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visualization.py
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visualization.py
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import numpy as np
import torch
import argparse
import math
import open3d as o3d
import os
from operator import itemgetter
# yapf:disable
COLOR_DETECTRON2 = (
np.array(
[
0.000,
0.447,
0.741,
0.850,
0.325,
0.098,
0.929,
0.694,
0.125,
0.494,
0.184,
0.556,
0.466,
0.674,
0.188,
0.301,
0.745,
0.933,
0.635,
0.078,
0.184,
# 0.300, 0.300, 0.300,
0.600,
0.600,
0.600,
1.000,
0.000,
0.000,
1.000,
0.500,
0.000,
0.749,
0.749,
0.000,
0.000,
1.000,
0.000,
0.000,
0.000,
1.000,
0.667,
0.000,
1.000,
0.333,
0.333,
0.000,
0.333,
0.667,
0.000,
0.333,
1.000,
0.000,
0.667,
0.333,
0.000,
0.667,
0.667,
0.000,
0.667,
1.000,
0.000,
1.000,
0.333,
0.000,
1.000,
0.667,
0.000,
1.000,
1.000,
0.000,
0.000,
0.333,
0.500,
0.000,
0.667,
0.500,
0.000,
1.000,
0.500,
0.333,
0.000,
0.500,
0.333,
0.333,
0.500,
0.333,
0.667,
0.500,
0.333,
1.000,
0.500,
0.667,
0.000,
0.500,
0.667,
0.333,
0.500,
0.667,
0.667,
0.500,
0.667,
1.000,
0.500,
1.000,
0.000,
0.500,
1.000,
0.333,
0.500,
1.000,
0.667,
0.500,
1.000,
1.000,
0.500,
0.000,
0.333,
1.000,
0.000,
0.667,
1.000,
0.000,
1.000,
1.000,
0.333,
0.000,
1.000,
0.333,
0.333,
1.000,
0.333,
0.667,
1.000,
0.333,
1.000,
1.000,
0.667,
0.000,
1.000,
0.667,
0.333,
1.000,
0.667,
0.667,
1.000,
0.667,
1.000,
1.000,
1.000,
0.000,
1.000,
1.000,
0.333,
1.000,
1.000,
0.667,
1.000,
# 0.333, 0.000, 0.000,
0.500,
0.000,
0.000,
0.667,
0.000,
0.000,
0.833,
0.000,
0.000,
1.000,
0.000,
0.000,
0.000,
0.167,
0.000,
# 0.000, 0.333, 0.000,
0.000,
0.500,
0.000,
0.000,
0.667,
0.000,
0.000,
0.833,
0.000,
0.000,
1.000,
0.000,
0.000,
0.000,
0.167,
# 0.000, 0.000, 0.333,
0.000,
0.000,
0.500,
0.000,
0.000,
0.667,
0.000,
0.000,
0.833,
0.000,
0.000,
1.000,
# 0.000, 0.000, 0.000,
0.143,
0.143,
0.143,
0.857,
0.857,
0.857,
# 1.000, 1.000, 1.000
]
)
.astype(np.float32)
.reshape(-1, 3)
* 255
)
# yapf:enable
SEMANTIC_IDXS = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39])
SEMANTIC_NAMES = np.array(
[
"wall",
"floor",
"cabinet",
"bed",
"chair",
"sofa",
"table",
"door",
"window",
"bookshelf",
"picture",
"counter",
"desk",
"curtain",
"refridgerator",
"shower curtain",
"toilet",
"sink",
"bathtub",
"otherfurniture",
]
)
CLASS_COLOR = {
"unannotated": [0, 0, 0],
"floor": [143, 223, 142],
"wall": [171, 198, 230],
"cabinet": [0, 120, 177],
"bed": [255, 188, 126],
"chair": [189, 189, 57],
"sofa": [144, 86, 76],
"table": [255, 152, 153],
"door": [222, 40, 47],
"window": [197, 176, 212],
"bookshelf": [150, 103, 185],
"picture": [200, 156, 149],
"counter": [0, 190, 206],
"desk": [252, 183, 210],
"curtain": [219, 219, 146],
"refridgerator": [255, 127, 43],
"bathtub": [234, 119, 192],
"shower curtain": [150, 218, 228],
"toilet": [0, 160, 55],
"sink": [110, 128, 143],
"otherfurniture": [80, 83, 160],
}
SEMANTIC_IDX2NAME = {
1: "wall",
2: "floor",
3: "cabinet",
4: "bed",
5: "chair",
6: "sofa",
7: "table",
8: "door",
9: "window",
10: "bookshelf",
11: "picture",
12: "counter",
14: "desk",
16: "curtain",
24: "refridgerator",
28: "shower curtain",
33: "toilet",
34: "sink",
36: "bathtub",
39: "otherfurniture",
}
def get_coords_color(opt):
if opt.dataset == "s3dis":
assert opt.data_split in [
"Area_1",
"Area_2",
"Area_3",
"Area_4",
"Area_5",
"Area_6",
], "data_split for s3dis should be one of [Area_1, Area_2, Area_3, Area_4, Area_5, Area_6]"
input_file = os.path.join("dataset", opt.dataset, "preprocess", opt.room_name + "_inst_nostuff.pth")
assert os.path.isfile(input_file), "File not exist - {}.".format(input_file)
xyz, rgb, label, inst_label, _, _ = torch.load(input_file)
# update variable to match scannet format
opt.data_split = os.path.join("val", opt.data_split)
else:
input_file = os.path.join("dataset", opt.dataset, opt.data_split, opt.room_name + "_inst_nostuff.pth")
assert os.path.isfile(input_file), "File not exist - {}.".format(input_file)
if opt.data_split == "test":
xyz, rgb = torch.load(input_file)
else:
xyz, rgb, label, inst_label = torch.load(input_file)
rgb = (rgb + 1) * 127.5
m = np.eye(3)
theta = 0.35 * math.pi
m = np.matmul(m, [[math.cos(theta), math.sin(theta), 0], [-math.sin(theta), math.cos(theta), 0], [0, 0, 1]])
xyz = np.matmul(xyz, m)
boxes = []
if opt.task == "semantic_gt":
assert opt.data_split != "test"
label = label.astype(np.int)
label_rgb = np.zeros(rgb.shape)
label_rgb[label >= 0] = np.array(itemgetter(*SEMANTIC_NAMES[label[label >= 0]])(CLASS_COLOR))
rgb = label_rgb
elif opt.task == "semantic_pred":
assert opt.data_split != "train"
semantic_file = os.path.join(opt.prediction_path, "semantic_pred", opt.room_name + ".npy")
assert os.path.isfile(semantic_file), "No semantic result - {}.".format(semantic_file)
label_pred = np.load(semantic_file).astype(np.int) # 0~19
label_pred_rgb = np.array(itemgetter(*SEMANTIC_NAMES[label_pred])(CLASS_COLOR))
rgb = label_pred_rgb
elif opt.task == "object_pred":
assert opt.data_split != "train"
obj_file = os.path.join(opt.prediction_path, "object_conditions", opt.room_name + ".npy")
assert os.path.isfile(obj_file), "No semantic result - {}.".format(obj_file)
obj_pred = np.load(obj_file).astype(np.int) # 0~19
# label_pred_rgb = np.array(itemgetter(*SEMANTIC_NAMES[label_pred])(CLASS_COLOR))
# rgb = label_pred_rgb
print("bug")
obj_pred_rgb = np.zeros_like(rgb)
obj_pred_rgb[obj_pred == 1] = CLASS_COLOR["floor"]
obj_pred_rgb[obj_pred == 0] = CLASS_COLOR["unannotated"]
rgb = obj_pred_rgb
elif opt.task == "offset_semantic_pred":
assert opt.data_split != "train"
semantic_file = os.path.join(opt.prediction_path, "semantic_pred", opt.room_name + ".npy")
assert os.path.isfile(semantic_file), "No semantic result - {}.".format(semantic_file)
label_pred = np.load(semantic_file).astype(np.int) # 0~19
label_pred_rgb = np.array(itemgetter(*SEMANTIC_NAMES[label_pred])(CLASS_COLOR))
rgb = label_pred_rgb
offset_file = os.path.join(opt.prediction_path, "offset_pred", opt.room_name + ".npy")
assert os.path.isfile(offset_file), "No offset result - {}.".format(offset_file)
offset_coords = np.load(offset_file)
# xyz = offset_coords[:, :3] + offset_coords[:, 3:]
# xyz[label_pred > 1] += offset_coords[label_pred > 1]
xyz += offset_coords
elif opt.task == "superpoint":
superpoint_file = os.path.join("dataset", opt.dataset, "superpoints", opt.room_name + ".pth")
spp = torch.load(superpoint_file)
spp_label_rgb = np.zeros(rgb.shape)
unique_spp = torch.unique(spp)
for i, uni_idx in enumerate(unique_spp):
if uni_idx == -100:
continue
spp_label_rgb[spp == uni_idx] = COLOR_DETECTRON2[i % len(COLOR_DETECTRON2)]
rgb = spp_label_rgb
# same color order according to instance pointnum
elif opt.task == "instance_gt":
assert opt.data_split != "test"
inst_label = inst_label.astype(np.int)
# unique_labels = np.unique(inst_label)
# unique_labels = [ins for ins in unique_labels if ins != -100]
inst_label_rgb = np.zeros(rgb.shape)
ins_num = inst_label.max() + 1
ins_pointnum = np.zeros(ins_num)
for _ins_id in range(ins_num):
ins_pointnum[_ins_id] = (inst_label == _ins_id).sum()
sort_idx = np.argsort(ins_pointnum)[::-1]
count_ins = 0
for _sort_id in range(ins_num):
inds = np.nonzero(inst_label == sort_idx[_sort_id])[0]
# print(inds)
if label[inds[0]] == -100:
continue
# inst_label_rgb[inds] = [255,0,0]
inst_label_rgb[inds] = COLOR_DETECTRON2[_sort_id % len(COLOR_DETECTRON2)]
print("Instance {}: pointnum: {}".format(_sort_id, ins_pointnum[_sort_id]))
count_ins += 1
print("Instance number: {}".format(count_ins))
rgb = inst_label_rgb
# xyz = xyz[inst_label_rgb==[255,0,0]]
# rgb = rgb[inst_label_rgb==[255,0,0]]
# same color order according to instance pointnum
elif opt.task == "instance_pred":
assert opt.data_split != "train"
instance_file = os.path.join(opt.prediction_path, "pred_instance", opt.room_name + ".txt")
assert os.path.isfile(instance_file), "No instance result - {}.".format(instance_file)
f = open(instance_file, "r")
masks = f.readlines()
masks = [mask.rstrip().split() for mask in masks]
inst_label_pred_rgb = np.zeros(rgb.shape) # np.ones(rgb.shape) * 255 #
# FIXME
# masks = masks[24:30]
ins_num = len(masks)
ins_pointnum = np.zeros(ins_num)
box_pred = np.zeros((ins_num, 6))
inst_label = -100 * np.ones(rgb.shape[0]).astype(np.int)
# sort score such that high score has high priority for visualization
scores = np.array([float(x[-1]) for x in masks])
sort_inds = np.argsort(scores)[::-1]
for i_ in range(len(masks) - 1, -1, -1):
i = sort_inds[i_]
mask_path = os.path.join(opt.prediction_path, "pred_instance", masks[i][0])
assert os.path.isfile(mask_path), mask_path
if float(masks[i][2]) < 0.09:
continue
mask = np.loadtxt(mask_path).astype(np.int)
if opt.dataset == "scannet":
print("{} {}: {} pointnum: {}".format(i, masks[i], SEMANTIC_IDX2NAME[int(masks[i][1])], mask.sum()))
else:
print("{} {}: pointnum: {}".format(i, masks[i], mask.sum()))
ins_pointnum[i] = mask.sum()
inst_label[mask == 1] = i
box = masks[i][3:9]
box_pred[i] = np.array(box)
sort_idx = np.argsort(ins_pointnum)[::-1]
for _sort_id in range(ins_num):
color = COLOR_DETECTRON2[_sort_id % len(COLOR_DETECTRON2)]
inst_label_pred_rgb[inst_label == sort_idx[_sort_id]] = color
box = o3d.geometry.AxisAlignedBoundingBox(
min_bound=box_pred[sort_idx[_sort_id], 0:3], max_bound=box_pred[sort_idx[_sort_id], 3:6]
)
box.color = color / 255.0
boxes.append(box)
rgb = inst_label_pred_rgb
# # NOTE plot box of context points
# offset_file = os.path.join(opt.prediction_path, 'offset_vertices_pred',
# opt.room_name + '.npy')
# assert os.path.isfile(offset_file), 'No offset result - {}.'.format(offset_file)
# offset_coords = np.load(offset_file)
# semantic_file = os.path.join(opt.prediction_path, "semantic_pred", opt.room_name + ".npy")
# assert os.path.isfile(semantic_file), "No semantic result - {}.".format(semantic_file)
# label_pred = np.load(semantic_file).astype(np.int) # 0~19
# for _sort_id in range(ins_num):
# color = COLOR_DETECTRON2[_sort_id % len(COLOR_DETECTRON2)]
# inst_label_rgb[inst_label == sort_idx[_sort_id]] = color
# # min_coord = np.mean(offset_coords_min[inst_label == sort_idx[_sort_id]], 0)
# # max_coord = np.mean(offset_coords_max[inst_label == sort_idx[_sort_id]], 0)
# # box = o3d.geometry.AxisAlignedBoundingBox(min_bound=min_coord, max_bound=max_coord)
# # box.color = color/255.
# # boxes.append(box)
# indices = np.nonzero(inst_label == sort_idx[_sort_id])[0][::500]
# for ind in indices:
# box = o3d.geometry.AxisAlignedBoundingBox(min_bound=offset_coords_min[ind], max_bound=offset_coords_max[ind])
# box.color = color/255.
# boxes.append(box)
elif opt.task == "offset_vertices_pred":
offset_file = os.path.join(opt.prediction_path, "offset_vertices_pred", opt.room_name + ".npy")
assert os.path.isfile(offset_file), "No offset result - {}.".format(offset_file)
offset_coords = np.load(offset_file)
semantic_file = os.path.join(opt.prediction_path, "semantic_pred", opt.room_name + ".npy")
assert os.path.isfile(semantic_file), "No semantic result - {}.".format(semantic_file)
label_pred = np.load(semantic_file).astype(np.int) # 0~19
# print(offset_coords.shape)
offset_coords_min = offset_coords[:, :3] + xyz
offset_coords_max = offset_coords[:, 3:] + xyz
assert opt.data_split != "test"
inst_label = inst_label.astype(np.int)
print("Instance number: {}".format(inst_label.max() + 1))
inst_label_rgb = np.zeros(rgb.shape)
ins_num = inst_label.max() + 1
ins_pointnum = np.zeros(ins_num)
for _ins_id in range(ins_num):
ins_pointnum[_ins_id] = (inst_label == _ins_id).sum()
sort_idx = np.argsort(ins_pointnum)[::-1]
for _sort_id in range(ins_num):
color = COLOR_DETECTRON2[_sort_id % len(COLOR_DETECTRON2)]
inst_label_rgb[inst_label == sort_idx[_sort_id]] = color
# min_coord = np.mean(offset_coords_min[inst_label == sort_idx[_sort_id]], 0)
# max_coord = np.mean(offset_coords_max[inst_label == sort_idx[_sort_id]], 0)
# box = o3d.geometry.AxisAlignedBoundingBox(min_bound=min_coord, max_bound=max_coord)
# box.color = color/255.
# boxes.append(box)
indices = np.nonzero(inst_label == sort_idx[_sort_id])[0][::500]
for ind in indices:
box = o3d.geometry.AxisAlignedBoundingBox(
min_bound=offset_coords_min[ind], max_bound=offset_coords_max[ind]
)
box.color = color / 255.0
boxes.append(box)
print("total boxes:", len(boxes))
rgb = inst_label_rgb
if opt.data_split != "test":
sem_valid = label != -100
xyz = xyz[sem_valid]
rgb = rgb[sem_valid]
return xyz, rgb, boxes
def write_ply(verts, colors, indices, output_file):
if colors is None:
colors = np.zeros_like(verts)
if indices is None:
indices = []
file = open(output_file, "w")
file.write("ply \n")
file.write("format ascii 1.0\n")
file.write("element vertex {:d}\n".format(len(verts)))
file.write("property float x\n")
file.write("property float y\n")
file.write("property float z\n")
file.write("property uchar red\n")
file.write("property uchar green\n")
file.write("property uchar blue\n")
file.write("element face {:d}\n".format(len(indices)))
file.write("property list uchar uint vertex_indices\n")
file.write("end_header\n")
for vert, color in zip(verts, colors):
file.write(
"{:f} {:f} {:f} {:d} {:d} {:d}\n".format(
vert[0], vert[1], vert[2], int(color[0] * 255), int(color[1] * 255), int(color[2] * 255)
)
)
for ind in indices:
file.write("3 {:d} {:d} {:d}\n".format(ind[0], ind[1], ind[2]))
file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset", choices=["scannetv2", "s3dis"], help="dataset for visualization", default="scannetv2"
)
parser.add_argument(
"--prediction_path",
help="path to the prediction results",
default="./results/pointnext_val",
)
parser.add_argument("--data_split", help="train/val/test for scannet or Area_ID for s3dis", default="val")
parser.add_argument("--room_name", help="room_name", default="scene0011_00")
parser.add_argument(
"--task",
help="input/semantic_gt/semantic_pred/offset_semantic_pred/instance_gt/instance_pred",
default="instance_pred",
)
parser.add_argument("--out", default="", help="output point cloud file in FILE.ply format")
opt = parser.parse_args()
xyz, rgb, boxes = get_coords_color(opt)
points = xyz[:, :3]
colors = rgb / 255
if opt.out != "":
assert ".ply" in opt.out, "output cloud file should be in FILE.ply format"
write_ply(points, colors, None, opt.out)
else:
pc = o3d.geometry.PointCloud()
pc.points = o3d.utility.Vector3dVector(points)
pc.colors = o3d.utility.Vector3dVector(colors)
vis = o3d.visualization.Visualizer()
vis.create_window()
if len(boxes) > 0:
for box in boxes:
# box.color = (1,0,0)
vis.add_geometry(box)
vis.add_geometry(pc)
vis.get_render_option().point_size = 1.5
vis.run()
vis.destroy_window()