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visualize_open3d.py
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visualize_open3d.py
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import numpy as np
import os, glob, argparse
import torch
from operator import itemgetter
import cv2
import open3d as o3d
import glob
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
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):
input_file = os.path.join(opt.data_path, 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
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, opt.data_split, 'semantic', 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 == 'offset_semantic_pred'):
assert opt.data_split != 'train'
semantic_file = os.path.join(opt.prediction_path, opt.data_split, 'semantic', 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, opt.data_split, 'coords_offsets', 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:]
# same color order according to instance pointnum
elif (opt.task == 'instance_gt'):
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)
object_idx = (inst_label >= 0)
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):
inst_label_rgb[inst_label == sort_idx[_sort_id] ] = COLOR_DETECTRON2[_sort_id % len(COLOR_DETECTRON2)]
rgb = inst_label_rgb
# 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, opt.data_split, 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 #
ins_num = len(masks)
ins_pointnum = np.zeros(ins_num)
inst_label = -100 * np.ones(rgb.shape[0]).astype(np.int)
for i in range(len(masks) - 1, -1, -1):
mask_path = os.path.join(opt.prediction_path, opt.data_split, 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)
print('{} {}: {} pointnum: {}'.format(i, masks[i], SEMANTIC_IDX2NAME[int(masks[i][1])], mask.sum()))
ins_pointnum[i] = mask.sum()
inst_label[mask == 1] = i
sort_idx = np.argsort(ins_pointnum)[::-1]
for _sort_id in range(ins_num):
inst_label_pred_rgb[inst_label == sort_idx[_sort_id] ] = COLOR_DETECTRON2[_sort_id % len(COLOR_DETECTRON2)]
rgb = inst_label_pred_rgb
if opt.data_split != 'test':
sem_valid = (label != -100)
xyz = xyz[sem_valid]
rgb = rgb[sem_valid]
return xyz, rgb
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', help='path to the dataset files')
parser.add_argument('--prediction_path', help='path to the prediction results')
parser.add_argument('--data_split', help='train / val / test', default='val')
parser.add_argument('--room_name', help='room_name', default='scene0146_01')
parser.add_argument('--task', help='input / semantic_gt / semantic_pred / offset_semantic_pred / instance_gt / instance_pred', default='input')
opt = parser.parse_args()
xyz, rgb = get_coords_color(opt)
points = xyz[:, :3]
colors = rgb / 255
pc = o3d.geometry.PointCloud()
pc.points = o3d.utility.Vector3dVector(points)
pc.colors = o3d.utility.Vector3dVector(colors)
vis = o3d.visualization.Visualizer()
vis.create_window()
vis.add_geometry(pc)
vis.get_render_option().point_size = 1.5
vis.run()
vis.destroy_window()