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5f95f895d2
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c5af524dac
23
img2cpi.c
23
img2cpi.c
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@ -95,8 +95,6 @@ struct k_means_state {
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float r, g, b;
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} sums;
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size_t count;
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union color closest_present_item;
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float closest_present_distance;
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} *centroid_intermediate;
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size_t item_count;
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};
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@ -677,20 +675,13 @@ struct palette *custom_palette_from(const struct palette *orig) {
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struct k_means_state k_means_init(const struct image *image, struct palette *starting_palette) {
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size_t item_count = image->w * image->h;
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uint8_t cluster_count = starting_palette->count;
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struct k_means_state state = {
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.items = image,
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.clusters = starting_palette,
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.predicted_cluster = calloc(image->w, image->h),
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.centroid_intermediate = calloc(cluster_count, sizeof(struct k_means_centroid_intermediate)),
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.centroid_intermediate = calloc(item_count, sizeof(struct k_means_centroid_intermediate)),
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.item_count = item_count,
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};
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if (state.centroid_intermediate) {
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for (size_t i = 0; i < cluster_count; i++) {
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state.centroid_intermediate[i].closest_present_item = starting_palette->colors[i];
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state.centroid_intermediate[i].closest_present_distance = 1e20;
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}
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}
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return state;
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}
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@ -705,16 +696,11 @@ bool k_means_iteration(struct k_means_state *state) {
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int closest_cluster = 0;
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float closest_distance = 1e20;
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for (int cluster = 0; cluster < state->clusters->count; cluster++) {
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union color item = state->items->pixels[i];
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float dist = get_color_difference(state->clusters->colors[cluster], item);
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float dist = get_color_difference(state->clusters->colors[cluster], state->items->pixels[i]);
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if (dist <= closest_distance) {
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closest_distance = dist;
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closest_cluster = cluster;
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}
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if (dist <= state->centroid_intermediate[cluster].closest_present_distance) {
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state->centroid_intermediate[cluster].closest_present_item = item;
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state->centroid_intermediate[cluster].closest_present_distance = dist;
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}
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}
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if (!changed) {
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changed = state->predicted_cluster[i] != closest_cluster;
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@ -744,10 +730,9 @@ bool k_means_iteration(struct k_means_state *state) {
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state->clusters->colors[i] = centroid;
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} else {
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// No pixels are closest to this color
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// Warp the centroid onto the closest item
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state->clusters->colors[i] = intermediate.closest_present_item;
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// TODO: wiggle the centroid?
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}
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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 };
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state->centroid_intermediate[i] = (struct k_means_centroid_intermediate) { .sums = {0, 0, 0}, .count = 0 };
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}
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return changed;
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