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fadb92b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | import argparse
import json
import os
import sys
from stru3d_utils import generate_coco_dict, generate_density, normalize_annotations, parse_floor_plan_polys
from tqdm import tqdm
sys.path.append("../.")
from common_utils import export_density, read_scene_pc
### Note: Some scenes have missing/wrong annotations. These are the indices that you should additionally exclude
### to be consistent with MonteFloor and HEAT:
invalid_scenes_ids = [
76,
183,
335,
491,
663,
681,
703,
728,
865,
936,
985,
986,
1009,
1104,
1155,
1221,
1282,
1365,
1378,
1635,
1745,
1772,
1774,
1816,
1866,
2037,
2076,
2274,
2334,
2357,
2580,
2665,
2706,
2713,
2771,
2868,
3156,
3192,
3198,
3261,
3271,
3276,
3296,
3342,
3387,
3398,
3466,
3496,
]
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 config():
a = argparse.ArgumentParser(description="Generate coco format data for Structured3D")
a.add_argument(
"--data_root", default="Structured3D_panorama", type=str, help="path to raw Structured3D_panorama folder"
)
a.add_argument("--output", default="coco_stru3d", type=str, help="path to output folder")
args = a.parse_args()
return args
def main(args):
data_root = args.data_root
data_parts = os.listdir(data_root)
### prepare
outFolder = args.output
if not os.path.exists(outFolder):
os.mkdir(outFolder)
annotation_outFolder = os.path.join(outFolder, "annotations")
if not os.path.exists(annotation_outFolder):
os.mkdir(annotation_outFolder)
train_img_folder = os.path.join(outFolder, "train")
val_img_folder = os.path.join(outFolder, "val")
test_img_folder = os.path.join(outFolder, "test")
for img_folder in [train_img_folder, val_img_folder, test_img_folder]:
if not os.path.exists(img_folder):
os.mkdir(img_folder)
coco_train_json_path = os.path.join(annotation_outFolder, "train.json")
coco_val_json_path = os.path.join(annotation_outFolder, "val.json")
coco_test_json_path = os.path.join(annotation_outFolder, "test.json")
coco_train_dict = {"images": [], "annotations": [], "categories": []}
coco_val_dict = {"images": [], "annotations": [], "categories": []}
coco_test_dict = {"images": [], "annotations": [], "categories": []}
for key, value in type2id.items():
type_dict = {"supercategory": "room", "id": value, "name": key}
coco_train_dict["categories"].append(type_dict)
coco_val_dict["categories"].append(type_dict)
coco_test_dict["categories"].append(type_dict)
### begin processing
instance_id = 0
for part in tqdm(data_parts):
scenes = os.listdir(os.path.join(data_root, part, "Structured3D"))
for scene in tqdm(scenes):
scene_path = os.path.join(data_root, part, "Structured3D", scene)
scene_id = scene.split("_")[-1]
if int(scene_id) in invalid_scenes_ids:
print("skip {}".format(scene))
continue
# load pre-generated point cloud
ply_path = os.path.join(scene_path, "point_cloud.ply")
points = read_scene_pc(ply_path)
xyz = points[:, :3]
### project point cloud to density map
density, normalization_dict = generate_density(xyz, width=256, height=256)
### rescale raw annotations
normalized_annos = normalize_annotations(scene_path, normalization_dict)
### prepare coco dict
img_id = int(scene_id)
img_dict = {}
img_dict["file_name"] = scene_id + ".png"
img_dict["id"] = img_id
img_dict["width"] = 256
img_dict["height"] = 256
### parse annotations
polys = parse_floor_plan_polys(normalized_annos)
polygons_list = generate_coco_dict(normalized_annos, polys, instance_id, img_id, ignore_types=["outwall"])
instance_id += len(polygons_list)
### train
if int(scene_id) < 3000:
coco_train_dict["images"].append(img_dict)
coco_train_dict["annotations"] += polygons_list
export_density(density, train_img_folder, scene_id)
### val
elif int(scene_id) >= 3000 and int(scene_id) < 3250:
coco_val_dict["images"].append(img_dict)
coco_val_dict["annotations"] += polygons_list
export_density(density, val_img_folder, scene_id)
### test
else:
coco_test_dict["images"].append(img_dict)
coco_test_dict["annotations"] += polygons_list
export_density(density, test_img_folder, scene_id)
print(scene_id)
with open(coco_train_json_path, "w") as f:
json.dump(coco_train_dict, f)
with open(coco_val_json_path, "w") as f:
json.dump(coco_val_dict, f)
with open(coco_test_json_path, "w") as f:
json.dump(coco_test_dict, f)
if __name__ == "__main__":
main(config())
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