exposures. In Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on, volume 3, pages 159–163. IEEE, 1999. @cite RB99 |
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3. Tom Mertens, Jan Kautz, and Frank Van Reeth. Exposure fusion. In Computer Graphics and Applications, 2007. PG'07. 15th Pacific Conference on, pages 382–390. IEEE, 2007. @cite MK07 |
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4. [Wikipedia-HDR](https://en.wikipedia.org/wiki/High-dynamic-range_imaging) |
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5. [Recovering High Dynamic Range Radiance Maps from Photographs (webpage)](http://www.pauldebevec.com/Research/HDR/)
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