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])