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sam3d.py
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"""
Main Script
Author: Yunhan Yang ([email protected])
"""
import os
import cv2
import numpy as np
import open3d as o3d
import torch
import copy
import multiprocessing as mp
import pointops
import random
import argparse
from segment_anything import build_sam, SamAutomaticMaskGenerator
from concurrent.futures import ProcessPoolExecutor
from itertools import repeat
from PIL import Image
from os.path import join
from util import *
def pcd_ensemble(org_path, new_path, data_path, vis_path):
new_pcd = torch.load(new_path)
new_pcd = num_to_natural(remove_small_group(new_pcd, 20))
with open(org_path) as f:
segments = json.load(f)
org_pcd = np.array(segments['segIndices'])
match_inds = [(i, i) for i in range(len(new_pcd))]
new_group = cal_group(dict(group=new_pcd), dict(group=org_pcd), match_inds)
print(new_group.shape)
data = torch.load(data_path)
visualize_partition(data["coord"], new_group, vis_path)
def get_sam(image, mask_generator):
masks = mask_generator.generate(image)
group_ids = np.full((image.shape[0], image.shape[1]), -1, dtype=int)
num_masks = len(masks)
group_counter = 0
for i in reversed(range(num_masks)):
# print(masks[i]["predicted_iou"])
group_ids[masks[i]["segmentation"]] = group_counter
group_counter += 1
return group_ids
def get_pcd(scene_name, color_name, rgb_path, mask_generator, save_2dmask_path):
intrinsic_path = join(rgb_path, scene_name, 'intrinsics', 'intrinsic_depth.txt')
depth_intrinsic = np.loadtxt(intrinsic_path)
pose = join(rgb_path, scene_name, 'pose', color_name[0:-4] + '.txt')
depth = join(rgb_path, scene_name, 'depth', color_name[0:-4] + '.png')
color = join(rgb_path, scene_name, 'color', color_name)
depth_img = cv2.imread(depth, -1) # read 16bit grayscale image
mask = (depth_img != 0)
color_image = cv2.imread(color)
color_image = cv2.resize(color_image, (640, 480))
save_2dmask_path = join(save_2dmask_path, scene_name)
if mask_generator is not None:
group_ids = get_sam(color_image, mask_generator)
if not os.path.exists(save_2dmask_path):
os.makedirs(save_2dmask_path)
img = Image.fromarray(num_to_natural(group_ids).astype(np.int16), mode='I;16')
img.save(join(save_2dmask_path, color_name[0:-4] + '.png'))
else:
group_path = join(save_2dmask_path, color_name[0:-4] + '.png')
img = Image.open(group_path)
group_ids = np.array(img, dtype=np.int16)
color_image = np.reshape(color_image[mask], [-1,3])
group_ids = group_ids[mask]
colors = np.zeros_like(color_image)
colors[:,0] = color_image[:,2]
colors[:,1] = color_image[:,1]
colors[:,2] = color_image[:,0]
pose = np.loadtxt(pose)
depth_shift = 1000.0
x,y = np.meshgrid(np.linspace(0,depth_img.shape[1]-1,depth_img.shape[1]), np.linspace(0,depth_img.shape[0]-1,depth_img.shape[0]))
uv_depth = np.zeros((depth_img.shape[0], depth_img.shape[1], 3))
uv_depth[:,:,0] = x
uv_depth[:,:,1] = y
uv_depth[:,:,2] = depth_img/depth_shift
uv_depth = np.reshape(uv_depth, [-1,3])
uv_depth = uv_depth[np.where(uv_depth[:,2]!=0),:].squeeze()
intrinsic_inv = np.linalg.inv(depth_intrinsic)
fx = depth_intrinsic[0,0]
fy = depth_intrinsic[1,1]
cx = depth_intrinsic[0,2]
cy = depth_intrinsic[1,2]
bx = depth_intrinsic[0,3]
by = depth_intrinsic[1,3]
n = uv_depth.shape[0]
points = np.ones((n,4))
X = (uv_depth[:,0]-cx)*uv_depth[:,2]/fx + bx
Y = (uv_depth[:,1]-cy)*uv_depth[:,2]/fy + by
points[:,0] = X
points[:,1] = Y
points[:,2] = uv_depth[:,2]
points_world = np.dot(points, np.transpose(pose))
group_ids = num_to_natural(group_ids)
save_dict = dict(coord=points_world[:,:3], color=colors, group=group_ids)
return save_dict
def make_open3d_point_cloud(input_dict, voxelize, th):
input_dict["group"] = remove_small_group(input_dict["group"], th)
# input_dict = voxelize(input_dict)
xyz = input_dict["coord"]
if np.isnan(xyz).any():
return None
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(xyz)
return pcd
def cal_group(input_dict, new_input_dict, match_inds, ratio=0.5):
group_0 = input_dict["group"]
group_1 = new_input_dict["group"]
group_1[group_1 != -1] += group_0.max() + 1
unique_groups, group_0_counts = np.unique(group_0, return_counts=True)
group_0_counts = dict(zip(unique_groups, group_0_counts))
unique_groups, group_1_counts = np.unique(group_1, return_counts=True)
group_1_counts = dict(zip(unique_groups, group_1_counts))
# Calculate the group number correspondence of overlapping points
group_overlap = {}
for i, j in match_inds:
group_i = group_1[i]
group_j = group_0[j]
if group_i == -1:
group_1[i] = group_0[j]
continue
if group_j == -1:
continue
if group_i not in group_overlap:
group_overlap[group_i] = {}
if group_j not in group_overlap[group_i]:
group_overlap[group_i][group_j] = 0
group_overlap[group_i][group_j] += 1
# Update group information for point cloud 1
for group_i, overlap_count in group_overlap.items():
# for group_j, count in overlap_count.items():
max_index = np.argmax(np.array(list(overlap_count.values())))
group_j = list(overlap_count.keys())[max_index]
count = list(overlap_count.values())[max_index]
total_count = min(group_0_counts[group_j], group_1_counts[group_i]).astype(np.float32)
# print(count / total_count)
if count / total_count >= ratio:
group_1[group_1 == group_i] = group_j
return group_1
def cal_2_scenes(pcd_list, index, voxel_size, voxelize, th=50):
if len(index) == 1:
return(pcd_list[index[0]])
# print(index, flush=True)
input_dict_0 = pcd_list[index[0]]
input_dict_1 = pcd_list[index[1]]
pcd0 = make_open3d_point_cloud(input_dict_0, voxelize, th)
pcd1 = make_open3d_point_cloud(input_dict_1, voxelize, th)
if pcd0 == None:
if pcd1 == None:
return None
else:
return input_dict_1
elif pcd1 == None:
return input_dict_0
# Cal Dul-overlap
pcd0_tree = o3d.geometry.KDTreeFlann(copy.deepcopy(pcd0))
match_inds = get_matching_indices(pcd1, pcd0_tree, 1.5 * voxel_size, 1)
pcd1_new_group = cal_group(input_dict_0, input_dict_1, match_inds)
# print(pcd1_new_group)
pcd1_tree = o3d.geometry.KDTreeFlann(copy.deepcopy(pcd1))
match_inds = get_matching_indices(pcd0, pcd1_tree, 1.5 * voxel_size, 1)
input_dict_1["group"] = pcd1_new_group
pcd0_new_group = cal_group(input_dict_1, input_dict_0, match_inds)
# print(pcd0_new_group)
pcd_new_group = np.concatenate((pcd0_new_group, pcd1_new_group), axis=0)
pcd_new_group = num_to_natural(pcd_new_group)
pcd_new_coord = np.concatenate((input_dict_0["coord"], input_dict_1["coord"]), axis=0)
pcd_new_color = np.concatenate((input_dict_0["color"], input_dict_1["color"]), axis=0)
pcd_dict = dict(coord=pcd_new_coord, color=pcd_new_color, group=pcd_new_group)
pcd_dict = voxelize(pcd_dict)
return pcd_dict
def seg_pcd(scene_name, rgb_path, data_path, save_path, mask_generator, voxel_size, voxelize, th, train_scenes, val_scenes, save_2dmask_path):
print(scene_name, flush=True)
if os.path.exists(join(save_path, scene_name + ".pth")):
return
color_names = sorted(os.listdir(join(rgb_path, scene_name, 'color')), key=lambda a: int(os.path.basename(a).split('.')[0]))
pcd_list = []
for color_name in color_names:
print(color_name, flush=True)
pcd_dict = get_pcd(scene_name, color_name, rgb_path, mask_generator, save_2dmask_path)
if len(pcd_dict["coord"]) == 0:
continue
pcd_dict = voxelize(pcd_dict)
pcd_list.append(pcd_dict)
while len(pcd_list) != 1:
print(len(pcd_list), flush=True)
new_pcd_list = []
for indice in pairwise_indices(len(pcd_list)):
# print(indice)
pcd_frame = cal_2_scenes(pcd_list, indice, voxel_size=voxel_size, voxelize=voxelize)
if pcd_frame is not None:
new_pcd_list.append(pcd_frame)
pcd_list = new_pcd_list
seg_dict = pcd_list[0]
seg_dict["group"] = num_to_natural(remove_small_group(seg_dict["group"], th))
if scene_name in train_scenes:
scene_path = join(data_path, "train", scene_name + ".pth")
elif scene_name in val_scenes:
scene_path = join(data_path, "val", scene_name + ".pth")
data_dict = torch.load(scene_path)
scene_coord = torch.tensor(data_dict["coord"]).cuda().contiguous()
new_offset = torch.tensor(scene_coord.shape[0]).cuda()
gen_coord = torch.tensor(seg_dict["coord"]).cuda().contiguous().float()
offset = torch.tensor(gen_coord.shape[0]).cuda()
gen_group = seg_dict["group"]
indices, dis = pointops.knn_query(1, gen_coord, offset, scene_coord, new_offset)
indices = indices.cpu().numpy()
group = gen_group[indices.reshape(-1)].astype(np.int16)
mask_dis = dis.reshape(-1).cpu().numpy() > 0.6
group[mask_dis] = -1
group = group.astype(np.int16)
torch.save(num_to_natural(group), join(save_path, scene_name + ".pth"))
def get_args():
'''Command line arguments.'''
parser = argparse.ArgumentParser(
description='Segment Anything on ScanNet.')
parser.add_argument('--rgb_path', type=str, help='the path of rgb data')
parser.add_argument('--data_path', type=str, default='', help='the path of pointcload data')
parser.add_argument('--save_path', type=str, help='Where to save the pcd results')
parser.add_argument('--save_2dmask_path', type=str, default='', help='Where to save 2D segmentation result from SAM')
parser.add_argument('--sam_checkpoint_path', type=str, default='', help='the path of checkpoint for SAM')
parser.add_argument('--scannetv2_train_path', type=str, default='scannet-preprocess/meta_data/scannetv2_train.txt', help='the path of scannetv2_train.txt')
parser.add_argument('--scannetv2_val_path', type=str, default='scannet-preprocess/meta_data/scannetv2_val.txt', help='the path of scannetv2_val.txt')
parser.add_argument('--img_size', default=[640,480])
parser.add_argument('--voxel_size', default=0.05)
parser.add_argument('--th', default=50, help='threshold of ignoring small groups to avoid noise pixel')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = get_args()
print("Arguments:")
print(args)
with open(args.scannetv2_train_path) as train_file:
train_scenes = train_file.read().splitlines()
with open(args.scannetv2_val_path) as val_file:
val_scenes = val_file.read().splitlines()
mask_generator = SamAutomaticMaskGenerator(build_sam(checkpoint=args.sam_checkpoint_path).to(device="cuda"))
voxelize = Voxelize(voxel_size=args.voxel_size, mode="train", keys=("coord", "color", "group"))
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
scene_names = sorted(os.listdir(args.rgb_path))
for scene_name in scene_names:
seg_pcd(scene_name, args.rgb_path, args.data_path, args.save_path, mask_generator, args.voxel_size,
voxelize, args.th, train_scenes, val_scenes, args.save_2dmask_path)