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import open3d as o3d
import matplotlib.pyplot as plt
import numpy as np
import os
import math
import torch
from PIL import Image
import random
from einops import einsum
@torch.no_grad()
def get_frustum_mask(points, H, W, intrinsics, view_matrices, near = 0.02, far = 10.):
ones = torch.ones_like(points[:, 0]).unsqueeze(-1)
homo_points = torch.cat([points, ones], dim=-1)
view_points = einsum(view_matrices, homo_points, "b c, N c -> N b")
view_points = view_points[:, :3]
uv_points = einsum(intrinsics, view_points, "b c, N c -> N b")
z = uv_points[:, -1:]
uv_points = uv_points[:, :2] / z
u, v = uv_points[:, 0], uv_points[:, 1]
depth = view_points[:, -1]
cull_near_fars = (depth >= near) & (depth <= far)
mask = cull_near_fars & (u >= 0) & (u <= W-1) & (v >= 0) & (v <= H-1)
return mask
def config_parser():
import configargparse
parser = configargparse.ArgumentParser()
# training options
parser.add_argument("--near", type=float, default=0.,
help='near distance')
parser.add_argument("--far", type=float, default=10.,
help='far distance')
parser.add_argument("--camera_height", type=int, default=24,
help='height of the feature map')
parser.add_argument("--camera_width", type=int, default=24,
help='width of the feature map')
parser.add_argument("--feature_fields_search_radius", type=float, default=1.,
help='search radius for near features')
parser.add_argument("--feature_fields_search_num", type=int, default=4,
help='The number of searched near features')
parser.add_argument("--mlp_net_layers", type=int, default=8,
help='layers in mlp network')
parser.add_argument("--mlp_net_width", type=int, default=768,
help='channels per layer in mlp net')
# rendering options
parser.add_argument("--N_samples", type=int, default=512,
help='number of coarse samples per ray')
parser.add_argument("--N_importance", type=int, default=16,
help='number of fine samples per ray')
return parser
parser = config_parser()
args, unknown = parser.parse_known_args() #parser.parse_args()
camera_intrinsic = np.eye(4)
with open('scannet_train_images/frames_square/scene0000_00/intrinsic_depth.txt', 'r') as file:
numbers = [line.strip() for line in file]
for i in range(4):
for j in range(4):
camera_intrinsic[i][j] = float(numbers[i].split()[j])
camera_intrinsic[0][0] *= args.camera_width / 320
camera_intrinsic[1][1] *= args.camera_height / 240
N_spacing = (args.far - args.near) / args.N_samples
sampled_points = o3d.geometry.PointCloud()
for N_index in range(args.N_samples):
N_distance = args.near + N_spacing * (N_index+1)
N_depth = np.full((args.camera_height,args.camera_width),N_distance,dtype=np.float32)
N_depth = o3d.geometry.Image(N_depth)
N_points = o3d.geometry.PointCloud.create_from_depth_image(N_depth, o3d.camera.PinholeCameraIntrinsic(args.camera_width,args.camera_height,camera_intrinsic[0][0]/2.,camera_intrinsic[1][1]/2.,args.camera_width/2,args.camera_height/2), depth_scale=1., depth_trunc=1.)
sampled_points += N_points
points_along_rays = o3d.geometry.PointCloud()
points_along_rays += sampled_points
scene_list = []
for i in range(800):
path = 'scannet_train_images/frames_square/'
scene = 'scene'+str(i).rjust(4, "0")+'_00/'
scene_list.append(path+scene)
for scene_id in scene_list:
image_list = []
for image_id in range(1000):
image_id = image_id * 20
image_path = scene_id + 'color/' + str(image_id) + ".jpg"
if not os.path.exists(image_path):
break
image_list.append(str(image_id))
image_list = image_list[:30]
#image_list = random.sample(image_list,min(30,len(image_list)))
target_image = random.choice(image_list)
pcd_all = o3d.geometry.PointCloud()
for image_id in image_list:
intrinsic = np.eye(4)
with open(scene_id + 'intrinsic_depth.txt', 'r') as file:
intrinsic_raw = [line.strip() for line in file]
for i in range(4):
for j in range(4):
intrinsic[i][j] = float(intrinsic_raw[i].split()[j])
extrinsic = np.eye(4)
with open(scene_id + 'pose/' + image_id + '.txt', 'r') as file:
extrinsic_raw = [line.strip() for line in file]
for i in range(4):
for j in range(4):
extrinsic[i][j] = float(extrinsic_raw[i].split()[j])
R = extrinsic[:3,:3]
T = extrinsic[:3,3:4]
if image_id == target_image:
points = np.asarray(sampled_points.points)
points = (R @ points.T + T).T
points_along_rays.points = o3d.utility.Vector3dVector(points)
continue
color_raw = o3d.io.read_image(scene_id + 'color/' + image_id + ".jpg")
depth_raw = o3d.io.read_image(scene_id + 'depth/' + image_id + ".png")
rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(color_raw, depth_raw, depth_scale=1000.0, depth_trunc=1000.0, convert_rgb_to_intensity=False)
# modify the intrinsic, because the image resolution is changed
intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2] = intrinsic[0][0]/2,intrinsic[1][1]/2,intrinsic[0][2]/2,intrinsic[1][2]/2
pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image,o3d.camera.PinholeCameraIntrinsic(320,240,intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2]))
#pcd = o3d.geometry.PointCloud.create_from_depth_image(depth_raw, o3d.camera.PinholeCameraIntrinsic(320,240,intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2]), depth_scale=1000.0, depth_trunc=1000.0)
points = np.asarray(pcd.points)
points = (R @ points.T + T).T
pcd.points = o3d.utility.Vector3dVector(points)
pcd_all += pcd
#o3d.visualization.draw_geometries([pcd_all])
mask = get_frustum_mask(torch.tensor(np.array(pcd_all.points)), 240, 320, intrinsic[:3,:3], np.linalg.inv(extrinsic), near = 0.02, far = 10.)
#exit()
#pcd_all += points_along_rays
o3d.visualization.draw_geometries([pcd_all]) |