| /frameworks/rs/java/tests/ImageProcessing/src/com/android/rs/image/ |
| threshold.rs | 31 static float gaussian[MAX_RADIUS * 2 + 1];
35 // Compute gaussian weights for the blur
45 // The larger the radius gets, the more our gaussian blur
47 // the gaussian curve begins to lose its shape
61 gaussian[r + radius] = coeff1 * pow(e, floatR * floatR * coeff2);
62 normalizeFactor += gaussian[r + radius];
69 gaussian[r + radius] *= normalizeFactor;
84 blurredPixel += i.xyz * gaussian[gi++];
90 blurredPixel += i.xyz * gaussian[gi++];
105 blurredPixel += i * gaussian[gi++]
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| /frameworks/rs/java/tests/ImageProcessing2/src/com/android/rs/image/ |
| threshold.rs | 31 static float gaussian[MAX_RADIUS * 2 + 1];
35 // Compute gaussian weights for the blur
45 // The larger the radius gets, the more our gaussian blur
47 // the gaussian curve begins to lose its shape
61 gaussian[r + radius] = coeff1 * pow(e, floatR * floatR * coeff2);
62 normalizeFactor += gaussian[r + radius];
69 gaussian[r + radius] *= normalizeFactor;
84 blurredPixel += i.xyz * gaussian[gi++];
90 blurredPixel += i.xyz * gaussian[gi++];
105 blurredPixel += i * gaussian[gi++]
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| /frameworks/rs/java/tests/ImageProcessing_jb/src/com/android/rs/image/ |
| threshold.rs | 31 static float gaussian[MAX_RADIUS * 2 + 1];
35 // Compute gaussian weights for the blur
45 // The larger the radius gets, the more our gaussian blur
47 // the gaussian curve begins to lose its shape
61 gaussian[r + radius] = coeff1 * pow(e, floatR * floatR * coeff2);
62 normalizeFactor += gaussian[r + radius];
69 gaussian[r + radius] *= normalizeFactor;
84 blurredPixel += i.xyz * gaussian[gi++];
90 blurredPixel += i.xyz * gaussian[gi++];
105 blurredPixel += i * gaussian[gi++]
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| /cts/tests/tests/renderscript/src/android/renderscript/cts/ |
| intrinsic_blur.rs | 28 static float gaussian[MAX_RADIUS * 2 + 1];
62 gaussian[r + radius] = coeff1 * pow(e, floatR * floatR * coeff2);
63 normalizeFactor += gaussian[r + radius];
69 gaussian[r + radius] *= normalizeFactor;
79 blurredPixel += i * gaussian[gi++];
91 blurredPixel += i * gaussian[gi++];
|
| /external/webrtc/webrtc/common_audio/vad/ |
| vad_core.c | 35 // Start values for the Gaussian models, Q7
114 // Gaussian Mixture Models (GMM). A hypothesis-test is performed to decide which
134 int gaussian; local
185 gaussian = channel + k * kNumChannels;
189 self->noise_means[gaussian],
190 self->noise_stds[gaussian],
191 &deltaN[gaussian]);
192 noise_probability[k] = kNoiseDataWeights[gaussian] * tmp1_s32;
198 self->speech_means[gaussian],
199 self->speech_stds[gaussian],
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| /external/ImageMagick/Magick++/demo/ |
| demos.tap | 25 for filter in bessel blackman box catrom cubic gaussian hamming hanning hermite lanczos mitchell point quadratic sample scale sinc triangle
|
| /external/opencv3/modules/features2d/src/kaze/ |
| utils.h | 31 * @brief This function computes the value of a 2D Gaussian function
36 inline float gaussian(float x, float y, float sigma) { function
|
| nldiffusion_functions.cpp | 18 * 2D Gaussian Derivatives
39 * @brief This function smoothes an image with a Gaussian kernel
65 // Perform the Gaussian Smoothing with border replication
207 * @param ksize_x Kernel size in X-direction (horizontal) for the Gaussian smoothing kernel
208 * @param ksize_y Kernel size in Y-direction (vertical) for the Gaussian smoothing kernel
222 Mat gaussian = Mat::zeros(img.rows, img.cols, CV_32F); local
226 // Perform the Gaussian convolution
227 gaussian_2D_convolution(img, gaussian, ksize_x, ksize_y, gscale);
229 // Compute the Gaussian derivatives Lx and Ly
230 Scharr(gaussian, Lx, CV_32F, 1, 0, 1, 0, cv::BORDER_DEFAULT)
[all...] |
| KAZEFeatures.cpp | 120 // Compute the Gaussian derivatives Lx and Ly
600 gweight = gaussian(iy - yf, ix - xf, 2.5f*s);
668 // Subregion centers for the 4x4 gaussian weighting
711 //Get the gaussian weighted x and y responses
712 gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.5f*scale);
751 gauss_s2 = gaussian(cx - 2.0f, cy - 2.0f, 1.5f);
796 // Subregion centers for the 4x4 gaussian weighting
844 // Get the gaussian weighted x and y response
[all...] |
| AKAZEFeatures.cpp | 133 // Compute the Gaussian derivatives Lx and Ly
772 /// Lookup table for 2d gaussian (sigma = 2.5) where (0,0) is top left and (6,6) is bottom right
867 // Subregion centers for the 4x4 gaussian weighting
911 //Get the gaussian weighted x and y responses
912 gauss_s1 = gaussian(xs - sample_x, ys - sample_y, 2.50f*scale);
[all...] |
| /external/caliper/caliper/src/main/java/com/google/caliper/worker/ |
| RuntimeWorker.java | 95 double gaussian) {
97 return Math.max(1L, Math.round((gaussian * (targetReps / 5)) + targetReps));
|
| /external/opencv3/modules/calib3d/test/ |
| test_decompose_projection.cpp | 78 double alpha = 0.01*rng.gaussian(1);
|
| test_fisheye.cpp | 170 alpha = 0.01*r.gaussian(1);
|
| /external/ImageMagick/www/api/ |
| morphology.php | 107 <p>AcquireKernelBuiltIn() returned one of the 'named' built-in types of kernels used for special purposes such as gaussian blurring, skeleton pruning, and edge distance determination.</p>
136 <dd> Gaussian:{radius},{sigma} Generate a two-dimensional gaussian kernel, as used by -gaussian. The sigma for the curve is required. The resulting kernel is normalized, </dd>
142 <dd> LoG:{radius},{sigma} "Laplacian of a Gaussian" or "Mexician Hat" Kernel. The supposed ideal edge detection, zero-summing kernel. </dd>
146 <dd> DoG:{radius},{sigma1},{sigma2} "Difference of Gaussians" Kernel. As "Gaussian" but with a gaussian produced by 'sigma2' subtracted from the gaussian produced by 'sigma1'. Typically sigma2 > sigma1. The result is a zero-summing kernel. </dd>
148 <dd> Blur:{radius},{sigma}[,{angle}] Generates a 1 dimensional or linear gaussian blur, at the angle given (current restricted to orthogonal angles). If a 'radius' is given the kernel is clipped to a width of 2*radius+1. Kernel can be rotated by a 90 degree angle. </dd>
152 <dd> Note that two convolutions with two "Blur" kernels perpendicular to each other, is equivalent to a far larger "Gaussian" kernel with the same sigma value, However it is much faster to apply. This is how the "-blur" ope (…)
[all...] |
| feature.php | 81 <dd>the radius of the gaussian smoothing filter. </dd>
85 <dd>the sigma of the gaussian smoothing filter. </dd>
|
| /frameworks/base/libs/hwui/ |
| FontRenderer.cpp | 745 std::unique_ptr<float[]> gaussian(new float[2 * intRadius + 1]);
746 Blur::generateGaussianWeights(gaussian.get(), radius);
749 Blur::horizontal(gaussian.get(), intRadius, *image, scratch.get(), width, height);
750 Blur::vertical(gaussian.get(), intRadius, scratch.get(), *image, width, height);
|
| /external/opencv3/modules/objdetect/src/opencl/ |
| objdetect_hog.cl | 64 // Use pre-computed gaussian and interp_weight lookup tables
120 float gaussian = gauss_w_lut[idx];
124 hist[bin.x * 48] += gaussian * interp_weight * vote.x;
125 hist[bin.y * 48] += gaussian * interp_weight * vote.y;
|
| /external/skia/tests/ |
| BlurTest.cpp | 202 // Implement a Gaussian function with 0 mean and std.dev. of 'sigma'.
203 static float gaussian(int x, SkScalar sigma) { function
209 // Perform a brute force convolution of a step function with a Gaussian.
220 sum += gaussian(j, gaussianSigma) * step(i-j, stepMin, stepMax);
|
| /external/opencv3/modules/video/src/ |
| bgfg_gaussmix2.cpp | 43 /*//Implementation of the Gaussian mixture model background subtraction from:
59 //additional selection of the number of the Gaussian components based on:
92 Interface of Gaussian mixture algorithm from:
105 // default parameters of gaussian background detection algorithm
149 // the number of gaussian mixtures, the background ratio parameter and the noise strength
222 // for each gaussian mixture of each pixel bg model we store ...
514 //additional selection of the number of the Gaussian components based on:
882 GMM gaussian = gmm[gaussianIdx]; local
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| /external/skia/src/effects/ |
| SkBlurMask.cpp | 498 // 6*rad+1 while the full Gaussian width is 6*sigma.
502 // Gaussian blur area (1.5*sigma on each side). The single pass box
508 // to approximate a Gaussian blur
849 // gaussian kernel. It's "ground truth" in a sense; too slow to be used, but very
873 float gaussian = expf(-x*x / (2*variance)); local
874 gaussWindow[halfWindow + x] = gaussWindow[halfWindow-x] = gaussian;
875 windowSum += 2*gaussian;
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| /external/opencv3/modules/cudaobjdetect/src/cuda/ |
| hog.cu | 163 float gaussian = ::expf(-(dist_center_y * dist_center_y +
168 hist[bin.x * 48 * nblocks] += gaussian * interp_weight * vote.x;
169 hist[bin.y * 48 * nblocks] += gaussian * interp_weight * vote.y;
212 // Precompute gaussian spatial window parameter
[all...] |
| /external/libvpx/libvpx/vp9/common/ |
| vp9_postproc.c | 537 static double gaussian(double sigma, double mu, double x) { function
553 * a gaussian distribution with sigma determined by q.
561 int a_i = (int)(0.5 + 256 * gaussian(sigma, 0, i));
|
| /hardware/intel/common/omx-components/videocodec/libvpx_internal/libvpx/vp9/common/ |
| vp9_postproc.c | 374 static double gaussian(double sigma, double mu, double x) { function
390 * a gaussian distribution with sigma determined by q.
399 int a = (int)(0.5 + 256 * gaussian(sigma, 0, i));
|
| /external/libvpx/libvpx/vp8/common/ |
| postproc.c | 430 static double gaussian(double sigma, double mu, double x) function
449 * a gaussian distribution with sigma determined by q.
458 const int v = (int)(.5 + 256 * gaussian(sigma, 0, i));
497 * INPUTS : unsigned char *Start starting address of buffer to add gaussian
509 * FUNCTION : adds gaussian noise to a plane of pixels
[all...] |
| /external/opencv3/modules/core/include/opencv2/ |
| core.hpp | [all...] |
