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authortrav90 <travawine@palemoon.org>2018-10-18 21:53:44 -0500
committertrav90 <travawine@palemoon.org>2018-10-18 21:53:44 -0500
commitec910d81405c736a4490383a250299a7837c2e64 (patch)
tree4f27cc226f93a863121aef6c56313e4153a69b3e /third_party/aom/av1/encoder/palette.h
parent01eb57073ba97b2d6cbf20f745dfcc508197adc3 (diff)
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Update aom to commit id e87fb2378f01103d5d6e477a4ef6892dc714e614
Diffstat (limited to 'third_party/aom/av1/encoder/palette.h')
-rw-r--r--third_party/aom/av1/encoder/palette.h40
1 files changed, 36 insertions, 4 deletions
diff --git a/third_party/aom/av1/encoder/palette.h b/third_party/aom/av1/encoder/palette.h
index 8afe5a782..efd89f66f 100644
--- a/third_party/aom/av1/encoder/palette.h
+++ b/third_party/aom/av1/encoder/palette.h
@@ -18,17 +18,49 @@
extern "C" {
#endif
+#define AV1_K_MEANS_RENAME(func, dim) func##_dim##dim
+
+void AV1_K_MEANS_RENAME(av1_calc_indices, 1)(const float *data,
+ const float *centroids,
+ uint8_t *indices, int n, int k);
+void AV1_K_MEANS_RENAME(av1_calc_indices, 2)(const float *data,
+ const float *centroids,
+ uint8_t *indices, int n, int k);
+void AV1_K_MEANS_RENAME(av1_k_means, 1)(const float *data, float *centroids,
+ uint8_t *indices, int n, int k,
+ int max_itr);
+void AV1_K_MEANS_RENAME(av1_k_means, 2)(const float *data, float *centroids,
+ uint8_t *indices, int n, int k,
+ int max_itr);
+
// Given 'n' 'data' points and 'k' 'centroids' each of dimension 'dim',
// calculate the centroid 'indices' for the data points.
-void av1_calc_indices(const float *data, const float *centroids,
- uint8_t *indices, int n, int k, int dim);
+static INLINE void av1_calc_indices(const float *data, const float *centroids,
+ uint8_t *indices, int n, int k, int dim) {
+ if (dim == 1) {
+ AV1_K_MEANS_RENAME(av1_calc_indices, 1)(data, centroids, indices, n, k);
+ } else if (dim == 2) {
+ AV1_K_MEANS_RENAME(av1_calc_indices, 2)(data, centroids, indices, n, k);
+ } else {
+ assert(0 && "Untemplated k means dimension");
+ }
+}
// Given 'n' 'data' points and an initial guess of 'k' 'centroids' each of
// dimension 'dim', runs up to 'max_itr' iterations of k-means algorithm to get
// updated 'centroids' and the centroid 'indices' for elements in 'data'.
// Note: the output centroids are rounded off to nearest integers.
-void av1_k_means(const float *data, float *centroids, uint8_t *indices, int n,
- int k, int dim, int max_itr);
+static INLINE void av1_k_means(const float *data, float *centroids,
+ uint8_t *indices, int n, int k, int dim,
+ int max_itr) {
+ if (dim == 1) {
+ AV1_K_MEANS_RENAME(av1_k_means, 1)(data, centroids, indices, n, k, max_itr);
+ } else if (dim == 2) {
+ AV1_K_MEANS_RENAME(av1_k_means, 2)(data, centroids, indices, n, k, max_itr);
+ } else {
+ assert(0 && "Untemplated k means dimension");
+ }
+}
// Given a list of centroids, returns the unique number of centroids 'k', and
// puts these unique centroids in first 'k' indices of 'centroids' array.