From a3e0e6b1866cbcfcf09c9bf62c70c2c465cd0a71 Mon Sep 17 00:00:00 2001 From: Lukas Radl Date: Thu, 7 Dec 2023 13:33:48 +0100 Subject: [PATCH] added scripts for evaluation --- README.md | 144 +++++++++++--- nerf/gui.py | 2 +- scripts/eval/consistency_metrics.py | 176 ++++++++++++++++++ scripts/eval/masks/llff/flower/image000.png | Bin 0 -> 262846 bytes scripts/eval/masks/llff/flower/image008.png | Bin 0 -> 273307 bytes scripts/eval/masks/llff/flower/image016.png | Bin 0 -> 264435 bytes scripts/eval/masks/llff/flower/image024.png | Bin 0 -> 272240 bytes scripts/eval/masks/llff/flower/image032.png | Bin 0 -> 265295 bytes scripts/eval/masks/llff/fortress/IMG_1800.png | Bin 0 -> 241771 bytes scripts/eval/masks/llff/fortress/IMG_1808.png | Bin 0 -> 240150 bytes scripts/eval/masks/llff/fortress/IMG_1816.png | Bin 0 -> 239730 bytes scripts/eval/masks/llff/fortress/IMG_1824.png | Bin 0 -> 238942 bytes scripts/eval/masks/llff/fortress/IMG_1832.png | Bin 0 -> 238676 bytes 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**R**adiance **F**ields. \ -\ - \ -\ - - - - +# LAENeRF: Local Appearance Editing for Neural Radiance Fields +### [Lukas Radl](https://r4dl.github.io/), [Michael Steiner](https://online.tugraz.at/tug_online/visitenkarte.show_vcard?pPersonenId=2999CD2743C7E977&pPersonenGruppe=3), [Andreas Kurz](https://scholar.google.com/citations?user=3yD4NZgAAAAJ&hl=de&oi=ao), [Markus Steinberger](https://www.markussteinberger.net/) +##### Institute of Computer Graphics and Vision, Graz University of Technology +#### [Project Page](https://r4dl.github.io/nerfinternals/) | [Preprint](https://arxiv.org/pdf/2306.00696.pdf) + +

+ +

+

+ + + + +

+ +We present LAENeRF, a method for **L**ocal **A**ppearance **E**diting of **Ne**ural **R**adiance **F**ields. +This repository contains the official implementation, based on [torch-ngp](https://github.com/ashawkey/torch-ngp). + ## Installation -First, setup the conda environment using: +First, clone our repository +```bash +git clone https://github.com/r4dl/LAENeRF.git +cd LAENeRF +``` +Next, setup the conda environment using: ```bash conda env create -f environment_laenerf.yml conda activate LAENeRF ``` -Next, install tiny-cuda-nn. +Finally, install [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn). ```bash pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch ``` @@ -33,7 +47,7 @@ We use the same data format torch-ngp and use the scripts from PaletteNeRF. Currently, three datasets are supported: [NeRF Synthetic](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1), [LLFF](https://drive.google.com/drive/folders/14boI-o5hGO9srnWaaogTU5_ji7wkX2S7) and [mip-NeRF 360](http://storage.googleapis.com/gresearch/refraw360/360_v2.zip). The folder structure should look like the following: ``` -code +LAENeRF ├── ... ├── data │ ├── nerf_synthetic @@ -52,7 +66,7 @@ For LLFF and mip-NeRF 360, data conversion is required: # mip-NeRF 360 python scripts/llff2nerf.py /path/to/the/scene/directory --images images_4 --downscale 4 --datatype mip360 # LLFF -python scripts/llff2nerf.py /path/to/the/scene/directory --images images_4 --downscale 4 --datatype llff +python scripts/llff2nerf.py /path/to/the/scene/directory --images images_4 --downscale 4 --datatype masks ``` ## Training a NeRF @@ -73,16 +87,104 @@ bash scripts/run_{dataset}.sh scripts/configs_{dataset}/{scene}.sh -m style ``` ### Results By default, we write the results to `test/{scene}_{style, recolor}`. -In this directory, we additionally save test/val set images, a video, LAENeRF (which may be loaded for recolorable stylization), +In this directory, we additionally save test/val set images, a video, the trained LAENeRF (which may be loaded for recolorable stylization), the fine-tuned NeRF and the grids. ### Style Images We provide a subset of style images in the `style_images` directory. Per-default, *the great wave* is loaded for stylization. -\ -\ - - - - - + +

+ + + + + +

+ +## Evaluation +We provide our evaluation scripts for our quantitative results to enable more future research in this direction. +All necessary scripts are located in the `scripts/eval` directory, with the required masks contained in the `scripts/eval/masks` directory. + +### MSE in the background +To evaluate the MSE in the background with respect to the ground truth test set images, run +```bash +# mip-NeRF 360 +python scripts/eval/mse_background_mip360.py --scene {scene} --results_dir {results_dir} +# LLFF +python scripts/eval/mse_background_llff.py --scene {scene} --results_dir {results_dir} +``` +Further, to compare to images from a pre-trained NeRF, provide a `--comparison_dir`. +To save the difference images as well as the results as a json file, provide a `--save_dir`. + +### View Consistency +To evaluate view-consistency, clone [RAFT](https://github.com/princeton-vl/RAFT), +install the conda environment according to their README.md and download their models. +Copy `scripts/eval/consistency_metrics.py` in the `RAFT` directory. +Finally, to obtain view-consistency metrics: +```bash +# Short-Range Consistency +python consistency_metrics.py --model=models/raft-sintel.pth --directory_gt={path_to_nerf_frames} --directory={path_to_stylized_frames} --step 1 +# Long-Range Consistency +python consistency_metrics.py --model=models/raft-sintel.pth --directory_gt={path_to_nerf_frames} --directory={path_to_stylized_frames} --step 7 +``` +Here, `{path_to_nerf_frames}` is a directory which contains frames from a video rendered by a pre-trained NeRF. +`{path_to_stylized_frames}` is a directory which contains the same frames from a recoloring/stylization method. +Importantly, we compute the optical flow from the former, and measure MSE with the latter. + +## Citation +If you find our work useful, consider citing us with: +```bibtex +@article(Radl2023laenerf, + title = {{LAENeRF: Local Appearance Editing of Neural Radiance Fields}}, + author = {Radl, Lukas and Steiner, Michael and Kurz, Andreas and Steinberger, Markus}, + journal = {arXiv preprint arXiv:}, + year = {2023}, +} +``` + +## Acknowledgements +The implementation of Instant-NGP is adapted from [torch-ngp](https://github.com/ashawkey/torch-ngp): +```bibtex +@misc{torch-ngp, + Author = {Jiaxiang Tang}, + Year = {2022}, + Note = {https://github.com/ashawkey/torch-ngp}, + Title = {Torch-ngp: a PyTorch implementation of instant-ngp} +} + +@article{tang2022compressible, + title = {Compressible-composable NeRF via Rank-residual Decomposition}, + author = {Tang, Jiaxiang and Chen, Xiaokang and Wang, Jingbo and Zeng, Gang}, + journal = {arXiv preprint arXiv:2205.14870}, + year = {2022} +} +``` +Some elements of the GUI as well as scripts for data conversion and running our method are adapted from [PaletteNeRF](https://github.com/zfkuang/PaletteNeRF/). +```bibtex +@article{kuang2022palettenerf, + title = {PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields}, + author = {Kuang, Zhengfei and Luan, Fujun and Bi, Sai and Shu, Zhixin and Wetzstein, Gordon and Sunkavalli, Kalyan}, + journal = {arXiv preprint arXiv:2212.10699}, + year = {2022} +} +``` +The segmentation masks for Mip-NeRF 360 were extracted with [Segment Anything](https://github.com/facebookresearch/segment-anything), +and the masks for LLFF were given to use by the authors of [ICE-NeRF](https://heuyklee.github.io/ice-nerf/). +```bibtex +@article{kirillov2023segany, + title = {Segment Anything}, + author = {Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross}, + journal = {arXiv:2304.02643}, + year = {2023} +} + +@InProceedings{Lee_2023_ICCV, + author = {Lee, Jae-Hyeok and Kim, Dae-Shik}, + title = {ICE-NeRF: Interactive Color Editing of NeRFs via Decomposition-Aware Weight Optimization}, + booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, + month = {October}, + year = {2023}, + pages = {3491-3501} +} +``` \ No newline at end of file diff --git a/nerf/gui.py b/nerf/gui.py index c7c773c..4762863 100755 --- a/nerf/gui.py +++ b/nerf/gui.py @@ -1905,7 +1905,7 @@ def callback_palet_close(sender, app_data): dpg.bind_font(default_font) - dpg.create_viewport(title='torch-ngp', width=self.W, height=self.H, resizable=False) + dpg.create_viewport(title='LAENeRF', width=self.W, height=self.H, resizable=False) ### global theme with dpg.theme() as theme_no_padding: diff --git a/scripts/eval/consistency_metrics.py b/scripts/eval/consistency_metrics.py new file mode 100644 index 0000000..2ab1f1b --- /dev/null +++ b/scripts/eval/consistency_metrics.py @@ -0,0 +1,176 @@ +import sys +sys.path.append('core') + +from PIL import Image +import argparse +import os +import time +import numpy as np +import torch +import torch.nn.functional as F +import matplotlib.pyplot as plt +import cv2, json +import lpips + +import datasets +from utils import flow_viz +from utils import frame_utils + +from raft import RAFT +from utils.utils import InputPadder, forward_interpolate + +def compute_flow_magnitude(flow): + + flow_mag = flow[:, :, 0] ** 2 + flow[:, :, 1] ** 2 + + return flow_mag + +def compute_flow_gradients(flow): + H = flow.shape[0] + W = flow.shape[1] + + flow_x_du = np.zeros((H, W)) + flow_x_dv = np.zeros((H, W)) + flow_y_du = np.zeros((H, W)) + flow_y_dv = np.zeros((H, W)) + + flow_x = flow[:, :, 0] + flow_y = flow[:, :, 1] + + flow_x_du[:, :-1] = flow_x[:, :-1] - flow_x[:, 1:] + flow_x_dv[:-1, :] = flow_x[:-1, :] - flow_x[1:, :] + flow_y_du[:, :-1] = flow_y[:, :-1] - flow_y[:, 1:] + flow_y_dv[:-1, :] = flow_y[:-1, :] - flow_y[1:, :] + + return flow_x_du, flow_x_dv, flow_y_du, flow_y_dv + +@torch.no_grad() +def detect_occlusion(fw_flow, bw_flow): + ## fw-flow: img1 => img2 + ## bw-flow: img2 => img1 + + fw_flow_t = torch.from_numpy(fw_flow).cuda() + bw_flow_t = torch.from_numpy(bw_flow).cuda() + + ## warp fw-flow to img2 + flow = torch.from_numpy(fw_flow).permute(2, 0, 1).float() + flow[0, :, :] += torch.arange(flow.shape[2]) + flow[1, :, :] += torch.arange(flow.shape[1])[:, None] + fw_flow_w = cv2.remap((bw_flow_t).cpu().numpy(), flow.permute(1, 2, 0).cpu().numpy(), None, cv2.INTER_LINEAR) + + ## convert to numpy array + fw_flow_w = (fw_flow_w) + + ## occlusion + fb_flow_sum = fw_flow_w + bw_flow + fb_flow_mag = compute_flow_magnitude(fb_flow_sum) + fw_flow_w_mag = compute_flow_magnitude(fw_flow_w) + bw_flow_mag = compute_flow_magnitude(bw_flow) + + mask1 = fb_flow_mag > 0.01 * (fw_flow_w_mag + bw_flow_mag) + 0.5 + + ## motion boundary + fx_du, fx_dv, fy_du, fy_dv = compute_flow_gradients(bw_flow) + fx_mag = fx_du ** 2 + fx_dv ** 2 + fy_mag = fy_du ** 2 + fy_dv ** 2 + + mask2 = (fx_mag + fy_mag) > 0.01 * bw_flow_mag + 0.002 + + ## combine mask + mask = np.logical_or(mask1, mask2) + occlusion = np.zeros((fw_flow.shape[0], fw_flow.shape[1])) + occlusion[mask == 1] = 1 + + return occlusion + +@torch.no_grad() +def validate_longrange(model, frames_directory, frames_directory_gt, step=7): + """ Peform validation using the Sintel (train) split """ + model.eval() + results = {} + iters=32 + + lpipsmeter = lpips.LPIPS(net='alex').cuda() + + frames_gt = sorted([i for i in os.listdir(frames_directory_gt) if 'png' in i]) + frames_ours = sorted([i for i in os.listdir(frames_directory) if 'png' in i]) + flow_prev, sequence_prev = None, None + + errors_ours = [] + lpips_ours = [] + + for test_id in range(len(frames_directory) - step): + image1 = frame_utils.read_gen(os.path.join(frames_directory_gt, frames_gt[test_id])) + image2 = frame_utils.read_gen(os.path.join(frames_directory_gt, frames_gt[test_id + step])) + image1 = np.array(image1).astype(np.uint8)[..., :3] + image2 = np.array(image2).astype(np.uint8)[..., :3] + image1 = torch.from_numpy(image1).permute(2, 0, 1).float() + image2 = torch.from_numpy(image2).permute(2, 0, 1).float() + + padder = InputPadder(image1.shape) + image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda()) + + flow_low, flow_pr = model(image2, image1, iters=iters, flow_init=flow_prev, test_mode=True) + flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy() + + flow_low, flow_pr = model(image1, image2, iters=iters, flow_init=flow_prev, test_mode=True) + flow_bw = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy() + + flow_clone = np.copy(flow) + occ = detect_occlusion(fw_flow=flow_clone, bw_flow=flow_bw) + + flow = torch.from_numpy(flow).permute(2, 0, 1).float() + flow[0, :, :] += torch.arange(flow.shape[2]) + flow[1, :, :] += torch.arange(flow.shape[1])[:, None] + + image1 = frame_utils.read_gen(os.path.join(frames_directory, frames_ours[test_id])) + image2 = frame_utils.read_gen(os.path.join(frames_directory, frames_ours[test_id + step])) + image1 = np.array(image1).astype(np.uint8)[..., :3] + image2 = np.array(image2).astype(np.uint8)[..., :3] + image1 = torch.from_numpy(image1).permute(2, 0, 1).float() / 255. + image2 = torch.from_numpy(image2).permute(2, 0, 1).float() / 255. + + warped_result = cv2.remap((image1).permute(1,2,0).cpu().numpy(), flow.permute(1,2,0).cpu().numpy(), None, cv2.INTER_LINEAR) + lpi = lpipsmeter(torch.from_numpy(warped_result).permute(-1,0,1).cuda(), image2.cuda()).item() + + mse = (((torch.from_numpy(warped_result).permute(2,0,1) - image2)**2).sum(0) * occ) + error = (mse.sum() / occ.sum()) + + errors_ours.append(error.item()) + lpips_ours.append(lpi) + + print(f'RSME of {np.mean(np.array(errors_ours)):.4f}') + print(f'LPIPS of {np.mean(np.array(lpips_ours))}') + + dict_ = { + 'rsme': np.mean(np.array(errors_ours)), + 'lpips': np.mean(np.array(lpips_ours)) + } + + with open('results.json', 'w') as outfile: + json.dump(dict_, outfile, indent=2) + + return results + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--model', help="restore checkpoint") + parser.add_argument('--step', type=int, help='number of steps to compute the optical flow') + parser.add_argument('--directory', type=str, help='directory in which the images to be evaluated lie') + parser.add_argument('--directory_gt', type=str, help='directory in which the gt images lie') + args = parser.parse_args() + + # custom args + args.small = False + args.mixed_precision = False + + model = torch.nn.DataParallel(RAFT(args)) + model.load_state_dict(torch.load(args.model)) + + model.cuda() + model.eval() + + with torch.no_grad(): + validate_longrange(model.module, frames_directory=args.directory, frames_directory_gt=args.directory_gt, + step=args.step) \ No newline at end of file diff --git a/scripts/eval/masks/llff/flower/image000.png b/scripts/eval/masks/llff/flower/image000.png new file mode 100644 index 0000000000000000000000000000000000000000..b323459e7ecd2fbb8127a85a79bf66f698e9b0e2 GIT binary patch literal 262846 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open the transform.json such that we can get the names of the test images + with open(f'data/{datatype}/{scene}/transforms_test.json') as fp: + file = json.load(fp) + + # get the reference/base images + # if the directory does not exist, take the reference images + if base_images is None: + paths = file['frames'] + imgs = [f'data/{datatype}/{scene}/{i["file_path"]}' for i in paths] + else: + imgs = [os.path.join(base_images, i) for i in sorted(os.listdir(base_images))] + + # get the masks + masks = [os.path.join('scripts', 'eval', 'masks', f'{datatype}', f'{scene}', i["file_path"][9:]) for i in file['frames']] + + # get the recolored images + imgs_recolored = [os.path.join(directory, i) for i in sorted(os.listdir(directory))] + + # create directory to save diff images to + if save_dir is not None: + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + errors = np.zeros((len(imgs))) + + for img_index, img in enumerate(imgs): + # load recolored image + out_senc = cv2.imread(imgs_recolored[img_index]) + out_senc = cv2.cvtColor(out_senc, cv2.COLOR_BGR2RGB) / 255. + + # load reference image + ref = cv2.imread(img) + ref = cv2.cvtColor(ref, cv2.COLOR_BGR2RGB) / 255. + + if out_senc.shape != ref.shape: + out_senc = cv2.resize(out_senc, (ref.shape[1], ref.shape[0])) + + # load the masks + # masks from ICE-NeRF are saved in the G channel + mask = cv2.imread(masks[img_index]) + if mask.shape != ref.shape: + mask = cv2.resize(mask, (ref.shape[1], ref.shape[0])) + mask = mask.max(-1)[..., None] / mask.max(-1)[..., None].max() + mask = 1 - mask + + error_img = (np.square(out_senc - ref) * mask) + if save_dir is not None: + cv2.imwrite(os.path.join(save_dir, f'error_{img_index:03d}.png'), + (error_img * 255).astype(np.byte)) + errors[img_index] = (error_img.sum() / mask.sum() / 3) + + # save the results if a save dir is there + if save_dir is not None: + results = { + 'errors': errors.tolist(), + 'mean': errors.mean() + } + with open(os.path.join(save_dir, 'results.json'), 'w') as fp: + json.dump(results, fp, indent=2) + + # in any case, print results + icecream.ic(np.mean(errors)) + icecream.ic(errors) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--scene', type=str, help='scene name') + parser.add_argument('--results_dir', type=str, help='directory in which the results reside', + required=True) + parser.add_argument('--comparison_dir', type=str, + help='directory for images before recoloring (optional)', required=False) + parser.add_argument('--save_dir', type=str, help='where to save the results (optional)', required=False) + + options = parser.parse_args() + + evaluate(directory=options.results_dir, + scene=options.scene, + save_dir=options.save_dir, + base_images=options.comparison_dir) + + + diff --git a/scripts/eval/mse_background_mip360.py b/scripts/eval/mse_background_mip360.py new file mode 100644 index 0000000..265f268 --- /dev/null +++ b/scripts/eval/mse_background_mip360.py @@ -0,0 +1,97 @@ +import argparse + +import cv2 +import json +import os +import icecream +import numpy as np + +datatype = 'mip360' +def evaluate(directory: str, scene: str, save_dir: str = None, base_images: str = None): + # open the transform.json such that we can get the names of the test images + with open(f'data/{datatype}/{scene}/transforms_test.json') as fp: + file = json.load(fp) + + # get the reference/base images + # if the directory does not exist, take the reference images + if base_images is None: + paths = file['frames'] + imgs = [f'data/{datatype}/{scene}/{i["file_path"]}' for i in paths] + else: + imgs = [os.path.join(base_images, i) for i in sorted(os.listdir(base_images))] + + # get the masks (we saved the masks as .png) + masks = [os.path.join('scripts', 'eval', 'masks', f'{datatype}', f'{scene}', i["file_path"][9:-3]+'png') for i in file['frames']] + + # get the recolored images + imgs_recolored = [os.path.join(directory, i) for i in sorted(os.listdir(directory))] + + # create directory to save diff images to + if save_dir is not None: + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + # we need a list here, because not all images may exist + errors = [] + + for img_index, img in enumerate(imgs): + # continue if the mask does not exist + if not os.path.exists(masks[img_index]): + continue + + # load recolored image + out_senc = cv2.imread(imgs_recolored[img_index]) + out_senc = cv2.cvtColor(out_senc, cv2.COLOR_BGR2RGB) / 255. + + # load reference image + ref = cv2.imread(img) + ref = cv2.cvtColor(ref, cv2.COLOR_BGR2RGB) / 255. + + if out_senc.shape != ref.shape: + out_senc = cv2.resize(out_senc, (ref.shape[1], ref.shape[0])) + + mask = cv2.imread(masks[img_index]) + if mask.shape != ref.shape: + mask = cv2.resize(mask, (ref.shape[1], ref.shape[0])) + mask = mask.max(-1)[..., None] / mask.max(-1)[..., None].max() + mask = 1 - mask + + error_img = (np.square(out_senc - ref) * mask) + if save_dir is not None: + cv2.imwrite(os.path.join(save_dir, f'error_{img_index:03d}.png'), + (error_img * 255).astype(np.byte)) + errors.append((error_img.sum() / mask.sum() / 3)) + + errors = np.array(errors) + # save the results if a save dir is there + if save_dir is not None: + results = { + 'errors': errors.tolist(), + 'mean': errors.mean() + } + with open(os.path.join(save_dir, 'results.json'), 'w') as fp: + json.dump(results, fp, indent=2) + + # in any case, print results + icecream.ic(np.mean(errors)) + icecream.ic(errors) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--scene', type=str, help='scene name') + parser.add_argument('--results_dir', type=str, help='directory in which the results reside', + required=True) + parser.add_argument('--comparison_dir', type=str, + help='directory for images before recoloring (optional)', required=False) + parser.add_argument('--save_dir', type=str, help='where to save the results (optional)', required=False) + + options = parser.parse_args() + + evaluate(directory=options.results_dir, + scene=options.scene, + save_dir=options.save_dir, + base_images=options.comparison_dir) + + +