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from typing import Any, Dict, Iterable, List, Optional, Tuple
import numpy as np
import cv2
import copy
def process_segmentation(
segmentation: np.ndarray,
segmentation_id_map: Optional[Dict[int, Any]],
color_map: Dict[int, List[int]],
current_segment: Any,
current_subgoal_segment: Optional[str],
previous_subgoal_segment: Optional[str],
current_task_name: str,
existing_points: Optional[List[List[int]]] = None,
existing_subgoal_filled: Optional[str] = None,
) -> Dict[str, Any]:
"""
Shared helper to compute segmentation filtering and grounded subgoal text.
Returns a dict with:
- segmentation_result: segmentation mask filtered to visible ids
- segmentation_result_2d: squeezed version of segmentation_result
- segmentation_points: cached center points for current targets
- current_subgoal_segment_filled: subgoal string with centers filled in
- no_object_flag: whether the target ids are missing in the mask
- updated_previous_subgoal_segment: equals current_subgoal_segment for caller caching
- vis_obj_id_list: ids kept in segmentation_result
"""
segmentation_2d = segmentation.squeeze() if segmentation.ndim > 2 else segmentation
if isinstance(current_segment, (list, tuple)):
active_segments = list(current_segment)
elif current_segment is None:
active_segments = []
else:
active_segments = [current_segment]
segment_ids_by_index = {idx: [] for idx in range(len(active_segments))}
vis_obj_id_list: List[int] = []
if isinstance(segmentation_id_map, dict):
for obj_id, obj in sorted(segmentation_id_map.items()):
if active_segments:
for idx, target in enumerate(active_segments):
if obj is target:
vis_obj_id_list.append(obj_id)
segment_ids_by_index[idx].append(obj_id)
break
if getattr(obj, "name", None) == "table-workspace":
color_map[obj_id] = [0, 0, 0]
segmentation_result = np.where(
np.isin(segmentation_2d, vis_obj_id_list), segmentation_2d, 0
)
segmentation_result_2d = segmentation_result.squeeze()
segmentation_points = existing_points or []
current_subgoal_segment_filled = existing_subgoal_filled
no_object_flag = False
if current_subgoal_segment != previous_subgoal_segment:
def compute_center_from_ids(segmentation_mask: np.ndarray, ids: Iterable[int]):
nonlocal no_object_flag
ids = list(ids)
if not ids:
return None
mask = np.isin(segmentation_mask, ids)
if not np.any(mask):
no_object_flag = True
return None
coords = np.argwhere(mask)
if coords.size == 0:
return None
center_y = int(coords[:, 0].mean())
center_x = int(coords[:, 1].mean())
return [center_y, center_x]
segment_centers: List[Optional[List[int]]] = []
if active_segments:
for idx in range(len(active_segments)):
segment_centers.append(
compute_center_from_ids(
segmentation_2d, segment_ids_by_index.get(idx, [])
)
)
else:
segment_centers.append(
compute_center_from_ids(segmentation_2d, vis_obj_id_list)
)
segmentation_points = [center for center in segment_centers if center is not None]
if current_subgoal_segment:
normalized_centers: List[Optional[str]] = []
for center in segment_centers:
if center is None:
normalized_centers.append(None)
continue
center_y, center_x = center
normalized_centers.append(f"<{center_y}, {center_x}>")
placeholder_pattern = re.compile(r"<[^>]*>")
placeholders = list(placeholder_pattern.finditer(current_subgoal_segment))
placeholder_count = len(placeholders)
if placeholder_count > 0 and normalized_centers:
replacements = normalized_centers.copy()
if len(replacements) == 1 and placeholder_count > 1:
replacements = replacements * placeholder_count
elif len(replacements) < placeholder_count:
replacements.extend([None] * (placeholder_count - len(replacements)))
missing_placeholder = False
new_text_parts: List[str] = []
last_idx = 0
for idx, match in enumerate(placeholders):
new_text_parts.append(
current_subgoal_segment[last_idx : match.start()]
)
replacement_text = replacements[idx]
if replacement_text is None:
missing_placeholder = True
else:
new_text_parts.append(replacement_text)
last_idx = match.end()
new_text_parts.append(current_subgoal_segment[last_idx:])
current_subgoal_segment_filled = (
current_task_name if missing_placeholder else "".join(new_text_parts)
)
else:
current_subgoal_segment_filled = current_subgoal_segment
else:
current_subgoal_segment_filled = current_subgoal_segment
return {
"segmentation_result": segmentation_result,
"segmentation_result_2d": segmentation_result_2d,
"segmentation_points": segmentation_points,
"current_subgoal_segment_filled": current_subgoal_segment_filled,
"no_object_flag": no_object_flag,
"updated_previous_subgoal_segment": current_subgoal_segment,
"vis_obj_id_list": vis_obj_id_list,
}
def create_segmentation_visuals(
segmentation: np.ndarray,
segmentation_result: np.ndarray,
base_frame: np.ndarray,
color_map: Dict[int, List[int]],
segmentation_points: List[List[int]],
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Build colored segmentation visualizations and target overlay for video export.
Returns (segmentation_vis, segmentation_result_vis, target_for_video).
"""
segmentation_for_video = copy.deepcopy(segmentation)
segmentation_result_for_video = copy.deepcopy(segmentation_result)
segmentation_vis = np.zeros(
(*segmentation_for_video.shape[:2], 3), dtype=np.uint8
)
segmentation_result_vis = np.zeros(
(*segmentation_result_for_video.shape[:2], 3), dtype=np.uint8
)
seg_2d = (
segmentation_for_video.squeeze()
if segmentation_for_video.ndim > 2
else segmentation_for_video
)
seg_result_2d = (
segmentation_result_for_video.squeeze()
if segmentation_result_for_video.ndim > 2
else segmentation_result_for_video
)
for seg_id in np.unique(seg_2d):
if seg_id > 0:
color = color_map.get(seg_id, [255, 255, 255])
mask = seg_2d == seg_id
segmentation_vis[mask] = color
for seg_id in np.unique(seg_result_2d):
if seg_id > 0:
color = color_map.get(seg_id, [255, 255, 255])
mask = seg_result_2d == seg_id
segmentation_result_vis[mask] = color
target_for_video = copy.deepcopy(base_frame)
if segmentation_vis.shape[:2] != base_frame.shape[:2]:
segmentation_vis = cv2.resize(
segmentation_vis,
(base_frame.shape[1], base_frame.shape[0]),
interpolation=cv2.INTER_NEAREST,
)
if segmentation_result_vis.shape[:2] != base_frame.shape[:2]:
segmentation_result_vis = cv2.resize(
segmentation_result_vis,
(base_frame.shape[1], base_frame.shape[0]),
interpolation=cv2.INTER_NEAREST,
)
if segmentation_points:
diameter = 5
for center_y, center_x in segmentation_points:
cv2.circle(target_for_video, (center_x, center_y), diameter, (255, 0, 0), -1)
return segmentation_vis, segmentation_result_vis, target_for_video
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