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This code is an adaptation that uses Structured 3D for the code base.
Reference: https://github.com/bertjiazheng/Structured3D
"""
import json
import os
import sys
import numpy as np
from shapely.geometry import Polygon
sys.path.append("../data_preprocess")
from common_utils import resort_corners
type2id = {
"living room": 0,
"kitchen": 1,
"bedroom": 2,
"bathroom": 3,
"balcony": 4,
"corridor": 5,
"dining room": 6,
"study": 7,
"studio": 8,
"store room": 9,
"garden": 10,
"laundry room": 11,
"office": 12,
"basement": 13,
"garage": 14,
"undefined": 15,
"door": 16,
"window": 17,
}
def generate_density(point_cloud, width=256, height=256):
ps = point_cloud * -1
ps[:, 0] *= -1
ps[:, 1] *= -1
image_res = np.array((width, height))
max_coords = np.max(ps, axis=0)
min_coords = np.min(ps, axis=0)
max_m_min = max_coords - min_coords
max_coords = max_coords + 0.1 * max_m_min
min_coords = min_coords - 0.1 * max_m_min
normalization_dict = {}
normalization_dict["min_coords"] = min_coords
normalization_dict["max_coords"] = max_coords
normalization_dict["image_res"] = image_res
# coordinates = np.round(points[:, :2] / max_coordinates[None,:2] * image_res[None])
coordinates = np.round(
(ps[:, :2] - min_coords[None, :2]) / (max_coords[None, :2] - min_coords[None, :2]) * image_res[None]
)
coordinates = np.minimum(np.maximum(coordinates, np.zeros_like(image_res)), image_res - 1)
density = np.zeros((height, width), dtype=np.float32)
unique_coordinates, counts = np.unique(coordinates, return_counts=True, axis=0)
# print(np.unique(counts))
# counts = np.minimum(counts, 1e2)
unique_coordinates = unique_coordinates.astype(np.int32)
density[unique_coordinates[:, 1], unique_coordinates[:, 0]] = counts
density = density / np.max(density)
return density, normalization_dict
def normalize_point(point, normalization_dict):
min_coords = normalization_dict["min_coords"]
max_coords = normalization_dict["max_coords"]
image_res = normalization_dict["image_res"]
point_2d = np.round((point[:2] - min_coords[:2]) / (max_coords[:2] - min_coords[:2]) * image_res)
point_2d = np.minimum(np.maximum(point_2d, np.zeros_like(image_res)), image_res - 1)
point[:2] = point_2d.tolist()
return point
def normalize_annotations(scene_path, normalization_dict):
annotation_path = os.path.join(scene_path, "annotation_3d.json")
with open(annotation_path, "r") as f:
annotation_json = json.load(f)
for line in annotation_json["lines"]:
point = line["point"]
point = normalize_point(point, normalization_dict)
line["point"] = point
for junction in annotation_json["junctions"]:
point = junction["coordinate"]
point = normalize_point(point, normalization_dict)
junction["coordinate"] = point
return annotation_json
def parse_floor_plan_polys(annos):
planes = []
for semantic in annos["semantics"]:
for planeID in semantic["planeID"]:
if annos["planes"][planeID]["type"] == "floor":
planes.append({"planeID": planeID, "type": semantic["type"]})
# if semantic["type"] == "outwall":
# outerwall_planes = semantic["planeID"]
# extract hole vertices
lines_holes = []
for semantic in annos["semantics"]:
if semantic["type"] in ["window", "door"]:
for planeID in semantic["planeID"]:
lines_holes.extend(np.where(np.array(annos["planeLineMatrix"][planeID]))[0].tolist())
lines_holes = np.unique(lines_holes)
## junctions on the floor
# junctions = np.array([junc["coordinate"] for junc in annos["junctions"]])
# construct each polygon
polygons = []
for plane in planes:
lineIDs = np.where(np.array(annos["planeLineMatrix"][plane["planeID"]]))[0].tolist()
junction_pairs = [np.where(np.array(annos["lineJunctionMatrix"][lineID]))[0].tolist() for lineID in lineIDs]
polygon = convert_lines_to_vertices(junction_pairs)
polygons.append([polygon[0], plane["type"]])
return polygons
def convert_lines_to_vertices(lines):
"""
convert line representation to polygon vertices
"""
polygons = []
lines = np.array(lines)
polygon = None
while len(lines) != 0:
if polygon is None:
polygon = lines[0].tolist()
lines = np.delete(lines, 0, 0)
lineID, juncID = np.where(lines == polygon[-1])
vertex = lines[lineID[0], 1 - juncID[0]]
lines = np.delete(lines, lineID, 0)
if vertex in polygon:
polygons.append(polygon)
polygon = None
else:
polygon.append(vertex)
return polygons
def generate_coco_dict(annos, polygons, curr_instance_id, curr_img_id, ignore_types):
junctions = np.array([junc["coordinate"][:2] for junc in annos["junctions"]])
coco_annotation_dict_list = []
for poly_ind, (polygon, poly_type) in enumerate(polygons):
if poly_type in ignore_types:
continue
polygon = junctions[np.array(polygon)]
poly_shapely = Polygon(polygon)
area = poly_shapely.area
# assert area > 10
# if area < 100:
if poly_type not in ["door", "window"] and area < 100:
continue
if poly_type in ["door", "window"] and area < 1:
continue
rectangle_shapely = poly_shapely.envelope
### here we convert door/window annotation into a single line
if poly_type in ["door", "window"]:
assert polygon.shape[0] == 4
midp_1 = (polygon[0] + polygon[1]) / 2
midp_2 = (polygon[1] + polygon[2]) / 2
midp_3 = (polygon[2] + polygon[3]) / 2
midp_4 = (polygon[3] + polygon[0]) / 2
dist_1_3 = np.square(midp_1 - midp_3).sum()
dist_2_4 = np.square(midp_2 - midp_4).sum()
if dist_1_3 > dist_2_4:
polygon = np.row_stack([midp_1, midp_3])
else:
polygon = np.row_stack([midp_2, midp_4])
coco_seg_poly = []
poly_sorted = resort_corners(polygon)
for p in poly_sorted:
coco_seg_poly += list(p)
# Slightly wider bounding box
bound_pad = 2
bb_x, bb_y = rectangle_shapely.exterior.xy
bb_x = np.unique(bb_x)
bb_y = np.unique(bb_y)
bb_x_min = np.maximum(np.min(bb_x) - bound_pad, 0)
bb_y_min = np.maximum(np.min(bb_y) - bound_pad, 0)
bb_x_max = np.minimum(np.max(bb_x) + bound_pad, 256 - 1)
bb_y_max = np.minimum(np.max(bb_y) + bound_pad, 256 - 1)
bb_width = bb_x_max - bb_x_min
bb_height = bb_y_max - bb_y_min
coco_bb = [bb_x_min, bb_y_min, bb_width, bb_height]
coco_annotation_dict = {
"segmentation": [coco_seg_poly],
"area": area,
"iscrowd": 0,
"image_id": curr_img_id,
"bbox": coco_bb,
"category_id": type2id[poly_type],
"id": curr_instance_id,
}
coco_annotation_dict_list.append(coco_annotation_dict)
curr_instance_id += 1
return coco_annotation_dict_list
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