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| # Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr | |
| # | |
| # Copyright 2023 LucidDreamer Authors | |
| # | |
| # Computer Vision Lab, SNU, its affiliates and licensors retain all intellectual | |
| # property and proprietary rights in and to this material, related | |
| # documentation and any modifications thereto. Any use, reproduction, | |
| # disclosure or distribution of this material and related documentation | |
| # without an express license agreement from the Computer Vision Lab, SNU or | |
| # its affiliates is strictly prohibited. | |
| # | |
| # For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. | |
| import os | |
| import glob | |
| import json | |
| import time | |
| import datetime | |
| import warnings | |
| import shutil | |
| from random import randint | |
| from argparse import ArgumentParser | |
| warnings.filterwarnings(action='ignore') | |
| import pickle | |
| import imageio | |
| import numpy as np | |
| import open3d as o3d | |
| from PIL import Image | |
| from tqdm import tqdm | |
| from scipy.interpolate import griddata as interp_grid | |
| from scipy.ndimage import minimum_filter, maximum_filter | |
| import torch | |
| import torch.nn.functional as F | |
| import gradio as gr | |
| from diffusers import ( | |
| StableDiffusionInpaintPipeline, StableDiffusionPipeline, ControlNetModel, StableDiffusionControlNetInpaintPipeline) | |
| from arguments import GSParams, CameraParams | |
| from gaussian_renderer import render | |
| from scene import Scene, GaussianModel | |
| from scene.dataset_readers import loadCameraPreset | |
| from utils.loss import l1_loss, ssim | |
| from utils.camera import load_json | |
| from utils.depth import colorize | |
| from utils.lama import LaMa | |
| from utils.trajectory import get_camerapaths, get_pcdGenPoses | |
| get_kernel = lambda p: torch.ones(1, 1, p * 2 + 1, p * 2 + 1).to('cuda') | |
| t2np = lambda x: (x[0].permute(1, 2, 0).clamp_(0, 1) * 255.0).to(torch.uint8).detach().cpu().numpy() | |
| np2t = lambda x: (torch.as_tensor(x).to(torch.float32).permute(2, 0, 1) / 255.0)[None, ...].to('cuda') | |
| pad_mask = lambda x, padamount=1: t2np( | |
| F.conv2d(np2t(x[..., None]), get_kernel(padamount), padding=padamount))[..., 0].astype(bool) | |
| class LucidDreamer: | |
| def __init__(self): | |
| self.opt = GSParams() | |
| self.cam = CameraParams() | |
| self.root = 'outputs' | |
| self.default_model = 'SD1.5 (default)' | |
| self.timestamp = datetime.datetime.now().strftime('%y%m%d_%H%M%S') | |
| self.gaussians = GaussianModel(self.opt.sh_degree) | |
| bg_color = [1, 1, 1] if self.opt.white_background else [0, 0, 0] | |
| self.background = torch.tensor(bg_color, dtype=torch.float32, device='cuda') | |
| self.rgb_model = StableDiffusionInpaintPipeline.from_pretrained( | |
| 'runwayml/stable-diffusion-inpainting', revision='fp16', torch_dtype=torch.float16).to('cuda') | |
| self.d_model = torch.hub.load('./ZoeDepth', 'ZoeD_N', source='local', pretrained=True).to('cuda') | |
| self.controlnet = None | |
| self.lama = None | |
| self.current_model = self.default_model | |
| def load_model(self, model_name, use_lama=False): | |
| if model_name is None: | |
| model_name = self.default_model | |
| if self.current_model == model_name: | |
| return | |
| if model_name == self.default_model: | |
| self.controlnet = None | |
| self.lama = None | |
| self.rgb_model = StableDiffusionInpaintPipeline.from_pretrained( | |
| 'runwayml/stable-diffusion-inpainting', | |
| revision='fp16', | |
| torch_dtype=torch.float16, | |
| safety_checker=None, | |
| ).to('cuda') | |
| else: | |
| if self.controlnet is None: | |
| self.controlnet = ControlNetModel.from_pretrained( | |
| 'lllyasviel/control_v11p_sd15_inpaint', torch_dtype=torch.float16) | |
| if self.lama is None and use_lama: | |
| self.lama = LaMa('cuda') | |
| self.rgb_model = StableDiffusionControlNetInpaintPipeline.from_pretrained( | |
| f'stablediffusion/{model_name}', | |
| controlnet=self.controlnet, | |
| revision='fp16', | |
| torch_dtype=torch.float16, | |
| safety_checker=None, | |
| ).to('cuda') | |
| # self.rgb_model.enable_model_cpu_offload() | |
| torch.cuda.empty_cache() | |
| self.current_model = model_name | |
| def rgb(self, prompt, image, negative_prompt='', generator=None, num_inference_steps=50, mask_image=None): | |
| if self.current_model == self.default_model: | |
| return self.rgb_model( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| generator=generator, | |
| num_inference_steps=num_inference_steps, | |
| image=image, | |
| mask_image=mask_image, | |
| ).images[0] | |
| kwargs = { | |
| 'negative_prompt': negative_prompt, | |
| 'generator': generator, | |
| 'strength': 0.8, | |
| 'num_inference_steps': num_inference_steps, | |
| 'height': self.cam.H, | |
| 'width': self.cam.W, | |
| } | |
| image_np = np.array(image).astype(float) / 255.0 | |
| mask_np = np.array(mask_image) / 255.0 | |
| mask_sum = np.clip((image_np.prod(axis=-1) == 0) + (1 - mask_np), 0, 1) | |
| mask_padded = pad_mask(mask_sum, 3) | |
| masked = image_np * np.logical_not(mask_padded[..., None]) | |
| if self.lama is not None: | |
| lama_image = Image.fromarray(lama(masked, mask_padded).astype(np.uint8)) | |
| else: | |
| lama_image = image | |
| mask_image = Image.fromarray(mask_padded.astype(np.uint8) * 255) | |
| control_image = self.make_controlnet_inpaint_condition(lama_image, mask_image) | |
| return self.rgb_model( | |
| prompt=prompt, | |
| image=lama_image, | |
| control_image=control_image, | |
| mask_image=mask_image, | |
| **kwargs, | |
| ).images[0] | |
| def d(self, im): | |
| return self.d_model.infer_pil(im) | |
| def make_controlnet_inpaint_condition(self, image, image_mask): | |
| image = np.array(image.convert("RGB")).astype(np.float32) / 255.0 | |
| image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0 | |
| assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size" | |
| image[image_mask > 0.5] = -1.0 # set as masked pixel | |
| image = np.expand_dims(image, 0).transpose(0, 3, 1, 2) | |
| image = torch.from_numpy(image) | |
| return image | |
| def run(self, rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps, render_camerapath, model_name=None, example_name=None): | |
| # gaussians, default_gallery = self.create( | |
| gaussians = self.create( | |
| rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps, model_name, example_name) | |
| gallery, depth = self.render_video(render_camerapath, example_name=example_name) | |
| return (gaussians, gallery, depth) | |
| # return (gaussians, default_gallery, gallery) | |
| def create(self, rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps, model_name=None, example_name=None): | |
| self.cleaner() | |
| self.load_model(model_name) | |
| if example_name and example_name != 'DON\'T': | |
| outfile = os.path.join('examples', f'{example_name}.ply') | |
| if not os.path.exists(outfile): | |
| self.traindata = self.generate_pcd(rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps) | |
| self.scene = Scene(self.traindata, self.gaussians, self.opt) | |
| self.training() | |
| outfile = self.save_ply(outfile) | |
| else: | |
| self.traindata = self.generate_pcd(rgb_cond, txt_cond, neg_txt_cond, pcdgenpath, seed, diff_steps) | |
| self.scene = Scene(self.traindata, self.gaussians, self.opt) | |
| self.training() | |
| self.timestamp = datetime.datetime.now().strftime('%y%m%d_%H%M%S') | |
| outfile = self.save_ply() | |
| # default_gallery = self.render_video('llff', example_name=example_name) | |
| return outfile #, default_gallery | |
| def save_ply(self, fpath=None): | |
| if fpath is None: | |
| dpath = os.path.join(self.root, self.timestamp) | |
| fpath = os.path.join(dpath, 'gsplat.ply') | |
| os.makedirs(dpath, exist_ok=True) | |
| if not os.path.exists(fpath): | |
| self.gaussians.save_ply(fpath) | |
| else: | |
| self.gaussians.load_ply(fpath) | |
| return fpath | |
| def cleaner(self): | |
| # Remove the temporary file created yesterday. | |
| for dpath in glob.glob(os.path.join(self.root, '*')): | |
| timestamp = datetime.datetime.strptime(os.path.basename(dpath), '%y%m%d_%H%M%S') | |
| if timestamp < datetime.datetime.now() - datetime.timedelta(days=1): | |
| try: | |
| shutil.rmtree(dpath) | |
| except OSError as e: | |
| print("Error: %s - %s." % (e.filename, e.strerror)) | |
| def render_video(self, preset, example_name=None): | |
| if example_name and example_name != 'DON\'T': | |
| videopath = os.path.join('examples', f'{example_name}_{preset}.mp4') | |
| depthpath = os.path.join('examples', f'depth_{example_name}_{preset}.mp4') | |
| else: | |
| videopath = os.path.join(self.root, self.timestamp, f'{preset}.mp4') | |
| depthpath = os.path.join(self.root, self.timestamp, f'depth_{preset}.mp4') | |
| if os.path.exists(videopath) and os.path.exists(depthpath): | |
| return videopath, depthpath | |
| if not hasattr(self, 'scene'): | |
| views = load_json(os.path.join('cameras', f'{preset}.json'), self.cam.H, self.cam.W) | |
| else: | |
| views = self.scene.getPresetCameras(preset) | |
| framelist = [] | |
| depthlist = [] | |
| dmin, dmax = 1e8, -1e8 | |
| for view in views: | |
| results = render(view, self.gaussians, self.opt, self.background, render_only=True) | |
| frame, depth = results['render'], results['depth'] | |
| framelist.append( | |
| np.round(frame.permute(1,2,0).detach().cpu().numpy().clip(0,1)*255.).astype(np.uint8)) | |
| depth = -(depth * (depth > 0)).detach().cpu().numpy() | |
| dmin_local = depth.min().item() | |
| dmax_local = depth.max().item() | |
| if dmin_local < dmin: | |
| dmin = dmin_local | |
| if dmax_local > dmax: | |
| dmax = dmax_local | |
| depthlist.append(depth) | |
| # depthlist = [colorize(depth, vmin=dmin, vmax=dmax) for depth in depthlist] | |
| depthlist = [colorize(depth) for depth in depthlist] | |
| if not os.path.exists(videopath): | |
| imageio.mimwrite(videopath, framelist, fps=60, quality=8) | |
| if not os.path.exists(depthpath): | |
| imageio.mimwrite(depthpath, depthlist, fps=60, quality=8) | |
| return videopath, depthpath | |
| def training(self): | |
| if not self.scene: | |
| raise('Build 3D Scene First!') | |
| for iteration in tqdm(range(1, self.opt.iterations + 1)): | |
| self.gaussians.update_learning_rate(iteration) | |
| # Every 1000 its we increase the levels of SH up to a maximum degree | |
| if iteration % 1000 == 0: | |
| self.gaussians.oneupSHdegree() | |
| # Pick a random Camera | |
| viewpoint_stack = self.scene.getTrainCameras().copy() | |
| viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) | |
| # import pdb; pdb.set_trace() | |
| # Render | |
| render_pkg = render(viewpoint_cam, self.gaussians, self.opt, self.background) | |
| image, viewspace_point_tensor, visibility_filter, radii = ( | |
| render_pkg['render'], render_pkg['viewspace_points'], render_pkg['visibility_filter'], render_pkg['radii']) | |
| # Loss | |
| gt_image = viewpoint_cam.original_image.cuda() | |
| Ll1 = l1_loss(image, gt_image) | |
| loss = (1.0 - self.opt.lambda_dssim) * Ll1 + self.opt.lambda_dssim * (1.0 - ssim(image, gt_image)) | |
| loss.backward() | |
| with torch.no_grad(): | |
| # Densification | |
| if iteration < self.opt.densify_until_iter: | |
| # Keep track of max radii in image-space for pruning | |
| self.gaussians.max_radii2D[visibility_filter] = torch.max( | |
| self.gaussians.max_radii2D[visibility_filter], radii[visibility_filter]) | |
| self.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter) | |
| if iteration > self.opt.densify_from_iter and iteration % self.opt.densification_interval == 0: | |
| size_threshold = 20 if iteration > self.opt.opacity_reset_interval else None | |
| self.gaussians.densify_and_prune( | |
| self.opt.densify_grad_threshold, 0.005, self.scene.cameras_extent, size_threshold) | |
| if (iteration % self.opt.opacity_reset_interval == 0 | |
| or (self.opt.white_background and iteration == self.opt.densify_from_iter) | |
| ): | |
| self.gaussians.reset_opacity() | |
| # Optimizer step | |
| if iteration < self.opt.iterations: | |
| self.gaussians.optimizer.step() | |
| self.gaussians.optimizer.zero_grad(set_to_none = True) | |
| def generate_pcd(self, rgb_cond, prompt, negative_prompt, pcdgenpath, seed, diff_steps, progress=gr.Progress()): | |
| ## processing inputs | |
| generator=torch.Generator(device='cuda').manual_seed(seed) | |
| w_in, h_in = rgb_cond.size | |
| if w_in/h_in > 1.1 or h_in/w_in > 1.1: # if height and width are similar, do center crop | |
| in_res = max(w_in, h_in) | |
| image_in, mask_in = np.zeros((in_res, in_res, 3), dtype=np.uint8), 255*np.ones((in_res, in_res, 3), dtype=np.uint8) | |
| image_in[int(in_res/2-h_in/2):int(in_res/2+h_in/2), int(in_res/2-w_in/2):int(in_res/2+w_in/2)] = np.array(rgb_cond) | |
| mask_in[int(in_res/2-h_in/2):int(in_res/2+h_in/2), int(in_res/2-w_in/2):int(in_res/2+w_in/2)] = 0 | |
| image_curr = self.rgb( | |
| prompt=prompt, image=Image.fromarray(image_in).resize((self.cam.W, self.cam.H)), | |
| negative_prompt=negative_prompt, generator=generator, | |
| mask_image=Image.fromarray(mask_in).resize((self.cam.W, self.cam.H))) | |
| else: # if there is a large gap between height and width, do inpainting | |
| if w_in > h_in: | |
| image_curr = rgb_cond.crop((int(w_in/2-h_in/2), 0, int(w_in/2+h_in/2), h_in)).resize((self.cam.W, self.cam.H)) | |
| else: # w <= h | |
| image_curr = rgb_cond.crop((0, int(h_in/2-w_in/2), w_in, int(h_in/2+w_in/2))).resize((self.cam.W, self.cam.H)) | |
| render_poses = get_pcdGenPoses(pcdgenpath) | |
| depth_curr = self.d(image_curr) | |
| center_depth = np.mean(depth_curr[h_in//2-10:h_in//2+10, w_in//2-10:w_in//2+10]) | |
| ########################################################################################################################### | |
| # Iterative scene generation | |
| H, W, K = self.cam.H, self.cam.W, self.cam.K | |
| x, y = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy') # pixels | |
| edgeN = 2 | |
| edgemask = np.ones((H-2*edgeN, W-2*edgeN)) | |
| edgemask = np.pad(edgemask, ((edgeN,edgeN),(edgeN,edgeN))) | |
| ### initialize | |
| R0, T0 = render_poses[0,:3,:3], render_poses[0,:3,3:4] | |
| pts_coord_cam = np.matmul(np.linalg.inv(K), np.stack((x*depth_curr, y*depth_curr, 1*depth_curr), axis=0).reshape(3,-1)) | |
| new_pts_coord_world2 = (np.linalg.inv(R0).dot(pts_coord_cam) - np.linalg.inv(R0).dot(T0)).astype(np.float32) ## new_pts_coord_world2 | |
| new_pts_colors2 = (np.array(image_curr).reshape(-1,3).astype(np.float32)/255.) ## new_pts_colors2 | |
| pts_coord_world, pts_colors = new_pts_coord_world2.copy(), new_pts_colors2.copy() | |
| progress(0, desc='Dreaming...') | |
| # time.sleep(0.5) | |
| for i in progress.tqdm(range(1, len(render_poses)), desc='Dreaming'): | |
| R, T = render_poses[i,:3,:3], render_poses[i,:3,3:4] | |
| ### Transform world to pixel | |
| pts_coord_cam2 = R.dot(pts_coord_world) + T ### Same with c2w*world_coord (in homogeneous space) | |
| pixel_coord_cam2 = np.matmul(K, pts_coord_cam2) #.reshape(3,H,W).transpose(1,2,0).astype(np.float32) | |
| valid_idx = np.where(np.logical_and.reduce((pixel_coord_cam2[2]>0, | |
| pixel_coord_cam2[0]/pixel_coord_cam2[2]>=0, | |
| pixel_coord_cam2[0]/pixel_coord_cam2[2]<=W-1, | |
| pixel_coord_cam2[1]/pixel_coord_cam2[2]>=0, | |
| pixel_coord_cam2[1]/pixel_coord_cam2[2]<=H-1)))[0] | |
| pixel_coord_cam2 = pixel_coord_cam2[:2, valid_idx]/pixel_coord_cam2[-1:, valid_idx] | |
| round_coord_cam2 = np.round(pixel_coord_cam2).astype(np.int32) | |
| x, y = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy') | |
| grid = np.stack((x,y), axis=-1).reshape(-1,2) | |
| image2 = interp_grid(pixel_coord_cam2.transpose(1,0), pts_colors[valid_idx], grid, method='linear', fill_value=0).reshape(H,W,3) | |
| image2 = edgemask[...,None]*image2 + (1-edgemask[...,None])*np.pad(image2[1:-1,1:-1], ((1,1),(1,1),(0,0)), mode='edge') | |
| round_mask2 = np.zeros((H,W), dtype=np.float32) | |
| round_mask2[round_coord_cam2[1], round_coord_cam2[0]] = 1 | |
| round_mask2 = maximum_filter(round_mask2, size=(9,9), axes=(0,1)) | |
| image2 = round_mask2[...,None]*image2 + (1-round_mask2[...,None])*(-1) | |
| mask2 = minimum_filter((image2.sum(-1)!=-3)*1, size=(11,11), axes=(0,1)) | |
| image2 = mask2[...,None]*image2 + (1-mask2[...,None])*0 | |
| mask_hf = np.abs(mask2[:H-1, :W-1] - mask2[1:, :W-1]) + np.abs(mask2[:H-1, :W-1] - mask2[:H-1, 1:]) | |
| mask_hf = np.pad(mask_hf, ((0,1), (0,1)), 'edge') | |
| mask_hf = np.where(mask_hf < 0.3, 0, 1) | |
| border_valid_idx = np.where(mask_hf[round_coord_cam2[1], round_coord_cam2[0]] == 1)[0] # use valid_idx[border_valid_idx] for world1 | |
| image_curr = self.rgb( | |
| prompt=prompt, image=Image.fromarray(np.round(image2*255.).astype(np.uint8)), | |
| negative_prompt=negative_prompt, generator=generator, num_inference_steps=diff_steps, | |
| mask_image=Image.fromarray(np.round((1-mask2[:,:])*255.).astype(np.uint8))) | |
| depth_curr = self.d(image_curr) | |
| ### depth optimize | |
| t_z2 = torch.tensor(depth_curr) | |
| sc = torch.ones(1).float().requires_grad_(True) | |
| optimizer = torch.optim.Adam(params=[sc], lr=0.001) | |
| for idx in range(100): | |
| trans3d = torch.tensor([[sc,0,0,0], [0,sc,0,0], [0,0,sc,0], [0,0,0,1]]).requires_grad_(True) | |
| coord_cam2 = torch.matmul(torch.tensor(np.linalg.inv(K)), torch.stack((torch.tensor(x)*t_z2, torch.tensor(y)*t_z2, 1*t_z2), axis=0)[:,round_coord_cam2[1], round_coord_cam2[0]].reshape(3,-1)) | |
| coord_world2 = (torch.tensor(np.linalg.inv(R)).float().matmul(coord_cam2) - torch.tensor(np.linalg.inv(R)).float().matmul(torch.tensor(T).float())) | |
| coord_world2_warp = torch.cat((coord_world2, torch.ones((1,valid_idx.shape[0]))), dim=0) | |
| coord_world2_trans = torch.matmul(trans3d, coord_world2_warp) | |
| coord_world2_trans = coord_world2_trans[:3] / coord_world2_trans[-1] | |
| loss = torch.mean((torch.tensor(pts_coord_world[:,valid_idx]).float() - coord_world2_trans)**2) | |
| optimizer.zero_grad() | |
| loss.backward() | |
| optimizer.step() | |
| with torch.no_grad(): | |
| coord_cam2 = torch.matmul(torch.tensor(np.linalg.inv(K)), torch.stack((torch.tensor(x)*t_z2, torch.tensor(y)*t_z2, 1*t_z2), axis=0)[:,round_coord_cam2[1, border_valid_idx], round_coord_cam2[0, border_valid_idx]].reshape(3,-1)) | |
| coord_world2 = (torch.tensor(np.linalg.inv(R)).float().matmul(coord_cam2) - torch.tensor(np.linalg.inv(R)).float().matmul(torch.tensor(T).float())) | |
| coord_world2_warp = torch.cat((coord_world2, torch.ones((1, border_valid_idx.shape[0]))), dim=0) | |
| coord_world2_trans = torch.matmul(trans3d, coord_world2_warp) | |
| coord_world2_trans = coord_world2_trans[:3] / coord_world2_trans[-1] | |
| trans3d = trans3d.detach().numpy() | |
| pts_coord_cam2 = np.matmul(np.linalg.inv(K), np.stack((x*depth_curr, y*depth_curr, 1*depth_curr), axis=0).reshape(3,-1))[:,np.where(1-mask2.reshape(-1))[0]] | |
| camera_origin_coord_world2 = - np.linalg.inv(R).dot(T).astype(np.float32) # 3, 1 | |
| new_pts_coord_world2 = (np.linalg.inv(R).dot(pts_coord_cam2) - np.linalg.inv(R).dot(T)).astype(np.float32) | |
| new_pts_coord_world2_warp = np.concatenate((new_pts_coord_world2, np.ones((1, new_pts_coord_world2.shape[1]))), axis=0) | |
| new_pts_coord_world2 = np.matmul(trans3d, new_pts_coord_world2_warp) | |
| new_pts_coord_world2 = new_pts_coord_world2[:3] / new_pts_coord_world2[-1] | |
| new_pts_colors2 = (np.array(image_curr).reshape(-1,3).astype(np.float32)/255.)[np.where(1-mask2.reshape(-1))[0]] | |
| vector_camorigin_to_campixels = coord_world2_trans.detach().numpy() - camera_origin_coord_world2 | |
| vector_camorigin_to_pcdpixels = pts_coord_world[:,valid_idx[border_valid_idx]] - camera_origin_coord_world2 | |
| compensate_depth_coeff = np.sum(vector_camorigin_to_pcdpixels * vector_camorigin_to_campixels, axis=0) / np.sum(vector_camorigin_to_campixels * vector_camorigin_to_campixels, axis=0) # N_correspond | |
| compensate_pts_coord_world2_correspond = camera_origin_coord_world2 + vector_camorigin_to_campixels * compensate_depth_coeff.reshape(1,-1) | |
| compensate_coord_cam2_correspond = R.dot(compensate_pts_coord_world2_correspond) + T | |
| homography_coord_cam2_correspond = R.dot(coord_world2_trans.detach().numpy()) + T | |
| compensate_depth_correspond = compensate_coord_cam2_correspond[-1] - homography_coord_cam2_correspond[-1] # N_correspond | |
| compensate_depth_zero = np.zeros(4) | |
| compensate_depth = np.concatenate((compensate_depth_correspond, compensate_depth_zero), axis=0) # N_correspond+4 | |
| pixel_cam2_correspond = pixel_coord_cam2[:, border_valid_idx] # 2, N_correspond (xy) | |
| pixel_cam2_zero = np.array([[0,0,W-1,W-1],[0,H-1,0,H-1]]) | |
| pixel_cam2 = np.concatenate((pixel_cam2_correspond, pixel_cam2_zero), axis=1).transpose(1,0) # N+H, 2 | |
| # Calculate for masked pixels | |
| masked_pixels_xy = np.stack(np.where(1-mask2), axis=1)[:, [1,0]] | |
| new_depth_linear, new_depth_nearest = interp_grid(pixel_cam2, compensate_depth, masked_pixels_xy), interp_grid(pixel_cam2, compensate_depth, masked_pixels_xy, method='nearest') | |
| new_depth = np.where(np.isnan(new_depth_linear), new_depth_nearest, new_depth_linear) | |
| pts_coord_cam2 = np.matmul(np.linalg.inv(K), np.stack((x*depth_curr, y*depth_curr, 1*depth_curr), axis=0).reshape(3,-1))[:,np.where(1-mask2.reshape(-1))[0]] | |
| x_nonmask, y_nonmask = x.reshape(-1)[np.where(1-mask2.reshape(-1))[0]], y.reshape(-1)[np.where(1-mask2.reshape(-1))[0]] | |
| compensate_pts_coord_cam2 = np.matmul(np.linalg.inv(K), np.stack((x_nonmask*new_depth, y_nonmask*new_depth, 1*new_depth), axis=0)) | |
| new_warp_pts_coord_cam2 = pts_coord_cam2 + compensate_pts_coord_cam2 | |
| new_pts_coord_world2 = (np.linalg.inv(R).dot(new_warp_pts_coord_cam2) - np.linalg.inv(R).dot(T)).astype(np.float32) | |
| new_pts_coord_world2_warp = np.concatenate((new_pts_coord_world2, np.ones((1, new_pts_coord_world2.shape[1]))), axis=0) | |
| new_pts_coord_world2 = np.matmul(trans3d, new_pts_coord_world2_warp) | |
| new_pts_coord_world2 = new_pts_coord_world2[:3] / new_pts_coord_world2[-1] | |
| new_pts_colors2 = (np.array(image_curr).reshape(-1,3).astype(np.float32)/255.)[np.where(1-mask2.reshape(-1))[0]] | |
| pts_coord_world = np.concatenate((pts_coord_world, new_pts_coord_world2), axis=-1) ### Same with inv(c2w) * cam_coord (in homogeneous space) | |
| pts_colors = np.concatenate((pts_colors, new_pts_colors2), axis=0) | |
| ################################################################################################# | |
| yz_reverse = np.array([[1,0,0], [0,-1,0], [0,0,-1]]) | |
| traindata = { | |
| 'camera_angle_x': self.cam.fov[0], | |
| 'W': W, | |
| 'H': H, | |
| 'pcd_points': pts_coord_world, | |
| 'pcd_colors': pts_colors, | |
| 'frames': [], | |
| } | |
| # render_poses = get_pcdGenPoses(pcdgenpath) | |
| internel_render_poses = get_pcdGenPoses('hemisphere', {'center_depth': center_depth}) | |
| progress(0, desc='Aligning...') | |
| # time.sleep(0.5) | |
| for i in progress.tqdm(range(len(render_poses)), desc='Aligning'): | |
| for j in range(len(internel_render_poses)): | |
| idx = i * len(internel_render_poses) + j | |
| print(f'{idx+1} / {len(render_poses)*len(internel_render_poses)}') | |
| ### Transform world to pixel | |
| Rw2i = render_poses[i,:3,:3] | |
| Tw2i = render_poses[i,:3,3:4] | |
| Ri2j = internel_render_poses[j,:3,:3] | |
| Ti2j = internel_render_poses[j,:3,3:4] | |
| Rw2j = np.matmul(Ri2j, Rw2i) | |
| Tw2j = np.matmul(Ri2j, Tw2i) + Ti2j | |
| # Transfrom cam2 to world + change sign of yz axis | |
| Rj2w = np.matmul(yz_reverse, Rw2j).T | |
| Tj2w = -np.matmul(Rj2w, np.matmul(yz_reverse, Tw2j)) | |
| Pc2w = np.concatenate((Rj2w, Tj2w), axis=1) | |
| Pc2w = np.concatenate((Pc2w, np.array([[0,0,0,1]])), axis=0) | |
| pts_coord_camj = Rw2j.dot(pts_coord_world) + Tw2j | |
| pixel_coord_camj = np.matmul(K, pts_coord_camj) | |
| valid_idxj = np.where(np.logical_and.reduce((pixel_coord_camj[2]>0, | |
| pixel_coord_camj[0]/pixel_coord_camj[2]>=0, | |
| pixel_coord_camj[0]/pixel_coord_camj[2]<=W-1, | |
| pixel_coord_camj[1]/pixel_coord_camj[2]>=0, | |
| pixel_coord_camj[1]/pixel_coord_camj[2]<=H-1)))[0] | |
| pts_depthsj = pixel_coord_camj[-1:, valid_idxj] | |
| pixel_coord_camj = pixel_coord_camj[:2, valid_idxj]/pixel_coord_camj[-1:, valid_idxj] | |
| round_coord_camj = np.round(pixel_coord_camj).astype(np.int32) | |
| x, y = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy') # pixels | |
| grid = np.stack((x,y), axis=-1).reshape(-1,2) | |
| imagej = interp_grid(pixel_coord_camj.transpose(1,0), pts_colors[valid_idxj], grid, method='linear', fill_value=0).reshape(H,W,3) | |
| imagej = edgemask[...,None]*imagej + (1-edgemask[...,None])*np.pad(imagej[1:-1,1:-1], ((1,1),(1,1),(0,0)), mode='edge') | |
| depthj = interp_grid(pixel_coord_camj.transpose(1,0), pts_depthsj.T, grid, method='linear', fill_value=0).reshape(H,W) | |
| depthj = edgemask*depthj + (1-edgemask)*np.pad(depthj[1:-1,1:-1], ((1,1),(1,1)), mode='edge') | |
| maskj = np.zeros((H,W), dtype=np.float32) | |
| maskj[round_coord_camj[1], round_coord_camj[0]] = 1 | |
| maskj = maximum_filter(maskj, size=(9,9), axes=(0,1)) | |
| imagej = maskj[...,None]*imagej + (1-maskj[...,None])*(-1) | |
| maskj = minimum_filter((imagej.sum(-1)!=-3)*1, size=(11,11), axes=(0,1)) | |
| imagej = maskj[...,None]*imagej + (1-maskj[...,None])*0 | |
| traindata['frames'].append({ | |
| 'image': Image.fromarray(np.round(imagej*255.).astype(np.uint8)), | |
| 'transform_matrix': Pc2w.tolist(), | |
| }) | |
| progress(1, desc='Baking Gaussians...') | |
| return traindata |