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@@ -2,17 +2,17 @@
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#define __PREPROCESSING_HPP__
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#include "ggml_extend.hpp"
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#define M_PI_ 3.14159265358979323846
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#define M_PI_ 3.14159265358979323846
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void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml_tensor* kernel, int padding) {
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struct ggml_init_params params;
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params.mem_size = 20 * 1024 * 1024; // 10
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params.mem_buffer = NULL;
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params.no_alloc = false;
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struct ggml_context* ctx0 = ggml_init(params);
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params.mem_size = 20 * 1024 * 1024; // 10
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params.mem_buffer = NULL;
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params.no_alloc = false;
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struct ggml_context* ctx0 = ggml_init(params);
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struct ggml_tensor* kernel_fp16 = ggml_new_tensor_4d(ctx0, GGML_TYPE_F16, kernel->ne[0], kernel->ne[1], 1, 1);
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ggml_fp32_to_fp16_row((float*)kernel->data, (ggml_fp16_t*) kernel_fp16->data, ggml_nelements(kernel));
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ggml_tensor* h = ggml_conv_2d(ctx0, kernel_fp16, input, 1, 1, padding, padding, 1, 1);
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ggml_fp32_to_fp16_row((float*)kernel->data, (ggml_fp16_t*)kernel_fp16->data, ggml_nelements(kernel));
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ggml_tensor* h = ggml_conv_2d(ctx0, kernel_fp16, input, 1, 1, padding, padding, 1, 1);
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ggml_cgraph* gf = ggml_new_graph(ctx0);
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, h, output));
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ggml_graph_compute_with_ctx(ctx0, gf, 1);
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@@ -20,14 +20,14 @@ void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml
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}
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void gaussian_kernel(struct ggml_tensor* kernel) {
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int ks_mid = kernel->ne[0] / 2;
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float sigma = 1.4f;
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int ks_mid = kernel->ne[0] / 2;
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float sigma = 1.4f;
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float normal = 1.f / (2.0f * M_PI_ * powf(sigma, 2.0f));
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for(int y = 0; y < kernel->ne[0]; y++) {
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for (int y = 0; y < kernel->ne[0]; y++) {
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float gx = -ks_mid + y;
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for(int x = 0; x < kernel->ne[1]; x++) {
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for (int x = 0; x < kernel->ne[1]; x++) {
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float gy = -ks_mid + x;
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float k_ = expf(-((gx*gx + gy*gy) / (2.0f * powf(sigma, 2.0f)))) * normal;
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float k_ = expf(-((gx * gx + gy * gy) / (2.0f * powf(sigma, 2.0f)))) * normal;
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ggml_tensor_set_f32(kernel, k_, x, y);
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}
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}
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@@ -36,9 +36,9 @@ void gaussian_kernel(struct ggml_tensor* kernel) {
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void grayscale(struct ggml_tensor* rgb_img, struct ggml_tensor* grayscale) {
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for (int iy = 0; iy < rgb_img->ne[1]; iy++) {
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for (int ix = 0; ix < rgb_img->ne[0]; ix++) {
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float r = ggml_tensor_get_f32(rgb_img, ix, iy);
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float g = ggml_tensor_get_f32(rgb_img, ix, iy, 1);
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float b = ggml_tensor_get_f32(rgb_img, ix, iy, 2);
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float r = ggml_tensor_get_f32(rgb_img, ix, iy);
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float g = ggml_tensor_get_f32(rgb_img, ix, iy, 1);
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float b = ggml_tensor_get_f32(rgb_img, ix, iy, 2);
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float gray = 0.2989f * r + 0.5870f * g + 0.1140f * b;
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ggml_tensor_set_f32(grayscale, gray, ix, iy);
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}
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@@ -47,19 +47,19 @@ void grayscale(struct ggml_tensor* rgb_img, struct ggml_tensor* grayscale) {
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void prop_hypot(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
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int n_elements = ggml_nelements(h);
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float* dx = (float*)x->data;
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float* dy = (float*)y->data;
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float* dh = (float*)h->data;
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for (int i = 0; i <n_elements; i++) {
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float* dx = (float*)x->data;
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float* dy = (float*)y->data;
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float* dh = (float*)h->data;
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for (int i = 0; i < n_elements; i++) {
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dh[i] = sqrtf(dx[i] * dx[i] + dy[i] * dy[i]);
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}
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}
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void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
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int n_elements = ggml_nelements(h);
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float* dx = (float*)x->data;
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float* dy = (float*)y->data;
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float* dh = (float*)h->data;
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float* dx = (float*)x->data;
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float* dy = (float*)y->data;
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float* dh = (float*)h->data;
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for (int i = 0; i < n_elements; i++) {
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dh[i] = atan2f(dy[i], dx[i]);
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}
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@@ -67,13 +67,13 @@ void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tens
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void normalize_tensor(struct ggml_tensor* g) {
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int n_elements = ggml_nelements(g);
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float* dg = (float*)g->data;
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float max = -INFINITY;
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for (int i = 0; i <n_elements; i++) {
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float* dg = (float*)g->data;
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float max = -INFINITY;
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for (int i = 0; i < n_elements; i++) {
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max = dg[i] > max ? dg[i] : max;
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}
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max = 1.0f / max;
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for (int i = 0; i <n_elements; i++) {
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for (int i = 0; i < n_elements; i++) {
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dg[i] *= max;
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}
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}
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@@ -82,12 +82,12 @@ void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struc
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for (int iy = 1; iy < result->ne[1] - 1; iy++) {
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for (int ix = 1; ix < result->ne[0] - 1; ix++) {
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float angle = ggml_tensor_get_f32(D, ix, iy) * 180.0f / M_PI_;
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angle = angle < 0.0f ? angle += 180.0f : angle;
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float q = 1.0f;
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float r = 1.0f;
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angle = angle < 0.0f ? angle += 180.0f : angle;
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float q = 1.0f;
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float r = 1.0f;
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// angle 0
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if((0 >= angle && angle < 22.5f) || (157.5f >= angle && angle <= 180)){
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if ((0 >= angle && angle < 22.5f) || (157.5f >= angle && angle <= 180)) {
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q = ggml_tensor_get_f32(G, ix, iy + 1);
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r = ggml_tensor_get_f32(G, ix, iy - 1);
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}
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@@ -119,8 +119,8 @@ void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struc
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void threshold_hystersis(struct ggml_tensor* img, float highThreshold, float lowThreshold, float weak, float strong) {
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int n_elements = ggml_nelements(img);
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float* imd = (float*)img->data;
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float max = -INFINITY;
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float* imd = (float*)img->data;
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float max = -INFINITY;
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for (int i = 0; i < n_elements; i++) {
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max = imd[i] > max ? imd[i] : max;
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}
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@@ -128,16 +128,16 @@ void threshold_hystersis(struct ggml_tensor* img, float highThreshold, float low
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float lt = ht * lowThreshold;
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for (int i = 0; i < n_elements; i++) {
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float img_v = imd[i];
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if(img_v >= ht) { // strong pixel
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if (img_v >= ht) { // strong pixel
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imd[i] = strong;
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} else if(img_v <= ht && img_v >= lt) { // strong pixel
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} else if (img_v <= ht && img_v >= lt) { // strong pixel
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imd[i] = weak;
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}
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}
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for (int iy = 0; iy < img->ne[1]; iy++) {
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for (int ix = 0; ix < img->ne[0]; ix++) {
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if(ix >= 3 && ix <= img->ne[0] - 3 && iy >= 3 && iy <= img->ne[1] - 3) {
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if (ix >= 3 && ix <= img->ne[0] - 3 && iy >= 3 && iy <= img->ne[1] - 3) {
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ggml_tensor_set_f32(img, ggml_tensor_get_f32(img, ix, iy), ix, iy);
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} else {
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ggml_tensor_set_f32(img, 0.0f, ix, iy);
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@@ -149,8 +149,8 @@ void threshold_hystersis(struct ggml_tensor* img, float highThreshold, float low
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for (int iy = 1; iy < img->ne[1] - 1; iy++) {
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for (int ix = 1; ix < img->ne[0] - 1; ix++) {
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float imd_v = ggml_tensor_get_f32(img, ix, iy);
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if(imd_v == weak) {
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if(ggml_tensor_get_f32(img, ix + 1, iy - 1) == strong || ggml_tensor_get_f32(img, ix + 1, iy) == strong ||
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if (imd_v == weak) {
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if (ggml_tensor_get_f32(img, ix + 1, iy - 1) == strong || ggml_tensor_get_f32(img, ix + 1, iy) == strong ||
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ggml_tensor_get_f32(img, ix, iy - 1) == strong || ggml_tensor_get_f32(img, ix, iy + 1) == strong ||
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ggml_tensor_get_f32(img, ix - 1, iy - 1) == strong || ggml_tensor_get_f32(img, ix - 1, iy) == strong) {
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ggml_tensor_set_f32(img, strong, ix, iy);
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@@ -164,9 +164,9 @@ void threshold_hystersis(struct ggml_tensor* img, float highThreshold, float low
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uint8_t* preprocess_canny(uint8_t* img, int width, int height, float highThreshold = 0.08f, float lowThreshold = 0.08f, float weak = 0.8f, float strong = 1.0f, bool inverse = false) {
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struct ggml_init_params params;
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params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10
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params.mem_buffer = NULL;
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params.no_alloc = false;
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params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10
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params.mem_buffer = NULL;
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params.no_alloc = false;
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struct ggml_context* work_ctx = ggml_init(params);
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if (!work_ctx) {
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@@ -177,29 +177,27 @@ uint8_t* preprocess_canny(uint8_t* img, int width, int height, float highThresho
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float kX[9] = {
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-1, 0, 1,
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-2, 0, 2,
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-1, 0, 1
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};
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-1, 0, 1};
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float kY[9] = {
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1, 2, 1,
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0, 0, 0,
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-1, -2, -1
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};
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-1, -2, -1};
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// generate kernel
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int kernel_size = 5;
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int kernel_size = 5;
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struct ggml_tensor* gkernel = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, kernel_size, kernel_size, 1, 1);
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struct ggml_tensor* sf_kx = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 3, 3, 1, 1);
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struct ggml_tensor* sf_kx = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 3, 3, 1, 1);
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memcpy(sf_kx->data, kX, ggml_nbytes(sf_kx));
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struct ggml_tensor* sf_ky = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 3, 3, 1, 1);
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memcpy(sf_ky->data, kY, ggml_nbytes(sf_ky));
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gaussian_kernel(gkernel);
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struct ggml_tensor* image = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
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struct ggml_tensor* image = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
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struct ggml_tensor* image_gray = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 1, 1);
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struct ggml_tensor* iX = ggml_dup_tensor(work_ctx, image_gray);
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struct ggml_tensor* iY = ggml_dup_tensor(work_ctx, image_gray);
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struct ggml_tensor* G = ggml_dup_tensor(work_ctx, image_gray);
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struct ggml_tensor* tetha = ggml_dup_tensor(work_ctx, image_gray);
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struct ggml_tensor* iX = ggml_dup_tensor(work_ctx, image_gray);
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struct ggml_tensor* iY = ggml_dup_tensor(work_ctx, image_gray);
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struct ggml_tensor* G = ggml_dup_tensor(work_ctx, image_gray);
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struct ggml_tensor* tetha = ggml_dup_tensor(work_ctx, image_gray);
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sd_image_to_tensor(img, image);
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grayscale(image, image_gray);
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convolve(image_gray, image_gray, gkernel, 2);
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@@ -214,7 +212,7 @@ uint8_t* preprocess_canny(uint8_t* img, int width, int height, float highThresho
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for (int iy = 0; iy < height; iy++) {
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for (int ix = 0; ix < width; ix++) {
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float gray = ggml_tensor_get_f32(image_gray, ix, iy);
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gray = inverse ? 1.0f - gray : gray;
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gray = inverse ? 1.0f - gray : gray;
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ggml_tensor_set_f32(image, gray, ix, iy);
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ggml_tensor_set_f32(image, gray, ix, iy, 1);
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ggml_tensor_set_f32(image, gray, ix, iy, 2);
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@@ -226,4 +224,4 @@ uint8_t* preprocess_canny(uint8_t* img, int width, int height, float highThresho
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return output;
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}
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#endif // __PREPROCESSING_HPP__
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#endif // __PREPROCESSING_HPP__
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