Removed fastNlMeansDenoisingColored[Multi]Abs
This commit is contained in:
Erik Karlsson
parent
82c54104d6
commit
9fff7896c5
@ -160,63 +160,10 @@ image in different colorspaces. Such approach is used in fastNlMeansDenoisingCol
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image to CIELAB colorspace and then separately denoise L and AB components with different h
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parameter.
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*/
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CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
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CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
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const std::vector<float>& h,
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int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Perform image denoising using Non-local Means Denoising
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algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/>
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with several computational optimizations. Noise expected to be a
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gaussian white noise. Uses squared sum of absolute value distances
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instead of sum of squared distances for weight calculation
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@param src Input 8-bit or 16-bit 1-channel, 2-channel, 3-channel or 4-channel image.
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@param dst Output image with the same size and type as src .
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
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removes image details, smaller h value preserves details but also preserves some noise
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This function expected to be applied to grayscale images. For colored images look at
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fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
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image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
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image to CIELAB colorspace and then separately denoise L and AB components with different h
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parameter.
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*/
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CV_EXPORTS_W void fastNlMeansDenoisingAbs( InputArray src, OutputArray dst, float h = 3,
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int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Perform image denoising using Non-local Means Denoising
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algorithm <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/>
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with several computational optimizations. Noise expected to be a
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gaussian white noise. Uses squared sum of absolute value distances
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instead of sum of squared distances for weight calculation
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@param src Input 8-bit or 16-bit 1-channel, 2-channel, 3-channel or 4-channel image.
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@param dst Output image with the same size and type as src .
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Array of parameters regulating filter strength, either one
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parameter applied to all channels or one per channel in dst. Big h value
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perfectly removes noise but also removes image details, smaller h
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value preserves details but also preserves some noise
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This function expected to be applied to grayscale images. For colored images look at
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fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
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image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
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image to CIELAB colorspace and then separately denoise L and AB components with different h
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parameter.
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*/
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CV_EXPORTS_W void fastNlMeansDenoisingAbs( InputArray src, OutputArray dst,
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const std::vector<float>& h,
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int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Modification of fastNlMeansDenoising function for colored images
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@param src Input 8-bit 3-channel image.
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@ -294,69 +241,6 @@ CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputA
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int imgToDenoiseIndex, int temporalWindowSize,
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const std::vector<float>& h , int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Modification of fastNlMeansDenoising function for images
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sequence where consequtive images have been captured in small period
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of time. For example video. This version of the function is for
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grayscale images or for manual manipulation with colorspaces. For more
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details see
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<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>. Uses
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squared sum of absolute value distances instead of sum of squared
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distances for weight calculation
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@param srcImgs Input 8-bit or 16-bit 1-channel, 2-channel, 3-channel
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or 4-channel images sequence. All images should have the same type and
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size.
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@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
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@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
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be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
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imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
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srcImgs[imgToDenoiseIndex] image.
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@param dst Output image with the same size and type as srcImgs images.
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Parameter regulating filter strength. Bigger h value
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perfectly removes noise but also removes image details, smaller h
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value preserves details but also preserves some noise
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*/
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CV_EXPORTS_W void fastNlMeansDenoisingMultiAbs( InputArrayOfArrays srcImgs, OutputArray dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Modification of fastNlMeansDenoising function for images
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sequence where consequtive images have been captured in small period
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of time. For example video. This version of the function is for
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grayscale images or for manual manipulation with colorspaces. For more
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details see
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<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>. Uses
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squared sum of absolute value distances instead of sum of squared
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distances for weight calculation
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@param srcImgs Input 8-bit or 16-bit 1-channel, 2-channel, 3-channel
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or 4-channel images sequence. All images should have the same type and
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size.
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@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
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@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
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be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
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imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
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srcImgs[imgToDenoiseIndex] image.
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@param dst Output image with the same size and type as srcImgs images.
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@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
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Should be odd. Recommended value 7 pixels
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@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
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given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
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denoising time. Recommended value 21 pixels
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@param h Array of parameters regulating filter strength, either one
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parameter applied to all channels or one per channel in dst. Big h value
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perfectly removes noise but also removes image details, smaller h
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value preserves details but also preserves some noise
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*/
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CV_EXPORTS_W void fastNlMeansDenoisingMultiAbs( InputArrayOfArrays srcImgs, OutputArray dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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const std::vector<float>& h, int templateWindowSize = 7, int searchWindowSize = 21);
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/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
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@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and
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@ -116,106 +116,6 @@ void cv::fastNlMeansDenoising( InputArray _src, OutputArray _dst, const std::vec
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}
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}
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void cv::fastNlMeansDenoisingAbs( InputArray _src, OutputArray _dst, float h,
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int templateWindowSize, int searchWindowSize)
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{
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fastNlMeansDenoisingAbs(_src, _dst, std::vector<float>(1, h),
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templateWindowSize, searchWindowSize);
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}
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void cv::fastNlMeansDenoisingAbs( InputArray _src, OutputArray _dst, const std::vector<float>& h,
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int templateWindowSize, int searchWindowSize)
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{
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int hn = (int)h.size();
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CV_Assert(hn == 1 || hn == CV_MAT_CN(_src.type()));
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Size src_size = _src.size();
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CV_OCL_RUN(_src.dims() <= 2 && (_src.isUMat() || _dst.isUMat()) &&
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src_size.width > 5 && src_size.height > 5, // low accuracy on small sizes
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ocl_fastNlMeansDenoising(_src, _dst, &h[0], hn,
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templateWindowSize, searchWindowSize, true))
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Mat src = _src.getMat();
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_dst.create(src_size, src.type());
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Mat dst = _dst.getMat();
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switch (src.type()) {
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case CV_8U:
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<uchar, int, unsigned, DistAbs, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_8UC2:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec2b, int, unsigned, DistAbs, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec2b, int, unsigned, DistAbs, Vec2i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_8UC3:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec3b, int, unsigned, DistAbs, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec3b, int, unsigned, DistAbs, Vec3i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_8UC4:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec4b, int, unsigned, DistAbs, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec4b, int, unsigned, DistAbs, Vec4i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_16U:
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<ushort, int64, uint64, DistAbs, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_16UC2:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ushort, 2>, int64, uint64, DistAbs, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ushort, 2>, int64, uint64, DistAbs, Vec2i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_16UC3:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ushort, 3>, int64, uint64, DistAbs, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ushort, 3>, int64, uint64, DistAbs, Vec3i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_16UC4:
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if (hn == 1)
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ushort, 4>, int64, uint64, DistAbs, int>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, src.rows),
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FastNlMeansDenoisingInvoker<Vec<ushort, 4>, int64, uint64, DistAbs, Vec4i>(
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src, dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported image format! Only CV_8U, CV_8UC2, CV_8UC3, CV_8UC4, CV_16U, CV_16UC2, CV_16UC3 and CV_16UC4 are supported");
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}
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}
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void cv::fastNlMeansDenoisingColored( InputArray _src, OutputArray _dst,
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float h, float hForColorComponents,
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int templateWindowSize, int searchWindowSize)
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@ -365,124 +265,6 @@ void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _ds
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}
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}
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void cv::fastNlMeansDenoisingMultiAbs( InputArrayOfArrays _srcImgs, OutputArray _dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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float h, int templateWindowSize, int searchWindowSize)
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{
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fastNlMeansDenoisingMulti(_srcImgs, _dst, imgToDenoiseIndex, temporalWindowSize,
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std::vector<float>(1, h), templateWindowSize, searchWindowSize);
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}
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void cv::fastNlMeansDenoisingMultiAbs( InputArrayOfArrays _srcImgs, OutputArray _dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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const std::vector<float>& h,
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int templateWindowSize, int searchWindowSize)
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{
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std::vector<Mat> srcImgs;
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_srcImgs.getMatVector(srcImgs);
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fastNlMeansDenoisingMultiCheckPreconditions(
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srcImgs, imgToDenoiseIndex,
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temporalWindowSize, templateWindowSize, searchWindowSize);
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int hn = (int)h.size();
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CV_Assert(hn == 1 || hn == CV_MAT_CN(srcImgs[0].type()));
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_dst.create(srcImgs[0].size(), srcImgs[0].type());
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Mat dst = _dst.getMat();
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switch (srcImgs[0].type())
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{
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case CV_8U:
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<uchar, int, unsigned, DistAbs, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_8UC2:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec2b, int, unsigned, DistAbs, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec2b, int, unsigned, DistAbs, Vec2i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_8UC3:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec3b, int, unsigned, DistAbs, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec3b, int, unsigned, DistAbs, Vec3i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_8UC4:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec4b, int, unsigned, DistAbs, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec4b, int, unsigned, DistAbs, Vec4i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_16U:
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<ushort, int64, uint64, DistAbs, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_16UC2:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ushort, 2>, int64, uint64, DistAbs, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ushort, 2>, int64, uint64, DistAbs, Vec2i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_16UC3:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ushort, 3>, int64, uint64, DistAbs, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ushort, 3>, int64, uint64, DistAbs, Vec3i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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case CV_16UC4:
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if (hn == 1)
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ushort, 4>, int64, uint64, DistAbs, int>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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else
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parallel_for_(cv::Range(0, srcImgs[0].rows),
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FastNlMeansMultiDenoisingInvoker<Vec<ushort, 4>, int64, uint64, DistAbs, Vec4i>(
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srcImgs, imgToDenoiseIndex, temporalWindowSize,
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dst, templateWindowSize, searchWindowSize, &h[0]));
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break;
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default:
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CV_Error(Error::StsBadArg,
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"Unsupported image format! Only CV_8U, CV_8UC2, CV_8UC3, CV_8UC4, CV_16U, CV_16UC2, CV_16UC3 and CV_16UC4 are supported");
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}
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}
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void cv::fastNlMeansDenoisingColoredMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
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int imgToDenoiseIndex, int temporalWindowSize,
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float h, float hForColorComponents,
|
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|
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@ -103,36 +103,6 @@ OCL_TEST_P(FastNlMeansDenoising_hsep, Mat)
|
||||
}
|
||||
}
|
||||
|
||||
typedef FastNlMeansDenoisingTestBase FastNlMeansDenoisingAbs;
|
||||
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OCL_TEST_P(FastNlMeansDenoisingAbs, Mat)
|
||||
{
|
||||
for (int j = 0; j < test_loop_times; j++)
|
||||
{
|
||||
generateTestData();
|
||||
|
||||
OCL_OFF(cv::fastNlMeansDenoisingAbs(src_roi, dst_roi, h[0], templateWindowSize, searchWindowSize));
|
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OCL_ON(cv::fastNlMeansDenoisingAbs(usrc_roi, udst_roi, h[0], templateWindowSize, searchWindowSize));
|
||||
|
||||
OCL_EXPECT_MATS_NEAR(dst, 1);
|
||||
}
|
||||
}
|
||||
|
||||
typedef FastNlMeansDenoisingTestBase FastNlMeansDenoisingAbs_hsep;
|
||||
|
||||
OCL_TEST_P(FastNlMeansDenoisingAbs_hsep, Mat)
|
||||
{
|
||||
for (int j = 0; j < test_loop_times; j++)
|
||||
{
|
||||
generateTestData();
|
||||
|
||||
OCL_OFF(cv::fastNlMeansDenoisingAbs(src_roi, dst_roi, h, templateWindowSize, searchWindowSize));
|
||||
OCL_ON(cv::fastNlMeansDenoisingAbs(usrc_roi, udst_roi, h, templateWindowSize, searchWindowSize));
|
||||
|
||||
OCL_EXPECT_MATS_NEAR(dst, 1);
|
||||
}
|
||||
}
|
||||
|
||||
typedef FastNlMeansDenoisingTestBase FastNlMeansDenoisingColored;
|
||||
|
||||
OCL_TEST_P(FastNlMeansDenoisingColored, Mat)
|
||||
@ -152,10 +122,6 @@ OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoising,
|
||||
Combine(Values(1, 2, 3, 4), Bool(), Values(true)));
|
||||
OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoising_hsep,
|
||||
Combine(Values(1, 2, 3, 4), Bool(), Values(true)));
|
||||
OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoisingAbs,
|
||||
Combine(Values(1, 2, 3, 4), Bool(), Values(true)));
|
||||
OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoisingAbs_hsep,
|
||||
Combine(Values(1, 2, 3, 4), Bool(), Values(true)));
|
||||
OCL_INSTANTIATE_TEST_CASE_P(Photo, FastNlMeansDenoisingColored,
|
||||
Combine(Values(3, 4), Bool(), Values(false)));
|
||||
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user