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1e3df84 e0e4431 1e3df84 e0e4431 1e3df84 e0e4431 1e3df84 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | """
Convert Tesseract++ graph JSON to Cytoscape.js elements format.
Tesseract++ JSON format:
nodes: [{id, type, position: [x,y], floor, ...}, ...]
edges: [{source, target, weight, distance}, ...]
Cytoscape.js elements format:
nodes: [{data: {id, type, floor, ...}, position: {x, y}}, ...]
edges: [{data: {id, source, target, weight, edgeColorType}, ...}, ...]
"""
from typing import Dict, Any, List
def _norm_type(t: str) -> str:
"""Normalize node types for the frontend. The pipeline emits 'transition'
for stairs/elevators, but the UI keys color, shape, size, and labels on
'floor_transition'. Map them so everything stays consistent."""
return "floor_transition" if t == "transition" else t
def _determine_edge_color_type(
source_id: str,
target_id: str,
node_type_map: Dict[str, str]
) -> str:
"""Determine edge color category based on endpoint node types."""
src_type = node_type_map.get(source_id, "unknown")
tgt_type = node_type_map.get(target_id, "unknown")
types = {src_type, tgt_type}
if "floor_transition" in types:
return "transition"
if "outside" in types:
return "outside"
if "corridor" in types:
return "corridor"
if "room" in types:
return "room"
return "default"
def convert_to_cytoscape(graph_json: Dict[str, Any]) -> Dict[str, Any]:
"""
Convert Tesseract++ graph JSON to Cytoscape.js elements format.
Args:
graph_json: Raw graph dict with 'nodes' and 'edges' lists.
Returns:
Dict with 'nodes', 'edges', and 'layout' keys in Cytoscape format.
"""
raw_nodes = graph_json.get("nodes", [])
raw_edges = graph_json.get("edges", [])
# Build node type lookup for edge coloring
node_type_map: Dict[str, str] = {}
for node in raw_nodes:
node_type_map[node["id"]] = _norm_type(node.get("type", "unknown"))
# Convert nodes
cy_nodes: List[Dict[str, Any]] = []
for node in raw_nodes:
pos = node.get("position", [0, 0])
x = pos[0] if isinstance(pos, (list, tuple)) else pos.get("x", 0)
y = pos[1] if isinstance(pos, (list, tuple)) else pos.get("y", 0)
data: Dict[str, Any] = {
"id": node["id"],
"label": node["id"],
"type": _norm_type(node.get("type", "unknown")),
"floor": node.get("floor", ""),
}
# Add room-specific attributes
if node.get("type") == "room":
if "room_area_px" in node:
data["area"] = node["room_area_px"]
if "anchor_door" in node:
data["anchorDoor"] = node["anchor_door"]
if "room_eq_radius" in node:
data["eqRadius"] = round(node["room_eq_radius"], 1)
# Add subnode info
if node.get("is_subnode"):
data["isSubnode"] = True
data["parentRoom"] = node.get("parent_room_id", "")
# Add door-specific attributes
if node.get("type") == "door":
if "door_type" in node:
data["doorType"] = node["door_type"]
cy_nodes.append({
"data": data,
"position": {"x": float(x), "y": float(y)}
})
# Convert edges
cy_edges: List[Dict[str, Any]] = []
for idx, edge in enumerate(raw_edges):
source = edge["source"]
target = edge["target"]
edge_id = f"e{idx}_{source}_{target}"
color_type = _determine_edge_color_type(source, target, node_type_map)
cy_edges.append({
"data": {
"id": edge_id,
"source": source,
"target": target,
"weight": edge.get("weight", 1.0),
"edgeColorType": color_type
}
})
return {
"nodes": cy_nodes,
"edges": cy_edges,
"layout": "preset"
}
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