Add Java and Python code for cascade classifier and HDR tutorials.

2018-06-07 20:14:16 +02:00
parent 1187a7fa34
commit afa5b0cc93
13 changed files with 557 additions and 100 deletions
@@ -126,9 +126,9 @@ Result looks like below:
 Additional Resources
 --------------------

-#  Video Lecture on [Face Detection and Tracking](http://www.youtube.com/watch?v=WfdYYNamHZ8)
-2.  An interesting interview regarding Face Detection by [Adam
-    Harvey](http://www.makematics.com/research/viola-jones/)
+-#  Video Lecture on [Face Detection and Tracking](https://www.youtube.com/watch?v=WfdYYNamHZ8)
+-#  An interesting interview regarding Face Detection by [Adam
+    Harvey](https://web.archive.org/web/20171204220159/http://www.makematics.com/research/viola-jones/)

 Exercises
 ---------
@@ -27,7 +27,7 @@ merged, it has to be converted back to 8-bit to view it on usual displays. This
 tonemapping. Additional complexities arise when objects of the scene or camera move between shots,
 since images with different exposures should be registered and aligned.

-In this tutorial we show 2 algorithms (Debvec, Robertson) to generate and display HDR image from an
+In this tutorial we show 2 algorithms (Debevec, Robertson) to generate and display HDR image from an
 exposure sequence, and demonstrate an alternative approach called exposure fusion (Mertens), that
 produces low dynamic range image and does not need the exposure times data.
 Furthermore, we estimate the camera response function (CRF) which is of great value for many computer
@@ -65,14 +65,14 @@ exposure_times = np.array([15.0, 2.5, 0.25, 0.0333], dtype=np.float32)
 ### 2. Merge exposures into HDR image

 In this stage we merge the exposure sequence into one HDR image, showing 2 possibilities
-which we have in OpenCV. The first method is Debvec and the second one is Robertson.
+which we have in OpenCV. The first method is Debevec and the second one is Robertson.
 Notice that the HDR image is of type float32, and not uint8, as it contains the
 full dynamic range of all exposure images.

@code{.py}
 # Merge exposures to HDR image
-merge_debvec = cv.createMergeDebevec()
-hdr_debvec = merge_debvec.process(img_list, times=exposure_times.copy())
+merge_debevec = cv.createMergeDebevec()
+hdr_debevec = merge_debevec.process(img_list, times=exposure_times.copy())
 merge_robertson = cv.createMergeRobertson()
 hdr_robertson = merge_robertson.process(img_list, times=exposure_times.copy())
@endcode
@@ -86,7 +86,7 @@ we will later have to clip the data in order to avoid overflow.
@code{.py}
 # Tonemap HDR image
 tonemap1 = cv.createTonemapDurand(gamma=2.2)
-res_debvec = tonemap1.process(hdr_debvec.copy())
+res_debevec = tonemap1.process(hdr_debevec.copy())
 tonemap2 = cv.createTonemapDurand(gamma=1.3)
 res_robertson = tonemap2.process(hdr_robertson.copy())
@endcode
@@ -111,11 +111,11 @@ integers in the range of [0..255].

@code{.py}
 # Convert datatype to 8-bit and save
-res_debvec_8bit = np.clip(res_debvec*255, 0, 255).astype('uint8')
+res_debevec_8bit = np.clip(res_debevec*255, 0, 255).astype('uint8')
 res_robertson_8bit = np.clip(res_robertson*255, 0, 255).astype('uint8')
 res_mertens_8bit = np.clip(res_mertens*255, 0, 255).astype('uint8')

-cv.imwrite("ldr_debvec.jpg", res_debvec_8bit)
+cv.imwrite("ldr_debevec.jpg", res_debevec_8bit)
 cv.imwrite("ldr_robertson.jpg", res_robertson_8bit)
 cv.imwrite("fusion_mertens.jpg", res_mertens_8bit)
@endcode
@@ -127,9 +127,9 @@ You can see the different results but consider that each algorithm have addition
 extra parameters that you should fit to get your desired outcome. Best practice is
 to try the different methods and see which one performs best for your scene.

-### Debvec:
+### Debevec:

-![image](images/ldr_debvec.jpg)
+![image](images/ldr_debevec.jpg)

 ### Robertson:

@@ -150,9 +150,9 @@ function and use it for the HDR merge.

@code{.py}
 # Estimate camera response function (CRF)
-cal_debvec = cv.createCalibrateDebevec()
-crf_debvec = cal_debvec.process(img_list, times=exposure_times)
-hdr_debvec = merge_debvec.process(img_list, times=exposure_times.copy(), response=crf_debvec.copy())
+cal_debevec = cv.createCalibrateDebevec()
+crf_debevec = cal_debevec.process(img_list, times=exposure_times)
+hdr_debevec = merge_debevec.process(img_list, times=exposure_times.copy(), response=crf_debevec.copy())
 cal_robertson = cv.createCalibrateRobertson()
 crf_robertson = cal_robertson.process(img_list, times=exposure_times)
 hdr_robertson = merge_robertson.process(img_list, times=exposure_times.copy(), response=crf_robertson.copy())
@@ -166,12 +166,12 @@ For this sequence we got the following estimation:
 Additional Resources
 --------------------

-1. Paul E Debevec and Jitendra Malik. Recovering high dynamic range radiance maps from photographs. In ACM SIGGRAPH 2008 classes, page 31. ACM, 2008.
-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.
-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.
+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
+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
+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
 4. Images from [Wikipedia-HDR](https://en.wikipedia.org/wiki/High-dynamic-range_imaging)

 Exercises
 ---------
-1. Try all tonemap algorithms: [Drago](http://docs.opencv.org/master/da/d53/classcv_1_1TonemapDrago.html), [Durand](http://docs.opencv.org/master/da/d3d/classcv_1_1TonemapDurand.html), [Mantiuk](http://docs.opencv.org/master/de/d76/classcv_1_1TonemapMantiuk.html) and [Reinhard](http://docs.opencv.org/master/d0/dec/classcv_1_1TonemapReinhard.html).
-2. Try changing the parameters in the HDR calibration and tonemap methods.
+1. Try all tonemap algorithms: cv::TonemapDrago, cv::TonemapDurand, cv::TonemapMantiuk and cv::TonemapReinhard
+2. Try changing the parameters in the HDR calibration and tonemap methods.
@@ -17,9 +17,23 @@ Theory
 Code
 ----

+@add_toggle_cpp
 This tutorial code's is shown lines below. You can also download it from
 [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp)
@include samples/cpp/tutorial_code/objectDetection/objectDetection.cpp
+@end_toggle
+
+@add_toggle_java
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/objectDetection/cascade_classifier/ObjectDetectionDemo.java)
+@include samples/java/tutorial_code/objectDetection/cascade_classifier/ObjectDetectionDemo.java
+@end_toggle
+
+@add_toggle_python
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/objectDetection/cascade_classifier/objectDetection.py)
+@include samples/python/tutorial_code/objectDetection/cascade_classifier/objectDetection.py
+@end_toggle

 Explanation
 -----------
@@ -40,3 +54,13 @@ Result
    detection. For the eyes we keep using the file used in the tutorial.

    ![](images/Cascade_Classifier_Tutorial_Result_LBP.jpg)
+
+Additional Resources
+--------------------
+
+-#  Paul Viola and Michael J. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137–154, 2004. @cite Viola04
+-#  Rainer Lienhart and Jochen Maydt. An extended set of haar-like features for rapid object detection. In Image Processing. 2002. Proceedings. 2002 International Conference on, volume 1, pages I–900. IEEE, 2002. @cite Lienhart02
+-#  Video Lecture on [Face Detection and Tracking](https://www.youtube.com/watch?v=WfdYYNamHZ8)
+-#  An interesting interview regarding Face Detection by [Adam
+    Harvey](https://web.archive.org/web/20171204220159/http://www.makematics.com/research/viola-jones/)
+-#  [OpenCV Face Detection: Visualized](https://vimeo.com/12774628) on Vimeo by Adam Harvey
@@ -5,6 +5,8 @@ Ever wondered how your digital camera detects peoples and faces? Look here to fi

 -   @subpage tutorial_cascade_classifier

+    *Languages:* C++, Java, Python
+
    *Compatibility:* \> OpenCV 2.0

    *Author:* Ana Huamán
@@ -31,21 +31,51 @@ Exposure sequence
 Source Code
 -----------

-@include cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp
+@add_toggle_cpp
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp)
+@include samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp
+@end_toggle
+
+@add_toggle_java
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java)
+@include samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java
+@end_toggle
+
+@add_toggle_python
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py)
+@include samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py
+@end_toggle
+
+Sample images
+-------------
+
+Data directory that contains images, exposure times and `list.txt` file can be downloaded from
+[here](https://github.com/opencv/opencv_extra/tree/3.4/testdata/cv/hdr/exposures).

 Explanation
 -----------

-#  **Load images and exposure times**
-    @code{.cpp}
-    vector<Mat> images;
-    vector<float> times;
-    loadExposureSeq(argv[1], images, times);
-    @endcode
-    Firstly we load input images and exposure times from user-defined folder. The folder should
-    contain images and *list.txt* - file that contains file names and inverse exposure times.
+-   **Load images and exposure times**

-    For our image sequence the list is following:
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Load images and exposure times
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Load images and exposure times
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Load images and exposure times
+@end_toggle
+
+Firstly we load input images and exposure times from user-defined folder. The folder should
+contain images and *list.txt* - file that contains file names and inverse exposure times.
+
+For our image sequence the list is following:
    @code{.none}
    memorial00.png 0.03125
    memorial01.png 0.0625
@@ -53,53 +83,96 @@ Explanation
    memorial15.png 1024
    @endcode

-#  **Estimate camera response**
-    @code{.cpp}
-    Mat response;
-    Ptr<CalibrateDebevec> calibrate = createCalibrateDebevec();
-    calibrate->process(images, response, times);
-    @endcode
-    It is necessary to know camera response function (CRF) for a lot of HDR construction algorithms.
-    We use one of the calibration algorithms to estimate inverse CRF for all 256 pixel values.
+-   **Estimate camera response**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Estimate camera response
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Estimate camera response
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Estimate camera response
+@end_toggle
+
+It is necessary to know camera response function (CRF) for a lot of HDR construction algorithms.
+We use one of the calibration algorithms to estimate inverse CRF for all 256 pixel values.
+
+-   **Make HDR image**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Make HDR image
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Make HDR image
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Make HDR image
+@end_toggle

-#  **Make HDR image**
-@code{.cpp}
-Mat hdr;
-Ptr<MergeDebevec> merge_debevec = createMergeDebevec();
-merge_debevec->process(images, hdr, times, response);
-@endcode
 We use Debevec's weighting scheme to construct HDR image using response calculated in the previous
 item.

-#  **Tonemap HDR image**
-    @code{.cpp}
-    Mat ldr;
-    Ptr<TonemapDurand> tonemap = createTonemapDurand(2.2f);
-    tonemap->process(hdr, ldr);
-    @endcode
-    Since we want to see our results on common LDR display we have to map our HDR image to 8-bit range
-    preserving most details. It is the main goal of tonemapping methods. We use tonemapper with
-    bilateral filtering and set 2.2 as the value for gamma correction.
+-   **Tonemap HDR image**

-#  **Perform exposure fusion**
-    @code{.cpp}
-    Mat fusion;
-    Ptr<MergeMertens> merge_mertens = createMergeMertens();
-    merge_mertens->process(images, fusion);
-    @endcode
-    There is an alternative way to merge our exposures in case when we don't need HDR image. This
-    process is called exposure fusion and produces LDR image that doesn't require gamma correction. It
-    also doesn't use exposure values of the photographs.
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Tonemap HDR image
+@end_toggle

-#  **Write results**
-    @code{.cpp}
-    imwrite("fusion.png", fusion * 255);
-    imwrite("ldr.png", ldr * 255);
-    imwrite("hdr.hdr", hdr);
-    @endcode
-    Now it's time to look at the results. Note that HDR image can't be stored in one of common image
-    formats, so we save it to Radiance image (.hdr). Also all HDR imaging functions return results in
-    [0, 1] range so we should multiply result by 255.
+@add_toggle_java
+@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Tonemap HDR image
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Tonemap HDR image
+@end_toggle
+
+Since we want to see our results on common LDR display we have to map our HDR image to 8-bit range
+preserving most details. It is the main goal of tonemapping methods. We use tonemapper with
+bilateral filtering and set 2.2 as the value for gamma correction.
+
+-   **Perform exposure fusion**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Perform exposure fusion
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Perform exposure fusion
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Perform exposure fusion
+@end_toggle
+
+There is an alternative way to merge our exposures in case when we don't need HDR image. This
+process is called exposure fusion and produces LDR image that doesn't require gamma correction. It
+also doesn't use exposure values of the photographs.
+
+-   **Write results**
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/photo/hdr_imaging/hdr_imaging.cpp Write results
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/photo/hdr_imaging/HDRImagingDemo.java Write results
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/photo/hdr_imaging/hdr_imaging.py Write results
+@end_toggle
+
+Now it's time to look at the results. Note that HDR image can't be stored in one of common image
+formats, so we save it to Radiance image (.hdr). Also all HDR imaging functions return results in
+[0, 1] range so we should multiply result by 255.
+
+You can try other tonemap algorithms: cv::TonemapDrago, cv::TonemapDurand, cv::TonemapMantiuk and cv::TonemapReinhard
+You can also adjust the parameters in the HDR calibration and tonemap methods for your own photos.

 Results
 -------
@@ -111,3 +184,12 @@ Results
 ### Exposure fusion

 ![](images/fusion.png)
+
+Additional Resources
+--------------------
+
+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
+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
+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
+4. [Wikipedia-HDR](https://en.wikipedia.org/wiki/High-dynamic-range_imaging)
+5. [Recovering High Dynamic Range Radiance Maps from Photographs (webpage)](http://www.pauldebevec.com/Research/HDR/)
@@ -5,6 +5,8 @@ Use OpenCV for advanced photo processing.

 -   @subpage tutorial_hdr_imaging

+    *Languages:* C++, Java, Python
+
    *Compatibility:* \> OpenCV 3.0

    *Author:* Fedor Morozov