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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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Alexander Alekhin 6 years ago
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  1. 2
      cmake/templates/opencv_abi.xml.in
  2. 36
      doc/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.markdown
  3. 68
      doc/py_tutorials/py_imgproc/py_filtering/py_filtering.markdown
  4. 28
      doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.markdown
  5. 12
      doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown
  6. 4
      doc/tutorials/introduction/cross_referencing/tutorial_cross_referencing.markdown
  7. 14
      doc/tutorials/introduction/documenting_opencv/documentation_tutorial.markdown
  8. 40
      doc/tutorials/introduction/ios_install/ios_install.markdown
  9. 2
      doc/tutorials/introduction/table_of_content_introduction.markdown
  10. 2
      doc/tutorials/introduction/windows_install/windows_install.markdown
  11. 38
      modules/core/include/opencv2/core/bindings_utils.hpp
  12. 11
      modules/core/include/opencv2/core/hal/intrin.hpp
  13. 146
      modules/core/include/opencv2/core/hal/simd_utils.impl.hpp
  14. 34
      modules/core/test/test_intrin_utils.hpp
  15. 2
      modules/dnn/src/tensorflow/tf_importer.cpp
  16. 2
      modules/ml/src/svm.cpp
  17. 200
      modules/ml/test/test_ann.cpp
  18. 56
      modules/ml/test/test_bayes.cpp
  19. 186
      modules/ml/test/test_em.cpp
  20. 727
      modules/ml/test/test_emknearestkmeans.cpp
  21. 286
      modules/ml/test/test_gbttest.cpp
  22. 53
      modules/ml/test/test_kmeans.cpp
  23. 77
      modules/ml/test/test_knearest.cpp
  24. 190
      modules/ml/test/test_lr.cpp
  25. 507
      modules/ml/test/test_mltests.cpp
  26. 793
      modules/ml/test/test_mltests2.cpp
  27. 65
      modules/ml/test/test_precomp.hpp
  28. 54
      modules/ml/test/test_rtrees.cpp
  29. 350
      modules/ml/test/test_save_load.cpp
  30. 296
      modules/ml/test/test_svmsgd.cpp
  31. 183
      modules/ml/test/test_svmtrainauto.cpp
  32. 189
      modules/ml/test/test_utils.cpp
  33. 273
      modules/python/test/test_misc.py
  34. 45
      modules/stitching/include/opencv2/stitching/detail/warpers.hpp
  35. 15
      modules/ts/include/opencv2/ts.hpp
  36. 10
      modules/ts/include/opencv2/ts/ts_ext.hpp
  37. 4
      modules/ts/include/opencv2/ts/ts_perf.hpp
  38. 25
      modules/ts/src/ts.cpp
  39. 8
      modules/ts/src/ts_perf.cpp
  40. 19
      modules/ts/src/ts_tags.cpp
  41. 2
      modules/ts/src/ts_tags.hpp
  42. 284
      modules/videoio/src/cap_v4l.cpp
  43. 6
      modules/videoio/src/precomp.hpp
  44. 2
      samples/dnn/tf_text_graph_ssd.py

2
cmake/templates/opencv_abi.xml.in
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@ -22,6 +22,8 @@
<skip_headers> <skip_headers>
opencv2/core/hal/intrin* opencv2/core/hal/intrin*
opencv2/core/hal/*macros.*
opencv2/core/hal/*.impl.*
opencv2/core/cuda* opencv2/core/cuda*
opencv2/core/opencl* opencv2/core/opencl*
opencv2/core/private* opencv2/core/private*

36
doc/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.markdown
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@ -5,44 +5,44 @@ Goal
---- ----
- In this tutorial, you will learn how to convert images from one color-space to another, like - In this tutorial, you will learn how to convert images from one color-space to another, like
BGR \f$\leftrightarrow\f$ Gray, BGR \f$\leftrightarrow\f$ HSV etc. BGR \f$\leftrightarrow\f$ Gray, BGR \f$\leftrightarrow\f$ HSV, etc.
- In addition to that, we will create an application which extracts a colored object in a video - In addition to that, we will create an application to extract a colored object in a video
- You will learn following functions : **cv.cvtColor()**, **cv.inRange()** etc. - You will learn the following functions: **cv.cvtColor()**, **cv.inRange()**, etc.
Changing Color-space Changing Color-space
-------------------- --------------------
There are more than 150 color-space conversion methods available in OpenCV. But we will look into There are more than 150 color-space conversion methods available in OpenCV. But we will look into
only two which are most widely used ones, BGR \f$\leftrightarrow\f$ Gray and BGR \f$\leftrightarrow\f$ HSV. only two, which are most widely used ones: BGR \f$\leftrightarrow\f$ Gray and BGR \f$\leftrightarrow\f$ HSV.
For color conversion, we use the function cv.cvtColor(input_image, flag) where flag determines the For color conversion, we use the function cv.cvtColor(input_image, flag) where flag determines the
type of conversion. type of conversion.
For BGR \f$\rightarrow\f$ Gray conversion we use the flags cv.COLOR_BGR2GRAY. Similarly for BGR For BGR \f$\rightarrow\f$ Gray conversion, we use the flag cv.COLOR_BGR2GRAY. Similarly for BGR
\f$\rightarrow\f$ HSV, we use the flag cv.COLOR_BGR2HSV. To get other flags, just run following \f$\rightarrow\f$ HSV, we use the flag cv.COLOR_BGR2HSV. To get other flags, just run following
commands in your Python terminal : commands in your Python terminal:
@code{.py} @code{.py}
>>> import cv2 as cv >>> import cv2 as cv
>>> flags = [i for i in dir(cv) if i.startswith('COLOR_')] >>> flags = [i for i in dir(cv) if i.startswith('COLOR_')]
>>> print( flags ) >>> print( flags )
@endcode @endcode
@note For HSV, Hue range is [0,179], Saturation range is [0,255] and Value range is [0,255]. @note For HSV, hue range is [0,179], saturation range is [0,255], and value range is [0,255].
Different software use different scales. So if you are comparing OpenCV values with them, you need Different software use different scales. So if you are comparing OpenCV values with them, you need
to normalize these ranges. to normalize these ranges.
Object Tracking Object Tracking
--------------- ---------------
Now we know how to convert BGR image to HSV, we can use this to extract a colored object. In HSV, it Now that we know how to convert a BGR image to HSV, we can use this to extract a colored object. In HSV, it
is more easier to represent a color than in BGR color-space. In our application, we will try to extract is easier to represent a color than in BGR color-space. In our application, we will try to extract
a blue colored object. So here is the method: a blue colored object. So here is the method:
- Take each frame of the video - Take each frame of the video
- Convert from BGR to HSV color-space - Convert from BGR to HSV color-space
- We threshold the HSV image for a range of blue color - We threshold the HSV image for a range of blue color
- Now extract the blue object alone, we can do whatever on that image we want. - Now extract the blue object alone, we can do whatever we want on that image.
Below is the code which are commented in detail : Below is the code which is commented in detail:
@code{.py} @code{.py}
import cv2 as cv import cv2 as cv
import numpy as np import numpy as np
@ -80,18 +80,18 @@ Below image shows tracking of the blue object:
![image](images/frame.jpg) ![image](images/frame.jpg)
@note There are some noises in the image. We will see how to remove them in later chapters. @note There is some noise in the image. We will see how to remove it in later chapters.
@note This is the simplest method in object tracking. Once you learn functions of contours, you can @note This is the simplest method in object tracking. Once you learn functions of contours, you can
do plenty of things like find centroid of this object and use it to track the object, draw diagrams do plenty of things like find the centroid of an object and use it to track the object, draw diagrams
just by moving your hand in front of camera and many other funny stuffs. just by moving your hand in front of a camera, and other fun stuff.
How to find HSV values to track? How to find HSV values to track?
-------------------------------- --------------------------------
This is a common question found in [stackoverflow.com](http://www.stackoverflow.com). It is very simple and This is a common question found in [stackoverflow.com](http://www.stackoverflow.com). It is very simple and
you can use the same function, cv.cvtColor(). Instead of passing an image, you just pass the BGR you can use the same function, cv.cvtColor(). Instead of passing an image, you just pass the BGR
values you want. For example, to find the HSV value of Green, try following commands in Python values you want. For example, to find the HSV value of Green, try the following commands in a Python
terminal: terminal:
@code{.py} @code{.py}
>>> green = np.uint8([[[0,255,0 ]]]) >>> green = np.uint8([[[0,255,0 ]]])
@ -99,7 +99,7 @@ terminal:
>>> print( hsv_green ) >>> print( hsv_green )
[[[ 60 255 255]]] [[[ 60 255 255]]]
@endcode @endcode
Now you take [H-10, 100,100] and [H+10, 255, 255] as lower bound and upper bound respectively. Apart Now you take [H-10, 100,100] and [H+10, 255, 255] as the lower bound and upper bound respectively. Apart
from this method, you can use any image editing tools like GIMP or any online converters to find from this method, you can use any image editing tools like GIMP or any online converters to find
these values, but don't forget to adjust the HSV ranges. these values, but don't forget to adjust the HSV ranges.
@ -109,5 +109,5 @@ Additional Resources
Exercises Exercises
--------- ---------
-# Try to find a way to extract more than one colored objects, for eg, extract red, blue, green -# Try to find a way to extract more than one colored object, for example, extract red, blue, and green
objects simultaneously. objects simultaneously.

68
doc/py_tutorials/py_imgproc/py_filtering/py_filtering.markdown
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@ -5,24 +5,24 @@ Goals
----- -----
Learn to: Learn to:
- Blur the images with various low pass filters - Blur images with various low pass filters
- Apply custom-made filters to images (2D convolution) - Apply custom-made filters to images (2D convolution)
2D Convolution ( Image Filtering ) 2D Convolution ( Image Filtering )
---------------------------------- ----------------------------------
As in one-dimensional signals, images also can be filtered with various low-pass filters(LPF), As in one-dimensional signals, images also can be filtered with various low-pass filters (LPF),
high-pass filters(HPF) etc. LPF helps in removing noises, blurring the images etc. HPF filters helps high-pass filters (HPF), etc. LPF helps in removing noise, blurring images, etc. HPF filters help
in finding edges in the images. in finding edges in images.
OpenCV provides a function **cv.filter2D()** to convolve a kernel with an image. As an example, we OpenCV provides a function **cv.filter2D()** to convolve a kernel with an image. As an example, we
will try an averaging filter on an image. A 5x5 averaging filter kernel will look like below: will try an averaging filter on an image. A 5x5 averaging filter kernel will look like the below:
\f[K = \frac{1}{25} \begin{bmatrix} 1 & 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 & 1 \end{bmatrix}\f] \f[K = \frac{1}{25} \begin{bmatrix} 1 & 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 & 1 \end{bmatrix}\f]
Operation is like this: keep this kernel above a pixel, add all the 25 pixels below this kernel, The operation works like this: keep this kernel above a pixel, add all the 25 pixels below this kernel,
take its average and replace the central pixel with the new average value. It continues this take the average, and replace the central pixel with the new average value. This operation is continued
operation for all the pixels in the image. Try this code and check the result: for all the pixels in the image. Try this code and check the result:
@code{.py} @code{.py}
import numpy as np import numpy as np
import cv2 as cv import cv2 as cv
@ -47,20 +47,20 @@ Image Blurring (Image Smoothing)
-------------------------------- --------------------------------
Image blurring is achieved by convolving the image with a low-pass filter kernel. It is useful for Image blurring is achieved by convolving the image with a low-pass filter kernel. It is useful for
removing noises. It actually removes high frequency content (eg: noise, edges) from the image. So removing noise. It actually removes high frequency content (eg: noise, edges) from the image. So
edges are blurred a little bit in this operation. (Well, there are blurring techniques which doesn't edges are blurred a little bit in this operation (there are also blurring techniques which don't
blur the edges too). OpenCV provides mainly four types of blurring techniques. blur the edges). OpenCV provides four main types of blurring techniques.
### 1. Averaging ### 1. Averaging
This is done by convolving image with a normalized box filter. It simply takes the average of all This is done by convolving an image with a normalized box filter. It simply takes the average of all
the pixels under kernel area and replace the central element. This is done by the function the pixels under the kernel area and replaces the central element. This is done by the function
**cv.blur()** or **cv.boxFilter()**. Check the docs for more details about the kernel. We should **cv.blur()** or **cv.boxFilter()**. Check the docs for more details about the kernel. We should
specify the width and height of kernel. A 3x3 normalized box filter would look like below: specify the width and height of the kernel. A 3x3 normalized box filter would look like the below:
\f[K = \frac{1}{9} \begin{bmatrix} 1 & 1 & 1 \\ 1 & 1 & 1 \\ 1 & 1 & 1 \end{bmatrix}\f] \f[K = \frac{1}{9} \begin{bmatrix} 1 & 1 & 1 \\ 1 & 1 & 1 \\ 1 & 1 & 1 \end{bmatrix}\f]
@note If you don't want to use normalized box filter, use **cv.boxFilter()**. Pass an argument @note If you don't want to use a normalized box filter, use **cv.boxFilter()**. Pass an argument
normalize=False to the function. normalize=False to the function.
Check a sample demo below with a kernel of 5x5 size: Check a sample demo below with a kernel of 5x5 size:
@ -85,12 +85,12 @@ Result:
### 2. Gaussian Blurring ### 2. Gaussian Blurring
In this, instead of box filter, gaussian kernel is used. It is done with the function, In this method, instead of a box filter, a Gaussian kernel is used. It is done with the function,
**cv.GaussianBlur()**. We should specify the width and height of kernel which should be positive **cv.GaussianBlur()**. We should specify the width and height of the kernel which should be positive
and odd. We also should specify the standard deviation in X and Y direction, sigmaX and sigmaY and odd. We also should specify the standard deviation in the X and Y directions, sigmaX and sigmaY
respectively. If only sigmaX is specified, sigmaY is taken as same as sigmaX. If both are given as respectively. If only sigmaX is specified, sigmaY is taken as the same as sigmaX. If both are given as
zeros, they are calculated from kernel size. Gaussian blurring is highly effective in removing zeros, they are calculated from the kernel size. Gaussian blurring is highly effective in removing
gaussian noise from the image. Gaussian noise from an image.
If you want, you can create a Gaussian kernel with the function, **cv.getGaussianKernel()**. If you want, you can create a Gaussian kernel with the function, **cv.getGaussianKernel()**.
@ -104,14 +104,14 @@ Result:
### 3. Median Blurring ### 3. Median Blurring
Here, the function **cv.medianBlur()** takes median of all the pixels under kernel area and central Here, the function **cv.medianBlur()** takes the median of all the pixels under the kernel area and the central
element is replaced with this median value. This is highly effective against salt-and-pepper noise element is replaced with this median value. This is highly effective against salt-and-pepper noise
in the images. Interesting thing is that, in the above filters, central element is a newly in an image. Interestingly, in the above filters, the central element is a newly
calculated value which may be a pixel value in the image or a new value. But in median blurring, calculated value which may be a pixel value in the image or a new value. But in median blurring,
central element is always replaced by some pixel value in the image. It reduces the noise the central element is always replaced by some pixel value in the image. It reduces the noise
effectively. Its kernel size should be a positive odd integer. effectively. Its kernel size should be a positive odd integer.
In this demo, I added a 50% noise to our original image and applied median blur. Check the result: In this demo, I added a 50% noise to our original image and applied median blurring. Check the result:
@code{.py} @code{.py}
median = cv.medianBlur(img,5) median = cv.medianBlur(img,5)
@endcode @endcode
@ -122,19 +122,19 @@ Result:
### 4. Bilateral Filtering ### 4. Bilateral Filtering
**cv.bilateralFilter()** is highly effective in noise removal while keeping edges sharp. But the **cv.bilateralFilter()** is highly effective in noise removal while keeping edges sharp. But the
operation is slower compared to other filters. We already saw that gaussian filter takes the a operation is slower compared to other filters. We already saw that a Gaussian filter takes the
neighbourhood around the pixel and find its gaussian weighted average. This gaussian filter is a neighbourhood around the pixel and finds its Gaussian weighted average. This Gaussian filter is a
function of space alone, that is, nearby pixels are considered while filtering. It doesn't consider function of space alone, that is, nearby pixels are considered while filtering. It doesn't consider
whether pixels have almost same intensity. It doesn't consider whether pixel is an edge pixel or whether pixels have almost the same intensity. It doesn't consider whether a pixel is an edge pixel or
not. So it blurs the edges also, which we don't want to do. not. So it blurs the edges also, which we don't want to do.
Bilateral filter also takes a gaussian filter in space, but one more gaussian filter which is a Bilateral filtering also takes a Gaussian filter in space, but one more Gaussian filter which is a
function of pixel difference. Gaussian function of space make sure only nearby pixels are considered function of pixel difference. The Gaussian function of space makes sure that only nearby pixels are considered
for blurring while gaussian function of intensity difference make sure only those pixels with for blurring, while the Gaussian function of intensity difference makes sure that only those pixels with
similar intensity to central pixel is considered for blurring. So it preserves the edges since similar intensities to the central pixel are considered for blurring. So it preserves the edges since
pixels at edges will have large intensity variation. pixels at edges will have large intensity variation.
Below samples shows use bilateral filter (For details on arguments, visit docs). The below sample shows use of a bilateral filter (For details on arguments, visit docs).
@code{.py} @code{.py}
blur = cv.bilateralFilter(img,9,75,75) blur = cv.bilateralFilter(img,9,75,75)
@endcode @endcode
@ -142,7 +142,7 @@ Result:
![image](images/bilateral.jpg) ![image](images/bilateral.jpg)
See, the texture on the surface is gone, but edges are still preserved. See, the texture on the surface is gone, but the edges are still preserved.
Additional Resources Additional Resources
-------------------- --------------------

28
doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.markdown
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@ -4,7 +4,7 @@ Geometric Transformations of Images {#tutorial_py_geometric_transformations}
Goals Goals
----- -----
- Learn to apply different geometric transformation to images like translation, rotation, affine - Learn to apply different geometric transformations to images, like translation, rotation, affine
transformation etc. transformation etc.
- You will see these functions: **cv.getPerspectiveTransform** - You will see these functions: **cv.getPerspectiveTransform**
@ -12,7 +12,7 @@ Transformations
--------------- ---------------
OpenCV provides two transformation functions, **cv.warpAffine** and **cv.warpPerspective**, with OpenCV provides two transformation functions, **cv.warpAffine** and **cv.warpPerspective**, with
which you can have all kinds of transformations. **cv.warpAffine** takes a 2x3 transformation which you can perform all kinds of transformations. **cv.warpAffine** takes a 2x3 transformation
matrix while **cv.warpPerspective** takes a 3x3 transformation matrix as input. matrix while **cv.warpPerspective** takes a 3x3 transformation matrix as input.
### Scaling ### Scaling
@ -21,8 +21,8 @@ Scaling is just resizing of the image. OpenCV comes with a function **cv.resize(
purpose. The size of the image can be specified manually, or you can specify the scaling factor. purpose. The size of the image can be specified manually, or you can specify the scaling factor.
Different interpolation methods are used. Preferable interpolation methods are **cv.INTER_AREA** Different interpolation methods are used. Preferable interpolation methods are **cv.INTER_AREA**
for shrinking and **cv.INTER_CUBIC** (slow) & **cv.INTER_LINEAR** for zooming. By default, for shrinking and **cv.INTER_CUBIC** (slow) & **cv.INTER_LINEAR** for zooming. By default,
interpolation method used is **cv.INTER_LINEAR** for all resizing purposes. You can resize an the interpolation method **cv.INTER_LINEAR** is used for all resizing purposes. You can resize an
input image either of following methods: input image with either of following methods:
@code{.py} @code{.py}
import numpy as np import numpy as np
import cv2 as cv import cv2 as cv
@ -38,13 +38,13 @@ res = cv.resize(img,(2*width, 2*height), interpolation = cv.INTER_CUBIC)
@endcode @endcode
### Translation ### Translation
Translation is the shifting of object's location. If you know the shift in (x,y) direction, let it Translation is the shifting of an object's location. If you know the shift in the (x,y) direction and let it
be \f$(t_x,t_y)\f$, you can create the transformation matrix \f$\textbf{M}\f$ as follows: be \f$(t_x,t_y)\f$, you can create the transformation matrix \f$\textbf{M}\f$ as follows:
\f[M = \begin{bmatrix} 1 & 0 & t_x \\ 0 & 1 & t_y \end{bmatrix}\f] \f[M = \begin{bmatrix} 1 & 0 & t_x \\ 0 & 1 & t_y \end{bmatrix}\f]
You can take make it into a Numpy array of type np.float32 and pass it into **cv.warpAffine()** You can take make it into a Numpy array of type np.float32 and pass it into the **cv.warpAffine()**
function. See below example for a shift of (100,50): function. See the below example for a shift of (100,50):
@code{.py} @code{.py}
import numpy as np import numpy as np
import cv2 as cv import cv2 as cv
@ -61,7 +61,7 @@ cv.destroyAllWindows()
@endcode @endcode
**warning** **warning**
Third argument of the **cv.warpAffine()** function is the size of the output image, which should The third argument of the **cv.warpAffine()** function is the size of the output image, which should
be in the form of **(width, height)**. Remember width = number of columns, and height = number of be in the form of **(width, height)**. Remember width = number of columns, and height = number of
rows. rows.
@ -76,7 +76,7 @@ Rotation of an image for an angle \f$\theta\f$ is achieved by the transformation
\f[M = \begin{bmatrix} cos\theta & -sin\theta \\ sin\theta & cos\theta \end{bmatrix}\f] \f[M = \begin{bmatrix} cos\theta & -sin\theta \\ sin\theta & cos\theta \end{bmatrix}\f]
But OpenCV provides scaled rotation with adjustable center of rotation so that you can rotate at any But OpenCV provides scaled rotation with adjustable center of rotation so that you can rotate at any
location you prefer. Modified transformation matrix is given by location you prefer. The modified transformation matrix is given by
\f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot center.x - \beta \cdot center.y \\ - \beta & \alpha & \beta \cdot center.x + (1- \alpha ) \cdot center.y \end{bmatrix}\f] \f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot center.x - \beta \cdot center.y \\ - \beta & \alpha & \beta \cdot center.x + (1- \alpha ) \cdot center.y \end{bmatrix}\f]
@ -84,7 +84,7 @@ where:
\f[\begin{array}{l} \alpha = scale \cdot \cos \theta , \\ \beta = scale \cdot \sin \theta \end{array}\f] \f[\begin{array}{l} \alpha = scale \cdot \cos \theta , \\ \beta = scale \cdot \sin \theta \end{array}\f]
To find this transformation matrix, OpenCV provides a function, **cv.getRotationMatrix2D**. Check To find this transformation matrix, OpenCV provides a function, **cv.getRotationMatrix2D**. Check out the
below example which rotates the image by 90 degree with respect to center without any scaling. below example which rotates the image by 90 degree with respect to center without any scaling.
@code{.py} @code{.py}
img = cv.imread('messi5.jpg',0) img = cv.imread('messi5.jpg',0)
@ -101,11 +101,11 @@ See the result:
### Affine Transformation ### Affine Transformation
In affine transformation, all parallel lines in the original image will still be parallel in the In affine transformation, all parallel lines in the original image will still be parallel in the
output image. To find the transformation matrix, we need three points from input image and their output image. To find the transformation matrix, we need three points from the input image and their
corresponding locations in output image. Then **cv.getAffineTransform** will create a 2x3 matrix corresponding locations in the output image. Then **cv.getAffineTransform** will create a 2x3 matrix
which is to be passed to **cv.warpAffine**. which is to be passed to **cv.warpAffine**.
Check below example, and also look at the points I selected (which are marked in Green color): Check the below example, and also look at the points I selected (which are marked in green color):
@code{.py} @code{.py}
img = cv.imread('drawing.png') img = cv.imread('drawing.png')
rows,cols,ch = img.shape rows,cols,ch = img.shape
@ -130,7 +130,7 @@ See the result:
For perspective transformation, you need a 3x3 transformation matrix. Straight lines will remain For perspective transformation, you need a 3x3 transformation matrix. Straight lines will remain
straight even after the transformation. To find this transformation matrix, you need 4 points on the straight even after the transformation. To find this transformation matrix, you need 4 points on the
input image and corresponding points on the output image. Among these 4 points, 3 of them should not input image and corresponding points on the output image. Among these 4 points, 3 of them should not
be collinear. Then transformation matrix can be found by the function be collinear. Then the transformation matrix can be found by the function
**cv.getPerspectiveTransform**. Then apply **cv.warpPerspective** with this 3x3 transformation **cv.getPerspectiveTransform**. Then apply **cv.warpPerspective** with this 3x3 transformation
matrix. matrix.

12
doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown
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@ -4,13 +4,13 @@ Image Thresholding {#tutorial_py_thresholding}
Goal Goal
---- ----
- In this tutorial, you will learn Simple thresholding, Adaptive thresholding and Otsu's thresholding. - In this tutorial, you will learn simple thresholding, adaptive thresholding and Otsu's thresholding.
- You will learn the functions **cv.threshold** and **cv.adaptiveThreshold**. - You will learn the functions **cv.threshold** and **cv.adaptiveThreshold**.
Simple Thresholding Simple Thresholding
------------------- -------------------
Here, the matter is straight forward. For every pixel, the same threshold value is applied. Here, the matter is straight-forward. For every pixel, the same threshold value is applied.
If the pixel value is smaller than the threshold, it is set to 0, otherwise it is set to a maximum value. If the pixel value is smaller than the threshold, it is set to 0, otherwise it is set to a maximum value.
The function **cv.threshold** is used to apply the thresholding. The function **cv.threshold** is used to apply the thresholding.
The first argument is the source image, which **should be a grayscale image**. The first argument is the source image, which **should be a grayscale image**.
@ -65,11 +65,11 @@ Adaptive Thresholding
In the previous section, we used one global value as a threshold. In the previous section, we used one global value as a threshold.
But this might not be good in all cases, e.g. if an image has different lighting conditions in different areas. But this might not be good in all cases, e.g. if an image has different lighting conditions in different areas.
In that case, adaptive thresholding thresholding can help. In that case, adaptive thresholding can help.
Here, the algorithm determines the threshold for a pixel based on a small region around it. Here, the algorithm determines the threshold for a pixel based on a small region around it.
So we get different thresholds for different regions of the same image which gives better results for images with varying illumination. So we get different thresholds for different regions of the same image which gives better results for images with varying illumination.
Additionally to the parameters described above, the method cv.adaptiveThreshold three input parameters: In addition to the parameters described above, the method cv.adaptiveThreshold takes three input parameters:
The **adaptiveMethod** decides how the threshold value is calculated: The **adaptiveMethod** decides how the threshold value is calculated:
- cv.ADAPTIVE_THRESH_MEAN_C: The threshold value is the mean of the neighbourhood area minus the constant **C**. - cv.ADAPTIVE_THRESH_MEAN_C: The threshold value is the mean of the neighbourhood area minus the constant **C**.
@ -168,8 +168,8 @@ Result:
### How does Otsu's Binarization work? ### How does Otsu's Binarization work?
This section demonstrates a Python implementation of Otsu's binarization to show how it works This section demonstrates a Python implementation of Otsu's binarization to show how it actually
actually. If you are not interested, you can skip this. works. If you are not interested, you can skip this.
Since we are working with bimodal images, Otsu's algorithm tries to find a threshold value (t) which Since we are working with bimodal images, Otsu's algorithm tries to find a threshold value (t) which
minimizes the **weighted within-class variance** given by the relation: minimizes the **weighted within-class variance** given by the relation:

4
doc/tutorials/introduction/cross_referencing/tutorial_cross_referencing.markdown
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@ -4,7 +4,7 @@ Cross referencing OpenCV from other Doxygen projects {#tutorial_cross_referencin
Cross referencing OpenCV Cross referencing OpenCV
------------------------ ------------------------
[Doxygen](https://www.stack.nl/~dimitri/doxygen/) is a tool to generate [Doxygen](http://www.doxygen.nl) is a tool to generate
documentations like the OpenCV documentation you are reading right now. documentations like the OpenCV documentation you are reading right now.
It is used by a variety of software projects and if you happen to use it It is used by a variety of software projects and if you happen to use it
to generate your own documentation, and you are using OpenCV inside your to generate your own documentation, and you are using OpenCV inside your
@ -57,5 +57,5 @@ contain a `your_project.tag` file in its root directory.
References References
---------- ----------
- [Doxygen: Linking to external documentation](https://www.stack.nl/~dimitri/doxygen/manual/external.html) - [Doxygen: Linking to external documentation](http://www.doxygen.nl/manual/external.html)
- [opencv.tag](opencv.tag) - [opencv.tag](opencv.tag)

14
doc/tutorials/introduction/documenting_opencv/documentation_tutorial.markdown
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@ -684,12 +684,12 @@ References {#tutorial_documentation_refs}
- [Command reference] - supported commands and their parameters - [Command reference] - supported commands and their parameters
<!-- invisible references list --> <!-- invisible references list -->
[Doxygen]: http://www.stack.nl/~dimitri/doxygen/index.html) [Doxygen]: http://www.doxygen.nl
[Doxygen download]: http://www.stack.nl/~dimitri/doxygen/download.html [Doxygen download]: http://doxygen.nl/download.html
[Doxygen installation]: http://www.stack.nl/~dimitri/doxygen/manual/install.html [Doxygen installation]: http://doxygen.nl/manual/install.html
[Documenting basics]: http://www.stack.nl/~dimitri/doxygen/manual/docblocks.html [Documenting basics]: http://www.doxygen.nl/manual/docblocks.html
[Markdown support]: http://www.stack.nl/~dimitri/doxygen/manual/markdown.html [Markdown support]: http://www.doxygen.nl/manual/markdown.html
[Formulas support]: http://www.stack.nl/~dimitri/doxygen/manual/formulas.html [Formulas support]: http://www.doxygen.nl/manual/formulas.html
[Supported formula commands]: http://docs.mathjax.org/en/latest/tex.html#supported-latex-commands [Supported formula commands]: http://docs.mathjax.org/en/latest/tex.html#supported-latex-commands
[Command reference]: http://www.stack.nl/~dimitri/doxygen/manual/commands.html [Command reference]: http://www.doxygen.nl/manual/commands.html
[Google Scholar]: http://scholar.google.ru/ [Google Scholar]: http://scholar.google.ru/

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doc/tutorials/introduction/ios_install/ios_install.markdown
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@ -9,7 +9,7 @@ Required Packages
### Getting the Cutting-edge OpenCV from Git Repository ### Getting the Cutting-edge OpenCV from Git Repository
Launch GIT client and clone OpenCV repository from [here](http://github.com/opencv/opencv) Launch Git client and clone OpenCV repository from [GitHub](http://github.com/opencv/opencv).
In MacOS it can be done using the following command in Terminal: In MacOS it can be done using the following command in Terminal:
@ -18,24 +18,48 @@ cd ~/<my_working _directory>
git clone https://github.com/opencv/opencv.git git clone https://github.com/opencv/opencv.git
@endcode @endcode
If you want to install OpenCV’s extra modules, clone the opencv_contrib repository as well:
@code{.bash}
cd ~/<my_working _directory>
git clone https://github.com/opencv/opencv_contrib.git
@endcode
Building OpenCV from Source, using CMake and Command Line Building OpenCV from Source, using CMake and Command Line
--------------------------------------------------------- ---------------------------------------------------------
-# Make symbolic link for Xcode to let OpenCV build scripts find the compiler, header files etc. 1. Make sure the xcode command line tools are installed:
@code{.bash} @code{.bash}
cd / xcode-select --install
sudo ln -s /Applications/Xcode.app/Contents/Developer Developer
@endcode @endcode
-# Build OpenCV framework: 2. Build OpenCV framework:
@code{.bash} @code{.bash}
cd ~/<my_working_directory> cd ~/<my_working_directory>
python opencv/platforms/ios/build_framework.py ios python opencv/platforms/ios/build_framework.py ios
@endcode @endcode
If everything's fine, a few minutes later you will get 3. To install OpenCV’s extra modules, append `--contrib opencv_contrib` to the python command above. **Note:** the extra modules are not included in the iOS Pack download at [OpenCV Releases](https://opencv.org/releases/). If you want to use the extra modules (e.g. aruco), you must build OpenCV yourself and include this option:
\~/\<my_working_directory\>/ios/opencv2.framework. You can add this framework to your Xcode @code{.bash}
projects. cd ~/<my_working_directory>
python opencv/platforms/ios/build_framework.py ios --contrib opencv_contrib
@endcode
4. To exclude a specific module, append `--without <module_name>`. For example, to exclude the "optflow" module from opencv_contrib:
@code{.bash}
cd ~/<my_working_directory>
python opencv/platforms/ios/build_framework.py ios --contrib opencv_contrib --without optflow
@endcode
5. The build process can take a significant amount of time. Currently (OpenCV 3.4 and 4.1), five separate architectures are built: armv7, armv7s, and arm64 for iOS plus i386 and x86_64 for the iPhone simulator. If you want to specify the architectures to include in the framework, use the `--iphoneos_archs` and/or `--iphonesimulator_archs` options. For example, to only build arm64 for iOS and x86_64 for the simulator:
@code{.bash}
cd ~/<my_working_directory>
python opencv/platforms/ios/build_framework.py ios --contrib opencv_contrib --iphoneos_archs arm64 --iphonesimulator_archs x86_64
@endcode
If everything’s fine, the build process will create
`~/<my_working_directory>/ios/opencv2.framework`. You can add this framework to your Xcode projects.
Further Reading Further Reading
--------------- ---------------

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@ -114,7 +114,7 @@ Additionally you can find very basic sample source code to introduce you to the
_Compatibility:_ \> OpenCV 2.4.2 _Compatibility:_ \> OpenCV 2.4.2
_Author:_ Artem Myagkov, Eduard Feicho _Author:_ Artem Myagkov, Eduard Feicho, Steve Nicholson
We will learn how to setup OpenCV for using it in iOS! We will learn how to setup OpenCV for using it in iOS!

2
doc/tutorials/introduction/windows_install/windows_install.markdown
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@ -151,7 +151,7 @@ of them, you need to download and install them on your system.
image file format. image file format.
- The OpenNI Framework contains a set of open source APIs that provide support for natural interaction with devices via methods such as voice command recognition, hand gestures, and body - The OpenNI Framework contains a set of open source APIs that provide support for natural interaction with devices via methods such as voice command recognition, hand gestures, and body
motion tracking. Prebuilt binaries can be found [here](http://structure.io/openni). The source code of [OpenNI](https://github.com/OpenNI/OpenNI) and [OpenNI2](https://github.com/OpenNI/OpenNI2) are also available on Github. motion tracking. Prebuilt binaries can be found [here](http://structure.io/openni). The source code of [OpenNI](https://github.com/OpenNI/OpenNI) and [OpenNI2](https://github.com/OpenNI/OpenNI2) are also available on Github.
- [Doxygen](http://www.stack.nl/~dimitri/doxygen/) is a documentation generator and is the tool that will actually create the - [Doxygen](http://www.doxygen.nl) is a documentation generator and is the tool that will actually create the
*OpenCV documentation*. *OpenCV documentation*.
Now we will describe the steps to follow for a full build (using all the above frameworks, tools and Now we will describe the steps to follow for a full build (using all the above frameworks, tools and

38
modules/core/include/opencv2/core/bindings_utils.hpp
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@ -20,6 +20,44 @@ CV_EXPORTS_W String dumpInputOutputArray(InputOutputArray argument);
CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argument); CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argument);
CV_WRAP static inline
String dumpBool(bool argument)
{
return (argument) ? String("Bool: True") : String("Bool: False");
}
CV_WRAP static inline
String dumpInt(int argument)
{
return cv::format("Int: %d", argument);
}
CV_WRAP static inline
String dumpSizeT(size_t argument)
{
std::ostringstream oss("size_t: ", std::ios::ate);
oss << argument;
return oss.str();
}
CV_WRAP static inline
String dumpFloat(float argument)
{
return cv::format("Float: %.2f", argument);
}
CV_WRAP static inline
String dumpDouble(double argument)
{
return cv::format("Double: %.2f", argument);
}
CV_WRAP static inline
String dumpCString(const char* argument)
{
return cv::format("String: %s", argument);
}
CV_WRAP static inline CV_WRAP static inline
AsyncArray testAsyncArray(InputArray argument) AsyncArray testAsyncArray(InputArray argument)
{ {

11
modules/core/include/opencv2/core/hal/intrin.hpp
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@ -457,10 +457,6 @@ namespace CV__SIMD_NAMESPACE {
using namespace CV__SIMD_NAMESPACE; using namespace CV__SIMD_NAMESPACE;
#endif #endif
#ifndef CV_DOXYGEN
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
#endif
#ifndef CV_SIMD_64F #ifndef CV_SIMD_64F
#define CV_SIMD_64F 0 #define CV_SIMD_64F 0
#endif #endif
@ -469,11 +465,16 @@ CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
#define CV_SIMD_FP16 0 //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types #define CV_SIMD_FP16 0 //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types
#endif #endif
#ifndef CV_SIMD #ifndef CV_SIMD
#define CV_SIMD 0 #define CV_SIMD 0
#endif #endif
#include "simd_utils.impl.hpp"
#ifndef CV_DOXYGEN
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
#endif
} // cv:: } // cv::
//! @endcond //! @endcond

146
modules/core/include/opencv2/core/hal/simd_utils.impl.hpp
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@ -0,0 +1,146 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
// This header is not standalone. Don't include directly, use "intrin.hpp" instead.
#ifdef OPENCV_HAL_INTRIN_HPP // defined in intrin.hpp
#if CV_SIMD128 || CV_SIMD128_CPP
template<typename _T> struct Type2Vec128_Traits;
#define CV_INTRIN_DEF_TYPE2VEC128_TRAITS(type_, vec_type_) \
template<> struct Type2Vec128_Traits<type_> \
{ \
typedef vec_type_ vec_type; \
}
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(uchar, v_uint8x16);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(schar, v_int8x16);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(ushort, v_uint16x8);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(short, v_int16x8);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(unsigned, v_uint32x4);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(int, v_int32x4);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(float, v_float32x4);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(uint64, v_uint64x2);
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(int64, v_int64x2);
#if CV_SIMD128_64F
CV_INTRIN_DEF_TYPE2VEC128_TRAITS(double, v_float64x2);
#endif
template<typename _T> static inline
typename Type2Vec128_Traits<_T>::vec_type v_setall(const _T& a);
template<> inline Type2Vec128_Traits< uchar>::vec_type v_setall< uchar>(const uchar& a) { return v_setall_u8(a); }
template<> inline Type2Vec128_Traits< schar>::vec_type v_setall< schar>(const schar& a) { return v_setall_s8(a); }
template<> inline Type2Vec128_Traits<ushort>::vec_type v_setall<ushort>(const ushort& a) { return v_setall_u16(a); }
template<> inline Type2Vec128_Traits< short>::vec_type v_setall< short>(const short& a) { return v_setall_s16(a); }
template<> inline Type2Vec128_Traits< uint>::vec_type v_setall< uint>(const uint& a) { return v_setall_u32(a); }
template<> inline Type2Vec128_Traits< int>::vec_type v_setall< int>(const int& a) { return v_setall_s32(a); }
template<> inline Type2Vec128_Traits<uint64>::vec_type v_setall<uint64>(const uint64& a) { return v_setall_u64(a); }
template<> inline Type2Vec128_Traits< int64>::vec_type v_setall< int64>(const int64& a) { return v_setall_s64(a); }
template<> inline Type2Vec128_Traits< float>::vec_type v_setall< float>(const float& a) { return v_setall_f32(a); }
#if CV_SIMD128_64F
template<> inline Type2Vec128_Traits<double>::vec_type v_setall<double>(const double& a) { return v_setall_f64(a); }
#endif
#endif // SIMD128
#if CV_SIMD256
template<typename _T> struct Type2Vec256_Traits;
#define CV_INTRIN_DEF_TYPE2VEC256_TRAITS(type_, vec_type_) \
template<> struct Type2Vec256_Traits<type_> \
{ \
typedef vec_type_ vec_type; \
}
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(uchar, v_uint8x32);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(schar, v_int8x32);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(ushort, v_uint16x16);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(short, v_int16x16);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(unsigned, v_uint32x8);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(int, v_int32x8);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(float, v_float32x8);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(uint64, v_uint64x4);
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(int64, v_int64x4);
#if CV_SIMD256_64F
CV_INTRIN_DEF_TYPE2VEC256_TRAITS(double, v_float64x4);
#endif
template<typename _T> static inline
typename Type2Vec256_Traits<_T>::vec_type v256_setall(const _T& a);
template<> inline Type2Vec256_Traits< uchar>::vec_type v256_setall< uchar>(const uchar& a) { return v256_setall_u8(a); }
template<> inline Type2Vec256_Traits< schar>::vec_type v256_setall< schar>(const schar& a) { return v256_setall_s8(a); }
template<> inline Type2Vec256_Traits<ushort>::vec_type v256_setall<ushort>(const ushort& a) { return v256_setall_u16(a); }
template<> inline Type2Vec256_Traits< short>::vec_type v256_setall< short>(const short& a) { return v256_setall_s16(a); }
template<> inline Type2Vec256_Traits< uint>::vec_type v256_setall< uint>(const uint& a) { return v256_setall_u32(a); }
template<> inline Type2Vec256_Traits< int>::vec_type v256_setall< int>(const int& a) { return v256_setall_s32(a); }
template<> inline Type2Vec256_Traits<uint64>::vec_type v256_setall<uint64>(const uint64& a) { return v256_setall_u64(a); }
template<> inline Type2Vec256_Traits< int64>::vec_type v256_setall< int64>(const int64& a) { return v256_setall_s64(a); }
template<> inline Type2Vec256_Traits< float>::vec_type v256_setall< float>(const float& a) { return v256_setall_f32(a); }
#if CV_SIMD256_64F
template<> inline Type2Vec256_Traits<double>::vec_type v256_setall<double>(const double& a) { return v256_setall_f64(a); }
#endif
#endif // SIMD256
#if CV_SIMD512
template<typename _T> struct Type2Vec512_Traits;
#define CV_INTRIN_DEF_TYPE2VEC512_TRAITS(type_, vec_type_) \
template<> struct Type2Vec512_Traits<type_> \
{ \
typedef vec_type_ vec_type; \
}
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(uchar, v_uint8x64);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(schar, v_int8x64);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(ushort, v_uint16x32);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(short, v_int16x32);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(unsigned, v_uint32x16);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(int, v_int32x16);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(float, v_float32x16);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(uint64, v_uint64x8);
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(int64, v_int64x8);
#if CV_SIMD512_64F
CV_INTRIN_DEF_TYPE2VEC512_TRAITS(double, v_float64x8);
#endif
template<typename _T> static inline
typename Type2Vec512_Traits<_T>::vec_type v512_setall(const _T& a);
template<> inline Type2Vec512_Traits< uchar>::vec_type v512_setall< uchar>(const uchar& a) { return v512_setall_u8(a); }
template<> inline Type2Vec512_Traits< schar>::vec_type v512_setall< schar>(const schar& a) { return v512_setall_s8(a); }
template<> inline Type2Vec512_Traits<ushort>::vec_type v512_setall<ushort>(const ushort& a) { return v512_setall_u16(a); }
template<> inline Type2Vec512_Traits< short>::vec_type v512_setall< short>(const short& a) { return v512_setall_s16(a); }
template<> inline Type2Vec512_Traits< uint>::vec_type v512_setall< uint>(const uint& a) { return v512_setall_u32(a); }
template<> inline Type2Vec512_Traits< int>::vec_type v512_setall< int>(const int& a) { return v512_setall_s32(a); }
template<> inline Type2Vec512_Traits<uint64>::vec_type v512_setall<uint64>(const uint64& a) { return v512_setall_u64(a); }
template<> inline Type2Vec512_Traits< int64>::vec_type v512_setall< int64>(const int64& a) { return v512_setall_s64(a); }
template<> inline Type2Vec512_Traits< float>::vec_type v512_setall< float>(const float& a) { return v512_setall_f32(a); }
#if CV_SIMD512_64F
template<> inline Type2Vec512_Traits<double>::vec_type v512_setall<double>(const double& a) { return v512_setall_f64(a); }
#endif
#endif // SIMD512
#if CV_SIMD_WIDTH == 16
template<typename _T> static inline
typename Type2Vec128_Traits<_T>::vec_type vx_setall(const _T& a) { return v_setall(a); }
#elif CV_SIMD_WIDTH == 32
template<typename _T> static inline
typename Type2Vec256_Traits<_T>::vec_type vx_setall(const _T& a) { return v256_setall(a); }
#elif CV_SIMD_WIDTH == 64
template<typename _T> static inline
typename Type2Vec512_Traits<_T>::vec_type vx_setall(const _T& a) { return v512_setall(a); }
#else
#error "Build configuration error, unsupported CV_SIMD_WIDTH"
#endif
#endif // OPENCV_HAL_INTRIN_HPP

34
modules/core/test/test_intrin_utils.hpp
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@ -336,6 +336,40 @@ template<typename R> struct TheTest
v_float64 vf64 = v_reinterpret_as_f64(r1); out.a.clear(); v_store((double*)out.a.d, vf64); EXPECT_EQ(data.a, out.a); v_float64 vf64 = v_reinterpret_as_f64(r1); out.a.clear(); v_store((double*)out.a.d, vf64); EXPECT_EQ(data.a, out.a);
#endif #endif
#if CV_SIMD_WIDTH == 16
R setall_res1 = v_setall((LaneType)5);
R setall_res2 = v_setall<LaneType>(6);
#elif CV_SIMD_WIDTH == 32
R setall_res1 = v256_setall((LaneType)5);
R setall_res2 = v256_setall<LaneType>(6);
#elif CV_SIMD_WIDTH == 64
R setall_res1 = v512_setall((LaneType)5);
R setall_res2 = v512_setall<LaneType>(6);
#else
#error "Configuration error"
#endif
#if CV_SIMD_WIDTH > 0
Data<R> setall_res1_; v_store(setall_res1_.d, setall_res1);
Data<R> setall_res2_; v_store(setall_res2_.d, setall_res2);
for (int i = 0; i < R::nlanes; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((LaneType)5, setall_res1_[i]);
EXPECT_EQ((LaneType)6, setall_res2_[i]);
}
#endif
R vx_setall_res1 = vx_setall((LaneType)11);
R vx_setall_res2 = vx_setall<LaneType>(12);
Data<R> vx_setall_res1_; v_store(vx_setall_res1_.d, vx_setall_res1);
Data<R> vx_setall_res2_; v_store(vx_setall_res2_.d, vx_setall_res2);
for (int i = 0; i < R::nlanes; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((LaneType)11, vx_setall_res1_[i]);
EXPECT_EQ((LaneType)12, vx_setall_res2_[i]);
}
return *this; return *this;
} }

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modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1585,7 +1585,7 @@ void TFImporter::populateNet(Net dstNet)
} }
} }
} }
else if (type == "FusedBatchNorm") else if (type == "FusedBatchNorm" || type == "FusedBatchNormV3")
{ {
// op: "FusedBatchNorm" // op: "FusedBatchNorm"
// input: "input" // input: "input"

2
modules/ml/src/svm.cpp
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@ -1451,7 +1451,7 @@ public:
sortSamplesByClasses( _samples, _responses, sidx_all, class_ranges ); sortSamplesByClasses( _samples, _responses, sidx_all, class_ranges );
//check that while cross-validation there were the samples from all the classes //check that while cross-validation there were the samples from all the classes
if( class_ranges[class_count] <= 0 ) if ((int)class_ranges.size() < class_count + 1)
CV_Error( CV_StsBadArg, "While cross-validation one or more of the classes have " CV_Error( CV_StsBadArg, "While cross-validation one or more of the classes have "
"been fell out of the sample. Try to reduce <Params::k_fold>" ); "been fell out of the sample. Try to reduce <Params::k_fold>" );

200
modules/ml/test/test_ann.cpp
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@ -0,0 +1,200 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
// #define GENERATE_TESTDATA
namespace opencv_test { namespace {
struct Activation
{
int id;
const char * name;
};
void PrintTo(const Activation &a, std::ostream *os) { *os << a.name; }
Activation activation_list[] =
{
{ ml::ANN_MLP::IDENTITY, "identity" },
{ ml::ANN_MLP::SIGMOID_SYM, "sigmoid_sym" },
{ ml::ANN_MLP::GAUSSIAN, "gaussian" },
{ ml::ANN_MLP::RELU, "relu" },
{ ml::ANN_MLP::LEAKYRELU, "leakyrelu" },
};
typedef testing::TestWithParam< Activation > ML_ANN_Params;
TEST_P(ML_ANN_Params, ActivationFunction)
{
const Activation &activation = GetParam();
const string dataname = "waveform";
const string data_path = findDataFile(dataname + ".data");
const string model_name = dataname + "_" + activation.name + ".yml";
Ptr<TrainData> tdata = TrainData::loadFromCSV(data_path, 0);
ASSERT_FALSE(tdata.empty());
// hack?
const uint64 old_state = theRNG().state;
theRNG().state = 1027401484159173092;
tdata->setTrainTestSplit(500);
theRNG().state = old_state;
Mat_<int> layerSizes(1, 4);
layerSizes(0, 0) = tdata->getNVars();
layerSizes(0, 1) = 100;
layerSizes(0, 2) = 100;
layerSizes(0, 3) = tdata->getResponses().cols;
Mat testSamples = tdata->getTestSamples();
Mat rx, ry;
{
Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
x->setActivationFunction(activation.id);
x->setLayerSizes(layerSizes);
x->setTrainMethod(ml::ANN_MLP::RPROP, 0.01, 0.1);
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 300, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE);
ASSERT_TRUE(x->isTrained());
x->predict(testSamples, rx);
#ifdef GENERATE_TESTDATA
x->save(cvtest::TS::ptr()->get_data_path() + model_name);
#endif
}
{
const string model_path = findDataFile(model_name);
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(model_path);
ASSERT_TRUE(y);
y->predict(testSamples, ry);
EXPECT_MAT_NEAR(rx, ry, FLT_EPSILON);
}
}
INSTANTIATE_TEST_CASE_P(/**/, ML_ANN_Params, testing::ValuesIn(activation_list));
//==================================================================================================
CV_ENUM(ANN_MLP_METHOD, ANN_MLP::RPROP, ANN_MLP::ANNEAL)
typedef tuple<ANN_MLP_METHOD, string, int> ML_ANN_METHOD_Params;
typedef TestWithParam<ML_ANN_METHOD_Params> ML_ANN_METHOD;
TEST_P(ML_ANN_METHOD, Test)
{
int methodType = get<0>(GetParam());
string methodName = get<1>(GetParam());
int N = get<2>(GetParam());
String folder = string(cvtest::TS::ptr()->get_data_path());
String original_path = findDataFile("waveform.data");
string dataname = "waveform_" + methodName;
string weight_name = dataname + "_init_weight.yml.gz";
string model_name = dataname + ".yml.gz";
string response_name = dataname + "_response.yml.gz";
Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
ASSERT_FALSE(tdata2.empty());
Mat samples = tdata2->getSamples()(Range(0, N), Range::all());
Mat responses(N, 3, CV_32FC1, Scalar(0));
for (int i = 0; i < N; i++)
responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
Ptr<TrainData> tdata = TrainData::create(samples, ml::ROW_SAMPLE, responses);
ASSERT_FALSE(tdata.empty());
// hack?
const uint64 old_state = theRNG().state;
theRNG().state = 0;
tdata->setTrainTestSplitRatio(0.8);
theRNG().state = old_state;
Mat testSamples = tdata->getTestSamples();
// train 1st stage
Ptr<ml::ANN_MLP> xx = ml::ANN_MLP::create();
Mat_<int> layerSizes(1, 4);
layerSizes(0, 0) = tdata->getNVars();
layerSizes(0, 1) = 30;
layerSizes(0, 2) = 30;
layerSizes(0, 3) = tdata->getResponses().cols;
xx->setLayerSizes(layerSizes);
xx->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
xx->setTrainMethod(ml::ANN_MLP::RPROP);
xx->setTermCriteria(TermCriteria(TermCriteria::COUNT, 1, 0.01));
xx->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE);
#ifdef GENERATE_TESTDATA
{
FileStorage fs;
fs.open(cvtest::TS::ptr()->get_data_path() + weight_name, FileStorage::WRITE + FileStorage::BASE64);
xx->write(fs);
}
#endif
// train 2nd stage
Mat r_gold;
Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
{
const string weight_file = findDataFile(weight_name);
FileStorage fs;
fs.open(weight_file, FileStorage::READ);
x->read(fs.root());
}
x->setTrainMethod(methodType);
if (methodType == ml::ANN_MLP::ANNEAL)
{
x->setAnnealEnergyRNG(RNG(CV_BIG_INT(0xffffffff)));
x->setAnnealInitialT(12);
x->setAnnealFinalT(0.15);
x->setAnnealCoolingRatio(0.96);
x->setAnnealItePerStep(11);
}
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 100, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE + ml::ANN_MLP::UPDATE_WEIGHTS);
ASSERT_TRUE(x->isTrained());
#ifdef GENERATE_TESTDATA
x->save(cvtest::TS::ptr()->get_data_path() + model_name);
x->predict(testSamples, r_gold);
{
FileStorage fs_response(cvtest::TS::ptr()->get_data_path() + response_name, FileStorage::WRITE + FileStorage::BASE64);
fs_response << "response" << r_gold;
}
#endif
{
const string response_file = findDataFile(response_name);
FileStorage fs_response(response_file, FileStorage::READ);
fs_response["response"] >> r_gold;
}
ASSERT_FALSE(r_gold.empty());
// verify
const string model_file = findDataFile(model_name);
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(model_file);
ASSERT_TRUE(y);
Mat rx, ry;
for (int j = 0; j < 4; j++)
{
rx = x->getWeights(j);
ry = y->getWeights(j);
EXPECT_MAT_NEAR(rx, ry, FLT_EPSILON) << "Weights are not equal for layer: " << j;
}
x->predict(testSamples, rx);
y->predict(testSamples, ry);
EXPECT_MAT_NEAR(ry, rx, FLT_EPSILON) << "Predict are not equal to result of the saved model";
EXPECT_MAT_NEAR(r_gold, rx, FLT_EPSILON) << "Predict are not equal to 'gold' response";
}
INSTANTIATE_TEST_CASE_P(/*none*/, ML_ANN_METHOD,
testing::Values(
ML_ANN_METHOD_Params(ml::ANN_MLP::RPROP, "rprop", 5000),
ML_ANN_METHOD_Params(ml::ANN_MLP::ANNEAL, "anneal", 1000)
// ML_ANN_METHOD_Params(ml::ANN_MLP::BACKPROP, "backprop", 500) -----> NO BACKPROP TEST
)
);
}} // namespace

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modules/ml/test/test_bayes.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
TEST(ML_NBAYES, regression_5911)
{
int N=12;
Ptr<ml::NormalBayesClassifier> nb = cv::ml::NormalBayesClassifier::create();
// data:
float X_data[] = {
1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4,
5,5,5,5, 5,5,5,5, 5,5,5,5, 5,5,5,5,
4,3,2,1, 4,3,2,1, 4,3,2,1, 4,3,2,1
};
Mat_<float> X(N, 4, X_data);
// labels:
int Y_data[] = { 0,0,0,0, 1,1,1,1, 2,2,2,2 };
Mat_<int> Y(N, 1, Y_data);
nb->train(X, ml::ROW_SAMPLE, Y);
// single prediction:
Mat R1,P1;
for (int i=0; i<N; i++)
{
Mat r,p;
nb->predictProb(X.row(i), r, p);
R1.push_back(r);
P1.push_back(p);
}
// bulk prediction (continuous memory):
Mat R2,P2;
nb->predictProb(X, R2, P2);
EXPECT_EQ(255 * R2.total(), sum(R1 == R2)[0]);
EXPECT_EQ(255 * P2.total(), sum(P1 == P2)[0]);
// bulk prediction, with non-continuous memory storage
Mat R3_(N, 1+1, CV_32S),
P3_(N, 3+1, CV_32F);
nb->predictProb(X, R3_.col(0), P3_.colRange(0,3));
Mat R3 = R3_.col(0).clone(),
P3 = P3_.colRange(0,3).clone();
EXPECT_EQ(255 * R3.total(), sum(R1 == R3)[0]);
EXPECT_EQ(255 * P3.total(), sum(P1 == P3)[0]);
}
}} // namespace

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modules/ml/test/test_em.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
CV_ENUM(EM_START_STEP, EM::START_AUTO_STEP, EM::START_M_STEP, EM::START_E_STEP)
CV_ENUM(EM_COV_MAT, EM::COV_MAT_GENERIC, EM::COV_MAT_DIAGONAL, EM::COV_MAT_SPHERICAL)
typedef testing::TestWithParam< tuple<EM_START_STEP, EM_COV_MAT> > ML_EM_Params;
TEST_P(ML_EM_Params, accuracy)
{
const int nclusters = 3;
const int sizesArr[] = { 500, 700, 800 };
const vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
const int pointsCount = sizesArr[0] + sizesArr[1] + sizesArr[2];
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs, CV_64FC1 );
Mat trainData(pointsCount, 2, CV_64FC1 );
Mat trainLabels;
generateData( trainData, trainLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );
Mat testData( pointsCount, 2, CV_64FC1 );
Mat testLabels;
generateData( testData, testLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );
Mat probs(trainData.rows, nclusters, CV_64FC1, cv::Scalar(1));
Mat weights(1, nclusters, CV_64FC1, cv::Scalar(1));
TermCriteria termCrit(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 100, FLT_EPSILON);
int startStep = get<0>(GetParam());
int covMatType = get<1>(GetParam());
cv::Mat labels;
Ptr<EM> em = EM::create();
em->setClustersNumber(nclusters);
em->setCovarianceMatrixType(covMatType);
em->setTermCriteria(termCrit);
if( startStep == EM::START_AUTO_STEP )
em->trainEM( trainData, noArray(), labels, noArray() );
else if( startStep == EM::START_E_STEP )
em->trainE( trainData, means, covs, weights, noArray(), labels, noArray() );
else if( startStep == EM::START_M_STEP )
em->trainM( trainData, probs, noArray(), labels, noArray() );
{
SCOPED_TRACE("Train");
float err = 1000;
EXPECT_TRUE(calcErr( labels, trainLabels, sizes, err , false, false ));
EXPECT_LE(err, 0.008f);
}
{
SCOPED_TRACE("Test");
float err = 1000;
labels.create( testData.rows, 1, CV_32SC1 );
for( int i = 0; i < testData.rows; i++ )
{
Mat sample = testData.row(i);
Mat out_probs;
labels.at<int>(i) = static_cast<int>(em->predict2( sample, out_probs )[1]);
}
EXPECT_TRUE(calcErr( labels, testLabels, sizes, err, false, false ));
EXPECT_LE(err, 0.008f);
}
}
INSTANTIATE_TEST_CASE_P(/**/, ML_EM_Params,
testing::Combine(
testing::Values(EM::START_AUTO_STEP, EM::START_M_STEP, EM::START_E_STEP),
testing::Values(EM::COV_MAT_GENERIC, EM::COV_MAT_DIAGONAL, EM::COV_MAT_SPHERICAL)
));
//==================================================================================================
TEST(ML_EM, save_load)
{
const int nclusters = 2;
Mat_<double> samples(3, 1);
samples << 1., 2., 3.;
std::vector<double> firstResult;
string filename = cv::tempfile(".xml");
{
Mat labels;
Ptr<EM> em = EM::create();
em->setClustersNumber(nclusters);
em->trainEM(samples, noArray(), labels, noArray());
for( int i = 0; i < samples.rows; i++)
{
Vec2d res = em->predict2(samples.row(i), noArray());
firstResult.push_back(res[1]);
}
{
FileStorage fs = FileStorage(filename, FileStorage::WRITE);
ASSERT_NO_THROW(fs << "em" << "{");
ASSERT_NO_THROW(em->write(fs));
ASSERT_NO_THROW(fs << "}");
}
}
{
Ptr<EM> em;
ASSERT_NO_THROW(em = Algorithm::load<EM>(filename));
for( int i = 0; i < samples.rows; i++)
{
SCOPED_TRACE(i);
Vec2d res = em->predict2(samples.row(i), noArray());
EXPECT_DOUBLE_EQ(firstResult[i], res[1]);
}
}
remove(filename.c_str());
}
//==================================================================================================
TEST(ML_EM, classification)
{
// This test classifies spam by the following way:
// 1. estimates distributions of "spam" / "not spam"
// 2. predict classID using Bayes classifier for estimated distributions.
string dataFilename = findDataFile("spambase.data");
Ptr<TrainData> data = TrainData::loadFromCSV(dataFilename, 0);
ASSERT_FALSE(data.empty());
Mat samples = data->getSamples();
ASSERT_EQ(samples.cols, 57);
Mat responses = data->getResponses();
vector<int> trainSamplesMask(samples.rows, 0);
const int trainSamplesCount = (int)(0.5f * samples.rows);
const int testSamplesCount = samples.rows - trainSamplesCount;
for(int i = 0; i < trainSamplesCount; i++)
trainSamplesMask[i] = 1;
RNG &rng = cv::theRNG();
for(size_t i = 0; i < trainSamplesMask.size(); i++)
{
int i1 = rng(static_cast<unsigned>(trainSamplesMask.size()));
int i2 = rng(static_cast<unsigned>(trainSamplesMask.size()));
std::swap(trainSamplesMask[i1], trainSamplesMask[i2]);
}
Mat samples0, samples1;
for(int i = 0; i < samples.rows; i++)
{
if(trainSamplesMask[i])
{
Mat sample = samples.row(i);
int resp = (int)responses.at<float>(i);
if(resp == 0)
samples0.push_back(sample);
else
samples1.push_back(sample);
}
}
Ptr<EM> model0 = EM::create();
model0->setClustersNumber(3);
model0->trainEM(samples0, noArray(), noArray(), noArray());
Ptr<EM> model1 = EM::create();
model1->setClustersNumber(3);
model1->trainEM(samples1, noArray(), noArray(), noArray());
// confusion matrices
Mat_<int> trainCM(2, 2, 0);
Mat_<int> testCM(2, 2, 0);
const double lambda = 1.;
for(int i = 0; i < samples.rows; i++)
{
Mat sample = samples.row(i);
double sampleLogLikelihoods0 = model0->predict2(sample, noArray())[0];
double sampleLogLikelihoods1 = model1->predict2(sample, noArray())[0];
int classID = (sampleLogLikelihoods0 >= lambda * sampleLogLikelihoods1) ? 0 : 1;
int resp = (int)responses.at<float>(i);
EXPECT_TRUE(resp == 0 || resp == 1);
if(trainSamplesMask[i])
trainCM(resp, classID)++;
else
testCM(resp, classID)++;
}
EXPECT_LE((double)(trainCM(1,0) + trainCM(0,1)) / trainSamplesCount, 0.23);
EXPECT_LE((double)(testCM(1,0) + testCM(0,1)) / testSamplesCount, 0.26);
}
}} // namespace

727
modules/ml/test/test_emknearestkmeans.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
using cv::ml::TrainData;
using cv::ml::EM;
using cv::ml::KNearest;
void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 )
{
CV_TRACE_FUNCTION();
float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
means.create(3, 2, type);
Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
means.resize(3), covs.resize(3);
Mat mr0 = means.row(0);
m0.convertTo(mr0, type);
c0.convertTo(covs[0], type);
Mat mr1 = means.row(1);
m1.convertTo(mr1, type);
c1.convertTo(covs[1], type);
Mat mr2 = means.row(2);
m2.convertTo(mr2, type);
c2.convertTo(covs[2], type);
}
// generate points sets by normal distributions
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType )
{
CV_TRACE_FUNCTION();
vector<int>::const_iterator sit = sizes.begin();
int total = 0;
for( ; sit != sizes.end(); ++sit )
total += *sit;
CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
CV_Assert( !data.empty() && data.rows == total );
CV_Assert( data.type() == dataType );
labels.create( data.rows, 1, labelType );
randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
vector<Mat> means(sizes.size());
for(int i = 0; i < _means.rows; i++)
means[i] = _means.row(i);
vector<Mat>::const_iterator mit = means.begin(), cit = covs.begin();
int bi, ei = 0;
sit = sizes.begin();
for( int p = 0, l = 0; sit != sizes.end(); ++sit, ++mit, ++cit, l++ )
{
bi = ei;
ei = bi + *sit;
assert( mit->rows == 1 && mit->cols == data.cols );
assert( cit->rows == data.cols && cit->cols == data.cols );
for( int i = bi; i < ei; i++, p++ )
{
Mat r = data.row(i);
r = r * (*cit) + *mit;
if( labelType == CV_32FC1 )
labels.at<float>(p, 0) = (float)l;
else if( labelType == CV_32SC1 )
labels.at<int>(p, 0) = l;
else
{
CV_DbgAssert(0);
}
}
}
}
int maxIdx( const vector<int>& count )
{
int idx = -1;
int maxVal = -1;
vector<int>::const_iterator it = count.begin();
for( int i = 0; it != count.end(); ++it, i++ )
{
if( *it > maxVal)
{
maxVal = *it;
idx = i;
}
}
assert( idx >= 0);
return idx;
}
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq=true )
{
size_t total = 0, nclusters = sizes.size();
for(size_t i = 0; i < sizes.size(); i++)
total += sizes[i];
assert( !labels.empty() );
assert( labels.total() == total && (labels.cols == 1 || labels.rows == 1));
assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
bool isFlt = labels.type() == CV_32FC1;
labelsMap.resize(nclusters);
vector<bool> buzy(nclusters, false);
int startIndex = 0;
for( size_t clusterIndex = 0; clusterIndex < sizes.size(); clusterIndex++ )
{
vector<int> count( nclusters, 0 );
for( int i = startIndex; i < startIndex + sizes[clusterIndex]; i++)
{
int lbl = isFlt ? (int)labels.at<float>(i) : labels.at<int>(i);
CV_Assert(lbl < (int)nclusters);
count[lbl]++;
CV_Assert(count[lbl] < (int)total);
}
startIndex += sizes[clusterIndex];
int cls = maxIdx( count );
CV_Assert( !checkClusterUniq || !buzy[cls] );
labelsMap[clusterIndex] = cls;
buzy[cls] = true;
}
if(checkClusterUniq)
{
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
}
return true;
}
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true, bool checkClusterUniq=true )
{
err = 0;
CV_Assert( !labels.empty() && !origLabels.empty() );
CV_Assert( labels.rows == 1 || labels.cols == 1 );
CV_Assert( origLabels.rows == 1 || origLabels.cols == 1 );
CV_Assert( labels.total() == origLabels.total() );
CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
CV_Assert( origLabels.type() == labels.type() );
vector<int> labelsMap;
bool isFlt = labels.type() == CV_32FC1;
if( !labelsEquivalent )
{
if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) )
return false;
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i) != labelsMap[(int)origLabels.at<float>(i)] ? 1.f : 0.f;
else
err += labels.at<int>(i) != labelsMap[origLabels.at<int>(i)] ? 1.f : 0.f;
}
else
{
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i) != origLabels.at<float>(i) ? 1.f : 0.f;
else
err += labels.at<int>(i) != origLabels.at<int>(i) ? 1.f : 0.f;
}
err /= (float)labels.rows;
return true;
}
//--------------------------------------------------------------------------------------------
class CV_KMeansTest : public cvtest::BaseTest {
public:
CV_KMeansTest() {}
protected:
virtual void run( int start_from );
};
void CV_KMeansTest::run( int /*start_from*/ )
{
CV_TRACE_FUNCTION();
const int iters = 100;
int sizesArr[] = { 5000, 7000, 8000 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
Mat data( pointsCount, 2, CV_32FC1 ), labels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
generateData( data, labels, sizes, means, covs, CV_32FC1, CV_32SC1 );
int code = cvtest::TS::OK;
float err;
Mat bestLabels;
// 1. flag==KMEANS_PP_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_PP_CENTERS, noArray() );
if( !calcErr( bestLabels, labels, sizes, err , false ) )
{
ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_PP_CENTERS.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_PP_CENTERS.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 2. flag==KMEANS_RANDOM_CENTERS
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_RANDOM_CENTERS, noArray() );
if( !calcErr( bestLabels, labels, sizes, err, false ) )
{
ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_RANDOM_CENTERS.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_RANDOM_CENTERS.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// 3. flag==KMEANS_USE_INITIAL_LABELS
labels.copyTo( bestLabels );
RNG rng;
for( int i = 0; i < 0.5f * pointsCount; i++ )
bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3;
kmeans( data, 3, bestLabels, TermCriteria( TermCriteria::COUNT, iters, 0.0), 0, KMEANS_USE_INITIAL_LABELS, noArray() );
if( !calcErr( bestLabels, labels, sizes, err, false ) )
{
ts->printf( cvtest::TS::LOG, "Bad output labels if flag==KMEANS_USE_INITIAL_LABELS.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) if flag==KMEANS_USE_INITIAL_LABELS.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info( code );
}
//--------------------------------------------------------------------------------------------
class CV_KNearestTest : public cvtest::BaseTest {
public:
CV_KNearestTest() {}
protected:
virtual void run( int start_from );
};
void CV_KNearestTest::run( int /*start_from*/ )
{
int sizesArr[] = { 500, 700, 800 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
// train data
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
// test data
Mat testData( pointsCount, 2, CV_32FC1 ), testLabels, bestLabels;
generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
int code = cvtest::TS::OK;
// KNearest default implementation
Ptr<KNearest> knearest = KNearest::create();
knearest->train(trainData, ml::ROW_SAMPLE, trainLabels);
knearest->findNearest(testData, 4, bestLabels);
float err;
if( !calcErr( bestLabels, testLabels, sizes, err, true ) )
{
ts->printf( cvtest::TS::LOG, "Bad output labels.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) on test data.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// KNearest KDTree implementation
Ptr<KNearest> knearestKdt = KNearest::create();
knearestKdt->setAlgorithmType(KNearest::KDTREE);
knearestKdt->train(trainData, ml::ROW_SAMPLE, trainLabels);
knearestKdt->findNearest(testData, 4, bestLabels);
if( !calcErr( bestLabels, testLabels, sizes, err, true ) )
{
ts->printf( cvtest::TS::LOG, "Bad output labels.\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.01f )
{
ts->printf( cvtest::TS::LOG, "Bad accuracy (%f) on test data.\n", err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info( code );
}
class EM_Params
{
public:
EM_Params(int _nclusters=10, int _covMatType=EM::COV_MAT_DIAGONAL, int _startStep=EM::START_AUTO_STEP,
const cv::TermCriteria& _termCrit=cv::TermCriteria(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, 100, FLT_EPSILON),
const cv::Mat* _probs=0, const cv::Mat* _weights=0,
const cv::Mat* _means=0, const std::vector<cv::Mat>* _covs=0)
: nclusters(_nclusters), covMatType(_covMatType), startStep(_startStep),
probs(_probs), weights(_weights), means(_means), covs(_covs), termCrit(_termCrit)
{}
int nclusters;
int covMatType;
int startStep;
// all 4 following matrices should have type CV_32FC1
const cv::Mat* probs;
const cv::Mat* weights;
const cv::Mat* means;
const std::vector<cv::Mat>* covs;
cv::TermCriteria termCrit;
};
//--------------------------------------------------------------------------------------------
class CV_EMTest : public cvtest::BaseTest
{
public:
CV_EMTest() {}
protected:
virtual void run( int start_from );
int runCase( int caseIndex, const EM_Params& params,
const cv::Mat& trainData, const cv::Mat& trainLabels,
const cv::Mat& testData, const cv::Mat& testLabels,
const vector<int>& sizes);
};
int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
const cv::Mat& trainData, const cv::Mat& trainLabels,
const cv::Mat& testData, const cv::Mat& testLabels,
const vector<int>& sizes )
{
int code = cvtest::TS::OK;
cv::Mat labels;
float err;
Ptr<EM> em = EM::create();
em->setClustersNumber(params.nclusters);
em->setCovarianceMatrixType(params.covMatType);
em->setTermCriteria(params.termCrit);
if( params.startStep == EM::START_AUTO_STEP )
em->trainEM( trainData, noArray(), labels, noArray() );
else if( params.startStep == EM::START_E_STEP )
em->trainE( trainData, *params.means, *params.covs,
*params.weights, noArray(), labels, noArray() );
else if( params.startStep == EM::START_M_STEP )
em->trainM( trainData, *params.probs,
noArray(), labels, noArray() );
// check train error
if( !calcErr( labels, trainLabels, sizes, err , false, false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.008f )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on train data.\n", caseIndex, err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
// check test error
labels.create( testData.rows, 1, CV_32SC1 );
for( int i = 0; i < testData.rows; i++ )
{
Mat sample = testData.row(i);
Mat probs;
labels.at<int>(i) = static_cast<int>(em->predict2( sample, probs )[1]);
}
if( !calcErr( labels, testLabels, sizes, err, false, false ) )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.008f )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on test data.\n", caseIndex, err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
return code;
}
void CV_EMTest::run( int /*start_from*/ )
{
int sizesArr[] = { 500, 700, 800 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
// Points distribution
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs, CV_64FC1 );
// train data
Mat trainData( pointsCount, 2, CV_64FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
generateData( trainData, trainLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );
// test data
Mat testData( pointsCount, 2, CV_64FC1 ), testLabels;
generateData( testData, testLabels, sizes, means, covs, CV_64FC1, CV_32SC1 );
EM_Params params;
params.nclusters = 3;
Mat probs(trainData.rows, params.nclusters, CV_64FC1, cv::Scalar(1));
params.probs = &probs;
Mat weights(1, params.nclusters, CV_64FC1, cv::Scalar(1));
params.weights = &weights;
params.means = &means;
params.covs = &covs;
int code = cvtest::TS::OK;
int caseIndex = 0;
{
params.startStep = EM::START_AUTO_STEP;
params.covMatType = EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = EM::START_AUTO_STEP;
params.covMatType = EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = EM::START_AUTO_STEP;
params.covMatType = EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = EM::START_M_STEP;
params.covMatType = EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = EM::START_M_STEP;
params.covMatType = EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = EM::START_M_STEP;
params.covMatType = EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = EM::START_E_STEP;
params.covMatType = EM::COV_MAT_GENERIC;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = EM::START_E_STEP;
params.covMatType = EM::COV_MAT_DIAGONAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
{
params.startStep = EM::START_E_STEP;
params.covMatType = EM::COV_MAT_SPHERICAL;
int currCode = runCase(caseIndex++, params, trainData, trainLabels, testData, testLabels, sizes);
code = currCode == cvtest::TS::OK ? code : currCode;
}
ts->set_failed_test_info( code );
}
class CV_EMTest_SaveLoad : public cvtest::BaseTest {
public:
CV_EMTest_SaveLoad() {}
protected:
virtual void run( int /*start_from*/ )
{
int code = cvtest::TS::OK;
const int nclusters = 2;
Mat samples = Mat(3,1,CV_64FC1);
samples.at<double>(0,0) = 1;
samples.at<double>(1,0) = 2;
samples.at<double>(2,0) = 3;
Mat labels;
Ptr<EM> em = EM::create();
em->setClustersNumber(nclusters);
em->trainEM(samples, noArray(), labels, noArray());
Mat firstResult(samples.rows, 1, CV_32SC1);
for( int i = 0; i < samples.rows; i++)
firstResult.at<int>(i) = static_cast<int>(em->predict2(samples.row(i), noArray())[1]);
// Write out
string filename = cv::tempfile(".xml");
{
FileStorage fs = FileStorage(filename, FileStorage::WRITE);
try
{
fs << "em" << "{";
em->write(fs);
fs << "}";
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "Crash in write method.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_EXCEPTION );
}
}
em.release();
// Read in
try
{
em = Algorithm::load<EM>(filename);
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "Crash in read method.\n" );
ts->set_failed_test_info( cvtest::TS::FAIL_EXCEPTION );
}
remove( filename.c_str() );
int errCaseCount = 0;
for( int i = 0; i < samples.rows; i++)
errCaseCount = std::abs(em->predict2(samples.row(i), noArray())[1] - firstResult.at<int>(i)) < FLT_EPSILON ? 0 : 1;
if( errCaseCount > 0 )
{
ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errCaseCount=%d).\n", errCaseCount );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info( code );
}
};
class CV_EMTest_Classification : public cvtest::BaseTest
{
public:
CV_EMTest_Classification() {}
protected:
virtual void run(int)
{
// This test classifies spam by the following way:
// 1. estimates distributions of "spam" / "not spam"
// 2. predict classID using Bayes classifier for estimated distributions.
string dataFilename = string(ts->get_data_path()) + "spambase.data";
Ptr<TrainData> data = TrainData::loadFromCSV(dataFilename, 0);
if( data.empty() )
{
ts->printf(cvtest::TS::LOG, "File with spambase dataset can't be read.\n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
Mat samples = data->getSamples();
CV_Assert(samples.cols == 57);
Mat responses = data->getResponses();
vector<int> trainSamplesMask(samples.rows, 0);
int trainSamplesCount = (int)(0.5f * samples.rows);
for(int i = 0; i < trainSamplesCount; i++)
trainSamplesMask[i] = 1;
RNG rng(0);
for(size_t i = 0; i < trainSamplesMask.size(); i++)
{
int i1 = rng(static_cast<unsigned>(trainSamplesMask.size()));
int i2 = rng(static_cast<unsigned>(trainSamplesMask.size()));
std::swap(trainSamplesMask[i1], trainSamplesMask[i2]);
}
Mat samples0, samples1;
for(int i = 0; i < samples.rows; i++)
{
if(trainSamplesMask[i])
{
Mat sample = samples.row(i);
int resp = (int)responses.at<float>(i);
if(resp == 0)
samples0.push_back(sample);
else
samples1.push_back(sample);
}
}
Ptr<EM> model0 = EM::create();
model0->setClustersNumber(3);
model0->trainEM(samples0, noArray(), noArray(), noArray());
Ptr<EM> model1 = EM::create();
model1->setClustersNumber(3);
model1->trainEM(samples1, noArray(), noArray(), noArray());
Mat trainConfusionMat(2, 2, CV_32SC1, Scalar(0)),
testConfusionMat(2, 2, CV_32SC1, Scalar(0));
const double lambda = 1.;
for(int i = 0; i < samples.rows; i++)
{
Mat sample = samples.row(i);
double sampleLogLikelihoods0 = model0->predict2(sample, noArray())[0];
double sampleLogLikelihoods1 = model1->predict2(sample, noArray())[0];
int classID = sampleLogLikelihoods0 >= lambda * sampleLogLikelihoods1 ? 0 : 1;
if(trainSamplesMask[i])
trainConfusionMat.at<int>((int)responses.at<float>(i), classID)++;
else
testConfusionMat.at<int>((int)responses.at<float>(i), classID)++;
}
// std::cout << trainConfusionMat << std::endl;
// std::cout << testConfusionMat << std::endl;
double trainError = (double)(trainConfusionMat.at<int>(1,0) + trainConfusionMat.at<int>(0,1)) / trainSamplesCount;
double testError = (double)(testConfusionMat.at<int>(1,0) + testConfusionMat.at<int>(0,1)) / (samples.rows - trainSamplesCount);
const double maxTrainError = 0.23;
const double maxTestError = 0.26;
int code = cvtest::TS::OK;
if(trainError > maxTrainError)
{
ts->printf(cvtest::TS::LOG, "Too large train classification error (calc = %f, valid=%f).\n", trainError, maxTrainError);
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
if(testError > maxTestError)
{
ts->printf(cvtest::TS::LOG, "Too large test classification error (calc = %f, valid=%f).\n", testError, maxTestError);
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
ts->set_failed_test_info(code);
}
};
TEST(ML_KMeans, accuracy) { CV_KMeansTest test; test.safe_run(); }
TEST(ML_KNearest, accuracy) { CV_KNearestTest test; test.safe_run(); }
TEST(ML_EM, accuracy) { CV_EMTest test; test.safe_run(); }
TEST(ML_EM, save_load) { CV_EMTest_SaveLoad test; test.safe_run(); }
TEST(ML_EM, classification) { CV_EMTest_Classification test; test.safe_run(); }
TEST(ML_KNearest, regression_12347)
{
Mat xTrainData = (Mat_<float>(5,2) << 1, 1.1, 1.1, 1, 2, 2, 2.1, 2, 2.1, 2.1);
Mat yTrainLabels = (Mat_<float>(5,1) << 1, 1, 2, 2, 2);
Ptr<KNearest> knn = KNearest::create();
knn->train(xTrainData, ml::ROW_SAMPLE, yTrainLabels);
Mat xTestData = (Mat_<float>(2,2) << 1.1, 1.1, 2, 2.2);
Mat zBestLabels, neighbours, dist;
// check output shapes:
int K = 16, Kexp = std::min(K, xTrainData.rows);
knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
EXPECT_EQ(xTestData.rows, zBestLabels.rows);
EXPECT_EQ(neighbours.cols, Kexp);
EXPECT_EQ(dist.cols, Kexp);
// see if the result is still correct:
K = 2;
knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
EXPECT_EQ(1, zBestLabels.at<float>(0,0));
EXPECT_EQ(2, zBestLabels.at<float>(1,0));
}
}} // namespace

286
modules/ml/test/test_gbttest.cpp
Unescape View File

@ -1,286 +0,0 @@
#include "test_precomp.hpp"
#if 0
using namespace std;
class CV_GBTreesTest : public cvtest::BaseTest
{
public:
CV_GBTreesTest();
~CV_GBTreesTest();
protected:
void run(int);
int TestTrainPredict(int test_num);
int TestSaveLoad();
int checkPredictError(int test_num);
int checkLoadSave();
string model_file_name1;
string model_file_name2;
string* datasets;
string data_path;
CvMLData* data;
CvGBTrees* gtb;
vector<float> test_resps1;
vector<float> test_resps2;
int64 initSeed;
};
int _get_len(const CvMat* mat)
{
return (mat->cols > mat->rows) ? mat->cols : mat->rows;
}
CV_GBTreesTest::CV_GBTreesTest()
{
int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52),
CV_BIG_INT(0x0000a17166072c7c),
CV_BIG_INT(0x0201b32115cd1f9a),
CV_BIG_INT(0x0513cb37abcd1234),
CV_BIG_INT(0x0001a2b3c4d5f678)
};
int seedCount = sizeof(seeds)/sizeof(seeds[0]);
cv::RNG& rng = cv::theRNG();
initSeed = rng.state;
rng.state = seeds[rng(seedCount)];
datasets = 0;
data = 0;
gtb = 0;
}
CV_GBTreesTest::~CV_GBTreesTest()
{
if (data)
delete data;
delete[] datasets;
cv::theRNG().state = initSeed;
}
int CV_GBTreesTest::TestTrainPredict(int test_num)
{
int code = cvtest::TS::OK;
int weak_count = 200;
float shrinkage = 0.1f;
float subsample_portion = 0.5f;
int max_depth = 5;
bool use_surrogates = false;
int loss_function_type = 0;
switch (test_num)
{
case (1) : loss_function_type = CvGBTrees::SQUARED_LOSS; break;
case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break;
case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break;
case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break;
default :
{
ts->printf( cvtest::TS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." );
return cvtest::TS::FAIL_BAD_ARG_CHECK;
}
}
int dataset_num = test_num == 0 ? 0 : 1;
if (!data)
{
data = new CvMLData();
data->set_delimiter(',');
if (data->read_csv(datasets[dataset_num].c_str()))
{
ts->printf( cvtest::TS::LOG, "File reading error." );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
if (test_num == 0)
{
data->set_response_idx(57);
data->set_var_types("ord[0-56],cat[57]");
}
else
{
data->set_response_idx(13);
data->set_var_types("ord[0-2,4-13],cat[3]");
subsample_portion = 0.7f;
}
int train_sample_count = cvFloor(_get_len(data->get_responses())*0.5f);
CvTrainTestSplit spl( train_sample_count );
data->set_train_test_split( &spl );
}
data->mix_train_and_test_idx();
if (gtb) delete gtb;
gtb = new CvGBTrees();
bool tmp_code = true;
tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count,
shrinkage, subsample_portion,
max_depth, use_surrogates));
if (!tmp_code)
{
ts->printf( cvtest::TS::LOG, "Model training was failed.");
return cvtest::TS::FAIL_INVALID_OUTPUT;
}
code = checkPredictError(test_num);
return code;
}
int CV_GBTreesTest::checkPredictError(int test_num)
{
if (!gtb)
return cvtest::TS::FAIL_GENERIC;
//float mean[] = {5.430247f, 13.5654f, 12.6569f, 13.1661f};
//float sigma[] = {0.4162694f, 3.21161f, 3.43297f, 3.00624f};
float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f};
float sigma[] = {0.4764534f, 3.166919f, 3.022405f, 2.868722f};
float current_error = gtb->calc_error(data, CV_TEST_ERROR);
if ( abs( current_error - mean[test_num]) > 6*sigma[test_num] )
{
ts->printf( cvtest::TS::LOG, "Test error is out of range:\n"
"abs(%f/*curEr*/ - %f/*mean*/ > %f/*6*sigma*/",
current_error, mean[test_num], 6*sigma[test_num] );
return cvtest::TS::FAIL_BAD_ACCURACY;
}
return cvtest::TS::OK;
}
int CV_GBTreesTest::TestSaveLoad()
{
if (!gtb)
return cvtest::TS::FAIL_GENERIC;
model_file_name1 = cv::tempfile();
model_file_name2 = cv::tempfile();
gtb->save(model_file_name1.c_str());
gtb->calc_error(data, CV_TEST_ERROR, &test_resps1);
gtb->load(model_file_name1.c_str());
gtb->calc_error(data, CV_TEST_ERROR, &test_resps2);
gtb->save(model_file_name2.c_str());
return checkLoadSave();
}
int CV_GBTreesTest::checkLoadSave()
{
int code = cvtest::TS::OK;
// 1. compare files
ifstream f1( model_file_name1.c_str() ), f2( model_file_name2.c_str() );
string s1, s2;
int lineIdx = 0;
CV_Assert( f1.is_open() && f2.is_open() );
for( ; !f1.eof() && !f2.eof(); lineIdx++ )
{
getline( f1, s1 );
getline( f2, s2 );
if( s1.compare(s2) )
{
ts->printf( cvtest::TS::LOG, "first and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
}
if( !f1.eof() || !f2.eof() )
{
ts->printf( cvtest::TS::LOG, "First and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
f1.close();
f2.close();
// delete temporary files
remove( model_file_name1.c_str() );
remove( model_file_name2.c_str() );
// 2. compare responses
CV_Assert( test_resps1.size() == test_resps2.size() );
vector<float>::const_iterator it1 = test_resps1.begin(), it2 = test_resps2.begin();
for( ; it1 != test_resps1.end(); ++it1, ++it2 )
{
if( fabs(*it1 - *it2) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "Responses predicted before saving and after loading are different" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
}
return code;
}
void CV_GBTreesTest::run(int)
{
string dataPath = string(ts->get_data_path());
datasets = new string[2];
datasets[0] = dataPath + string("spambase.data"); /*string("dataset_classification.csv");*/
datasets[1] = dataPath + string("housing_.data"); /*string("dataset_regression.csv");*/
int code = cvtest::TS::OK;
for (int i = 0; i < 4; i++)
{
int temp_code = TestTrainPredict(i);
if (temp_code != cvtest::TS::OK)
{
code = temp_code;
break;
}
else if (i==0)
{
temp_code = TestSaveLoad();
if (temp_code != cvtest::TS::OK)
code = temp_code;
delete data;
data = 0;
}
delete gtb;
gtb = 0;
}
delete data;
data = 0;
ts->set_failed_test_info( code );
}
/////////////////////////////////////////////////////////////////////////////
//////////////////// test registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
TEST(ML_GBTrees, regression) { CV_GBTreesTest test; test.safe_run(); }
#endif

53
modules/ml/test/test_kmeans.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
TEST(ML_KMeans, accuracy)
{
const int iters = 100;
int sizesArr[] = { 5000, 7000, 8000 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
Mat data( pointsCount, 2, CV_32FC1 ), labels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
generateData( data, labels, sizes, means, covs, CV_32FC1, CV_32SC1 );
TermCriteria termCriteria( TermCriteria::COUNT, iters, 0.0);
{
SCOPED_TRACE("KMEANS_PP_CENTERS");
float err = 1000;
Mat bestLabels;
kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_PP_CENTERS, noArray() );
EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err , false ));
EXPECT_LE(err, 0.01f);
}
{
SCOPED_TRACE("KMEANS_RANDOM_CENTERS");
float err = 1000;
Mat bestLabels;
kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_RANDOM_CENTERS, noArray() );
EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err, false ));
EXPECT_LE(err, 0.01f);
}
{
SCOPED_TRACE("KMEANS_USE_INITIAL_LABELS");
float err = 1000;
Mat bestLabels;
labels.copyTo( bestLabels );
RNG &rng = cv::theRNG();
for( int i = 0; i < 0.5f * pointsCount; i++ )
bestLabels.at<int>( rng.next() % pointsCount, 0 ) = rng.next() % 3;
kmeans( data, 3, bestLabels, termCriteria, 0, KMEANS_USE_INITIAL_LABELS, noArray() );
EXPECT_TRUE(calcErr( bestLabels, labels, sizes, err, false ));
EXPECT_LE(err, 0.01f);
}
}
}} // namespace

77
modules/ml/test/test_knearest.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
using cv::ml::TrainData;
using cv::ml::EM;
using cv::ml::KNearest;
TEST(ML_KNearest, accuracy)
{
int sizesArr[] = { 500, 700, 800 };
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
Mat means;
vector<Mat> covs;
defaultDistribs( means, covs );
generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
Mat testData( pointsCount, 2, CV_32FC1 );
Mat testLabels;
generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
{
SCOPED_TRACE("Default");
Mat bestLabels;
float err = 1000;
Ptr<KNearest> knn = KNearest::create();
knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
knn->findNearest(testData, 4, bestLabels);
EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
EXPECT_LE(err, 0.01f);
}
{
// TODO: broken
#if 0
SCOPED_TRACE("KDTree");
Mat bestLabels;
float err = 1000;
Ptr<KNearest> knn = KNearest::create();
knn->setAlgorithmType(KNearest::KDTREE);
knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
knn->findNearest(testData, 4, bestLabels);
EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
EXPECT_LE(err, 0.01f);
#endif
}
}
TEST(ML_KNearest, regression_12347)
{
Mat xTrainData = (Mat_<float>(5,2) << 1, 1.1, 1.1, 1, 2, 2, 2.1, 2, 2.1, 2.1);
Mat yTrainLabels = (Mat_<float>(5,1) << 1, 1, 2, 2, 2);
Ptr<KNearest> knn = KNearest::create();
knn->train(xTrainData, ml::ROW_SAMPLE, yTrainLabels);
Mat xTestData = (Mat_<float>(2,2) << 1.1, 1.1, 2, 2.2);
Mat zBestLabels, neighbours, dist;
// check output shapes:
int K = 16, Kexp = std::min(K, xTrainData.rows);
knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
EXPECT_EQ(xTestData.rows, zBestLabels.rows);
EXPECT_EQ(neighbours.cols, Kexp);
EXPECT_EQ(dist.cols, Kexp);
// see if the result is still correct:
K = 2;
knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
EXPECT_EQ(1, zBestLabels.at<float>(0,0));
EXPECT_EQ(2, zBestLabels.at<float>(1,0));
}
}} // namespace

190
modules/ml/test/test_lr.cpp
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@ -1,9 +1,6 @@
/////////////////////////////////////////////////////////////////////////////////////// // This file is part of OpenCV project.
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV. // This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
@ -11,92 +8,16 @@
// Rahul Kavi rahulkavi[at]live[at]com // Rahul Kavi rahulkavi[at]live[at]com
// //
// contains a subset of data from the popular Iris Dataset (taken from "http://archive.ics.uci.edu/ml/datasets/Iris")
// # You are free to use, change, or redistribute the code in any way you wish for
// # non-commercial purposes, but please maintain the name of the original author.
// # This code comes with no warranty of any kind.
// #
// # You are free to use, change, or redistribute the code in any way you wish for
// # non-commercial purposes, but please maintain the name of the original author.
// # This code comes with no warranty of any kind.
// # Logistic Regression ALGORITHM
// License Agreement
// For Open Source Computer Vision Library
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
#include "test_precomp.hpp" #include "test_precomp.hpp"
namespace opencv_test { namespace { namespace opencv_test { namespace {
bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error) TEST(ML_LR, accuracy)
{
CV_TRACE_FUNCTION();
error = 0.0f;
float accuracy = 0.0f;
Mat _p_labels_temp;
Mat _o_labels_temp;
_p_labels.convertTo(_p_labels_temp, CV_32S);
_o_labels.convertTo(_o_labels_temp, CV_32S);
CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
accuracy = (float)countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
error = 1 - accuracy;
return true;
}
//--------------------------------------------------------------------------------------------
class CV_LRTest : public cvtest::BaseTest
{
public:
CV_LRTest() {}
protected:
virtual void run( int start_from );
};
void CV_LRTest::run( int /*start_from*/ )
{ {
CV_TRACE_FUNCTION(); std::string dataFileName = findDataFile("iris.data");
// initialize variables from the popular Iris Dataset
string dataFileName = ts->get_data_path() + "iris.data";
Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0); Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << dataFileName; ASSERT_FALSE(tdata.empty());
// run LR classifier train classifier
Ptr<LogisticRegression> p = LogisticRegression::create(); Ptr<LogisticRegression> p = LogisticRegression::create();
p->setLearningRate(1.0); p->setLearningRate(1.0);
p->setIterations(10001); p->setIterations(10001);
@ -105,121 +26,54 @@ void CV_LRTest::run( int /*start_from*/ )
p->setMiniBatchSize(10); p->setMiniBatchSize(10);
p->train(tdata); p->train(tdata);
// predict using the same data
Mat responses; Mat responses;
p->predict(tdata->getSamples(), responses); p->predict(tdata->getSamples(), responses);
// calculate error float error = 1000;
int test_code = cvtest::TS::OK; EXPECT_TRUE(calculateError(responses, tdata->getResponses(), error));
float error = 0.0f; EXPECT_LE(error, 0.05f);
if(!calculateError(responses, tdata->getResponses(), error))
{
ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if(error > 0.05f)
{
ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
test_code = cvtest::TS::FAIL_BAD_ACCURACY;
}
{
FileStorage s("debug.xml", FileStorage::WRITE);
s << "original" << tdata->getResponses();
s << "predicted1" << responses;
s << "learnt" << p->get_learnt_thetas();
s << "error" << error;
s.release();
}
ts->set_failed_test_info(test_code);
} }
//-------------------------------------------------------------------------------------------- //==================================================================================================
class CV_LRTest_SaveLoad : public cvtest::BaseTest
{
public:
CV_LRTest_SaveLoad(){}
protected:
virtual void run(int start_from);
};
void CV_LRTest_SaveLoad::run( int /*start_from*/ ) TEST(ML_LR, save_load)
{ {
CV_TRACE_FUNCTION(); string dataFileName = findDataFile("iris.data");
int code = cvtest::TS::OK;
// initialize variables from the popular Iris Dataset
string dataFileName = ts->get_data_path() + "iris.data";
Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0); Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << dataFileName; ASSERT_FALSE(tdata.empty());
Mat responses1, responses2; Mat responses1, responses2;
Mat learnt_mat1, learnt_mat2; Mat learnt_mat1, learnt_mat2;
// train and save the classifier
String filename = tempfile(".xml"); String filename = tempfile(".xml");
try
{ {
// run LR classifier train classifier
Ptr<LogisticRegression> lr1 = LogisticRegression::create(); Ptr<LogisticRegression> lr1 = LogisticRegression::create();
lr1->setLearningRate(1.0); lr1->setLearningRate(1.0);
lr1->setIterations(10001); lr1->setIterations(10001);
lr1->setRegularization(LogisticRegression::REG_L2); lr1->setRegularization(LogisticRegression::REG_L2);
lr1->setTrainMethod(LogisticRegression::BATCH); lr1->setTrainMethod(LogisticRegression::BATCH);
lr1->setMiniBatchSize(10); lr1->setMiniBatchSize(10);
lr1->train(tdata); ASSERT_NO_THROW(lr1->train(tdata));
lr1->predict(tdata->getSamples(), responses1); ASSERT_NO_THROW(lr1->predict(tdata->getSamples(), responses1));
ASSERT_NO_THROW(lr1->save(filename));
learnt_mat1 = lr1->get_learnt_thetas(); learnt_mat1 = lr1->get_learnt_thetas();
lr1->save(filename);
} }
catch(...)
{
ts->printf(cvtest::TS::LOG, "Crash in write method.\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_EXCEPTION);
}
// and load to another
try
{ {
Ptr<LogisticRegression> lr2 = Algorithm::load<LogisticRegression>(filename); Ptr<LogisticRegression> lr2;
lr2->predict(tdata->getSamples(), responses2); ASSERT_NO_THROW(lr2 = Algorithm::load<LogisticRegression>(filename));
ASSERT_NO_THROW(lr2->predict(tdata->getSamples(), responses2));
learnt_mat2 = lr2->get_learnt_thetas(); learnt_mat2 = lr2->get_learnt_thetas();
} }
catch(...) // compare difference in prediction outputs and stored inputs
{ EXPECT_MAT_NEAR(responses1, responses2, 0.f);
ts->printf(cvtest::TS::LOG, "Crash in write method.\n" );
ts->set_failed_test_info(cvtest::TS::FAIL_EXCEPTION);
}
CV_Assert(responses1.rows == responses2.rows);
// compare difference in learnt matrices before and after loading from disk
Mat comp_learnt_mats; Mat comp_learnt_mats;
comp_learnt_mats = (learnt_mat1 == learnt_mat2); comp_learnt_mats = (learnt_mat1 == learnt_mat2);
comp_learnt_mats = comp_learnt_mats.reshape(1, comp_learnt_mats.rows*comp_learnt_mats.cols); comp_learnt_mats = comp_learnt_mats.reshape(1, comp_learnt_mats.rows*comp_learnt_mats.cols);
comp_learnt_mats.convertTo(comp_learnt_mats, CV_32S); comp_learnt_mats.convertTo(comp_learnt_mats, CV_32S);
comp_learnt_mats = comp_learnt_mats/255; comp_learnt_mats = comp_learnt_mats/255;
// compare difference in prediction outputs and stored inputs
// check if there is any difference between computed learnt mat and retrieved mat // check if there is any difference between computed learnt mat and retrieved mat
EXPECT_EQ(comp_learnt_mats.rows, sum(comp_learnt_mats)[0]);
float errorCount = 0.0;
errorCount += 1 - (float)countNonZero(responses1 == responses2)/responses1.rows;
errorCount += 1 - (float)sum(comp_learnt_mats)[0]/comp_learnt_mats.rows;
if(errorCount>0)
{
ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errorCount=%d).\n", errorCount );
code = cvtest::TS::FAIL_BAD_ACCURACY;
}
remove( filename.c_str() ); remove( filename.c_str() );
ts->set_failed_test_info( code );
} }
TEST(ML_LR, accuracy) { CV_LRTest test; test.safe_run(); }
TEST(ML_LR, save_load) { CV_LRTest_SaveLoad test; test.safe_run(); }
}} // namespace }} // namespace

507
modules/ml/test/test_mltests.cpp
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@ -1,224 +1,373 @@
/*M/////////////////////////////////////////////////////////////////////////////////////// // This file is part of OpenCV project.
// // It is subject to the license terms in the LICENSE file found in the top-level directory
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // of this distribution and at http://opencv.org/license.html.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp" #include "test_precomp.hpp"
namespace opencv_test { namespace opencv_test { namespace {
CV_AMLTest::CV_AMLTest( const char* _modelName ) : CV_MLBaseTest( _modelName ) struct DatasetDesc
{ {
validationFN = "avalidation.xml"; string name;
} int resp_idx;
int train_count;
int cat_num;
string type_desc;
public:
Ptr<TrainData> load()
{
string filename = findDataFile(name + ".data");
Ptr<TrainData> data = TrainData::loadFromCSV(filename, 0, resp_idx, resp_idx + 1, type_desc);
data->setTrainTestSplit(train_count);
data->shuffleTrainTest();
return data;
}
};
int CV_AMLTest::run_test_case( int testCaseIdx ) // see testdata/ml/protocol.txt (?)
{ DatasetDesc datasets[] = {
CV_TRACE_FUNCTION(); { "mushroom", 0, 4000, 16, "cat" },
int code = cvtest::TS::OK; { "adult", 14, 22561, 16, "ord[0,2,4,10-12],cat[1,3,5-9,13,14]" },
code = prepare_test_case( testCaseIdx ); { "vehicle", 18, 761, 4, "ord[0-17],cat[18]" },
{ "abalone", 8, 3133, 16, "ord[1-8],cat[0]" },
{ "ringnorm", 20, 300, 2, "ord[0-19],cat[20]" },
{ "spambase", 57, 3221, 3, "ord[0-56],cat[57]" },
{ "waveform", 21, 300, 3, "ord[0-20],cat[21]" },
{ "elevators", 18, 5000, 0, "ord" },
{ "letter", 16, 10000, 26, "ord[0-15],cat[16]" },
{ "twonorm", 20, 300, 3, "ord[0-19],cat[20]" },
{ "poletelecomm", 48, 2500, 0, "ord" },
};
if (code == cvtest::TS::OK) static DatasetDesc & getDataset(const string & name)
{
const int sz = sizeof(datasets)/sizeof(datasets[0]);
for (int i = 0; i < sz; ++i)
{ {
//#define GET_STAT DatasetDesc & desc = datasets[i];
#ifdef GET_STAT if (desc.name == name)
const char* data_name = ((CvFileNode*)cvGetSeqElem( dataSetNames, testCaseIdx ))->data.str.ptr; return desc;
printf("%s, %s ", name, data_name);
const int icount = 100;
float res[icount];
for (int k = 0; k < icount; k++)
{
#endif
data->shuffleTrainTest();
code = train( testCaseIdx );
#ifdef GET_STAT
float case_result = get_error();
res[k] = case_result;
}
float mean = 0, sigma = 0;
for (int k = 0; k < icount; k++)
{
mean += res[k];
}
mean = mean /icount;
for (int k = 0; k < icount; k++)
{
sigma += (res[k] - mean)*(res[k] - mean);
}
sigma = sqrt(sigma/icount);
printf("%f, %f\n", mean, sigma);
#endif
} }
return code; CV_Error(Error::StsInternal, "");
} }
int CV_AMLTest::validate_test_results( int testCaseIdx ) //==================================================================================================
// interfaces and templates
template <typename T> string modelName() { return "Unknown"; };
template <typename T> Ptr<T> tuneModel(const DatasetDesc &, Ptr<T> m) { return m; }
struct IModelFactory
{ {
CV_TRACE_FUNCTION(); virtual Ptr<StatModel> createNew(const DatasetDesc &dataset) const = 0;
int iters; virtual Ptr<StatModel> loadFromFile(const string &filename) const = 0;
float mean, sigma; virtual string name() const = 0;
// read validation params virtual ~IModelFactory() {}
FileNode resultNode = };
validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["result"];
resultNode["iter_count"] >> iters;
if ( iters > 0)
{
resultNode["mean"] >> mean;
resultNode["sigma"] >> sigma;
model->save(format("/Users/vp/tmp/dtree/testcase_%02d.cur.yml", testCaseIdx));
float curErr = get_test_error( testCaseIdx );
const int coeff = 4;
ts->printf( cvtest::TS::LOG, "Test case = %d; test error = %f; mean error = %f (diff=%f), %d*sigma = %f\n",
testCaseIdx, curErr, mean, abs( curErr - mean), coeff, coeff*sigma );
if ( abs( curErr - mean) > coeff*sigma )
{
ts->printf( cvtest::TS::LOG, "abs(%f - %f) > %f - OUT OF RANGE!\n", curErr, mean, coeff*sigma, coeff );
return cvtest::TS::FAIL_BAD_ACCURACY;
}
else
ts->printf( cvtest::TS::LOG, ".\n" );
template <typename T>
struct ModelFactory : public IModelFactory
{
Ptr<StatModel> createNew(const DatasetDesc &dataset) const CV_OVERRIDE
{
return tuneModel<T>(dataset, T::create());
} }
else Ptr<StatModel> loadFromFile(const string & filename) const CV_OVERRIDE
{ {
ts->printf( cvtest::TS::LOG, "validation info is not suitable" ); return T::load(filename);
return cvtest::TS::FAIL_INVALID_TEST_DATA;
} }
return cvtest::TS::OK; string name() const CV_OVERRIDE { return modelName<T>(); }
} };
// implementation
namespace { template <> string modelName<NormalBayesClassifier>() { return "NormalBayesClassifier"; }
template <> string modelName<DTrees>() { return "DTrees"; }
template <> string modelName<KNearest>() { return "KNearest"; }
template <> string modelName<RTrees>() { return "RTrees"; }
template <> string modelName<SVMSGD>() { return "SVMSGD"; }
TEST(ML_DTree, regression) { CV_AMLTest test( CV_DTREE ); test.safe_run(); } template<> Ptr<DTrees> tuneModel<DTrees>(const DatasetDesc &dataset, Ptr<DTrees> m)
TEST(ML_Boost, regression) { CV_AMLTest test( CV_BOOST ); test.safe_run(); } {
TEST(ML_RTrees, regression) { CV_AMLTest test( CV_RTREES ); test.safe_run(); } m->setMaxDepth(10);
TEST(DISABLED_ML_ERTrees, regression) { CV_AMLTest test( CV_ERTREES ); test.safe_run(); } m->setMinSampleCount(2);
m->setRegressionAccuracy(0);
m->setUseSurrogates(false);
m->setCVFolds(0);
m->setUse1SERule(false);
m->setTruncatePrunedTree(false);
m->setPriors(Mat());
m->setMaxCategories(dataset.cat_num);
return m;
}
TEST(ML_NBAYES, regression_5911) template<> Ptr<RTrees> tuneModel<RTrees>(const DatasetDesc &dataset, Ptr<RTrees> m)
{ {
int N=12; m->setMaxDepth(20);
Ptr<ml::NormalBayesClassifier> nb = cv::ml::NormalBayesClassifier::create(); m->setMinSampleCount(2);
m->setRegressionAccuracy(0);
m->setUseSurrogates(false);
m->setPriors(Mat());
m->setCalculateVarImportance(true);
m->setActiveVarCount(0);
m->setTermCriteria(TermCriteria(TermCriteria::COUNT, 100, 0.0));
m->setMaxCategories(dataset.cat_num);
return m;
}
// data: template<> Ptr<SVMSGD> tuneModel<SVMSGD>(const DatasetDesc &, Ptr<SVMSGD> m)
Mat_<float> X(N,4); {
X << 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4, m->setSvmsgdType(SVMSGD::ASGD);
5,5,5,5, 5,5,5,5, 5,5,5,5, 5,5,5,5, m->setMarginType(SVMSGD::SOFT_MARGIN);
4,3,2,1, 4,3,2,1, 4,3,2,1, 4,3,2,1; m->setMarginRegularization(0.00001f);
m->setInitialStepSize(0.1f);
m->setStepDecreasingPower(0.75);
m->setTermCriteria(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 10000, 0.00001));
return m;
}
// labels: template <>
Mat_<int> Y(N,1); struct ModelFactory<Boost> : public IModelFactory
Y << 0,0,0,0, 1,1,1,1, 2,2,2,2; {
nb->train(X, ml::ROW_SAMPLE, Y); ModelFactory(int boostType_) : boostType(boostType_) {}
Ptr<StatModel> createNew(const DatasetDesc &) const CV_OVERRIDE
{
Ptr<Boost> m = Boost::create();
m->setBoostType(boostType);
m->setWeakCount(20);
m->setWeightTrimRate(0.95);
m->setMaxDepth(4);
m->setUseSurrogates(false);
m->setPriors(Mat());
return m;
}
Ptr<StatModel> loadFromFile(const string &filename) const { return Boost::load(filename); }
string name() const CV_OVERRIDE { return "Boost"; }
int boostType;
};
// single prediction: template <>
Mat R1,P1; struct ModelFactory<SVM> : public IModelFactory
for (int i=0; i<N; i++) {
ModelFactory(int svmType_, int kernelType_, double gamma_, double c_, double nu_)
: svmType(svmType_), kernelType(kernelType_), gamma(gamma_), c(c_), nu(nu_) {}
Ptr<StatModel> createNew(const DatasetDesc &) const CV_OVERRIDE
{ {
Mat r,p; Ptr<SVM> m = SVM::create();
nb->predictProb(X.row(i), r, p); m->setType(svmType);
R1.push_back(r); m->setKernel(kernelType);
P1.push_back(p); m->setDegree(0);
m->setGamma(gamma);
m->setCoef0(0);
m->setC(c);
m->setNu(nu);
m->setP(0);
return m;
} }
Ptr<StatModel> loadFromFile(const string &filename) const { return SVM::load(filename); }
string name() const CV_OVERRIDE { return "SVM"; }
int svmType;
int kernelType;
double gamma;
double c;
double nu;
};
//==================================================================================================
// bulk prediction (continuous memory): struct ML_Params_t
Mat R2,P2; {
nb->predictProb(X, R2, P2); Ptr<IModelFactory> factory;
string dataset;
float mean;
float sigma;
};
void PrintTo(const ML_Params_t & param, std::ostream *os)
{
*os << param.factory->name() << "_" << param.dataset;
}
EXPECT_EQ(sum(R1 == R2)[0], 255 * R2.total()); ML_Params_t ML_Params_List[] = {
EXPECT_EQ(sum(P1 == P2)[0], 255 * P2.total()); { makePtr< ModelFactory<DTrees> >(), "mushroom", 0.027401f, 0.036236f },
{ makePtr< ModelFactory<DTrees> >(), "adult", 14.279000f, 0.354323f },
{ makePtr< ModelFactory<DTrees> >(), "vehicle", 29.761162f, 4.823927f },
{ makePtr< ModelFactory<DTrees> >(), "abalone", 7.297540f, 0.510058f },
{ makePtr< ModelFactory<Boost> >(Boost::REAL), "adult", 13.894001f, 0.337763f },
{ makePtr< ModelFactory<Boost> >(Boost::DISCRETE), "mushroom", 0.007274f, 0.029400f },
{ makePtr< ModelFactory<Boost> >(Boost::LOGIT), "ringnorm", 9.993943f, 0.860256f },
{ makePtr< ModelFactory<Boost> >(Boost::GENTLE), "spambase", 5.404347f, 0.581716f },
{ makePtr< ModelFactory<RTrees> >(), "waveform", 17.100641f, 0.630052f },
{ makePtr< ModelFactory<RTrees> >(), "mushroom", 0.006547f, 0.028248f },
{ makePtr< ModelFactory<RTrees> >(), "adult", 13.5129f, 0.266065f },
{ makePtr< ModelFactory<RTrees> >(), "abalone", 4.745199f, 0.282112f },
{ makePtr< ModelFactory<RTrees> >(), "vehicle", 24.964712f, 4.469287f },
{ makePtr< ModelFactory<RTrees> >(), "letter", 5.334999f, 0.261142f },
{ makePtr< ModelFactory<RTrees> >(), "ringnorm", 6.248733f, 0.904713f },
{ makePtr< ModelFactory<RTrees> >(), "twonorm", 4.506479f, 0.449739f },
{ makePtr< ModelFactory<RTrees> >(), "spambase", 5.243477f, 0.54232f },
};
// bulk prediction, with non-continuous memory storage typedef testing::TestWithParam<ML_Params_t> ML_Params;
Mat R3_(N, 1+1, CV_32S),
P3_(N, 3+1, CV_32F);
nb->predictProb(X, R3_.col(0), P3_.colRange(0,3));
Mat R3 = R3_.col(0).clone(),
P3 = P3_.colRange(0,3).clone();
EXPECT_EQ(sum(R1 == R3)[0], 255 * R3.total()); TEST_P(ML_Params, accuracy)
EXPECT_EQ(sum(P1 == P3)[0], 255 * P3.total()); {
const ML_Params_t & param = GetParam();
DatasetDesc &dataset = getDataset(param.dataset);
Ptr<TrainData> data = dataset.load();
ASSERT_TRUE(data);
ASSERT_TRUE(data->getNSamples() > 0);
Ptr<StatModel> m = param.factory->createNew(dataset);
ASSERT_TRUE(m);
ASSERT_TRUE(m->train(data, 0));
float err = m->calcError(data, true, noArray());
EXPECT_NEAR(err, param.mean, 4 * param.sigma);
} }
TEST(ML_RTrees, getVotes) INSTANTIATE_TEST_CASE_P(/**/, ML_Params, testing::ValuesIn(ML_Params_List));
//==================================================================================================
struct ML_SL_Params_t
{
Ptr<IModelFactory> factory;
string dataset;
};
void PrintTo(const ML_SL_Params_t & param, std::ostream *os)
{ {
int n = 12; *os << param.factory->name() << "_" << param.dataset;
int count, i; }
int label_size = 3;
int predicted_class = 0;
int max_votes = -1;
int val;
// RTrees for classification
Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
//data ML_SL_Params_t ML_SL_Params_List[] = {
Mat data(n, 4, CV_32F); { makePtr< ModelFactory<NormalBayesClassifier> >(), "waveform" },
randu(data, 0, 10); { makePtr< ModelFactory<KNearest> >(), "waveform" },
{ makePtr< ModelFactory<KNearest> >(), "abalone" },
{ makePtr< ModelFactory<SVM> >(SVM::C_SVC, SVM::LINEAR, 1, 0.5, 0), "waveform" },
{ makePtr< ModelFactory<SVM> >(SVM::NU_SVR, SVM::RBF, 0.00225, 62.5, 0.03), "poletelecomm" },
{ makePtr< ModelFactory<DTrees> >(), "mushroom" },
{ makePtr< ModelFactory<DTrees> >(), "abalone" },
{ makePtr< ModelFactory<Boost> >(Boost::REAL), "adult" },
{ makePtr< ModelFactory<RTrees> >(), "waveform" },
{ makePtr< ModelFactory<RTrees> >(), "abalone" },
{ makePtr< ModelFactory<SVMSGD> >(), "waveform" },
};
//labels typedef testing::TestWithParam<ML_SL_Params_t> ML_SL_Params;
Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
rt->train(data, ml::ROW_SAMPLE, labels); TEST_P(ML_SL_Params, save_load)
{
const ML_SL_Params_t & param = GetParam();
//run function DatasetDesc &dataset = getDataset(param.dataset);
Mat test(1, 4, CV_32F); Ptr<TrainData> data = dataset.load();
Mat result; ASSERT_TRUE(data);
randu(test, 0, 10); ASSERT_TRUE(data->getNSamples() > 0);
rt->getVotes(test, result, 0);
//count vote amount and find highest vote Mat responses1, responses2;
count = 0; string file1 = tempfile(".json.gz");
const int* result_row = result.ptr<int>(1); string file2 = tempfile(".json.gz");
for( i = 0; i < label_size; i++ ) {
Ptr<StatModel> m = param.factory->createNew(dataset);
ASSERT_TRUE(m);
ASSERT_TRUE(m->train(data, 0));
m->calcError(data, true, responses1);
m->save(file1 + "?base64");
}
{ {
val = result_row[i]; Ptr<StatModel> m = param.factory->loadFromFile(file1);
//predicted_class = max_votes < val? i; ASSERT_TRUE(m);
if( max_votes < val ) m->calcError(data, true, responses2);
m->save(file2 + "?base64");
}
EXPECT_MAT_NEAR(responses1, responses2, 0.0);
{
ifstream f1(file1.c_str(), std::ios_base::binary);
ifstream f2(file2.c_str(), std::ios_base::binary);
ASSERT_TRUE(f1.is_open() && f2.is_open());
const size_t BUFSZ = 10000;
vector<char> buf1(BUFSZ, 0);
vector<char> buf2(BUFSZ, 0);
while (true)
{ {
max_votes = val; f1.read(&buf1[0], BUFSZ);
predicted_class = i; f2.read(&buf2[0], BUFSZ);
EXPECT_EQ(f1.gcount(), f2.gcount());
EXPECT_EQ(f1.eof(), f2.eof());
if (!f1.good() || !f2.good() || f1.gcount() != f2.gcount())
break;
ASSERT_EQ(buf1, buf2);
} }
count += val;
} }
remove(file1.c_str());
remove(file2.c_str());
}
INSTANTIATE_TEST_CASE_P(/**/, ML_SL_Params, testing::ValuesIn(ML_SL_Params_List));
//==================================================================================================
TEST(TrainDataGet, layout_ROW_SAMPLE) // Details: #12236
{
cv::Mat test = cv::Mat::ones(150, 30, CV_32FC1) * 2;
test.col(3) += Scalar::all(3);
cv::Mat labels = cv::Mat::ones(150, 3, CV_32SC1) * 5;
labels.col(1) += 1;
cv::Ptr<cv::ml::TrainData> train_data = cv::ml::TrainData::create(test, cv::ml::ROW_SAMPLE, labels);
train_data->setTrainTestSplitRatio(0.9);
Mat tidx = train_data->getTestSampleIdx();
EXPECT_EQ((size_t)15, tidx.total());
Mat tresp = train_data->getTestResponses();
EXPECT_EQ(15, tresp.rows);
EXPECT_EQ(labels.cols, tresp.cols);
EXPECT_EQ(5, tresp.at<int>(0, 0)) << tresp;
EXPECT_EQ(6, tresp.at<int>(0, 1)) << tresp;
EXPECT_EQ(6, tresp.at<int>(14, 1)) << tresp;
EXPECT_EQ(5, tresp.at<int>(14, 2)) << tresp;
Mat tsamples = train_data->getTestSamples();
EXPECT_EQ(15, tsamples.rows);
EXPECT_EQ(test.cols, tsamples.cols);
EXPECT_EQ(2, tsamples.at<float>(0, 0)) << tsamples;
EXPECT_EQ(5, tsamples.at<float>(0, 3)) << tsamples;
EXPECT_EQ(2, tsamples.at<float>(14, test.cols - 1)) << tsamples;
EXPECT_EQ(5, tsamples.at<float>(14, 3)) << tsamples;
}
TEST(TrainDataGet, layout_COL_SAMPLE) // Details: #12236
{
cv::Mat test = cv::Mat::ones(30, 150, CV_32FC1) * 3;
test.row(3) += Scalar::all(3);
cv::Mat labels = cv::Mat::ones(3, 150, CV_32SC1) * 5;
labels.row(1) += 1;
cv::Ptr<cv::ml::TrainData> train_data = cv::ml::TrainData::create(test, cv::ml::COL_SAMPLE, labels);
train_data->setTrainTestSplitRatio(0.9);
Mat tidx = train_data->getTestSampleIdx();
EXPECT_EQ((size_t)15, tidx.total());
Mat tresp = train_data->getTestResponses(); // always row-based, transposed
EXPECT_EQ(15, tresp.rows);
EXPECT_EQ(labels.rows, tresp.cols);
EXPECT_EQ(5, tresp.at<int>(0, 0)) << tresp;
EXPECT_EQ(6, tresp.at<int>(0, 1)) << tresp;
EXPECT_EQ(6, tresp.at<int>(14, 1)) << tresp;
EXPECT_EQ(5, tresp.at<int>(14, 2)) << tresp;
EXPECT_EQ(count, (int)rt->getRoots().size()); Mat tsamples = train_data->getTestSamples();
EXPECT_EQ(result.at<float>(0, predicted_class), rt->predict(test)); EXPECT_EQ(15, tsamples.cols);
EXPECT_EQ(test.rows, tsamples.rows);
EXPECT_EQ(3, tsamples.at<float>(0, 0)) << tsamples;
EXPECT_EQ(6, tsamples.at<float>(3, 0)) << tsamples;
EXPECT_EQ(6, tsamples.at<float>(3, 14)) << tsamples;
EXPECT_EQ(3, tsamples.at<float>(test.rows - 1, 14)) << tsamples;
} }
}} // namespace }} // namespace
/* End of file. */

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modules/ml/test/test_mltests2.cpp
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@ -1,793 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
//#define GENERATE_TESTDATA
namespace opencv_test { namespace {
int str_to_svm_type(String& str)
{
if( !str.compare("C_SVC") )
return SVM::C_SVC;
if( !str.compare("NU_SVC") )
return SVM::NU_SVC;
if( !str.compare("ONE_CLASS") )
return SVM::ONE_CLASS;
if( !str.compare("EPS_SVR") )
return SVM::EPS_SVR;
if( !str.compare("NU_SVR") )
return SVM::NU_SVR;
CV_Error( CV_StsBadArg, "incorrect svm type string" );
}
int str_to_svm_kernel_type( String& str )
{
if( !str.compare("LINEAR") )
return SVM::LINEAR;
if( !str.compare("POLY") )
return SVM::POLY;
if( !str.compare("RBF") )
return SVM::RBF;
if( !str.compare("SIGMOID") )
return SVM::SIGMOID;
CV_Error( CV_StsBadArg, "incorrect svm type string" );
}
// 4. em
// 5. ann
int str_to_ann_train_method( String& str )
{
if( !str.compare("BACKPROP") )
return ANN_MLP::BACKPROP;
if (!str.compare("RPROP"))
return ANN_MLP::RPROP;
if (!str.compare("ANNEAL"))
return ANN_MLP::ANNEAL;
CV_Error( CV_StsBadArg, "incorrect ann train method string" );
}
#if 0
int str_to_ann_activation_function(String& str)
{
if (!str.compare("IDENTITY"))
return ANN_MLP::IDENTITY;
if (!str.compare("SIGMOID_SYM"))
return ANN_MLP::SIGMOID_SYM;
if (!str.compare("GAUSSIAN"))
return ANN_MLP::GAUSSIAN;
if (!str.compare("RELU"))
return ANN_MLP::RELU;
if (!str.compare("LEAKYRELU"))
return ANN_MLP::LEAKYRELU;
CV_Error(CV_StsBadArg, "incorrect ann activation function string");
}
#endif
void ann_check_data( Ptr<TrainData> _data )
{
CV_TRACE_FUNCTION();
CV_Assert(!_data.empty());
Mat values = _data->getSamples();
Mat var_idx = _data->getVarIdx();
int nvars = (int)var_idx.total();
if( nvars != 0 && nvars != values.cols )
CV_Error( CV_StsBadArg, "var_idx is not supported" );
if( !_data->getMissing().empty() )
CV_Error( CV_StsBadArg, "missing values are not supported" );
}
// unroll the categorical responses to binary vectors
Mat ann_get_new_responses( Ptr<TrainData> _data, map<int, int>& cls_map )
{
CV_TRACE_FUNCTION();
CV_Assert(!_data.empty());
Mat train_sidx = _data->getTrainSampleIdx();
int* train_sidx_ptr = train_sidx.ptr<int>();
Mat responses = _data->getResponses();
int cls_count = 0;
// construct cls_map
cls_map.clear();
int nresponses = (int)responses.total();
int si, n = !train_sidx.empty() ? (int)train_sidx.total() : nresponses;
for( si = 0; si < n; si++ )
{
int sidx = train_sidx_ptr ? train_sidx_ptr[si] : si;
int r = cvRound(responses.at<float>(sidx));
CV_DbgAssert( fabs(responses.at<float>(sidx) - r) < FLT_EPSILON );
map<int,int>::iterator it = cls_map.find(r);
if( it == cls_map.end() )
cls_map[r] = cls_count++;
}
Mat new_responses = Mat::zeros( nresponses, cls_count, CV_32F );
for( si = 0; si < n; si++ )
{
int sidx = train_sidx_ptr ? train_sidx_ptr[si] : si;
int r = cvRound(responses.at<float>(sidx));
int cidx = cls_map[r];
new_responses.at<float>(sidx, cidx) = 1.f;
}
return new_responses;
}
float ann_calc_error( Ptr<StatModel> ann, Ptr<TrainData> _data, map<int, int>& cls_map, int type, vector<float> *resp_labels )
{
CV_TRACE_FUNCTION();
CV_Assert(!ann.empty());
CV_Assert(!_data.empty());
float err = 0;
Mat samples = _data->getSamples();
Mat responses = _data->getResponses();
Mat sample_idx = (type == CV_TEST_ERROR) ? _data->getTestSampleIdx() : _data->getTrainSampleIdx();
int* sidx = !sample_idx.empty() ? sample_idx.ptr<int>() : 0;
ann_check_data( _data );
int sample_count = (int)sample_idx.total();
sample_count = (type == CV_TRAIN_ERROR && sample_count == 0) ? samples.rows : sample_count;
float* pred_resp = 0;
vector<float> innresp;
if( sample_count > 0 )
{
if( resp_labels )
{
resp_labels->resize( sample_count );
pred_resp = &((*resp_labels)[0]);
}
else
{
innresp.resize( sample_count );
pred_resp = &(innresp[0]);
}
}
int cls_count = (int)cls_map.size();
Mat output( 1, cls_count, CV_32FC1 );
for( int i = 0; i < sample_count; i++ )
{
int si = sidx ? sidx[i] : i;
Mat sample = samples.row(si);
ann->predict( sample, output );
Point best_cls;
minMaxLoc(output, 0, 0, 0, &best_cls, 0);
int r = cvRound(responses.at<float>(si));
CV_DbgAssert( fabs(responses.at<float>(si) - r) < FLT_EPSILON );
r = cls_map[r];
int d = best_cls.x == r ? 0 : 1;
err += d;
pred_resp[i] = (float)best_cls.x;
}
err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX;
return err;
}
TEST(ML_ANN, ActivationFunction)
{
String folder = string(cvtest::TS::ptr()->get_data_path());
String original_path = folder + "waveform.data";
String dataname = folder + "waveform";
Ptr<TrainData> tdata = TrainData::loadFromCSV(original_path, 0);
ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << original_path;
RNG& rng = theRNG();
rng.state = 1027401484159173092;
tdata->setTrainTestSplit(500);
vector<int> activationType;
activationType.push_back(ml::ANN_MLP::IDENTITY);
activationType.push_back(ml::ANN_MLP::SIGMOID_SYM);
activationType.push_back(ml::ANN_MLP::GAUSSIAN);
activationType.push_back(ml::ANN_MLP::RELU);
activationType.push_back(ml::ANN_MLP::LEAKYRELU);
vector<String> activationName;
activationName.push_back("_identity");
activationName.push_back("_sigmoid_sym");
activationName.push_back("_gaussian");
activationName.push_back("_relu");
activationName.push_back("_leakyrelu");
for (size_t i = 0; i < activationType.size(); i++)
{
Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
Mat_<int> layerSizes(1, 4);
layerSizes(0, 0) = tdata->getNVars();
layerSizes(0, 1) = 100;
layerSizes(0, 2) = 100;
layerSizes(0, 3) = tdata->getResponses().cols;
x->setLayerSizes(layerSizes);
x->setActivationFunction(activationType[i]);
x->setTrainMethod(ml::ANN_MLP::RPROP, 0.01, 0.1);
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 300, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE);
ASSERT_TRUE(x->isTrained()) << "Could not train networks with " << activationName[i];
#ifdef GENERATE_TESTDATA
x->save(dataname + activationName[i] + ".yml");
#else
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(dataname + activationName[i] + ".yml");
ASSERT_TRUE(y) << "Could not load " << dataname + activationName[i] + ".yml";
Mat testSamples = tdata->getTestSamples();
Mat rx, ry, dst;
x->predict(testSamples, rx);
y->predict(testSamples, ry);
double n = cvtest::norm(rx, ry, NORM_INF);
EXPECT_LT(n,FLT_EPSILON) << "Predict are not equal for " << dataname + activationName[i] + ".yml and " << activationName[i];
#endif
}
}
CV_ENUM(ANN_MLP_METHOD, ANN_MLP::RPROP, ANN_MLP::ANNEAL)
typedef tuple<ANN_MLP_METHOD, string, int> ML_ANN_METHOD_Params;
typedef TestWithParam<ML_ANN_METHOD_Params> ML_ANN_METHOD;
TEST_P(ML_ANN_METHOD, Test)
{
int methodType = get<0>(GetParam());
string methodName = get<1>(GetParam());
int N = get<2>(GetParam());
String folder = string(cvtest::TS::ptr()->get_data_path());
String original_path = folder + "waveform.data";
String dataname = folder + "waveform" + '_' + methodName;
Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
ASSERT_FALSE(tdata2.empty()) << "Could not find test data file : " << original_path;
Mat samples = tdata2->getSamples()(Range(0, N), Range::all());
Mat responses(N, 3, CV_32FC1, Scalar(0));
for (int i = 0; i < N; i++)
responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
Ptr<TrainData> tdata = TrainData::create(samples, ml::ROW_SAMPLE, responses);
ASSERT_FALSE(tdata.empty());
RNG& rng = theRNG();
rng.state = 0;
tdata->setTrainTestSplitRatio(0.8);
Mat testSamples = tdata->getTestSamples();
#ifdef GENERATE_TESTDATA
{
Ptr<ml::ANN_MLP> xx = ml::ANN_MLP::create();
Mat_<int> layerSizesXX(1, 4);
layerSizesXX(0, 0) = tdata->getNVars();
layerSizesXX(0, 1) = 30;
layerSizesXX(0, 2) = 30;
layerSizesXX(0, 3) = tdata->getResponses().cols;
xx->setLayerSizes(layerSizesXX);
xx->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
xx->setTrainMethod(ml::ANN_MLP::RPROP);
xx->setTermCriteria(TermCriteria(TermCriteria::COUNT, 1, 0.01));
xx->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE);
FileStorage fs;
fs.open(dataname + "_init_weight.yml.gz", FileStorage::WRITE + FileStorage::BASE64);
xx->write(fs);
fs.release();
}
#endif
{
FileStorage fs;
fs.open(dataname + "_init_weight.yml.gz", FileStorage::READ);
Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
x->read(fs.root());
x->setTrainMethod(methodType);
if (methodType == ml::ANN_MLP::ANNEAL)
{
x->setAnnealEnergyRNG(RNG(CV_BIG_INT(0xffffffff)));
x->setAnnealInitialT(12);
x->setAnnealFinalT(0.15);
x->setAnnealCoolingRatio(0.96);
x->setAnnealItePerStep(11);
}
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 100, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE + ml::ANN_MLP::UPDATE_WEIGHTS);
ASSERT_TRUE(x->isTrained()) << "Could not train networks with " << methodName;
string filename = dataname + ".yml.gz";
Mat r_gold;
#ifdef GENERATE_TESTDATA
x->save(filename);
x->predict(testSamples, r_gold);
{
FileStorage fs_response(dataname + "_response.yml.gz", FileStorage::WRITE + FileStorage::BASE64);
fs_response << "response" << r_gold;
}
#else
{
FileStorage fs_response(dataname + "_response.yml.gz", FileStorage::READ);
fs_response["response"] >> r_gold;
}
#endif
ASSERT_FALSE(r_gold.empty());
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(filename);
ASSERT_TRUE(y) << "Could not load " << filename;
Mat rx, ry;
for (int j = 0; j < 4; j++)
{
rx = x->getWeights(j);
ry = y->getWeights(j);
double n = cvtest::norm(rx, ry, NORM_INF);
EXPECT_LT(n, FLT_EPSILON) << "Weights are not equal for layer: " << j;
}
x->predict(testSamples, rx);
y->predict(testSamples, ry);
double n = cvtest::norm(ry, rx, NORM_INF);
EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal to result of the saved model";
n = cvtest::norm(r_gold, rx, NORM_INF);
EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal to 'gold' response";
}
}
INSTANTIATE_TEST_CASE_P(/*none*/, ML_ANN_METHOD,
testing::Values(
make_tuple<ANN_MLP_METHOD, string, int>(ml::ANN_MLP::RPROP, "rprop", 5000),
make_tuple<ANN_MLP_METHOD, string, int>(ml::ANN_MLP::ANNEAL, "anneal", 1000)
//make_pair<ANN_MLP_METHOD, string>(ml::ANN_MLP::BACKPROP, "backprop", 5000); -----> NO BACKPROP TEST
)
);
// 6. dtree
// 7. boost
int str_to_boost_type( String& str )
{
if ( !str.compare("DISCRETE") )
return Boost::DISCRETE;
if ( !str.compare("REAL") )
return Boost::REAL;
if ( !str.compare("LOGIT") )
return Boost::LOGIT;
if ( !str.compare("GENTLE") )
return Boost::GENTLE;
CV_Error( CV_StsBadArg, "incorrect boost type string" );
}
// 8. rtrees
// 9. ertrees
int str_to_svmsgd_type( String& str )
{
if ( !str.compare("SGD") )
return SVMSGD::SGD;
if ( !str.compare("ASGD") )
return SVMSGD::ASGD;
CV_Error( CV_StsBadArg, "incorrect svmsgd type string" );
}
int str_to_margin_type( String& str )
{
if ( !str.compare("SOFT_MARGIN") )
return SVMSGD::SOFT_MARGIN;
if ( !str.compare("HARD_MARGIN") )
return SVMSGD::HARD_MARGIN;
CV_Error( CV_StsBadArg, "incorrect svmsgd margin type string" );
}
}
// ---------------------------------- MLBaseTest ---------------------------------------------------
CV_MLBaseTest::CV_MLBaseTest(const char* _modelName)
{
int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52),
CV_BIG_INT(0x0000a17166072c7c),
CV_BIG_INT(0x0201b32115cd1f9a),
CV_BIG_INT(0x0513cb37abcd1234),
CV_BIG_INT(0x0001a2b3c4d5f678)
};
int seedCount = sizeof(seeds)/sizeof(seeds[0]);
RNG& rng = theRNG();
initSeed = rng.state;
rng.state = seeds[rng(seedCount)];
modelName = _modelName;
}
CV_MLBaseTest::~CV_MLBaseTest()
{
if( validationFS.isOpened() )
validationFS.release();
theRNG().state = initSeed;
}
int CV_MLBaseTest::read_params( const cv::FileStorage& _fs )
{
CV_TRACE_FUNCTION();
if( !_fs.isOpened() )
test_case_count = -1;
else
{
FileNode fn = _fs.getFirstTopLevelNode()["run_params"][modelName];
test_case_count = (int)fn.size();
if( test_case_count <= 0 )
test_case_count = -1;
if( test_case_count > 0 )
{
dataSetNames.resize( test_case_count );
FileNodeIterator it = fn.begin();
for( int i = 0; i < test_case_count; i++, ++it )
{
dataSetNames[i] = (string)*it;
}
}
}
return cvtest::TS::OK;;
}
void CV_MLBaseTest::run( int )
{
CV_TRACE_FUNCTION();
string filename = ts->get_data_path();
filename += get_validation_filename();
validationFS.open( filename, FileStorage::READ );
read_params( validationFS );
int code = cvtest::TS::OK;
for (int i = 0; i < test_case_count; i++)
{
CV_TRACE_REGION("iteration");
int temp_code = run_test_case( i );
if (temp_code == cvtest::TS::OK)
temp_code = validate_test_results( i );
if (temp_code != cvtest::TS::OK)
code = temp_code;
}
if ( test_case_count <= 0)
{
ts->printf( cvtest::TS::LOG, "validation file is not determined or not correct" );
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
ts->set_failed_test_info( code );
}
int CV_MLBaseTest::prepare_test_case( int test_case_idx )
{
CV_TRACE_FUNCTION();
clear();
string dataPath = ts->get_data_path();
if ( dataPath.empty() )
{
ts->printf( cvtest::TS::LOG, "data path is empty" );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
string dataName = dataSetNames[test_case_idx],
filename = dataPath + dataName + ".data";
FileNode dataParamsNode = validationFS.getFirstTopLevelNode()["validation"][modelName][dataName]["data_params"];
CV_DbgAssert( !dataParamsNode.empty() );
CV_DbgAssert( !dataParamsNode["LS"].empty() );
int trainSampleCount = (int)dataParamsNode["LS"];
CV_DbgAssert( !dataParamsNode["resp_idx"].empty() );
int respIdx = (int)dataParamsNode["resp_idx"];
CV_DbgAssert( !dataParamsNode["types"].empty() );
String varTypes = (String)dataParamsNode["types"];
data = TrainData::loadFromCSV(filename, 0, respIdx, respIdx+1, varTypes);
if( data.empty() )
{
ts->printf( cvtest::TS::LOG, "file %s can not be read\n", filename.c_str() );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
data->setTrainTestSplit(trainSampleCount);
return cvtest::TS::OK;
}
string& CV_MLBaseTest::get_validation_filename()
{
return validationFN;
}
int CV_MLBaseTest::train( int testCaseIdx )
{
CV_TRACE_FUNCTION();
bool is_trained = false;
FileNode modelParamsNode =
validationFS.getFirstTopLevelNode()["validation"][modelName][dataSetNames[testCaseIdx]]["model_params"];
if( modelName == CV_NBAYES )
model = NormalBayesClassifier::create();
else if( modelName == CV_KNEAREST )
{
model = KNearest::create();
}
else if( modelName == CV_SVM )
{
String svm_type_str, kernel_type_str;
modelParamsNode["svm_type"] >> svm_type_str;
modelParamsNode["kernel_type"] >> kernel_type_str;
Ptr<SVM> m = SVM::create();
m->setType(str_to_svm_type( svm_type_str ));
m->setKernel(str_to_svm_kernel_type( kernel_type_str ));
m->setDegree(modelParamsNode["degree"]);
m->setGamma(modelParamsNode["gamma"]);
m->setCoef0(modelParamsNode["coef0"]);
m->setC(modelParamsNode["C"]);
m->setNu(modelParamsNode["nu"]);
m->setP(modelParamsNode["p"]);
model = m;
}
else if( modelName == CV_EM )
{
assert( 0 );
}
else if( modelName == CV_ANN )
{
String train_method_str;
double param1, param2;
modelParamsNode["train_method"] >> train_method_str;
modelParamsNode["param1"] >> param1;
modelParamsNode["param2"] >> param2;
Mat new_responses = ann_get_new_responses( data, cls_map );
// binarize the responses
data = TrainData::create(data->getSamples(), data->getLayout(), new_responses,
data->getVarIdx(), data->getTrainSampleIdx());
int layer_sz[] = { data->getNAllVars(), 100, 100, (int)cls_map.size() };
Mat layer_sizes( 1, (int)(sizeof(layer_sz)/sizeof(layer_sz[0])), CV_32S, layer_sz );
Ptr<ANN_MLP> m = ANN_MLP::create();
m->setLayerSizes(layer_sizes);
m->setActivationFunction(ANN_MLP::SIGMOID_SYM, 0, 0);
m->setTermCriteria(TermCriteria(TermCriteria::COUNT,300,0.01));
m->setTrainMethod(str_to_ann_train_method(train_method_str), param1, param2);
model = m;
}
else if( modelName == CV_DTREE )
{
int MAX_DEPTH, MIN_SAMPLE_COUNT, MAX_CATEGORIES, CV_FOLDS;
float REG_ACCURACY = 0;
bool USE_SURROGATE = false, IS_PRUNED;
modelParamsNode["max_depth"] >> MAX_DEPTH;
modelParamsNode["min_sample_count"] >> MIN_SAMPLE_COUNT;
//modelParamsNode["use_surrogate"] >> USE_SURROGATE;
modelParamsNode["max_categories"] >> MAX_CATEGORIES;
modelParamsNode["cv_folds"] >> CV_FOLDS;
modelParamsNode["is_pruned"] >> IS_PRUNED;
Ptr<DTrees> m = DTrees::create();
m->setMaxDepth(MAX_DEPTH);
m->setMinSampleCount(MIN_SAMPLE_COUNT);
m->setRegressionAccuracy(REG_ACCURACY);
m->setUseSurrogates(USE_SURROGATE);
m->setMaxCategories(MAX_CATEGORIES);
m->setCVFolds(CV_FOLDS);
m->setUse1SERule(false);
m->setTruncatePrunedTree(IS_PRUNED);
m->setPriors(Mat());
model = m;
}
else if( modelName == CV_BOOST )
{
int BOOST_TYPE, WEAK_COUNT, MAX_DEPTH;
float WEIGHT_TRIM_RATE;
bool USE_SURROGATE = false;
String typeStr;
modelParamsNode["type"] >> typeStr;
BOOST_TYPE = str_to_boost_type( typeStr );
modelParamsNode["weak_count"] >> WEAK_COUNT;
modelParamsNode["weight_trim_rate"] >> WEIGHT_TRIM_RATE;
modelParamsNode["max_depth"] >> MAX_DEPTH;
//modelParamsNode["use_surrogate"] >> USE_SURROGATE;
Ptr<Boost> m = Boost::create();
m->setBoostType(BOOST_TYPE);
m->setWeakCount(WEAK_COUNT);
m->setWeightTrimRate(WEIGHT_TRIM_RATE);
m->setMaxDepth(MAX_DEPTH);
m->setUseSurrogates(USE_SURROGATE);
m->setPriors(Mat());
model = m;
}
else if( modelName == CV_RTREES )
{
int MAX_DEPTH, MIN_SAMPLE_COUNT, MAX_CATEGORIES, CV_FOLDS, NACTIVE_VARS, MAX_TREES_NUM;
float REG_ACCURACY = 0, OOB_EPS = 0.0;
bool USE_SURROGATE = false, IS_PRUNED;
modelParamsNode["max_depth"] >> MAX_DEPTH;
modelParamsNode["min_sample_count"] >> MIN_SAMPLE_COUNT;
//modelParamsNode["use_surrogate"] >> USE_SURROGATE;
modelParamsNode["max_categories"] >> MAX_CATEGORIES;
modelParamsNode["cv_folds"] >> CV_FOLDS;
modelParamsNode["is_pruned"] >> IS_PRUNED;
modelParamsNode["nactive_vars"] >> NACTIVE_VARS;
modelParamsNode["max_trees_num"] >> MAX_TREES_NUM;
Ptr<RTrees> m = RTrees::create();
m->setMaxDepth(MAX_DEPTH);
m->setMinSampleCount(MIN_SAMPLE_COUNT);
m->setRegressionAccuracy(REG_ACCURACY);
m->setUseSurrogates(USE_SURROGATE);
m->setMaxCategories(MAX_CATEGORIES);
m->setPriors(Mat());
m->setCalculateVarImportance(true);
m->setActiveVarCount(NACTIVE_VARS);
m->setTermCriteria(TermCriteria(TermCriteria::COUNT, MAX_TREES_NUM, OOB_EPS));
model = m;
}
else if( modelName == CV_SVMSGD )
{
String svmsgdTypeStr;
modelParamsNode["svmsgdType"] >> svmsgdTypeStr;
Ptr<SVMSGD> m = SVMSGD::create();
int svmsgdType = str_to_svmsgd_type( svmsgdTypeStr );
m->setSvmsgdType(svmsgdType);
String marginTypeStr;
modelParamsNode["marginType"] >> marginTypeStr;
int marginType = str_to_margin_type( marginTypeStr );
m->setMarginType(marginType);
m->setMarginRegularization(modelParamsNode["marginRegularization"]);
m->setInitialStepSize(modelParamsNode["initialStepSize"]);
m->setStepDecreasingPower(modelParamsNode["stepDecreasingPower"]);
m->setTermCriteria(TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 10000, 0.00001));
model = m;
}
if( !model.empty() )
is_trained = model->train(data, 0);
if( !is_trained )
{
ts->printf( cvtest::TS::LOG, "in test case %d model training was failed", testCaseIdx );
return cvtest::TS::FAIL_INVALID_OUTPUT;
}
return cvtest::TS::OK;
}
float CV_MLBaseTest::get_test_error( int /*testCaseIdx*/, vector<float> *resp )
{
CV_TRACE_FUNCTION();
int type = CV_TEST_ERROR;
float err = 0;
Mat _resp;
if( modelName == CV_EM )
assert( 0 );
else if( modelName == CV_ANN )
err = ann_calc_error( model, data, cls_map, type, resp );
else if( modelName == CV_DTREE || modelName == CV_BOOST || modelName == CV_RTREES ||
modelName == CV_SVM || modelName == CV_NBAYES || modelName == CV_KNEAREST || modelName == CV_SVMSGD )
err = model->calcError( data, true, _resp );
if( !_resp.empty() && resp )
_resp.convertTo(*resp, CV_32F);
return err;
}
void CV_MLBaseTest::save( const char* filename )
{
CV_TRACE_FUNCTION();
model->save( filename );
}
void CV_MLBaseTest::load( const char* filename )
{
CV_TRACE_FUNCTION();
if( modelName == CV_NBAYES )
model = Algorithm::load<NormalBayesClassifier>( filename );
else if( modelName == CV_KNEAREST )
model = Algorithm::load<KNearest>( filename );
else if( modelName == CV_SVM )
model = Algorithm::load<SVM>( filename );
else if( modelName == CV_ANN )
model = Algorithm::load<ANN_MLP>( filename );
else if( modelName == CV_DTREE )
model = Algorithm::load<DTrees>( filename );
else if( modelName == CV_BOOST )
model = Algorithm::load<Boost>( filename );
else if( modelName == CV_RTREES )
model = Algorithm::load<RTrees>( filename );
else if( modelName == CV_SVMSGD )
model = Algorithm::load<SVMSGD>( filename );
else
CV_Error( CV_StsNotImplemented, "invalid stat model name");
}
TEST(TrainDataGet, layout_ROW_SAMPLE) // Details: #12236
{
cv::Mat test = cv::Mat::ones(150, 30, CV_32FC1) * 2;
test.col(3) += Scalar::all(3);
cv::Mat labels = cv::Mat::ones(150, 3, CV_32SC1) * 5;
labels.col(1) += 1;
cv::Ptr<cv::ml::TrainData> train_data = cv::ml::TrainData::create(test, cv::ml::ROW_SAMPLE, labels);
train_data->setTrainTestSplitRatio(0.9);
Mat tidx = train_data->getTestSampleIdx();
EXPECT_EQ((size_t)15, tidx.total());
Mat tresp = train_data->getTestResponses();
EXPECT_EQ(15, tresp.rows);
EXPECT_EQ(labels.cols, tresp.cols);
EXPECT_EQ(5, tresp.at<int>(0, 0)) << tresp;
EXPECT_EQ(6, tresp.at<int>(0, 1)) << tresp;
EXPECT_EQ(6, tresp.at<int>(14, 1)) << tresp;
EXPECT_EQ(5, tresp.at<int>(14, 2)) << tresp;
Mat tsamples = train_data->getTestSamples();
EXPECT_EQ(15, tsamples.rows);
EXPECT_EQ(test.cols, tsamples.cols);
EXPECT_EQ(2, tsamples.at<float>(0, 0)) << tsamples;
EXPECT_EQ(5, tsamples.at<float>(0, 3)) << tsamples;
EXPECT_EQ(2, tsamples.at<float>(14, test.cols - 1)) << tsamples;
EXPECT_EQ(5, tsamples.at<float>(14, 3)) << tsamples;
}
TEST(TrainDataGet, layout_COL_SAMPLE) // Details: #12236
{
cv::Mat test = cv::Mat::ones(30, 150, CV_32FC1) * 3;
test.row(3) += Scalar::all(3);
cv::Mat labels = cv::Mat::ones(3, 150, CV_32SC1) * 5;
labels.row(1) += 1;
cv::Ptr<cv::ml::TrainData> train_data = cv::ml::TrainData::create(test, cv::ml::COL_SAMPLE, labels);
train_data->setTrainTestSplitRatio(0.9);
Mat tidx = train_data->getTestSampleIdx();
EXPECT_EQ((size_t)15, tidx.total());
Mat tresp = train_data->getTestResponses(); // always row-based, transposed
EXPECT_EQ(15, tresp.rows);
EXPECT_EQ(labels.rows, tresp.cols);
EXPECT_EQ(5, tresp.at<int>(0, 0)) << tresp;
EXPECT_EQ(6, tresp.at<int>(0, 1)) << tresp;
EXPECT_EQ(6, tresp.at<int>(14, 1)) << tresp;
EXPECT_EQ(5, tresp.at<int>(14, 2)) << tresp;
Mat tsamples = train_data->getTestSamples();
EXPECT_EQ(15, tsamples.cols);
EXPECT_EQ(test.rows, tsamples.rows);
EXPECT_EQ(3, tsamples.at<float>(0, 0)) << tsamples;
EXPECT_EQ(6, tsamples.at<float>(3, 0)) << tsamples;
EXPECT_EQ(6, tsamples.at<float>(3, 14)) << tsamples;
EXPECT_EQ(3, tsamples.at<float>(test.rows - 1, 14)) << tsamples;
}
} // namespace
/* End of file. */

65
modules/ml/test/test_precomp.hpp
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@ -2,10 +2,15 @@
#define __OPENCV_TEST_PRECOMP_HPP__ #define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts.hpp" #include "opencv2/ts.hpp"
#include <opencv2/ts/cuda_test.hpp> // EXPECT_MAT_NEAR
#include "opencv2/ml.hpp" #include "opencv2/ml.hpp"
#include "opencv2/core/core_c.h" #include "opencv2/core/core_c.h"
#include <fstream>
using std::ifstream;
namespace opencv_test { namespace opencv_test {
using namespace cv::ml; using namespace cv::ml;
#define CV_NBAYES "nbayes" #define CV_NBAYES "nbayes"
@ -19,8 +24,6 @@ using namespace cv::ml;
#define CV_ERTREES "ertrees" #define CV_ERTREES "ertrees"
#define CV_SVMSGD "svmsgd" #define CV_SVMSGD "svmsgd"
enum { CV_TRAIN_ERROR=0, CV_TEST_ERROR=1 };
using cv::Ptr; using cv::Ptr;
using cv::ml::StatModel; using cv::ml::StatModel;
using cv::ml::TrainData; using cv::ml::TrainData;
@ -34,58 +37,14 @@ using cv::ml::Boost;
using cv::ml::RTrees; using cv::ml::RTrees;
using cv::ml::SVMSGD; using cv::ml::SVMSGD;
class CV_MLBaseTest : public cvtest::BaseTest void defaultDistribs( Mat& means, vector<Mat>& covs, int type=CV_32FC1 );
{ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType );
public: int maxIdx( const vector<int>& count );
CV_MLBaseTest( const char* _modelName ); bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq=true );
virtual ~CV_MLBaseTest(); bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent = true, bool checkClusterUniq=true );
protected:
virtual int read_params( const cv::FileStorage& fs );
virtual void run( int startFrom );
virtual int prepare_test_case( int testCaseIdx );
virtual std::string& get_validation_filename();
virtual int run_test_case( int testCaseIdx ) = 0;
virtual int validate_test_results( int testCaseIdx ) = 0;
int train( int testCaseIdx );
float get_test_error( int testCaseIdx, std::vector<float> *resp = 0 );
void save( const char* filename );
void load( const char* filename );
Ptr<TrainData> data;
std::string modelName, validationFN;
std::vector<std::string> dataSetNames;
cv::FileStorage validationFS;
Ptr<StatModel> model;
std::map<int, int> cls_map;
int64 initSeed;
};
class CV_AMLTest : public CV_MLBaseTest
{
public:
CV_AMLTest( const char* _modelName );
virtual ~CV_AMLTest() {}
protected:
virtual int run_test_case( int testCaseIdx );
virtual int validate_test_results( int testCaseIdx );
};
class CV_SLMLTest : public CV_MLBaseTest
{
public:
CV_SLMLTest( const char* _modelName );
virtual ~CV_SLMLTest() {}
protected:
virtual int run_test_case( int testCaseIdx );
virtual int validate_test_results( int testCaseIdx );
std::vector<float> test_resps1, test_resps2; // predicted responses for test data // used in LR test
std::string fname1, fname2; bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error);
};
} // namespace } // namespace

54
modules/ml/test/test_rtrees.cpp
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@ -0,0 +1,54 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
TEST(ML_RTrees, getVotes)
{
int n = 12;
int count, i;
int label_size = 3;
int predicted_class = 0;
int max_votes = -1;
int val;
// RTrees for classification
Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
//data
Mat data(n, 4, CV_32F);
randu(data, 0, 10);
//labels
Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
rt->train(data, ml::ROW_SAMPLE, labels);
//run function
Mat test(1, 4, CV_32F);
Mat result;
randu(test, 0, 10);
rt->getVotes(test, result, 0);
//count vote amount and find highest vote
count = 0;
const int* result_row = result.ptr<int>(1);
for( i = 0; i < label_size; i++ )
{
val = result_row[i];
//predicted_class = max_votes < val? i;
if( max_votes < val )
{
max_votes = val;
predicted_class = i;
}
count += val;
}
EXPECT_EQ(count, (int)rt->getRoots().size());
EXPECT_EQ(result.at<float>(0, predicted_class), rt->predict(test));
}
}} // namespace

350
modules/ml/test/test_save_load.cpp
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@ -1,267 +1,100 @@
/*M/////////////////////////////////////////////////////////////////////////////////////// // This file is part of OpenCV project.
// // It is subject to the license terms in the LICENSE file found in the top-level directory
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // of this distribution and at http://opencv.org/license.html.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp" #include "test_precomp.hpp"
namespace opencv_test { namespace opencv_test { namespace {
CV_SLMLTest::CV_SLMLTest( const char* _modelName ) : CV_MLBaseTest( _modelName )
{
validationFN = "slvalidation.xml";
}
int CV_SLMLTest::run_test_case( int testCaseIdx ) void randomFillCategories(const string & filename, Mat & input)
{ {
int code = cvtest::TS::OK; Mat catMap;
code = prepare_test_case( testCaseIdx ); Mat catCount;
std::vector<uchar> varTypes;
if( code == cvtest::TS::OK )
{ FileStorage fs(filename, FileStorage::READ);
data->setTrainTestSplit(data->getNTrainSamples(), true); FileNode root = fs.getFirstTopLevelNode();
code = train( testCaseIdx ); root["cat_map"] >> catMap;
if( code == cvtest::TS::OK ) root["cat_count"] >> catCount;
{ root["var_type"] >> varTypes;
get_test_error( testCaseIdx, &test_resps1 );
fname1 = tempfile(".json.gz"); int offset = 0;
save( (fname1 + "?base64").c_str() ); int countOffset = 0;
load( fname1.c_str() ); uint var = 0, varCount = (uint)varTypes.size();
get_test_error( testCaseIdx, &test_resps2 ); for (; var < varCount; ++var)
fname2 = tempfile(".json.gz");
save( (fname2 + "?base64").c_str() );
}
else
ts->printf( cvtest::TS::LOG, "model can not be trained" );
}
return code;
}
int CV_SLMLTest::validate_test_results( int testCaseIdx )
{
int code = cvtest::TS::OK;
// 1. compare files
FILE *fs1 = fopen(fname1.c_str(), "rb"), *fs2 = fopen(fname2.c_str(), "rb");
size_t sz1 = 0, sz2 = 0;
if( !fs1 || !fs2 )
code = cvtest::TS::FAIL_MISSING_TEST_DATA;
if( code >= 0 )
{
fseek(fs1, 0, SEEK_END); fseek(fs2, 0, SEEK_END);
sz1 = ftell(fs1);
sz2 = ftell(fs2);
fseek(fs1, 0, SEEK_SET); fseek(fs2, 0, SEEK_SET);
}
if( sz1 != sz2 )
code = cvtest::TS::FAIL_INVALID_OUTPUT;
if( code >= 0 )
{ {
const int BUFSZ = 1024; if (varTypes[var] == ml::VAR_CATEGORICAL)
uchar buf1[BUFSZ], buf2[BUFSZ];
for( size_t pos = 0; pos < sz1; )
{ {
size_t r1 = fread(buf1, 1, BUFSZ, fs1); int size = catCount.at<int>(0, countOffset);
size_t r2 = fread(buf2, 1, BUFSZ, fs2); for (int row = 0; row < input.rows; ++row)
if( r1 != r2 || memcmp(buf1, buf2, r1) != 0 )
{ {
ts->printf( cvtest::TS::LOG, int randomChosenIndex = offset + ((uint)cv::theRNG()) % size;
"in test case %d first (%s) and second (%s) saved files differ in %d-th kb\n", int value = catMap.at<int>(0, randomChosenIndex);
testCaseIdx, fname1.c_str(), fname2.c_str(), input.at<float>(row, var) = (float)value;
(int)pos );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
break;
} }
pos += r1; offset += size;
++countOffset;
} }
} }
if(fs1)
fclose(fs1);
if(fs2)
fclose(fs2);
// delete temporary files
if( code >= 0 )
{
remove( fname1.c_str() );
remove( fname2.c_str() );
}
if( code >= 0 )
{
// 2. compare responses
CV_Assert( test_resps1.size() == test_resps2.size() );
vector<float>::const_iterator it1 = test_resps1.begin(), it2 = test_resps2.begin();
for( ; it1 != test_resps1.end(); ++it1, ++it2 )
{
if( fabs(*it1 - *it2) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "in test case %d responses predicted before saving and after loading is different", testCaseIdx );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
break;
}
}
}
return code;
} }
namespace { //==================================================================================================
TEST(ML_NaiveBayes, save_load) { CV_SLMLTest test( CV_NBAYES ); test.safe_run(); } typedef tuple<string, string> ML_Legacy_Param;
TEST(ML_KNearest, save_load) { CV_SLMLTest test( CV_KNEAREST ); test.safe_run(); } typedef testing::TestWithParam< ML_Legacy_Param > ML_Legacy_Params;
TEST(ML_SVM, save_load) { CV_SLMLTest test( CV_SVM ); test.safe_run(); }
TEST(ML_ANN, save_load) { CV_SLMLTest test( CV_ANN ); test.safe_run(); }
TEST(ML_DTree, save_load) { CV_SLMLTest test( CV_DTREE ); test.safe_run(); }
TEST(ML_Boost, save_load) { CV_SLMLTest test( CV_BOOST ); test.safe_run(); }
TEST(ML_RTrees, save_load) { CV_SLMLTest test( CV_RTREES ); test.safe_run(); }
TEST(DISABLED_ML_ERTrees, save_load) { CV_SLMLTest test( CV_ERTREES ); test.safe_run(); }
TEST(MV_SVMSGD, save_load){ CV_SLMLTest test( CV_SVMSGD ); test.safe_run(); }
class CV_LegacyTest : public cvtest::BaseTest TEST_P(ML_Legacy_Params, legacy_load)
{ {
public: const string modelName = get<0>(GetParam());
CV_LegacyTest(const std::string &_modelName, const std::string &_suffixes = std::string()) const string dataName = get<1>(GetParam());
: cvtest::BaseTest(), modelName(_modelName), suffixes(_suffixes) const string filename = findDataFile("legacy/" + modelName + "_" + dataName + ".xml");
{ const bool isTree = modelName == CV_BOOST || modelName == CV_DTREE || modelName == CV_RTREES;
}
virtual ~CV_LegacyTest() {} Ptr<StatModel> model;
protected: if (modelName == CV_BOOST)
void run(int) model = Algorithm::load<Boost>(filename);
{ else if (modelName == CV_ANN)
unsigned int idx = 0; model = Algorithm::load<ANN_MLP>(filename);
for (;;) else if (modelName == CV_DTREE)
{ model = Algorithm::load<DTrees>(filename);
if (idx >= suffixes.size()) else if (modelName == CV_NBAYES)
break; model = Algorithm::load<NormalBayesClassifier>(filename);
int found = (int)suffixes.find(';', idx); else if (modelName == CV_SVM)
string piece = suffixes.substr(idx, found - idx); model = Algorithm::load<SVM>(filename);
if (piece.empty()) else if (modelName == CV_RTREES)
break; model = Algorithm::load<RTrees>(filename);
oneTest(piece); else if (modelName == CV_SVMSGD)
idx += (unsigned int)piece.size() + 1; model = Algorithm::load<SVMSGD>(filename);
} ASSERT_TRUE(model);
}
void oneTest(const string & suffix) Mat input = Mat(isTree ? 10 : 1, model->getVarCount(), CV_32F);
{ cv::theRNG().fill(input, RNG::UNIFORM, 0, 40);
using namespace cv::ml;
if (isTree)
int code = cvtest::TS::OK; randomFillCategories(filename, input);
string filename = ts->get_data_path() + "legacy/" + modelName + suffix;
bool isTree = modelName == CV_BOOST || modelName == CV_DTREE || modelName == CV_RTREES; Mat output;
Ptr<StatModel> model; EXPECT_NO_THROW(model->predict(input, output, StatModel::RAW_OUTPUT | (isTree ? DTrees::PREDICT_SUM : 0)));
if (modelName == CV_BOOST) // just check if no internal assertions or errors thrown
model = Algorithm::load<Boost>(filename); }
else if (modelName == CV_ANN)
model = Algorithm::load<ANN_MLP>(filename);
else if (modelName == CV_DTREE)
model = Algorithm::load<DTrees>(filename);
else if (modelName == CV_NBAYES)
model = Algorithm::load<NormalBayesClassifier>(filename);
else if (modelName == CV_SVM)
model = Algorithm::load<SVM>(filename);
else if (modelName == CV_RTREES)
model = Algorithm::load<RTrees>(filename);
else if (modelName == CV_SVMSGD)
model = Algorithm::load<SVMSGD>(filename);
if (!model)
{
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
else
{
Mat input = Mat(isTree ? 10 : 1, model->getVarCount(), CV_32F);
ts->get_rng().fill(input, RNG::UNIFORM, 0, 40);
if (isTree)
randomFillCategories(filename, input);
Mat output;
model->predict(input, output, StatModel::RAW_OUTPUT | (isTree ? DTrees::PREDICT_SUM : 0));
// just check if no internal assertions or errors thrown
}
ts->set_failed_test_info(code);
}
void randomFillCategories(const string & filename, Mat & input)
{
Mat catMap;
Mat catCount;
std::vector<uchar> varTypes;
FileStorage fs(filename, FileStorage::READ);
FileNode root = fs.getFirstTopLevelNode();
root["cat_map"] >> catMap;
root["cat_count"] >> catCount;
root["var_type"] >> varTypes;
int offset = 0; ML_Legacy_Param param_list[] = {
int countOffset = 0; ML_Legacy_Param(CV_ANN, "waveform"),
uint var = 0, varCount = (uint)varTypes.size(); ML_Legacy_Param(CV_BOOST, "adult"),
for (; var < varCount; ++var) ML_Legacy_Param(CV_BOOST, "1"),
{ ML_Legacy_Param(CV_BOOST, "2"),
if (varTypes[var] == ml::VAR_CATEGORICAL) ML_Legacy_Param(CV_BOOST, "3"),
{ ML_Legacy_Param(CV_DTREE, "abalone"),
int size = catCount.at<int>(0, countOffset); ML_Legacy_Param(CV_DTREE, "mushroom"),
for (int row = 0; row < input.rows; ++row) ML_Legacy_Param(CV_NBAYES, "waveform"),
{ ML_Legacy_Param(CV_SVM, "poletelecomm"),
int randomChosenIndex = offset + ((uint)ts->get_rng()) % size; ML_Legacy_Param(CV_SVM, "waveform"),
int value = catMap.at<int>(0, randomChosenIndex); ML_Legacy_Param(CV_RTREES, "waveform"),
input.at<float>(row, var) = (float)value; ML_Legacy_Param(CV_SVMSGD, "waveform"),
}
offset += size;
++countOffset;
}
}
}
string modelName;
string suffixes;
}; };
TEST(ML_ANN, legacy_load) { CV_LegacyTest test(CV_ANN, "_waveform.xml"); test.safe_run(); } INSTANTIATE_TEST_CASE_P(/**/, ML_Legacy_Params, testing::ValuesIn(param_list));
TEST(ML_Boost, legacy_load) { CV_LegacyTest test(CV_BOOST, "_adult.xml;_1.xml;_2.xml;_3.xml"); test.safe_run(); }
TEST(ML_DTree, legacy_load) { CV_LegacyTest test(CV_DTREE, "_abalone.xml;_mushroom.xml"); test.safe_run(); }
TEST(ML_NBayes, legacy_load) { CV_LegacyTest test(CV_NBAYES, "_waveform.xml"); test.safe_run(); }
TEST(ML_SVM, legacy_load) { CV_LegacyTest test(CV_SVM, "_poletelecomm.xml;_waveform.xml"); test.safe_run(); }
TEST(ML_RTrees, legacy_load) { CV_LegacyTest test(CV_RTREES, "_waveform.xml"); test.safe_run(); }
TEST(ML_SVMSGD, legacy_load) { CV_LegacyTest test(CV_SVMSGD, "_waveform.xml"); test.safe_run(); }
/*TEST(ML_SVM, throw_exception_when_save_untrained_model) /*TEST(ML_SVM, throw_exception_when_save_untrained_model)
{ {
@ -271,33 +104,4 @@ TEST(ML_SVMSGD, legacy_load) { CV_LegacyTest test(CV_SVMSGD, "_waveform.xml"); t
remove(filename.c_str()); remove(filename.c_str());
}*/ }*/
TEST(DISABLED_ML_SVM, linear_save_load)
{
Ptr<cv::ml::SVM> svm1, svm2, svm3;
svm1 = Algorithm::load<SVM>("SVM45_X_38-1.xml");
svm2 = Algorithm::load<SVM>("SVM45_X_38-2.xml");
string tname = tempfile("a.json");
svm2->save(tname + "?base64");
svm3 = Algorithm::load<SVM>(tname);
ASSERT_EQ(svm1->getVarCount(), svm2->getVarCount());
ASSERT_EQ(svm1->getVarCount(), svm3->getVarCount());
int m = 10000, n = svm1->getVarCount();
Mat samples(m, n, CV_32F), r1, r2, r3;
randu(samples, 0., 1.);
svm1->predict(samples, r1);
svm2->predict(samples, r2);
svm3->predict(samples, r3);
double eps = 1e-4;
EXPECT_LE(cvtest::norm(r1, r2, NORM_INF), eps);
EXPECT_LE(cvtest::norm(r1, r3, NORM_INF), eps);
remove(tname.c_str());
}
}} // namespace }} // namespace
/* End of file. */

296
modules/ml/test/test_svmsgd.cpp
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@ -1,281 +1,119 @@
/*M/////////////////////////////////////////////////////////////////////////////////////// // This file is part of OpenCV project.
// // It is subject to the license terms in the LICENSE file found in the top-level directory
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // of this distribution and at http://opencv.org/license.html.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp" #include "test_precomp.hpp"
namespace opencv_test { namespace { namespace opencv_test { namespace {
using cv::ml::SVMSGD; static const int TEST_VALUE_LIMIT = 500;
using cv::ml::TrainData; enum
class CV_SVMSGDTrainTest : public cvtest::BaseTest
{ {
public: UNIFORM_SAME_SCALE,
enum TrainDataType UNIFORM_DIFFERENT_SCALES
{
UNIFORM_SAME_SCALE,
UNIFORM_DIFFERENT_SCALES
};
CV_SVMSGDTrainTest(const Mat &_weights, float shift, TrainDataType type, double precision = 0.01);
private:
virtual void run( int start_from );
static float decisionFunction(const Mat &sample, const Mat &weights, float shift);
void makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses);
void generateSameBorders(int featureCount);
void generateDifferentBorders(int featureCount);
TrainDataType type;
double precision;
std::vector<std::pair<float,float> > borders;
cv::Ptr<TrainData> data;
cv::Mat testSamples;
cv::Mat testResponses;
static const int TEST_VALUE_LIMIT = 500;
}; };
void CV_SVMSGDTrainTest::generateSameBorders(int featureCount) CV_ENUM(SVMSGD_TYPE, UNIFORM_SAME_SCALE, UNIFORM_DIFFERENT_SCALES)
{
float lowerLimit = -TEST_VALUE_LIMIT;
float upperLimit = TEST_VALUE_LIMIT;
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
{
borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
}
}
void CV_SVMSGDTrainTest::generateDifferentBorders(int featureCount)
{
float lowerLimit = -TEST_VALUE_LIMIT;
float upperLimit = TEST_VALUE_LIMIT;
cv::RNG rng(0);
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
{
int crit = rng.uniform(0, 2);
if (crit > 0)
{
borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
}
else
{
borders.push_back(std::pair<float,float>(lowerLimit/1000, upperLimit/1000));
}
}
}
float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights, float shift) typedef std::vector< std::pair<float,float> > BorderList;
{
return static_cast<float>(sample.dot(weights)) + shift;
}
void CV_SVMSGDTrainTest::makeData(int samplesCount, const Mat &weights, float shift, RNG &rng, Mat &samples, Mat & responses) static void makeData(RNG &rng, int samplesCount, const Mat &weights, float shift, const BorderList & borders, Mat &samples, Mat & responses)
{ {
int featureCount = weights.cols; int featureCount = weights.cols;
samples.create(samplesCount, featureCount, CV_32FC1); samples.create(samplesCount, featureCount, CV_32FC1);
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++) for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
{
rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second); rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
}
responses.create(samplesCount, 1, CV_32FC1); responses.create(samplesCount, 1, CV_32FC1);
for (int i = 0 ; i < samplesCount; i++) for (int i = 0 ; i < samplesCount; i++)
{ {
responses.at<float>(i) = decisionFunction(samples.row(i), weights, shift) > 0 ? 1.f : -1.f; double res = samples.row(i).dot(weights) + shift;
responses.at<float>(i) = res > 0 ? 1.f : -1.f;
} }
} }
CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(const Mat &weights, float shift, TrainDataType _type, double _precision) //==================================================================================================
typedef tuple<SVMSGD_TYPE, int, double> ML_SVMSGD_Param;
typedef testing::TestWithParam<ML_SVMSGD_Param> ML_SVMSGD_Params;
TEST_P(ML_SVMSGD_Params, scale_and_features)
{ {
type = _type; const int type = get<0>(GetParam());
precision = _precision; const int featureCount = get<1>(GetParam());
const double precision = get<2>(GetParam());
int featureCount = weights.cols; RNG &rng = cv::theRNG();
switch(type) Mat_<float> weights(1, featureCount);
rng.fill(weights, RNG::UNIFORM, -1, 1);
const float shift = static_cast<float>(rng.uniform(-featureCount, featureCount));
BorderList borders;
float lowerLimit = -TEST_VALUE_LIMIT;
float upperLimit = TEST_VALUE_LIMIT;
if (type == UNIFORM_SAME_SCALE)
{ {
case UNIFORM_SAME_SCALE: for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
generateSameBorders(featureCount); borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
break;
case UNIFORM_DIFFERENT_SCALES:
generateDifferentBorders(featureCount);
break;
default:
CV_Error(CV_StsBadArg, "Unknown train data type");
} }
else if (type == UNIFORM_DIFFERENT_SCALES)
RNG rng(0); {
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
{
int crit = rng.uniform(0, 2);
if (crit > 0)
borders.push_back(std::pair<float,float>(lowerLimit, upperLimit));
else
borders.push_back(std::pair<float,float>(lowerLimit/1000, upperLimit/1000));
}
}
ASSERT_FALSE(borders.empty());
Mat trainSamples; Mat trainSamples;
Mat trainResponses; Mat trainResponses;
int trainSamplesCount = 10000; int trainSamplesCount = 10000;
makeData(trainSamplesCount, weights, shift, rng, trainSamples, trainResponses); makeData(rng, trainSamplesCount, weights, shift, borders, trainSamples, trainResponses);
data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses); ASSERT_EQ(trainResponses.type(), CV_32FC1);
Mat testSamples;
Mat testResponses;
int testSamplesCount = 100000; int testSamplesCount = 100000;
makeData(testSamplesCount, weights, shift, rng, testSamples, testResponses); makeData(rng, testSamplesCount, weights, shift, borders, testSamples, testResponses);
} ASSERT_EQ(testResponses.type(), CV_32FC1);
Ptr<TrainData> data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
ASSERT_TRUE(data);
void CV_SVMSGDTrainTest::run( int /*start_from*/ )
{
cv::Ptr<SVMSGD> svmsgd = SVMSGD::create(); cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
ASSERT_TRUE(svmsgd);
svmsgd->train(data); svmsgd->train(data);
Mat responses; Mat responses;
svmsgd->predict(testSamples, responses); svmsgd->predict(testSamples, responses);
ASSERT_EQ(responses.type(), CV_32FC1);
ASSERT_EQ(responses.rows, testSamplesCount);
int errCount = 0; int errCount = 0;
int testSamplesCount = testSamples.rows;
CV_Assert((responses.type() == CV_32FC1) && (testResponses.type() == CV_32FC1));
for (int i = 0; i < testSamplesCount; i++) for (int i = 0; i < testSamplesCount; i++)
{
if (responses.at<float>(i) * testResponses.at<float>(i) < 0) if (responses.at<float>(i) * testResponses.at<float>(i) < 0)
errCount++; errCount++;
}
float err = (float)errCount / testSamplesCount; float err = (float)errCount / testSamplesCount;
EXPECT_LE(err, precision);
if ( err > precision )
{
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
}
}
void makeWeightsAndShift(int featureCount, Mat &weights, float &shift)
{
weights.create(1, featureCount, CV_32FC1);
cv::RNG rng(0);
double lowerLimit = -1;
double upperLimit = 1;
rng.fill(weights, RNG::UNIFORM, lowerLimit, upperLimit);
shift = static_cast<float>(rng.uniform(-featureCount, featureCount));
} }
ML_SVMSGD_Param params_list[] = {
ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 2, 0.01),
ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 5, 0.01),
ML_SVMSGD_Param(UNIFORM_SAME_SCALE, 100, 0.02),
ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 2, 0.01),
ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 5, 0.01),
ML_SVMSGD_Param(UNIFORM_DIFFERENT_SCALES, 100, 0.01),
};
TEST(ML_SVMSGD, trainSameScale2) INSTANTIATE_TEST_CASE_P(/**/, ML_SVMSGD_Params, testing::ValuesIn(params_list));
{
int featureCount = 2;
Mat weights;
float shift = 0;
makeWeightsAndShift(featureCount, weights, shift);
CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
test.safe_run();
}
TEST(ML_SVMSGD, trainSameScale5)
{
int featureCount = 5;
Mat weights;
float shift = 0;
makeWeightsAndShift(featureCount, weights, shift);
CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
test.safe_run();
}
TEST(ML_SVMSGD, trainSameScale100)
{
int featureCount = 100;
Mat weights;
float shift = 0;
makeWeightsAndShift(featureCount, weights, shift);
CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE, 0.02);
test.safe_run();
}
TEST(ML_SVMSGD, trainDifferentScales2)
{
int featureCount = 2;
Mat weights;
float shift = 0;
makeWeightsAndShift(featureCount, weights, shift);
CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
test.safe_run();
}
TEST(ML_SVMSGD, trainDifferentScales5)
{
int featureCount = 5;
Mat weights;
float shift = 0;
makeWeightsAndShift(featureCount, weights, shift);
CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01);
test.safe_run();
}
TEST(ML_SVMSGD, trainDifferentScales100)
{
int featureCount = 100;
Mat weights;
float shift = 0;
makeWeightsAndShift(featureCount, weights, shift);
CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_DIFFERENT_SCALES, 0.01); //==================================================================================================
test.safe_run();
}
TEST(ML_SVMSGD, twoPoints) TEST(ML_SVMSGD, twoPoints)
{ {

183
modules/ml/test/test_svmtrainauto.cpp
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@ -1,43 +1,6 @@
/*M/////////////////////////////////////////////////////////////////////////////////////// // This file is part of OpenCV project.
// // It is subject to the license terms in the LICENSE file found in the top-level directory
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // of this distribution and at http://opencv.org/license.html.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp" #include "test_precomp.hpp"
@ -46,21 +9,11 @@ namespace opencv_test { namespace {
using cv::ml::SVM; using cv::ml::SVM;
using cv::ml::TrainData; using cv::ml::TrainData;
//-------------------------------------------------------------------------------------------- static Ptr<TrainData> makeRandomData(int datasize)
class CV_SVMTrainAutoTest : public cvtest::BaseTest {
public:
CV_SVMTrainAutoTest() {}
protected:
virtual void run( int start_from );
};
void CV_SVMTrainAutoTest::run( int /*start_from*/ )
{ {
int datasize = 100;
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 ); cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S ); cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
RNG &rng = cv::theRNG();
RNG rng(0);
for (int i = 0; i < datasize; ++i) for (int i = 0; i < datasize; ++i)
{ {
int response = rng.uniform(0, 2); // Random from {0, 1}. int response = rng.uniform(0, 2); // Random from {0, 1}.
@ -68,36 +21,14 @@ void CV_SVMTrainAutoTest::run( int /*start_from*/ )
samples.at<float>( i, 1 ) = rng.uniform(0.f, 0.5f) + response * 0.5f; samples.at<float>( i, 1 ) = rng.uniform(0.f, 0.5f) + response * 0.5f;
responses.at<int>( i, 0 ) = response; responses.at<int>( i, 0 ) = response;
} }
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
cv::Ptr<SVM> svm = SVM::create();
svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {0.25f, 0.25f};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
float result0 = svm->predict( test_point0 );
float test_data1[2] = {0.75f, 0.75f};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
float result1 = svm->predict( test_point1 );
if ( fabs( result0 - 0 ) > 0.001 || fabs( result1 - 1 ) > 0.001 )
{
ts->set_failed_test_info( cvtest::TS::FAIL_BAD_ACCURACY );
}
} }
TEST(ML_SVM, trainauto) { CV_SVMTrainAutoTest test; test.safe_run(); } static Ptr<TrainData> makeCircleData(int datasize, float scale_factor, float radius)
TEST(ML_SVM, trainauto_sigmoid)
{ {
const int datasize = 100; // Populate samples with data that can be split into two concentric circles
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 ); cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S ); cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
const float scale_factor = 0.5;
const float radius = 2.0;
// Populate samples with data that can be split into two concentric circles
for (int i = 0; i < datasize; i+=2) for (int i = 0; i < datasize; i+=2)
{ {
const float pi = 3.14159f; const float pi = 3.14159f;
@ -115,41 +46,74 @@ TEST(ML_SVM, trainauto_sigmoid)
samples.at<float>( i + 1, 1 ) = y * scale_factor; samples.at<float>( i + 1, 1 ) = y * scale_factor;
responses.at<int>( i + 1, 0 ) = 1; responses.at<int>( i + 1, 0 ) = 1;
} }
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
}
static Ptr<TrainData> makeRandomData2(int datasize)
{
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 );
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
RNG &rng = cv::theRNG();
for (int i = 0; i < datasize; ++i)
{
int response = rng.uniform(0, 2); // Random from {0, 1}.
samples.at<float>( i, 0 ) = 0;
samples.at<float>( i, 1 ) = (0.5f - response) * rng.uniform(0.f, 1.2f) + response;
responses.at<int>( i, 0 ) = response;
}
return TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
}
//==================================================================================================
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses ); TEST(ML_SVM, trainauto)
{
const int datasize = 100;
cv::Ptr<TrainData> data = makeRandomData(datasize);
ASSERT_TRUE(data);
cv::Ptr<SVM> svm = SVM::create(); cv::Ptr<SVM> svm = SVM::create();
svm->setKernel(SVM::SIGMOID); ASSERT_TRUE(svm);
svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {0.25f, 0.25f};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
float result0 = svm->predict( test_point0 );
float test_data1[2] = {0.75f, 0.75f};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
float result1 = svm->predict( test_point1 );
EXPECT_NEAR(result0, 0, 0.001);
EXPECT_NEAR(result1, 1, 0.001);
}
TEST(ML_SVM, trainauto_sigmoid)
{
const int datasize = 100;
const float scale_factor = 0.5;
const float radius = 2.0;
cv::Ptr<TrainData> data = makeCircleData(datasize, scale_factor, radius);
ASSERT_TRUE(data);
cv::Ptr<SVM> svm = SVM::create();
ASSERT_TRUE(svm);
svm->setKernel(SVM::SIGMOID);
svm->setGamma(10.0); svm->setGamma(10.0);
svm->setCoef0(-10.0); svm->setCoef0(-10.0);
svm->trainAuto( data, 10 ); // 2-fold cross validation. svm->trainAuto( data, 10 ); // 2-fold cross validation.
float test_data0[2] = {radius, radius}; float test_data0[2] = {radius, radius};
cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 ); cv::Mat test_point0 = cv::Mat( 1, 2, CV_32FC1, test_data0 );
ASSERT_EQ(0, svm->predict( test_point0 )); EXPECT_FLOAT_EQ(svm->predict( test_point0 ), 0);
float test_data1[2] = {scale_factor * radius, scale_factor * radius}; float test_data1[2] = {scale_factor * radius, scale_factor * radius};
cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 ); cv::Mat test_point1 = cv::Mat( 1, 2, CV_32FC1, test_data1 );
ASSERT_EQ(1, svm->predict( test_point1 )); EXPECT_FLOAT_EQ(svm->predict( test_point1 ), 1);
} }
TEST(ML_SVM, trainAuto_regression_5369) TEST(ML_SVM, trainAuto_regression_5369)
{ {
int datasize = 100; const int datasize = 100;
cv::Mat samples = cv::Mat::zeros( datasize, 2, CV_32FC1 ); Ptr<TrainData> data = makeRandomData2(datasize);
cv::Mat responses = cv::Mat::zeros( datasize, 1, CV_32S );
RNG rng(0); // fixed!
for (int i = 0; i < datasize; ++i)
{
int response = rng.uniform(0, 2); // Random from {0, 1}.
samples.at<float>( i, 0 ) = 0;
samples.at<float>( i, 1 ) = (0.5f - response) * rng.uniform(0.f, 1.2f) + response;
responses.at<int>( i, 0 ) = response;
}
cv::Ptr<TrainData> data = TrainData::create( samples, cv::ml::ROW_SAMPLE, responses );
cv::Ptr<SVM> svm = SVM::create(); cv::Ptr<SVM> svm = SVM::create();
svm->trainAuto( data, 10 ); // 2-fold cross validation. svm->trainAuto( data, 10 ); // 2-fold cross validation.
@ -164,16 +128,8 @@ TEST(ML_SVM, trainAuto_regression_5369)
EXPECT_EQ(1., result1); EXPECT_EQ(1., result1);
} }
class CV_SVMGetSupportVectorsTest : public cvtest::BaseTest { TEST(ML_SVM, getSupportVectors)
public:
CV_SVMGetSupportVectorsTest() {}
protected:
virtual void run( int startFrom );
};
void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
{ {
int code = cvtest::TS::OK;
// Set up training data // Set up training data
int labels[4] = {1, -1, -1, -1}; int labels[4] = {1, -1, -1, -1};
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} }; float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
@ -181,19 +137,18 @@ void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
Mat labelsMat(4, 1, CV_32SC1, labels); Mat labelsMat(4, 1, CV_32SC1, labels);
Ptr<SVM> svm = SVM::create(); Ptr<SVM> svm = SVM::create();
ASSERT_TRUE(svm);
svm->setType(SVM::C_SVC); svm->setType(SVM::C_SVC);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6)); svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
// Test retrieval of SVs and compressed SVs on linear SVM // Test retrieval of SVs and compressed SVs on linear SVM
svm->setKernel(SVM::LINEAR); svm->setKernel(SVM::LINEAR);
svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat); svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
Mat sv = svm->getSupportVectors(); Mat sv = svm->getSupportVectors();
CV_Assert(sv.rows == 1); // by default compressed SV returned EXPECT_EQ(1, sv.rows); // by default compressed SV returned
sv = svm->getUncompressedSupportVectors(); sv = svm->getUncompressedSupportVectors();
CV_Assert(sv.rows == 3); EXPECT_EQ(3, sv.rows);
// Test retrieval of SVs and compressed SVs on non-linear SVM // Test retrieval of SVs and compressed SVs on non-linear SVM
svm->setKernel(SVM::POLY); svm->setKernel(SVM::POLY);
@ -201,15 +156,9 @@ void CV_SVMGetSupportVectorsTest::run(int /*startFrom*/ )
svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat); svm->train(trainingDataMat, cv::ml::ROW_SAMPLE, labelsMat);
sv = svm->getSupportVectors(); sv = svm->getSupportVectors();
CV_Assert(sv.rows == 3); EXPECT_EQ(3, sv.rows);
sv = svm->getUncompressedSupportVectors(); sv = svm->getUncompressedSupportVectors();
CV_Assert(sv.rows == 0); // inapplicable for non-linear SVMs EXPECT_EQ(0, sv.rows); // inapplicable for non-linear SVMs
ts->set_failed_test_info(code);
} }
TEST(ML_SVM, getSupportVectors) { CV_SVMGetSupportVectorsTest test; test.safe_run(); }
}} // namespace }} // namespace

189
modules/ml/test/test_utils.cpp
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@ -0,0 +1,189 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test {
void defaultDistribs( Mat& means, vector<Mat>& covs, int type)
{
float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
means.create(3, 2, type);
Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
means.resize(3), covs.resize(3);
Mat mr0 = means.row(0);
m0.convertTo(mr0, type);
c0.convertTo(covs[0], type);
Mat mr1 = means.row(1);
m1.convertTo(mr1, type);
c1.convertTo(covs[1], type);
Mat mr2 = means.row(2);
m2.convertTo(mr2, type);
c2.convertTo(covs[2], type);
}
// generate points sets by normal distributions
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType )
{
vector<int>::const_iterator sit = sizes.begin();
int total = 0;
for( ; sit != sizes.end(); ++sit )
total += *sit;
CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
CV_Assert( !data.empty() && data.rows == total );
CV_Assert( data.type() == dataType );
labels.create( data.rows, 1, labelType );
randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
vector<Mat> means(sizes.size());
for(int i = 0; i < _means.rows; i++)
means[i] = _means.row(i);
vector<Mat>::const_iterator mit = means.begin(), cit = covs.begin();
int bi, ei = 0;
sit = sizes.begin();
for( int p = 0, l = 0; sit != sizes.end(); ++sit, ++mit, ++cit, l++ )
{
bi = ei;
ei = bi + *sit;
CV_Assert( mit->rows == 1 && mit->cols == data.cols );
CV_Assert( cit->rows == data.cols && cit->cols == data.cols );
for( int i = bi; i < ei; i++, p++ )
{
Mat r = data.row(i);
r = r * (*cit) + *mit;
if( labelType == CV_32FC1 )
labels.at<float>(p, 0) = (float)l;
else if( labelType == CV_32SC1 )
labels.at<int>(p, 0) = l;
else
{
CV_DbgAssert(0);
}
}
}
}
int maxIdx( const vector<int>& count )
{
int idx = -1;
int maxVal = -1;
vector<int>::const_iterator it = count.begin();
for( int i = 0; it != count.end(); ++it, i++ )
{
if( *it > maxVal)
{
maxVal = *it;
idx = i;
}
}
CV_Assert( idx >= 0);
return idx;
}
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq)
{
size_t total = 0, nclusters = sizes.size();
for(size_t i = 0; i < sizes.size(); i++)
total += sizes[i];
CV_Assert( !labels.empty() );
CV_Assert( labels.total() == total && (labels.cols == 1 || labels.rows == 1));
CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
bool isFlt = labels.type() == CV_32FC1;
labelsMap.resize(nclusters);
vector<bool> buzy(nclusters, false);
int startIndex = 0;
for( size_t clusterIndex = 0; clusterIndex < sizes.size(); clusterIndex++ )
{
vector<int> count( nclusters, 0 );
for( int i = startIndex; i < startIndex + sizes[clusterIndex]; i++)
{
int lbl = isFlt ? (int)labels.at<float>(i) : labels.at<int>(i);
CV_Assert(lbl < (int)nclusters);
count[lbl]++;
CV_Assert(count[lbl] < (int)total);
}
startIndex += sizes[clusterIndex];
int cls = maxIdx( count );
CV_Assert( !checkClusterUniq || !buzy[cls] );
labelsMap[clusterIndex] = cls;
buzy[cls] = true;
}
if(checkClusterUniq)
{
for(size_t i = 0; i < buzy.size(); i++)
if(!buzy[i])
return false;
}
return true;
}
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent, bool checkClusterUniq)
{
err = 0;
CV_Assert( !labels.empty() && !origLabels.empty() );
CV_Assert( labels.rows == 1 || labels.cols == 1 );
CV_Assert( origLabels.rows == 1 || origLabels.cols == 1 );
CV_Assert( labels.total() == origLabels.total() );
CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
CV_Assert( origLabels.type() == labels.type() );
vector<int> labelsMap;
bool isFlt = labels.type() == CV_32FC1;
if( !labelsEquivalent )
{
if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) )
return false;
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i) != labelsMap[(int)origLabels.at<float>(i)] ? 1.f : 0.f;
else
err += labels.at<int>(i) != labelsMap[origLabels.at<int>(i)] ? 1.f : 0.f;
}
else
{
for( int i = 0; i < labels.rows; i++ )
if( isFlt )
err += labels.at<float>(i) != origLabels.at<float>(i) ? 1.f : 0.f;
else
err += labels.at<int>(i) != origLabels.at<int>(i) ? 1.f : 0.f;
}
err /= (float)labels.rows;
return true;
}
bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error)
{
error = 0.0f;
float accuracy = 0.0f;
Mat _p_labels_temp;
Mat _o_labels_temp;
_p_labels.convertTo(_p_labels_temp, CV_32S);
_o_labels.convertTo(_o_labels_temp, CV_32S);
CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
accuracy = (float)countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
error = 1 - accuracy;
return true;
}
} // namespace

273
modules/python/test/test_misc.py
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@ -1,38 +1,66 @@
#!/usr/bin/env python #!/usr/bin/env python
from __future__ import print_function from __future__ import print_function
import ctypes
from functools import partial
import numpy as np import numpy as np
import cv2 as cv import cv2 as cv
from tests_common import NewOpenCVTests from tests_common import NewOpenCVTests, unittest
def is_numeric(dtype):
return np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating)
def get_limits(dtype):
if not is_numeric(dtype):
return None, None
if np.issubdtype(dtype, np.integer):
info = np.iinfo(dtype)
else:
info = np.finfo(dtype)
return info.min, info.max
def get_conversion_error_msg(value, expected, actual):
return 'Conversion "{}" of type "{}" failed\nExpected: "{}" vs Actual "{}"'.format(
value, type(value).__name__, expected, actual
)
def get_no_exception_msg(value):
return 'Exception is not risen for {} of type {}'.format(value, type(value).__name__)
class Bindings(NewOpenCVTests): class Bindings(NewOpenCVTests):
def test_inheritance(self): def test_inheritance(self):
bm = cv.StereoBM_create() bm = cv.StereoBM_create()
bm.getPreFilterCap() # from StereoBM bm.getPreFilterCap() # from StereoBM
bm.getBlockSize() # from SteroMatcher bm.getBlockSize() # from SteroMatcher
boost = cv.ml.Boost_create() boost = cv.ml.Boost_create()
boost.getBoostType() # from ml::Boost boost.getBoostType() # from ml::Boost
boost.getMaxDepth() # from ml::DTrees boost.getMaxDepth() # from ml::DTrees
boost.isClassifier() # from ml::StatModel boost.isClassifier() # from ml::StatModel
def test_redirectError(self): def test_redirectError(self):
try: try:
cv.imshow("", None) # This causes an assert cv.imshow("", None) # This causes an assert
self.assertEqual("Dead code", 0) self.assertEqual("Dead code", 0)
except cv.error as _e: except cv.error as _e:
pass pass
handler_called = [False] handler_called = [False]
def test_error_handler(status, func_name, err_msg, file_name, line): def test_error_handler(status, func_name, err_msg, file_name, line):
handler_called[0] = True handler_called[0] = True
cv.redirectError(test_error_handler) cv.redirectError(test_error_handler)
try: try:
cv.imshow("", None) # This causes an assert cv.imshow("", None) # This causes an assert
self.assertEqual("Dead code", 0) self.assertEqual("Dead code", 0)
except cv.error as _e: except cv.error as _e:
self.assertEqual(handler_called[0], True) self.assertEqual(handler_called[0], True)
@ -40,7 +68,7 @@ class Bindings(NewOpenCVTests):
cv.redirectError(None) cv.redirectError(None)
try: try:
cv.imshow("", None) # This causes an assert cv.imshow("", None) # This causes an assert
self.assertEqual("Dead code", 0) self.assertEqual("Dead code", 0)
except cv.error as _e: except cv.error as _e:
pass pass
@ -48,41 +76,248 @@ class Bindings(NewOpenCVTests):
class Arguments(NewOpenCVTests): class Arguments(NewOpenCVTests):
def _try_to_convert(self, conversion, value):
try:
result = conversion(value).lower()
except Exception as e:
self.fail(
'{} "{}" is risen for conversion {} of type {}'.format(
type(e).__name__, e, value, type(value).__name__
)
)
else:
return result
def test_InputArray(self): def test_InputArray(self):
res1 = cv.utils.dumpInputArray(None) res1 = cv.utils.dumpInputArray(None)
#self.assertEqual(res1, "InputArray: noArray()") # not supported # self.assertEqual(res1, "InputArray: noArray()") # not supported
self.assertEqual(res1, "InputArray: empty()=true kind=0x00010000 flags=0x01010000 total(-1)=0 dims(-1)=0 size(-1)=0x0 type(-1)=CV_8UC1") self.assertEqual(res1, "InputArray: empty()=true kind=0x00010000 flags=0x01010000 total(-1)=0 dims(-1)=0 size(-1)=0x0 type(-1)=CV_8UC1")
res2_1 = cv.utils.dumpInputArray((1, 2)) res2_1 = cv.utils.dumpInputArray((1, 2))
self.assertEqual(res2_1, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=2 dims(-1)=2 size(-1)=1x2 type(-1)=CV_64FC1") self.assertEqual(res2_1, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=2 dims(-1)=2 size(-1)=1x2 type(-1)=CV_64FC1")
res2_2 = cv.utils.dumpInputArray(1.5) # Scalar(1.5, 1.5, 1.5, 1.5) res2_2 = cv.utils.dumpInputArray(1.5) # Scalar(1.5, 1.5, 1.5, 1.5)
self.assertEqual(res2_2, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=4 dims(-1)=2 size(-1)=1x4 type(-1)=CV_64FC1") self.assertEqual(res2_2, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=4 dims(-1)=2 size(-1)=1x4 type(-1)=CV_64FC1")
a = np.array([[1,2],[3,4],[5,6]]) a = np.array([[1, 2], [3, 4], [5, 6]])
res3 = cv.utils.dumpInputArray(a) # 32SC1 res3 = cv.utils.dumpInputArray(a) # 32SC1
self.assertEqual(res3, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=6 dims(-1)=2 size(-1)=2x3 type(-1)=CV_32SC1") self.assertEqual(res3, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=6 dims(-1)=2 size(-1)=2x3 type(-1)=CV_32SC1")
a = np.array([[[1,2],[3,4],[5,6]]], dtype='f') a = np.array([[[1, 2], [3, 4], [5, 6]]], dtype='f')
res4 = cv.utils.dumpInputArray(a) # 32FC2 res4 = cv.utils.dumpInputArray(a) # 32FC2
self.assertEqual(res4, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=3x1 type(-1)=CV_32FC2") self.assertEqual(res4, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=3x1 type(-1)=CV_32FC2")
a = np.array([[[1,2]],[[3,4]],[[5,6]]], dtype=float) a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]], dtype=float)
res5 = cv.utils.dumpInputArray(a) # 64FC2 res5 = cv.utils.dumpInputArray(a) # 64FC2
self.assertEqual(res5, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=1x3 type(-1)=CV_64FC2") self.assertEqual(res5, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=3 dims(-1)=2 size(-1)=1x3 type(-1)=CV_64FC2")
def test_InputArrayOfArrays(self): def test_InputArrayOfArrays(self):
res1 = cv.utils.dumpInputArrayOfArrays(None) res1 = cv.utils.dumpInputArrayOfArrays(None)
#self.assertEqual(res1, "InputArray: noArray()") # not supported # self.assertEqual(res1, "InputArray: noArray()") # not supported
self.assertEqual(res1, "InputArrayOfArrays: empty()=true kind=0x00050000 flags=0x01050000 total(-1)=0 dims(-1)=1 size(-1)=0x0") self.assertEqual(res1, "InputArrayOfArrays: empty()=true kind=0x00050000 flags=0x01050000 total(-1)=0 dims(-1)=1 size(-1)=0x0")
res2_1 = cv.utils.dumpInputArrayOfArrays((1, 2)) # { Scalar:all(1), Scalar::all(2) } res2_1 = cv.utils.dumpInputArrayOfArrays((1, 2)) # { Scalar:all(1), Scalar::all(2) }
self.assertEqual(res2_1, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_64FC1 dims(0)=2 size(0)=1x4 type(0)=CV_64FC1") self.assertEqual(res2_1, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_64FC1 dims(0)=2 size(0)=1x4 type(0)=CV_64FC1")
res2_2 = cv.utils.dumpInputArrayOfArrays([1.5]) res2_2 = cv.utils.dumpInputArrayOfArrays([1.5])
self.assertEqual(res2_2, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=1 dims(-1)=1 size(-1)=1x1 type(0)=CV_64FC1 dims(0)=2 size(0)=1x4 type(0)=CV_64FC1") self.assertEqual(res2_2, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=1 dims(-1)=1 size(-1)=1x1 type(0)=CV_64FC1 dims(0)=2 size(0)=1x4 type(0)=CV_64FC1")
a = np.array([[1,2],[3,4],[5,6]]) a = np.array([[1, 2], [3, 4], [5, 6]])
b = np.array([[1,2,3],[4,5,6],[7,8,9]]) b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
res3 = cv.utils.dumpInputArrayOfArrays([a, b]) res3 = cv.utils.dumpInputArrayOfArrays([a, b])
self.assertEqual(res3, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_32SC1 dims(0)=2 size(0)=2x3 type(0)=CV_32SC1") self.assertEqual(res3, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=2 dims(-1)=1 size(-1)=2x1 type(0)=CV_32SC1 dims(0)=2 size(0)=2x3 type(0)=CV_32SC1")
c = np.array([[[1,2],[3,4],[5,6]]], dtype='f') c = np.array([[[1, 2], [3, 4], [5, 6]]], dtype='f')
res4 = cv.utils.dumpInputArrayOfArrays([c, a, b]) res4 = cv.utils.dumpInputArrayOfArrays([c, a, b])
self.assertEqual(res4, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=3 dims(-1)=1 size(-1)=3x1 type(0)=CV_32FC2 dims(0)=2 size(0)=3x1 type(0)=CV_32FC2") self.assertEqual(res4, "InputArrayOfArrays: empty()=false kind=0x00050000 flags=0x01050000 total(-1)=3 dims(-1)=1 size(-1)=3x1 type(0)=CV_32FC2 dims(0)=2 size(0)=3x1 type(0)=CV_32FC2")
def test_parse_to_bool_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpBool)
for convertible_true in (True, 1, 64, np.bool(1), np.int8(123), np.int16(11), np.int32(2),
np.int64(1), np.bool_(3), np.bool8(12)):
actual = try_to_convert(convertible_true)
self.assertEqual('bool: true', actual,
msg=get_conversion_error_msg(convertible_true, 'bool: true', actual))
for convertible_false in (False, 0, np.uint8(0), np.bool_(0), np.int_(0)):
actual = try_to_convert(convertible_false)
self.assertEqual('bool: false', actual,
msg=get_conversion_error_msg(convertible_false, 'bool: false', actual))
def test_parse_to_bool_not_convertible(self):
for not_convertible in (1.2, np.float(2.3), 's', 'str', (1, 2), [1, 2], complex(1, 1), None,
complex(imag=2), complex(1.1), np.array([1, 0], dtype=np.bool)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpBool(not_convertible)
@unittest.skip('Wrong conversion behavior')
def test_parse_to_bool_convertible_extra(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpBool)
_, max_size_t = get_limits(ctypes.c_size_t)
for convertible_true in (-1, max_size_t):
actual = try_to_convert(convertible_true)
self.assertEqual('bool: true', actual,
msg=get_conversion_error_msg(convertible_true, 'bool: true', actual))
@unittest.skip('Wrong conversion behavior')
def test_parse_to_bool_not_convertible_extra(self):
for not_convertible in (np.array([False]), np.array([True], dtype=np.bool)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpBool(not_convertible)
def test_parse_to_int_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpInt)
min_int, max_int = get_limits(ctypes.c_int)
for convertible in (-10, -1, 2, int(43.2), np.uint8(15), np.int8(33), np.int16(-13),
np.int32(4), np.int64(345), (23), min_int, max_int, np.int_(33)):
expected = 'int: {0:d}'.format(convertible)
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
def test_parse_to_int_not_convertible(self):
min_int, max_int = get_limits(ctypes.c_int)
for not_convertible in (1.2, np.float(4), float(3), np.double(45), 's', 'str',
np.array([1, 2]), (1,), [1, 2], min_int - 1, max_int + 1,
complex(1, 1), complex(imag=2), complex(1.1), None):
with self.assertRaises((TypeError, OverflowError, ValueError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpInt(not_convertible)
@unittest.skip('Wrong conversion behavior')
def test_parse_to_int_not_convertible_extra(self):
for not_convertible in (np.bool_(True), True, False, np.float32(2.3),
np.array([3, ], dtype=int), np.array([-2, ], dtype=np.int32),
np.array([1, ], dtype=np.int), np.array([11, ], dtype=np.uint8)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpInt(not_convertible)
def test_parse_to_size_t_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpSizeT)
_, max_uint = get_limits(ctypes.c_uint)
for convertible in (2, True, False, max_uint, (12), np.uint8(34), np.int8(12), np.int16(23),
np.int32(123), np.int64(344), np.uint64(3), np.uint16(2), np.uint32(5),
np.uint(44)):
expected = 'size_t: {0:d}'.format(convertible).lower()
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
def test_parse_to_size_t_not_convertible(self):
for not_convertible in (1.2, np.float(4), float(3), np.double(45), 's', 'str',
np.array([1, 2]), (1,), [1, 2], np.float64(6), complex(1, 1),
complex(imag=2), complex(1.1), None):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpSizeT(not_convertible)
@unittest.skip('Wrong conversion behavior')
def test_parse_to_size_t_convertible_extra(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpSizeT)
_, max_size_t = get_limits(ctypes.c_size_t)
for convertible in (max_size_t,):
expected = 'size_t: {0:d}'.format(convertible).lower()
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
@unittest.skip('Wrong conversion behavior')
def test_parse_to_size_t_not_convertible_extra(self):
for not_convertible in (np.bool_(True), True, False, np.array([123, ], dtype=np.uint8),):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpSizeT(not_convertible)
def test_parse_to_float_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpFloat)
min_float, max_float = get_limits(ctypes.c_float)
for convertible in (2, -13, 1.24, float(32), np.float(32.45), np.double(12.23),
np.float32(-12.3), np.float64(3.22), np.float_(-1.5), min_float,
max_float, np.inf, -np.inf, float('Inf'), -float('Inf'),
np.double(np.inf), np.double(-np.inf), np.double(float('Inf')),
np.double(-float('Inf'))):
expected = 'Float: {0:.2f}'.format(convertible).lower()
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
# Workaround for Windows NaN tests due to Visual C runtime
# special floating point values (indefinite NaN)
for nan in (float('NaN'), np.nan, np.float32(np.nan), np.double(np.nan),
np.double(float('NaN'))):
actual = try_to_convert(nan)
self.assertIn('nan', actual, msg="Can't convert nan of type {} to float. "
"Actual: {}".format(type(nan).__name__, actual))
min_double, max_double = get_limits(ctypes.c_double)
for inf in (min_float * 10, max_float * 10, min_double, max_double):
expected = 'float: {}inf'.format('-' if inf < 0 else '')
actual = try_to_convert(inf)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(inf, expected, actual))
def test_parse_to_float_not_convertible(self):
for not_convertible in ('s', 'str', (12,), [1, 2], None, np.array([1, 2], dtype=np.float),
np.array([1, 2], dtype=np.double), complex(1, 1), complex(imag=2),
complex(1.1)):
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpFloat(not_convertible)
@unittest.skip('Wrong conversion behavior')
def test_parse_to_float_not_convertible_extra(self):
for not_convertible in (np.bool_(False), True, False, np.array([123, ], dtype=int),
np.array([1., ]), np.array([False]),
np.array([True], dtype=np.bool)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpFloat(not_convertible)
def test_parse_to_double_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpDouble)
min_float, max_float = get_limits(ctypes.c_float)
min_double, max_double = get_limits(ctypes.c_double)
for convertible in (2, -13, 1.24, np.float(32.45), float(2), np.double(12.23),
np.float32(-12.3), np.float64(3.22), np.float_(-1.5), min_float,
max_float, min_double, max_double, np.inf, -np.inf, float('Inf'),
-float('Inf'), np.double(np.inf), np.double(-np.inf),
np.double(float('Inf')), np.double(-float('Inf'))):
expected = 'Double: {0:.2f}'.format(convertible).lower()
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
# Workaround for Windows NaN tests due to Visual C runtime
# special floating point values (indefinite NaN)
for nan in (float('NaN'), np.nan, np.double(np.nan),
np.double(float('NaN'))):
actual = try_to_convert(nan)
self.assertIn('nan', actual, msg="Can't convert nan of type {} to double. "
"Actual: {}".format(type(nan).__name__, actual))
def test_parse_to_double_not_convertible(self):
for not_convertible in ('s', 'str', (12,), [1, 2], None, np.array([1, 2], dtype=np.float),
np.array([1, 2], dtype=np.double), complex(1, 1), complex(imag=2),
complex(1.1)):
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpDouble(not_convertible)
@unittest.skip('Wrong conversion behavior')
def test_parse_to_double_not_convertible_extra(self):
for not_convertible in (np.bool_(False), True, False, np.array([123, ], dtype=int),
np.array([1., ]), np.array([False]),
np.array([12.4], dtype=np.double), np.array([True], dtype=np.bool)):
with self.assertRaises((TypeError, OverflowError),
msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpDouble(not_convertible)
def test_parse_to_cstring_convertible(self):
try_to_convert = partial(self._try_to_convert, cv.utils.dumpCString)
for convertible in ('s', 'str', str(123), ('char'), np.str('test1'), np.str_('test2')):
expected = 'string: ' + convertible
actual = try_to_convert(convertible)
self.assertEqual(expected, actual,
msg=get_conversion_error_msg(convertible, expected, actual))
def test_parse_to_cstring_not_convertible(self):
for not_convertible in ((12,), ('t', 'e', 's', 't'), np.array(['123', ]),
np.array(['t', 'e', 's', 't']), 1, -1.4, True, False, None):
with self.assertRaises((TypeError), msg=get_no_exception_msg(not_convertible)):
_ = cv.utils.dumpCString(not_convertible)
class SamplesFindFile(NewOpenCVTests): class SamplesFindFile(NewOpenCVTests):

45
modules/stitching/include/opencv2/stitching/detail/warpers.hpp
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@ -219,11 +219,46 @@ public:
*/ */
AffineWarper(float scale = 1.f) : PlaneWarper(scale) {} AffineWarper(float scale = 1.f) : PlaneWarper(scale) {}
Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE; /** @brief Projects the image point.
Rect buildMaps(Size src_size, InputArray K, InputArray R, CV_OUT OutputArray xmap, CV_OUT OutputArray ymap) CV_OVERRIDE;
Point warp(InputArray src, InputArray K, InputArray R, @param pt Source point
int interp_mode, int border_mode, CV_OUT OutputArray dst) CV_OVERRIDE; @param K Camera intrinsic parameters
Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE; @param H Camera extrinsic parameters
@return Projected point
*/
Point2f warpPoint(const Point2f &pt, InputArray K, InputArray H) CV_OVERRIDE;
/** @brief Builds the projection maps according to the given camera data.
@param src_size Source image size
@param K Camera intrinsic parameters
@param H Camera extrinsic parameters
@param xmap Projection map for the x axis
@param ymap Projection map for the y axis
@return Projected image minimum bounding box
*/
Rect buildMaps(Size src_size, InputArray K, InputArray H, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
/** @brief Projects the image.
@param src Source image
@param K Camera intrinsic parameters
@param H Camera extrinsic parameters
@param interp_mode Interpolation mode
@param border_mode Border extrapolation mode
@param dst Projected image
@return Project image top-left corner
*/
Point warp(InputArray src, InputArray K, InputArray H,
int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE;
/**
@param src_size Source image bounding box
@param K Camera intrinsic parameters
@param H Camera extrinsic parameters
@return Projected image minimum bounding box
*/
Rect warpRoi(Size src_size, InputArray K, InputArray H) CV_OVERRIDE;
protected: protected:
/** @brief Extracts rotation and translation matrices from matrix H representing /** @brief Extracts rotation and translation matrices from matrix H representing

15
modules/ts/include/opencv2/ts.hpp
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@ -180,12 +180,21 @@ using testing::tuple_size;
using testing::tuple_element; using testing::tuple_element;
class SkipTestException: public cv::Exception namespace details {
class SkipTestExceptionBase: public cv::Exception
{
public:
SkipTestExceptionBase(bool handlingTags);
SkipTestExceptionBase(const cv::String& message, bool handlingTags);
};
}
class SkipTestException: public details::SkipTestExceptionBase
{ {
public: public:
int dummy; // workaround for MacOSX Xcode 7.3 bug (don't make class "empty") int dummy; // workaround for MacOSX Xcode 7.3 bug (don't make class "empty")
SkipTestException() : dummy(0) {} SkipTestException() : details::SkipTestExceptionBase(false), dummy(0) {}
SkipTestException(const cv::String& message) : dummy(0) { this->msg = message; } SkipTestException(const cv::String& message) : details::SkipTestExceptionBase(message, false), dummy(0) { }
}; };
/** Apply tag to the current test /** Apply tag to the current test

10
modules/ts/include/opencv2/ts/ts_ext.hpp
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@ -16,6 +16,9 @@ extern int testThreads;
void testSetUp(); void testSetUp();
void testTearDown(); void testTearDown();
bool checkBigDataTests();
} }
// check for required "opencv_test" namespace // check for required "opencv_test" namespace
@ -37,7 +40,7 @@ void testTearDown();
Body(); \ Body(); \
CV__TEST_CLEANUP \ CV__TEST_CLEANUP \
} \ } \
catch (const cvtest::SkipTestException& e) \ catch (const cvtest::details::SkipTestExceptionBase& e) \
{ \ { \
printf("[ SKIP ] %s\n", e.what()); \ printf("[ SKIP ] %s\n", e.what()); \
} \ } \
@ -74,9 +77,8 @@ void testTearDown();
#define CV__TEST_BIGDATA_BODY_IMPL(name) \ #define CV__TEST_BIGDATA_BODY_IMPL(name) \
{ \ { \
if (!cvtest::runBigDataTests) \ if (!cvtest::checkBigDataTests()) \
{ \ { \
printf("[ SKIP ] BigData tests are disabled\n"); \
return; \ return; \
} \ } \
CV__TRACE_APP_FUNCTION_NAME(name); \ CV__TRACE_APP_FUNCTION_NAME(name); \
@ -85,7 +87,7 @@ void testTearDown();
Body(); \ Body(); \
CV__TEST_CLEANUP \ CV__TEST_CLEANUP \
} \ } \
catch (const cvtest::SkipTestException& e) \ catch (const cvtest::details::SkipTestExceptionBase& e) \
{ \ { \
printf("[ SKIP ] %s\n", e.what()); \ printf("[ SKIP ] %s\n", e.what()); \
} \ } \

4
modules/ts/include/opencv2/ts/ts_perf.hpp
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@ -386,7 +386,7 @@ public:
static enum PERF_STRATEGY getCurrentModulePerformanceStrategy(); static enum PERF_STRATEGY getCurrentModulePerformanceStrategy();
static enum PERF_STRATEGY setModulePerformanceStrategy(enum PERF_STRATEGY strategy); static enum PERF_STRATEGY setModulePerformanceStrategy(enum PERF_STRATEGY strategy);
class PerfSkipTestException: public cv::Exception class PerfSkipTestException: public cvtest::SkipTestException
{ {
public: public:
int dummy; // workaround for MacOSX Xcode 7.3 bug (don't make class "empty") int dummy; // workaround for MacOSX Xcode 7.3 bug (don't make class "empty")
@ -531,7 +531,7 @@ void PrintTo(const Size& sz, ::std::ostream* os);
::cvtest::testSetUp(); \ ::cvtest::testSetUp(); \
RunPerfTestBody(); \ RunPerfTestBody(); \
} \ } \
catch (cvtest::SkipTestException& e) \ catch (cvtest::details::SkipTestExceptionBase& e) \
{ \ { \
printf("[ SKIP ] %s\n", e.what()); \ printf("[ SKIP ] %s\n", e.what()); \
} \ } \

25
modules/ts/src/ts.cpp
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@ -125,6 +125,20 @@ bool required_opencv_test_namespace = false; // compilation check for non-refac
namespace cvtest namespace cvtest
{ {
details::SkipTestExceptionBase::SkipTestExceptionBase(bool handlingTags)
{
if (!handlingTags)
{
testTagIncreaseSkipCount("skip_other", true, true);
}
}
details::SkipTestExceptionBase::SkipTestExceptionBase(const cv::String& message, bool handlingTags)
{
if (!handlingTags)
testTagIncreaseSkipCount("skip_other", true, true);
this->msg = message;
}
uint64 param_seed = 0x12345678; // real value is passed via parseCustomOptions function uint64 param_seed = 0x12345678; // real value is passed via parseCustomOptions function
static std::string path_join(const std::string& prefix, const std::string& subpath) static std::string path_join(const std::string& prefix, const std::string& subpath)
@ -850,6 +864,17 @@ void testTearDown()
} }
} }
bool checkBigDataTests()
{
if (!runBigDataTests)
{
testTagIncreaseSkipCount("skip_bigdata", true, true);
printf("[ SKIP ] BigData tests are disabled\n");
return false;
}
return true;
}
void parseCustomOptions(int argc, char **argv) void parseCustomOptions(int argc, char **argv)
{ {
const string command_line_keys = string( const string command_line_keys = string(

8
modules/ts/src/ts_perf.cpp
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@ -2003,15 +2003,15 @@ void TestBase::RunPerfTestBody()
implConf.GetImpl(); implConf.GetImpl();
#endif #endif
} }
catch(const SkipTestException&) catch(const PerfSkipTestException&)
{ {
metrics.terminationReason = performance_metrics::TERM_SKIP_TEST; metrics.terminationReason = performance_metrics::TERM_SKIP_TEST;
throw; return;
} }
catch(const PerfSkipTestException&) catch(const cvtest::details::SkipTestExceptionBase&)
{ {
metrics.terminationReason = performance_metrics::TERM_SKIP_TEST; metrics.terminationReason = performance_metrics::TERM_SKIP_TEST;
return; throw;
} }
catch(const PerfEarlyExitException&) catch(const PerfEarlyExitException&)
{ {

19
modules/ts/src/ts_tags.cpp
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@ -23,8 +23,10 @@ static std::map<std::string, int>& getTestTagsSkipExtraCounts()
static std::map<std::string, int> testTagsSkipExtraCounts; static std::map<std::string, int> testTagsSkipExtraCounts;
return testTagsSkipExtraCounts; return testTagsSkipExtraCounts;
} }
static void increaseTagsSkipCount(const std::string& tag, bool isMain) void testTagIncreaseSkipCount(const std::string& tag, bool isMain, bool appendSkipTests)
{ {
if (appendSkipTests)
skipped_tests.push_back(::testing::UnitTest::GetInstance()->current_test_info());
std::map<std::string, int>& counts = isMain ? getTestTagsSkipCounts() : getTestTagsSkipExtraCounts(); std::map<std::string, int>& counts = isMain ? getTestTagsSkipCounts() : getTestTagsSkipExtraCounts();
std::map<std::string, int>::iterator i = counts.find(tag); std::map<std::string, int>::iterator i = counts.find(tag);
if (i == counts.end()) if (i == counts.end())
@ -192,11 +194,14 @@ public:
{ {
if (!skipped_tests.empty()) if (!skipped_tests.empty())
{ {
std::cout << "[ SKIPSTAT ] " << skipped_tests.size() << " tests via tags" << std::endl; std::cout << "[ SKIPSTAT ] " << skipped_tests.size() << " tests skipped" << std::endl;
const std::vector<std::string>& skipTags = getTestTagsSkipList(); const std::vector<std::string>& skipTags = getTestTagsSkipList();
const std::map<std::string, int>& counts = getTestTagsSkipCounts(); const std::map<std::string, int>& counts = getTestTagsSkipCounts();
const std::map<std::string, int>& countsExtra = getTestTagsSkipExtraCounts(); const std::map<std::string, int>& countsExtra = getTestTagsSkipExtraCounts();
for (std::vector<std::string>::const_iterator i = skipTags.begin(); i != skipTags.end(); ++i) std::vector<std::string> skipTags_all = skipTags;
skipTags_all.push_back("skip_bigdata");
skipTags_all.push_back("skip_other");
for (std::vector<std::string>::const_iterator i = skipTags_all.begin(); i != skipTags_all.end(); ++i)
{ {
int c1 = 0; int c1 = 0;
std::map<std::string, int>::const_iterator i1 = counts.find(*i); std::map<std::string, int>::const_iterator i1 = counts.find(*i);
@ -301,7 +306,7 @@ void checkTestTags()
if (found != tags.size()) if (found != tags.size())
{ {
skipped_tests.push_back(::testing::UnitTest::GetInstance()->current_test_info()); skipped_tests.push_back(::testing::UnitTest::GetInstance()->current_test_info());
throw SkipTestException("Test tags don't pass required tags list (--test_tag parameter)"); throw details::SkipTestExceptionBase("Test tags don't pass required tags list (--test_tag parameter)", true);
} }
} }
} }
@ -317,7 +322,7 @@ void checkTestTags()
const std::string& testTag = testTags[i]; const std::string& testTag = testTags[i];
if (isTestTagSkipped(testTag, skipTag)) if (isTestTagSkipped(testTag, skipTag))
{ {
increaseTagsSkipCount(skipTag, skip_message.empty()); testTagIncreaseSkipCount(skipTag, skip_message.empty());
if (skip_message.empty()) skip_message = "Test with tag '" + testTag + "' is skipped ('" + skipTag + "' is in skip list)"; if (skip_message.empty()) skip_message = "Test with tag '" + testTag + "' is skipped ('" + skipTag + "' is in skip list)";
} }
} }
@ -327,7 +332,7 @@ void checkTestTags()
const std::string& testTag = testTagsImplied[i]; const std::string& testTag = testTagsImplied[i];
if (isTestTagSkipped(testTag, skipTag)) if (isTestTagSkipped(testTag, skipTag))
{ {
increaseTagsSkipCount(skipTag, skip_message.empty()); testTagIncreaseSkipCount(skipTag, skip_message.empty());
if (skip_message.empty()) skip_message = "Test with tag '" + testTag + "' is skipped (implied '" + skipTag + "' is in skip list)"; if (skip_message.empty()) skip_message = "Test with tag '" + testTag + "' is skipped (implied '" + skipTag + "' is in skip list)";
} }
} }
@ -335,7 +340,7 @@ void checkTestTags()
if (!skip_message.empty()) if (!skip_message.empty())
{ {
skipped_tests.push_back(::testing::UnitTest::GetInstance()->current_test_info()); skipped_tests.push_back(::testing::UnitTest::GetInstance()->current_test_info());
throw SkipTestException(skip_message); throw details::SkipTestExceptionBase(skip_message, true);
} }
} }

2
modules/ts/src/ts_tags.hpp
Unescape View File

@ -21,6 +21,8 @@ namespace cvtest {
void activateTestTags(const cv::CommandLineParser& parser); void activateTestTags(const cv::CommandLineParser& parser);
void testTagIncreaseSkipCount(const std::string& tag, bool isMain = true, bool appendSkipTests = false);
} // namespace } // namespace
#endif // OPENCV_TS_SRC_TAGS_HPP #endif // OPENCV_TS_SRC_TAGS_HPP

284
modules/videoio/src/cap_v4l.cpp
Unescape View File

@ -284,6 +284,32 @@ typedef uint32_t __u32;
namespace cv { namespace cv {
static const char* decode_ioctl_code(unsigned long ioctlCode)
{
switch (ioctlCode)
{
#define CV_ADD_IOCTL_CODE(id) case id: return #id
CV_ADD_IOCTL_CODE(VIDIOC_G_FMT);
CV_ADD_IOCTL_CODE(VIDIOC_S_FMT);
CV_ADD_IOCTL_CODE(VIDIOC_REQBUFS);
CV_ADD_IOCTL_CODE(VIDIOC_DQBUF);
CV_ADD_IOCTL_CODE(VIDIOC_QUERYCAP);
CV_ADD_IOCTL_CODE(VIDIOC_S_PARM);
CV_ADD_IOCTL_CODE(VIDIOC_G_PARM);
CV_ADD_IOCTL_CODE(VIDIOC_QUERYBUF);
CV_ADD_IOCTL_CODE(VIDIOC_QBUF);
CV_ADD_IOCTL_CODE(VIDIOC_STREAMON);
CV_ADD_IOCTL_CODE(VIDIOC_STREAMOFF);
CV_ADD_IOCTL_CODE(VIDIOC_ENUMINPUT);
CV_ADD_IOCTL_CODE(VIDIOC_G_INPUT);
CV_ADD_IOCTL_CODE(VIDIOC_S_INPUT);
CV_ADD_IOCTL_CODE(VIDIOC_G_CTRL);
CV_ADD_IOCTL_CODE(VIDIOC_S_CTRL);
#undef CV_ADD_IOCTL_CODE
}
return "unknown";
}
/* Device Capture Objects */ /* Device Capture Objects */
/* V4L2 structure */ /* V4L2 structure */
struct Buffer struct Buffer
@ -305,6 +331,9 @@ struct CvCaptureCAM_V4L CV_FINAL : public CvCapture
int getCaptureDomain() /*const*/ CV_OVERRIDE { return cv::CAP_V4L; } int getCaptureDomain() /*const*/ CV_OVERRIDE { return cv::CAP_V4L; }
int deviceHandle; int deviceHandle;
bool v4l_buffersRequested;
bool v4l_streamStarted;
int bufferIndex; int bufferIndex;
bool FirstCapture; bool FirstCapture;
String deviceName; String deviceName;
@ -345,6 +374,8 @@ struct CvCaptureCAM_V4L CV_FINAL : public CvCapture
bool open(const char* deviceName); bool open(const char* deviceName);
bool isOpened() const; bool isOpened() const;
void closeDevice();
virtual double getProperty(int) const CV_OVERRIDE; virtual double getProperty(int) const CV_OVERRIDE;
virtual bool setProperty(int, double) CV_OVERRIDE; virtual bool setProperty(int, double) CV_OVERRIDE;
virtual bool grabFrame() CV_OVERRIDE; virtual bool grabFrame() CV_OVERRIDE;
@ -381,7 +412,10 @@ struct CvCaptureCAM_V4L CV_FINAL : public CvCapture
/*********************** Implementations ***************************************/ /*********************** Implementations ***************************************/
CvCaptureCAM_V4L::CvCaptureCAM_V4L() : CvCaptureCAM_V4L::CvCaptureCAM_V4L() :
deviceHandle(-1), bufferIndex(-1), deviceHandle(-1),
v4l_buffersRequested(false),
v4l_streamStarted(false),
bufferIndex(-1),
FirstCapture(true), FirstCapture(true),
palette(0), palette(0),
width(0), height(0), width_set(0), height_set(0), width(0), height(0), width_set(0), height_set(0),
@ -395,11 +429,32 @@ CvCaptureCAM_V4L::CvCaptureCAM_V4L() :
memset(&timestamp, 0, sizeof(timestamp)); memset(&timestamp, 0, sizeof(timestamp));
} }
CvCaptureCAM_V4L::~CvCaptureCAM_V4L() { CvCaptureCAM_V4L::~CvCaptureCAM_V4L()
streaming(false); {
releaseBuffers(); try
{
closeDevice();
}
catch (...)
{
CV_LOG_WARNING(NULL, "VIDEOIO(V4L2): unable properly close device: " << deviceName);
if (deviceHandle != -1)
close(deviceHandle);
}
}
void CvCaptureCAM_V4L::closeDevice()
{
if (v4l_streamStarted)
streaming(false);
if (v4l_buffersRequested)
releaseBuffers();
if(deviceHandle != -1) if(deviceHandle != -1)
{
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): close(" << deviceHandle << ")");
close(deviceHandle); close(deviceHandle);
}
deviceHandle = -1;
} }
bool CvCaptureCAM_V4L::isOpened() const bool CvCaptureCAM_V4L::isOpened() const
@ -415,7 +470,7 @@ bool CvCaptureCAM_V4L::try_palette_v4l2()
form.fmt.pix.field = V4L2_FIELD_ANY; form.fmt.pix.field = V4L2_FIELD_ANY;
form.fmt.pix.width = width; form.fmt.pix.width = width;
form.fmt.pix.height = height; form.fmt.pix.height = height;
if (!tryIoctl(VIDIOC_S_FMT, &form)) if (!tryIoctl(VIDIOC_S_FMT, &form, true))
{ {
return false; return false;
} }
@ -460,9 +515,7 @@ bool CvCaptureCAM_V4L::try_init_v4l2()
// The cv::CAP_PROP_MODE used for set the video input channel number // The cv::CAP_PROP_MODE used for set the video input channel number
if (!setVideoInputChannel()) if (!setVideoInputChannel())
{ {
#ifndef NDEBUG CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): Unable to set Video Input Channel");
fprintf(stderr, "(DEBUG) V4L2: Unable to set Video Input Channel.");
#endif
return false; return false;
} }
@ -470,16 +523,14 @@ bool CvCaptureCAM_V4L::try_init_v4l2()
capability = v4l2_capability(); capability = v4l2_capability();
if (!tryIoctl(VIDIOC_QUERYCAP, &capability)) if (!tryIoctl(VIDIOC_QUERYCAP, &capability))
{ {
#ifndef NDEBUG CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): Unable to query capability");
fprintf(stderr, "(DEBUG) V4L2: Unable to query capability.");
#endif
return false; return false;
} }
if ((capability.capabilities & V4L2_CAP_VIDEO_CAPTURE) == 0) if ((capability.capabilities & V4L2_CAP_VIDEO_CAPTURE) == 0)
{ {
/* Nope. */ /* Nope. */
fprintf(stderr, "VIDEOIO ERROR: V4L2: Unable to capture video memory."); CV_LOG_INFO(NULL, "VIDEOIO(V4L2:" << deviceName << "): not supported - device is unable to capture video (missing V4L2_CAP_VIDEO_CAPTURE)");
return false; return false;
} }
return true; return true;
@ -488,10 +539,18 @@ bool CvCaptureCAM_V4L::try_init_v4l2()
bool CvCaptureCAM_V4L::autosetup_capture_mode_v4l2() bool CvCaptureCAM_V4L::autosetup_capture_mode_v4l2()
{ {
//in case palette is already set and works, no need to setup. //in case palette is already set and works, no need to setup.
if (palette != 0 && try_palette_v4l2()) { if (palette != 0)
return true; {
} else if (errno == EBUSY) { if (try_palette_v4l2())
return false; {
return true;
}
else if (errno == EBUSY)
{
CV_LOG_INFO(NULL, "VIDEOIO(V4L2:" << deviceName << "): device is busy");
closeDevice();
return false;
}
} }
__u32 try_order[] = { __u32 try_order[] = {
V4L2_PIX_FMT_BGR24, V4L2_PIX_FMT_BGR24,
@ -519,6 +578,10 @@ bool CvCaptureCAM_V4L::autosetup_capture_mode_v4l2()
palette = try_order[i]; palette = try_order[i];
if (try_palette_v4l2()) { if (try_palette_v4l2()) {
return true; return true;
} else if (errno == EBUSY) {
CV_LOG_INFO(NULL, "VIDEOIO(V4L2:" << deviceName << "): device is busy");
closeDevice();
return false;
} }
} }
return false; return false;
@ -534,9 +597,15 @@ bool CvCaptureCAM_V4L::setFps(int value)
streamparm.parm.capture.timeperframe.numerator = 1; streamparm.parm.capture.timeperframe.numerator = 1;
streamparm.parm.capture.timeperframe.denominator = __u32(value); streamparm.parm.capture.timeperframe.denominator = __u32(value);
if (!tryIoctl(VIDIOC_S_PARM, &streamparm) || !tryIoctl(VIDIOC_G_PARM, &streamparm)) if (!tryIoctl(VIDIOC_S_PARM, &streamparm) || !tryIoctl(VIDIOC_G_PARM, &streamparm))
{
CV_LOG_INFO(NULL, "VIDEOIO(V4L2:" << deviceName << "): can't set FPS: " << value);
return false; return false;
}
fps = streamparm.parm.capture.timeperframe.denominator; CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): FPS="
<< streamparm.parm.capture.timeperframe.denominator << "/"
<< streamparm.parm.capture.timeperframe.numerator);
fps = streamparm.parm.capture.timeperframe.denominator; // TODO use numerator
return true; return true;
} }
@ -631,10 +700,9 @@ bool CvCaptureCAM_V4L::initCapture()
if (!isOpened()) if (!isOpened())
return false; return false;
if (!try_init_v4l2()) { if (!try_init_v4l2())
#ifndef NDEBUG {
fprintf(stderr, " try_init_v4l2 open \"%s\": %s\n", deviceName.c_str(), strerror(errno)); CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): init failed: errno=" << errno << " (" << strerror(errno) << ")");
#endif
return false; return false;
} }
@ -642,14 +710,17 @@ bool CvCaptureCAM_V4L::initCapture()
form = v4l2_format(); form = v4l2_format();
form.type = V4L2_BUF_TYPE_VIDEO_CAPTURE; form.type = V4L2_BUF_TYPE_VIDEO_CAPTURE;
if (!tryIoctl(VIDIOC_G_FMT, &form)) { if (!tryIoctl(VIDIOC_G_FMT, &form))
fprintf( stderr, "VIDEOIO ERROR: V4L2: Could not obtain specifics of capture window.\n"); {
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): Could not obtain specifics of capture window (VIDIOC_G_FMT): errno=" << errno << " (" << strerror(errno) << ")");
return false; return false;
} }
if (!autosetup_capture_mode_v4l2()) { if (!autosetup_capture_mode_v4l2())
if (errno != EBUSY) { {
fprintf(stderr, "VIDEOIO ERROR: V4L2: Pixel format of incoming image is unsupported by OpenCV\n"); if (errno != EBUSY)
{
CV_LOG_INFO(NULL, "VIDEOIO(V4L2:" << deviceName << "): Pixel format of incoming image is unsupported by OpenCV");
} }
return false; return false;
} }
@ -691,16 +762,16 @@ bool CvCaptureCAM_V4L::requestBuffers()
{ {
unsigned int buffer_number = bufferSize; unsigned int buffer_number = bufferSize;
while (buffer_number > 0) { while (buffer_number > 0) {
if (!requestBuffers(buffer_number)) if (requestBuffers(buffer_number) && req.count >= buffer_number)
return false; {
if (req.count >= buffer_number)
break; break;
}
buffer_number--; buffer_number--;
fprintf(stderr, "Insufficient buffer memory on %s -- decreasing buffers\n", deviceName.c_str()); CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): Insufficient buffer memory -- decreasing buffers: " << buffer_number);
} }
if (buffer_number < 1) { if (buffer_number < 1) {
fprintf(stderr, "Insufficient buffer memory on %s\n", deviceName.c_str()); CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): Insufficient buffer memory");
return false; return false;
} }
bufferSize = req.count; bufferSize = req.count;
@ -718,13 +789,18 @@ bool CvCaptureCAM_V4L::requestBuffers(unsigned int buffer_number)
req.memory = V4L2_MEMORY_MMAP; req.memory = V4L2_MEMORY_MMAP;
if (!tryIoctl(VIDIOC_REQBUFS, &req)) { if (!tryIoctl(VIDIOC_REQBUFS, &req)) {
if (EINVAL == errno) { int err = errno;
fprintf(stderr, "%s does not support memory mapping\n", deviceName.c_str()); if (EINVAL == err)
} else { {
perror("VIDIOC_REQBUFS"); CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): no support for memory mapping");
}
else
{
CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed VIDIOC_REQBUFS: errno=" << err << " (" << strerror(err) << ")");
} }
return false; return false;
} }
v4l_buffersRequested = true;
return true; return true;
} }
@ -738,7 +814,7 @@ bool CvCaptureCAM_V4L::createBuffers()
buf.index = n_buffers; buf.index = n_buffers;
if (!tryIoctl(VIDIOC_QUERYBUF, &buf)) { if (!tryIoctl(VIDIOC_QUERYBUF, &buf)) {
perror("VIDIOC_QUERYBUF"); CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed VIDIOC_QUERYBUF: errno=" << errno << " (" << strerror(errno) << ")");
return false; return false;
} }
@ -751,7 +827,7 @@ bool CvCaptureCAM_V4L::createBuffers()
deviceHandle, buf.m.offset); deviceHandle, buf.m.offset);
if (MAP_FAILED == buffers[n_buffers].start) { if (MAP_FAILED == buffers[n_buffers].start) {
perror("mmap"); CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed mmap(" << buf.length << "): errno=" << errno << " (" << strerror(errno) << ")");
return false; return false;
} }
maxLength = maxLength > buf.length ? maxLength : buf.length; maxLength = maxLength > buf.length ? maxLength : buf.length;
@ -795,7 +871,7 @@ bool CvCaptureCAM_V4L::open(int _index)
} }
if (_index < 0) if (_index < 0)
{ {
fprintf(stderr, "VIDEOIO ERROR: V4L: can't find camera device\n"); CV_LOG_WARNING(NULL, "VIDEOIO(V4L2): can't find camera device");
name.clear(); name.clear();
return false; return false;
} }
@ -808,16 +884,15 @@ bool CvCaptureCAM_V4L::open(int _index)
bool res = open(name.c_str()); bool res = open(name.c_str());
if (!res) if (!res)
{ {
CV_LOG_WARNING(NULL, cv::format("VIDEOIO ERROR: V4L: can't open camera by index %d", _index)); CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): can't open camera by index");
} }
return res; return res;
} }
bool CvCaptureCAM_V4L::open(const char* _deviceName) bool CvCaptureCAM_V4L::open(const char* _deviceName)
{ {
#ifndef NDEBUG CV_Assert(_deviceName);
fprintf(stderr, "(DEBUG) V4L: opening %s\n", _deviceName); CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << _deviceName << "): opening...");
#endif
FirstCapture = true; FirstCapture = true;
width = DEFAULT_V4L_WIDTH; width = DEFAULT_V4L_WIDTH;
height = DEFAULT_V4L_HEIGHT; height = DEFAULT_V4L_HEIGHT;
@ -833,6 +908,7 @@ bool CvCaptureCAM_V4L::open(const char* _deviceName)
bufferIndex = -1; bufferIndex = -1;
deviceHandle = ::open(deviceName.c_str(), O_RDWR /* required */ | O_NONBLOCK, 0); deviceHandle = ::open(deviceName.c_str(), O_RDWR /* required */ | O_NONBLOCK, 0);
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << _deviceName << "): deviceHandle=" << deviceHandle);
if (deviceHandle == -1) if (deviceHandle == -1)
return false; return false;
@ -846,7 +922,8 @@ bool CvCaptureCAM_V4L::read_frame_v4l2()
buf.memory = V4L2_MEMORY_MMAP; buf.memory = V4L2_MEMORY_MMAP;
while (!tryIoctl(VIDIOC_DQBUF, &buf)) { while (!tryIoctl(VIDIOC_DQBUF, &buf)) {
if (errno == EIO && !(buf.flags & (V4L2_BUF_FLAG_QUEUED | V4L2_BUF_FLAG_DONE))) { int err = errno;
if (err == EIO && !(buf.flags & (V4L2_BUF_FLAG_QUEUED | V4L2_BUF_FLAG_DONE))) {
// Maybe buffer not in the queue? Try to put there // Maybe buffer not in the queue? Try to put there
if (!tryIoctl(VIDIOC_QBUF, &buf)) if (!tryIoctl(VIDIOC_QBUF, &buf))
return false; return false;
@ -854,7 +931,7 @@ bool CvCaptureCAM_V4L::read_frame_v4l2()
} }
/* display the error and stop processing */ /* display the error and stop processing */
returnFrame = false; returnFrame = false;
perror("VIDIOC_DQBUF"); CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): can't read frame (VIDIOC_DQBUF): errno=" << err << " (" << strerror(err) << ")");
return false; return false;
} }
@ -872,38 +949,64 @@ bool CvCaptureCAM_V4L::read_frame_v4l2()
bool CvCaptureCAM_V4L::tryIoctl(unsigned long ioctlCode, void *parameter, bool failIfBusy, int attempts) const bool CvCaptureCAM_V4L::tryIoctl(unsigned long ioctlCode, void *parameter, bool failIfBusy, int attempts) const
{ {
CV_LOG_DEBUG(NULL, "tryIoctl(handle=" << deviceHandle << ", ioctl=0x" << std::hex << ioctlCode << ", ...)"); CV_Assert(attempts > 0);
if (attempts == 0) { CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): tryIoctl(" << deviceHandle << ", "
return false; << decode_ioctl_code(ioctlCode) << "(" << ioctlCode << "), failIfBusy=" << failIfBusy << ")"
} );
while (-1 == ioctl(deviceHandle, ioctlCode, parameter)) { while (true)
const bool isBusy = (errno == EBUSY); {
if (isBusy & failIfBusy) { errno = 0;
int result = ioctl(deviceHandle, ioctlCode, parameter);
int err = errno;
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): call ioctl(" << deviceHandle << ", "
<< decode_ioctl_code(ioctlCode) << "(" << ioctlCode << "), ...) => "
<< result << " errno=" << err << " (" << strerror(err) << ")"
);
if (result != -1)
return true; // success
const bool isBusy = (err == EBUSY);
if (isBusy && failIfBusy)
{
CV_LOG_INFO(NULL, "VIDEOIO(V4L2:" << deviceName << "): ioctl returns with errno=EBUSY");
return false; return false;
} }
if ((attempts > 0) && (--attempts == 0)) { if (!(isBusy || errno == EAGAIN))
return false; return false;
}
if (!(isBusy || errno == EAGAIN)) if (--attempts == 0) {
return false; return false;
}
fd_set fds; fd_set fds;
FD_ZERO(&fds); FD_ZERO(&fds);
FD_SET(deviceHandle, &fds); FD_SET(deviceHandle, &fds);
/* Timeout. */ /* Timeout. */
static int param_v4l_select_timeout = (int)utils::getConfigurationParameterSizeT("OPENCV_VIDEOIO_V4L_SELECT_TIMEOUT", 10);
struct timeval tv; struct timeval tv;
tv.tv_sec = 10; tv.tv_sec = param_v4l_select_timeout;
tv.tv_usec = 0; tv.tv_usec = 0;
int result = select(deviceHandle + 1, &fds, NULL, NULL, &tv); errno = 0;
if (0 == result) { result = select(deviceHandle + 1, &fds, NULL, NULL, &tv);
fprintf(stderr, "select timeout\n"); err = errno;
if (0 == result)
{
CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): select() timeout.");
return false;
}
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): select(" << deviceHandle << ") => "
<< result << " errno = " << err << " (" << strerror(err) << ")"
);
if (EINTR == err) // don't loop if signal occurred, like Ctrl+C
{
return false; return false;
} }
if (-1 == result && EINTR != errno)
perror("select");
} }
return true; return true;
} }
@ -930,14 +1033,12 @@ bool CvCaptureCAM_V4L::grabFrame()
buf.index = index; buf.index = index;
if (!tryIoctl(VIDIOC_QBUF, &buf)) { if (!tryIoctl(VIDIOC_QBUF, &buf)) {
perror("VIDIOC_QBUF"); CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed VIDIOC_QBUF (buffer=" << index << "): errno=" << errno << " (" << strerror(errno) << ")");
return false; return false;
} }
} }
if(!streaming(true)) { if (!streaming(true)) {
/* error enabling the stream */
perror("VIDIOC_STREAMON");
return false; return false;
} }
@ -952,9 +1053,12 @@ bool CvCaptureCAM_V4L::grabFrame()
FirstCapture = false; FirstCapture = false;
} }
// In the case that the grab frame was without retrieveFrame // In the case that the grab frame was without retrieveFrame
if (bufferIndex >= 0) { if (bufferIndex >= 0)
{
if (!tryIoctl(VIDIOC_QBUF, &buffers[bufferIndex].buffer)) if (!tryIoctl(VIDIOC_QBUF, &buffers[bufferIndex].buffer))
perror("VIDIOC_QBUF"); {
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed VIDIOC_QBUF (buffer=" << bufferIndex << "): errno=" << errno << " (" << strerror(errno) << ")");
}
} }
return read_frame_v4l2(); return read_frame_v4l2();
} }
@ -1469,6 +1573,7 @@ void CvCaptureCAM_V4L::convertToRgb(const Buffer &currentBuffer)
#ifdef HAVE_JPEG #ifdef HAVE_JPEG
case V4L2_PIX_FMT_MJPEG: case V4L2_PIX_FMT_MJPEG:
case V4L2_PIX_FMT_JPEG: case V4L2_PIX_FMT_JPEG:
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): decoding JPEG frame: size=" << currentBuffer.buffer.bytesused);
cv::imdecode(Mat(1, currentBuffer.buffer.bytesused, CV_8U, currentBuffer.start), IMREAD_COLOR, &destination); cv::imdecode(Mat(1, currentBuffer.buffer.bytesused, CV_8U, currentBuffer.start), IMREAD_COLOR, &destination);
return; return;
#endif #endif
@ -1711,7 +1816,7 @@ bool CvCaptureCAM_V4L::controlInfo(int property_id, __u32 &_v4l2id, cv::Range &r
v4l2_queryctrl queryctrl = v4l2_queryctrl(); v4l2_queryctrl queryctrl = v4l2_queryctrl();
queryctrl.id = __u32(v4l2id); queryctrl.id = __u32(v4l2id);
if (v4l2id == -1 || !tryIoctl(VIDIOC_QUERYCTRL, &queryctrl)) { if (v4l2id == -1 || !tryIoctl(VIDIOC_QUERYCTRL, &queryctrl)) {
fprintf(stderr, "VIDEOIO ERROR: V4L2: property %s is not supported\n", capPropertyName(property_id).c_str()); CV_LOG_INFO(NULL, "VIDEOIO(V4L2:" << deviceName << "): property " << capPropertyName(property_id) << " is not supported");
return false; return false;
} }
_v4l2id = __u32(v4l2id); _v4l2id = __u32(v4l2id);
@ -1742,7 +1847,9 @@ bool CvCaptureCAM_V4L::icvControl(__u32 v4l2id, int &value, bool isSet) const
/* The driver may clamp the value or return ERANGE, ignored here */ /* The driver may clamp the value or return ERANGE, ignored here */
if (!tryIoctl(isSet ? VIDIOC_S_CTRL : VIDIOC_G_CTRL, &control)) { if (!tryIoctl(isSet ? VIDIOC_S_CTRL : VIDIOC_G_CTRL, &control)) {
switch (errno) { int err = errno;
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed " << (isSet ? "VIDIOC_S_CTRL" : "VIDIOC_G_CTRL") << ": errno=" << err << " (" << strerror(err) << ")");
switch (err) {
#ifndef NDEBUG #ifndef NDEBUG
case EINVAL: case EINVAL:
fprintf(stderr, fprintf(stderr,
@ -1757,7 +1864,6 @@ bool CvCaptureCAM_V4L::icvControl(__u32 v4l2id, int &value, bool isSet) const
break; break;
#endif #endif
default: default:
perror(isSet ? "VIDIOC_S_CTRL" : "VIDIOC_G_CTRL");
break; break;
} }
return false; return false;
@ -1791,7 +1897,7 @@ double CvCaptureCAM_V4L::getProperty(int property_id) const
v4l2_streamparm sp = v4l2_streamparm(); v4l2_streamparm sp = v4l2_streamparm();
sp.type = V4L2_BUF_TYPE_VIDEO_CAPTURE; sp.type = V4L2_BUF_TYPE_VIDEO_CAPTURE;
if (!tryIoctl(VIDIOC_G_PARM, &sp)) { if (!tryIoctl(VIDIOC_G_PARM, &sp)) {
fprintf(stderr, "VIDEOIO ERROR: V4L: Unable to get camera FPS\n"); CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): Unable to get camera FPS");
return -1; return -1;
} }
return sp.parm.capture.timeperframe.denominator / (double)sp.parm.capture.timeperframe.numerator; return sp.parm.capture.timeperframe.denominator / (double)sp.parm.capture.timeperframe.numerator;
@ -1881,7 +1987,7 @@ bool CvCaptureCAM_V4L::setProperty( int property_id, double _value )
return true; return true;
if (value > MAX_V4L_BUFFERS || value < 1) { if (value > MAX_V4L_BUFFERS || value < 1) {
fprintf(stderr, "V4L: Bad buffer size %d, buffer size must be from 1 to %d\n", value, MAX_V4L_BUFFERS); CV_LOG_WARNING(NULL, "VIDEOIO(V4L2:" << deviceName << "): Bad buffer size " << value << ", buffer size must be from 1 to " << MAX_V4L_BUFFERS);
return false; return false;
} }
bufferSize = value; bufferSize = value;
@ -1937,13 +2043,15 @@ void CvCaptureCAM_V4L::releaseBuffers()
bufferIndex = -1; bufferIndex = -1;
FirstCapture = true; FirstCapture = true;
if (!isOpened())
if (!v4l_buffersRequested)
return; return;
v4l_buffersRequested = false;
for (unsigned int n_buffers = 0; n_buffers < MAX_V4L_BUFFERS; ++n_buffers) { for (unsigned int n_buffers = 0; n_buffers < MAX_V4L_BUFFERS; ++n_buffers) {
if (buffers[n_buffers].start) { if (buffers[n_buffers].start) {
if (-1 == munmap(buffers[n_buffers].start, buffers[n_buffers].length)) { if (-1 == munmap(buffers[n_buffers].start, buffers[n_buffers].length)) {
perror("munmap"); CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed munmap(): errno=" << errno << " (" << strerror(errno) << ")");
} else { } else {
buffers[n_buffers].start = 0; buffers[n_buffers].start = 0;
} }
@ -1957,11 +2065,28 @@ void CvCaptureCAM_V4L::releaseBuffers()
bool CvCaptureCAM_V4L::streaming(bool startStream) bool CvCaptureCAM_V4L::streaming(bool startStream)
{ {
if (!isOpened()) if (startStream != v4l_streamStarted)
return !startStream; {
if (!isOpened())
{
CV_Assert(v4l_streamStarted == false);
return !startStream;
}
type = V4L2_BUF_TYPE_VIDEO_CAPTURE; type = V4L2_BUF_TYPE_VIDEO_CAPTURE;
return tryIoctl(startStream ? VIDIOC_STREAMON : VIDIOC_STREAMOFF, &type); bool result = tryIoctl(startStream ? VIDIOC_STREAMON : VIDIOC_STREAMOFF, &type);
if (result)
{
v4l_streamStarted = startStream;
return true;
}
if (startStream)
{
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed VIDIOC_STREAMON: errno=" << errno << " (" << strerror(errno) << ")");
}
return false;
}
return startStream;
} }
IplImage *CvCaptureCAM_V4L::retrieveFrame(int) IplImage *CvCaptureCAM_V4L::retrieveFrame(int)
@ -1981,6 +2106,7 @@ IplImage *CvCaptureCAM_V4L::retrieveFrame(int)
} else { } else {
// for mjpeg streams the size might change in between, so we have to change the header // for mjpeg streams the size might change in between, so we have to change the header
// We didn't allocate memory when not convert_rgb, but we have to recreate the header // We didn't allocate memory when not convert_rgb, but we have to recreate the header
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): buffer input size=" << currentBuffer.buffer.bytesused);
if (frame.imageSize != (int)currentBuffer.buffer.bytesused) if (frame.imageSize != (int)currentBuffer.buffer.bytesused)
v4l2_create_frame(); v4l2_create_frame();
@ -1990,7 +2116,9 @@ IplImage *CvCaptureCAM_V4L::retrieveFrame(int)
} }
//Revert buffer to the queue //Revert buffer to the queue
if (!tryIoctl(VIDIOC_QBUF, &buffers[bufferIndex].buffer)) if (!tryIoctl(VIDIOC_QBUF, &buffers[bufferIndex].buffer))
perror("VIDIOC_QBUF"); {
CV_LOG_DEBUG(NULL, "VIDEOIO(V4L2:" << deviceName << "): failed VIDIOC_QBUF: errno=" << errno << " (" << strerror(errno) << ")");
}
bufferIndex = -1; bufferIndex = -1;
return &frame; return &frame;

6
modules/videoio/src/precomp.hpp
Unescape View File

@ -51,6 +51,12 @@
#endif #endif
#include <opencv2/core/utils/configuration.private.hpp> #include <opencv2/core/utils/configuration.private.hpp>
#include <opencv2/core/utils/logger.defines.hpp>
#ifdef NDEBUG
#define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG + 1
#else
#define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE + 1
#endif
#include <opencv2/core/utils/logger.hpp> #include <opencv2/core/utils/logger.hpp>
#include "opencv2/imgcodecs.hpp" #include "opencv2/imgcodecs.hpp"

2
samples/dnn/tf_text_graph_ssd.py
Unescape View File

@ -64,7 +64,7 @@ def createSSDGraph(modelPath, configPath, outputPath):
# Nodes that should be kept. # Nodes that should be kept.
keepOps = ['Conv2D', 'BiasAdd', 'Add', 'Relu', 'Relu6', 'Placeholder', 'FusedBatchNorm', keepOps = ['Conv2D', 'BiasAdd', 'Add', 'Relu', 'Relu6', 'Placeholder', 'FusedBatchNorm',
'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool', 'Identity', 'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool', 'Identity',
'Sub', 'ResizeNearestNeighbor', 'Pad'] 'Sub', 'ResizeNearestNeighbor', 'Pad', 'FusedBatchNormV3']
# Node with which prefixes should be removed # Node with which prefixes should be removed
prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Concatenate/', 'Postprocessor/', 'Preprocessor/map') prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Concatenate/', 'Postprocessor/', 'Preprocessor/map')

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