img2cpi.c

@@ -95,8 +95,6 @@ struct k_means_state {
       float r, g, b;
     } sums;
     size_t count;
-    union color closest_present_item;
-    float closest_present_distance;
   } *centroid_intermediate;
   size_t item_count;
 };
@@ -677,20 +675,13 @@ struct palette *custom_palette_from(const struct palette *orig) {
 
 struct k_means_state k_means_init(const struct image *image, struct palette *starting_palette) {
   size_t item_count = image->w * image->h;
-  uint8_t cluster_count = starting_palette->count;
   struct k_means_state state = {
     .items = image,
     .clusters = starting_palette,
     .predicted_cluster = calloc(image->w, image->h),
-    .centroid_intermediate = calloc(cluster_count, sizeof(struct k_means_centroid_intermediate)),
+    .centroid_intermediate = calloc(item_count, sizeof(struct k_means_centroid_intermediate)),
     .item_count = item_count,
   };
-  if (state.centroid_intermediate) {
-    for (size_t i = 0; i < cluster_count; i++) {
-      state.centroid_intermediate[i].closest_present_item = starting_palette->colors[i];
-      state.centroid_intermediate[i].closest_present_distance = 1e20;
-    }
-  }
   return state;
 }
 
@@ -705,16 +696,11 @@ bool k_means_iteration(struct k_means_state *state) {
     int closest_cluster = 0;
     float closest_distance = 1e20;
     for (int cluster = 0; cluster < state->clusters->count; cluster++) {
-      union color item = state->items->pixels[i];
-      float dist = get_color_difference(state->clusters->colors[cluster], item);
+      float dist = get_color_difference(state->clusters->colors[cluster], state->items->pixels[i]);
       if (dist <= closest_distance) {
         closest_distance = dist;
         closest_cluster = cluster;
       }
-      if (dist <= state->centroid_intermediate[cluster].closest_present_distance) {
-        state->centroid_intermediate[cluster].closest_present_item = item;
-        state->centroid_intermediate[cluster].closest_present_distance = dist;
-      }
     }
     if (!changed) {
       changed = state->predicted_cluster[i] != closest_cluster;
@@ -744,10 +730,9 @@ bool k_means_iteration(struct k_means_state *state) {
       state->clusters->colors[i] = centroid;
     } else {
       // No pixels are closest to this color
-      // Warp the centroid onto the closest item
-      state->clusters->colors[i] = intermediate.closest_present_item;
+      // TODO: wiggle the centroid?
     }
-    state->centroid_intermediate[i] = (struct k_means_centroid_intermediate) { .sums = {0, 0, 0}, .count = 0, .closest_present_item = state->clusters->colors[i], .closest_present_distance = 1e20 };
+    state->centroid_intermediate[i] = (struct k_means_centroid_intermediate) { .sums = {0, 0, 0}, .count = 0 };
   }
 
   return changed;