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# -*- coding: utf-8 -*-
import os
import torch
import argparse
import numpy as np
import open3d as o3d
from huggingface_hub import hf_hub_download, HfFolder
from segment import seg_point, seg_box, seg_mask
import sam2point.dataset as dataset
import sam2point.configs as configs
from sam2point.voxelizer import Voxelizer
from sam2point.utils import cal
import matplotlib.pyplot as plt
import plotly.graph_objects as go
print("Torch CUDA:", torch.cuda.is_available())
torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
def run_demo(dataset_name, prompt_type, sample_idx, prompt_idx, voxel_size, theta, mode, ret_prompt):
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', choices=['S3DIS', 'ScanNet', 'Objaverse', 'KITTI', 'Semantic3D'], default='Objaverse', help='dataset selected')
parser.add_argument('--prompt_type', choices=['point', 'box', 'mask'], default='point', help='prompt type selected')
parser.add_argument('--sample_idx', type=int, default=2, help='the index of the scene or object')
parser.add_argument('--prompt_idx', type=int, default=0, help='the index of the prompt')
parser.add_argument('--voxel_size', type=float, default=0.02, help='voxel size')
parser.add_argument('--theta', type=float, default=0.5)
parser.add_argument('--mode', type=str, default='bilinear')
parser.add_argument("--ret_prompt", action="store_true")
args = parser.parse_args()
args.dataset, args.prompt_type, args.sample_idx, args.prompt_idx = dataset_name, prompt_type, sample_idx, prompt_idx
args.voxel_size, args.theta, args.mode, args.ret_prompt = voxel_size, theta, mode, ret_prompt
print(args)
name_list = [args.dataset, "sample" + str(args.sample_idx), args.prompt_type + "-prompt" + str(args.prompt_idx)]
name = '_'.join(name_list)
# use cache result for speeding up
repo_id = "ZiyuG/Cache"
result_name = "cache_results/" + name + '.npy'
prompt_name = "cache_prompt/" + name + '.npy'
token = os.getenv('HF_TOKEN')
try:
result_file = hf_hub_download(repo_id=repo_id, filename=result_name, use_auth_token=token, repo_type='dataset')
prompt_file = hf_hub_download(repo_id=repo_id, filename=prompt_name, use_auth_token=token, repo_type='dataset')
new_color = np.load(result_file)
PROMPT = np.load(prompt_file)
if not args.ret_prompt: return new_color, PROMPT
else: return PROMPT
except Exception as e:
if os.path.exists("./cache_results/" + name + '.npy') and os.path.exists("./cache_prompt/" + name + '.npy'):
new_color = np.load("./cache_results/" + name + '.npy')
PROMPT = np.load("./cache_prompt/" + name + '.npy')
if not args.ret_prompt: return new_color, PROMPT
else: return PROMPT
if args.dataset == 'S3DIS':
info = configs.S3DIS_samples[args.sample_idx]
# early return
if args.prompt_type == 'point' and args.ret_prompt: return list(np.array(info['point_prompts'])[args.prompt_idx])
elif args.prompt_type == 'box' and args.ret_prompt: return list(np.array(info['box_prompts'])[args.prompt_idx])
point, color = dataset.load_S3DIS_sample(info['path'])
elif args.dataset == 'ScanNet':
info = configs.ScanNet_samples[args.sample_idx]
# early return
if args.prompt_type == 'point' and args.ret_prompt: return list(np.array(info['point_prompts'])[args.prompt_idx])
elif args.prompt_type == 'box' and args.ret_prompt: return list(np.array(info['box_prompts'])[args.prompt_idx])
point, color = dataset.load_ScanNet_sample(info['path'])
elif args.dataset == 'Objaverse':
info = configs.Objaverse_samples[args.sample_idx]
# early return
if args.prompt_type == 'point' and args.ret_prompt: return list(np.array(info['point_prompts'])[args.prompt_idx])
elif args.prompt_type == 'box' and args.ret_prompt: return list(np.array(info['box_prompts'])[args.prompt_idx])
point, color = dataset.load_Objaverse_sample(info['path'])
args.voxel_size = info[configs.VOXEL[args.prompt_type]][args.prompt_idx]
elif args.dataset == 'KITTI':
info = configs.KITTI_samples[args.sample_idx]
# early return
if args.prompt_type == 'point' and args.ret_prompt: return list(np.array(info['point_prompts'])[args.prompt_idx])
elif args.prompt_type == 'box' and args.ret_prompt: return list(np.array(info['box_prompts'])[args.prompt_idx])
point, color = dataset.load_KITTI_sample(info['path'])
args.voxel_size = info[configs.VOXEL[args.prompt_type]][args.prompt_idx]
elif args.dataset == 'Semantic3D':
info = configs.Semantic3D_samples[args.sample_idx]
# early return
if args.prompt_type == 'point' and args.ret_prompt: return list(np.array(info['point_prompts'])[args.prompt_idx])
elif args.prompt_type == 'box' and args.ret_prompt: return list(np.array(info['box_prompts'])[args.prompt_idx])
point, color = dataset.load_Semantic3D_sample(info['path'], args.sample_idx)
args.voxel_size = info[configs.VOXEL[args.prompt_type]][args.prompt_idx]
point_color = np.concatenate([point, color], axis=1)
voxelizer = Voxelizer(voxel_size=args.voxel_size, clip_bound=None)
labels_in = point[:, :1].astype(int)
locs, feats, labels, inds_reconstruct = voxelizer.voxelize(point, color, labels_in)
if args.prompt_type == 'point':
if args.ret_prompt: return list(np.array(info['point_prompts'])[args.prompt_idx])
mask = seg_point(locs, feats, info['point_prompts'], args)
point_prompts = np.array(info['point_prompts'])
prompt_point = list(point_prompts[args.prompt_idx])
prompt_box = None
PROMPT = prompt_point
elif args.prompt_type == 'box':
if args.ret_prompt: return list(np.array(info['box_prompts'])[args.prompt_idx])
mask = seg_box(locs, feats, info['box_prompts'], args)
point_prompts = np.array(info['box_prompts'])
prompt_point = None
prompt_box = list(point_prompts[args.prompt_idx])
PROMPT = prompt_box
elif args.prompt_type == 'mask':
if 'mask_prompts' not in info: info['mask_prompts'] = info['point_prompts']
mask, prompt_mask = seg_mask(locs, feats, info['mask_prompts'], args)
prompt_point, prompt_box = None, None
point_locs = locs[inds_reconstruct]
point_prompt_mask = prompt_mask[point_locs[:, 0], point_locs[:, 1], point_locs[:, 2]]
point_prompt_mask = point_prompt_mask.unsqueeze(-1)
point_prompt_mask_not = ~point_prompt_mask
color_prompt_mask = color * point_prompt_mask_not.numpy() + (color * 0 + np.array([[1., 0., 0.]])) * point_prompt_mask.numpy()
PROMPT = color_prompt_mask
if args.ret_prompt:
return color_prompt_mask
point_locs = locs[inds_reconstruct]
point_mask = mask[point_locs[:, 0], point_locs[:, 1], point_locs[:, 2]]
point_mask = point_mask.unsqueeze(-1)
point_mask_not = ~point_mask
point, color = point_color[:, :3], point_color[:, 3:]
new_color = color * point_mask_not.numpy() + (color * 0 + np.array([[0., 1., 0.]])) * point_mask.numpy()
name_list = [args.dataset, "sample" + str(args.sample_idx), args.prompt_type + "-prompt" + str(args.prompt_idx)]
name = '_'.join(name_list) + 'frames'
#cache for speeding up
name_list = [args.dataset, "sample" + str(args.sample_idx), args.prompt_type + "-prompt" + str(args.prompt_idx)]
name = '_'.join(name_list)
os.makedirs("cache_results", exist_ok=True)
os.makedirs("cache_prompt", exist_ok=True)
np.save("./cache_results/" + name + '.npy', new_color)
np.save("./cache_prompt/" + name + '.npy', PROMPT)
return new_color, PROMPT
def create_box(prompt):
x_min, y_min, z_min, x_max, y_max, z_max = tuple(prompt)
bbox_points = np.array([
[x_min, y_min, z_min],
[x_max, y_min, z_min],
[x_max, y_max, z_min],
[x_min, y_max, z_min],
[x_min, y_min, z_max],
[x_max, y_min, z_max],
[x_max, y_max, z_max],
[x_min, y_max, z_max]
])
edges = [
(0, 1), (1, 2), (2, 3), (3, 0), # Bottom face
(4, 5), (5, 6), (6, 7), (7, 4), # Top face
(0, 4), (1, 5), (2, 6), (3, 7) # Vertical edges
]
bbox_lines = []
f = 1
for start, end in edges:
bbox_lines.append(go.Scatter3d(
x=[bbox_points[start, 0], bbox_points[end, 0]],
y=[bbox_points[start, 1], bbox_points[end, 1]],
z=[bbox_points[start, 2], bbox_points[end, 2]],
mode='lines',
line=dict(color='rgb(220, 20, 60)', width=6),
name="Box Prompt" if f == 1 else "",
showlegend=True if f == 1 else False
))
f = 0
return bbox_lines |