mirrors/Graphite
1
0
Fork 0
mirror of https://github.com/GraphiteEditor/Graphite.git synced 2025-07-07 15:55:00 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 2

Fix 'Boolean Operation' node merge-by-distance post-processing

Browse source 
Fixes #2750
This commit is contained in:
Keavon Chambers 2025-06-24 18:23:09 -07:00
parent 11ba2cc0fe
commit 4a65ad290c
3 changed files with 123 additions and 125 deletions
Download patch file Download diff file Expand all files Collapse all files

120
node-graph/gcore/src/vector/algorithms/merge_by_distance.rs
View file

@ -1,16 +1,18 @@
use crate::vector::{PointId, VectorData, VectorDataIndex};
use glam::DVec2;
use crate::vector::{PointDomain, PointId, SegmentDomain, VectorData, VectorDataIndex};
use glam::{DAffine2, DVec2};
use petgraph::prelude::UnGraphMap;
use rustc_hash::FxHashSet;
impl VectorData {
/// Collapse all points with edges shorter than the specified distance
pub fn merge_by_distance(&mut self, distance: f64) {
pub fn merge_by_distance_topological(&mut self, distance: f64) {
// Treat self as an undirected graph
let indices = VectorDataIndex::build_from(self);
// TODO: We lose information on the winding order by using an undirected graph. Switch to a directed graph and fix the algorithm to handle that.
// Graph containing only short edges, referencing the data graph
let mut short_edges = UnGraphMap::new();
for segment_id in self.segment_ids().iter().copied() {
let length = indices.segment_chord_length(segment_id);
if length < distance {
@ -92,4 +94,116 @@ impl VectorData {
self.segment_domain.retain(|id| !segments_to_delete.contains(id), usize::MAX);
self.point_domain.retain(&mut self.segment_domain, |id| !points_to_delete.contains(id));
}
pub fn merge_by_distance_spatial(&mut self, transform: DAffine2, distance: f64) {
let point_count = self.point_domain.positions().len();
// Find min x and y for grid cell normalization
let mut min_x = f64::MAX;
let mut min_y = f64::MAX;
// Calculate mins without collecting all positions
for &pos in self.point_domain.positions() {
let transformed_pos = transform.transform_point2(pos);
min_x = min_x.min(transformed_pos.x);
min_y = min_y.min(transformed_pos.y);
}
// Create a spatial grid with cell size of 'distance'
use std::collections::HashMap;
let mut grid: HashMap<(i32, i32), Vec<usize>> = HashMap::new();
// Add points to grid cells without collecting all positions first
for i in 0..point_count {
let pos = transform.transform_point2(self.point_domain.positions()[i]);
let grid_x = ((pos.x - min_x) / distance).floor() as i32;
let grid_y = ((pos.y - min_y) / distance).floor() as i32;
grid.entry((grid_x, grid_y)).or_default().push(i);
}
// Create point index mapping for merged points
let mut point_index_map = vec![None; point_count];
let mut merged_positions = Vec::new();
let mut merged_indices = Vec::new();
// Process each point
for i in 0..point_count {
// Skip points that have already been processed
if point_index_map[i].is_some() {
continue;
}
let pos_i = transform.transform_point2(self.point_domain.positions()[i]);
let grid_x = ((pos_i.x - min_x) / distance).floor() as i32;
let grid_y = ((pos_i.y - min_y) / distance).floor() as i32;
let mut group = vec![i];
// Check only neighboring cells (3x3 grid around current cell)
for dx in -1..=1 {
for dy in -1..=1 {
let neighbor_cell = (grid_x + dx, grid_y + dy);
if let Some(indices) = grid.get(&neighbor_cell) {
for &j in indices {
if j > i && point_index_map[j].is_none() {
let pos_j = transform.transform_point2(self.point_domain.positions()[j]);
if pos_i.distance(pos_j) <= distance {
group.push(j);
}
}
}
}
}
}
// Create merged point - calculate positions as needed
let merged_position = group
.iter()
.map(|&idx| transform.transform_point2(self.point_domain.positions()[idx]))
.fold(DVec2::ZERO, |sum, pos| sum + pos)
/ group.len() as f64;
let merged_position = transform.inverse().transform_point2(merged_position);
let merged_index = merged_positions.len();
merged_positions.push(merged_position);
merged_indices.push(self.point_domain.ids()[group[0]]);
// Update mapping for all points in the group
for &idx in &group {
point_index_map[idx] = Some(merged_index);
}
}
// Create new point domain with merged points
let mut new_point_domain = PointDomain::new();
for (idx, pos) in merged_indices.into_iter().zip(merged_positions) {
new_point_domain.push(idx, pos);
}
// Update segment domain
let mut new_segment_domain = SegmentDomain::new();
for segment_idx in 0..self.segment_domain.ids().len() {
let id = self.segment_domain.ids()[segment_idx];
let start = self.segment_domain.start_point()[segment_idx];
let end = self.segment_domain.end_point()[segment_idx];
let handles = self.segment_domain.handles()[segment_idx];
let stroke = self.segment_domain.stroke()[segment_idx];
// Get new indices for start and end points
let new_start = point_index_map[start].unwrap();
let new_end = point_index_map[end].unwrap();
// Skip segments where start and end points were merged
if new_start != new_end {
new_segment_domain.push(id, new_start, new_end, handles, stroke);
}
}
// Create new vector data
self.point_domain = new_point_domain;
self.segment_domain = new_segment_domain;
}
}

126
node-graph/gcore/src/vector/vector_nodes.rs
View file

@ -555,7 +555,7 @@ pub fn merge_by_distance(
vector_data: VectorDataTable,
#[default(0.1)]
#[hard_min(0.0001)]
distance: Length,
distance: PixelLength,
algorithm: MergeByDistanceAlgorithm,
) -> VectorDataTable {
let mut result_table = VectorDataTable::default();
@ -563,130 +563,14 @@ pub fn merge_by_distance(
match algorithm {
MergeByDistanceAlgorithm::Spatial => {
for mut vector_data_instance in vector_data.instance_iter() {
let vector_data_transform = vector_data_instance.transform;
let vector_data = vector_data_instance.instance;
let point_count = vector_data.point_domain.positions().len();
// Find min x and y for grid cell normalization
let mut min_x = f64::MAX;
let mut min_y = f64::MAX;
// Calculate mins without collecting all positions
for &pos in vector_data.point_domain.positions() {
let transformed_pos = vector_data_transform.transform_point2(pos);
min_x = min_x.min(transformed_pos.x);
min_y = min_y.min(transformed_pos.y);
}
// Create a spatial grid with cell size of 'distance'
use std::collections::HashMap;
let mut grid: HashMap<(i32, i32), Vec<usize>> = HashMap::new();
// Add points to grid cells without collecting all positions first
for i in 0..point_count {
let pos = vector_data_transform.transform_point2(vector_data.point_domain.positions()[i]);
let grid_x = ((pos.x - min_x) / distance).floor() as i32;
let grid_y = ((pos.y - min_y) / distance).floor() as i32;
grid.entry((grid_x, grid_y)).or_default().push(i);
}
// Create point index mapping for merged points
let mut point_index_map = vec![None; point_count];
let mut merged_positions = Vec::new();
let mut merged_indices = Vec::new();
// Process each point
for i in 0..point_count {
// Skip points that have already been processed
if point_index_map[i].is_some() {
continue;
}
let pos_i = vector_data_transform.transform_point2(vector_data.point_domain.positions()[i]);
let grid_x = ((pos_i.x - min_x) / distance).floor() as i32;
let grid_y = ((pos_i.y - min_y) / distance).floor() as i32;
let mut group = vec![i];
// Check only neighboring cells (3x3 grid around current cell)
for dx in -1..=1 {
for dy in -1..=1 {
let neighbor_cell = (grid_x + dx, grid_y + dy);
if let Some(indices) = grid.get(&neighbor_cell) {
for &j in indices {
if j > i && point_index_map[j].is_none() {
let pos_j = vector_data_transform.transform_point2(vector_data.point_domain.positions()[j]);
if pos_i.distance(pos_j) <= distance {
group.push(j);
}
}
}
}
}
}
// Create merged point - calculate positions as needed
let merged_position = group
.iter()
.map(|&idx| vector_data_transform.transform_point2(vector_data.point_domain.positions()[idx]))
.fold(DVec2::ZERO, |sum, pos| sum + pos)
/ group.len() as f64;
let merged_position = vector_data_transform.inverse().transform_point2(merged_position);
let merged_index = merged_positions.len();
merged_positions.push(merged_position);
merged_indices.push(vector_data.point_domain.ids()[group[0]]);
// Update mapping for all points in the group
for &idx in &group {
point_index_map[idx] = Some(merged_index);
}
}
// Create new point domain with merged points
let mut new_point_domain = PointDomain::new();
for (idx, pos) in merged_indices.into_iter().zip(merged_positions) {
new_point_domain.push(idx, pos);
}
// Update segment domain
let mut new_segment_domain = SegmentDomain::new();
for segment_idx in 0..vector_data.segment_domain.ids().len() {
let id = vector_data.segment_domain.ids()[segment_idx];
let start = vector_data.segment_domain.start_point()[segment_idx];
let end = vector_data.segment_domain.end_point()[segment_idx];
let handles = vector_data.segment_domain.handles()[segment_idx];
let stroke = vector_data.segment_domain.stroke()[segment_idx];
// Get new indices for start and end points
let new_start = point_index_map[start].unwrap();
let new_end = point_index_map[end].unwrap();
// Skip segments where start and end points were merged
if new_start != new_end {
new_segment_domain.push(id, new_start, new_end, handles, stroke);
}
}
// Create new vector data
let mut result = vector_data.clone();
result.point_domain = new_point_domain;
result.segment_domain = new_segment_domain;
// Create and return the result
vector_data_instance.instance = result;
vector_data_instance.source_node_id = None;
vector_data_instance.instance.merge_by_distance_spatial(vector_data_instance.transform, distance);
result_table.push(vector_data_instance);
}
}
MergeByDistanceAlgorithm::Topological => {
for mut source_instance in vector_data.instance_iter() {
source_instance.instance.merge_by_distance(distance);
result_table.push(source_instance);
for mut vector_data_instance in vector_data.instance_iter() {
vector_data_instance.instance.merge_by_distance_topological(distance);
result_table.push(vector_data_instance);
}
}
}

2
node-graph/gstd/src/vector.rs
View file

@ -43,7 +43,7 @@ async fn boolean_operation<I: Into<GraphicGroupTable> + 'n + Send + Clone>(
result_vector_data.instance.upstream_graphic_group = Some(group_of_paths.clone());
// Clean up the boolean operation result by merging duplicated points
result_vector_data.instance.merge_by_distance(0.001);
result_vector_data.instance.merge_by_distance_spatial(*result_vector_data.transform, 0.0001);
}
result_vector_data_table