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| import argparse | |
| import os | |
| import glob | |
| import torch | |
| from PIL import Image | |
| from copy import deepcopy | |
| import sys | |
| import tempfile | |
| import subprocess | |
| from huggingface_hub import snapshot_download | |
| LOCAL_CODE = os.environ.get("LOCAL_CODE", "1") == "1" | |
| AUTH = ("admin", os.environ["PASSWD"]) if "PASSWD" in os.environ else None | |
| code_dir = snapshot_download("zouzx/TriplaneGaussian", local_dir="./code", token=os.environ["HF_TOKEN"]) if not LOCAL_CODE else "./code" | |
| sys.path.append(code_dir) | |
| if not LOCAL_CODE: | |
| subprocess.run(["pip", "install", "--upgrade", "gradio"]) | |
| import gradio as gr | |
| print("gr version: ", gr.__version__) | |
| from utils import image_preprocess, pred_bbox, sam_init, sam_out_nosave, todevice | |
| from gradio_splatting.backend.gradio_model3dgs import Model3DGS | |
| import tgs | |
| from tgs.utils.config import ExperimentConfig, load_config | |
| from tgs.systems.infer import TGS | |
| SAM_CKPT_PATH = "code/checkpoints/sam_vit_h_4b8939.pth" | |
| MODEL_CKPT_PATH = "code/checkpoints/tgs_lvis_100v_rel.ckpt" | |
| CONFIG = "code/configs/single-rel.yaml" | |
| EXP_ROOT_DIR = "./outputs-gradio" | |
| os.makedirs(EXP_ROOT_DIR, exist_ok=True) | |
| gpu = os.environ.get("CUDA_VISIBLE_DEVICES", "0") | |
| device = "cuda:{}".format(gpu) if torch.cuda.is_available() else "cpu" | |
| print("device: ", device) | |
| # load SAM checkpoint | |
| # sam_predictor = sam_init(SAM_CKPT_PATH, gpu) | |
| # print("load sam ckpt done.") | |
| # init system | |
| base_cfg: ExperimentConfig | |
| base_cfg = load_config(CONFIG, cli_args=[], n_gpus=1) | |
| base_cfg.system.weights = MODEL_CKPT_PATH | |
| system = TGS(cfg=base_cfg.system).to(device) | |
| print("load model ckpt done.") | |
| HEADER = """ | |
| # Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers | |
| <div> | |
| <a style="display: inline-block;" href="https://arxiv.org/abs/2312.09147"><img src="https://img.shields.io/badge/arxiv-2312.09147-B31B1B.svg"></a> | |
| </div> | |
| TGS enables fast reconstruction from single-view image in a few seconds based on a hybrid Triplane-Gaussian 3D representation. | |
| This model is trained on Objaverse-LVIS (**~45K** synthetic objects) only. And note that we normalize the input camera pose to a pre-set viewpoint during training stage following LRM, rather than directly using camera pose of input camera as implemented in our original paper. | |
| **Tips:** | |
| 1. If you find the result is unsatisfied, please try to change the camera distance. It perhaps improves the results. | |
| 2. Please wait until the completion of the reconstruction of the previous model before proceeding with the next one, otherwise, it may cause bug. We will fix it soon. | |
| """ | |
| def assert_input_image(input_image): | |
| if input_image is None: | |
| raise gr.Error("No image selected or uploaded!") | |
| def resize_image(input_raw, size): | |
| w, h = input_raw.size | |
| ratio = size / max(w, h) | |
| resized_w = int(w * ratio) | |
| resized_h = int(h * ratio) | |
| return input_raw.resize((resized_w, resized_h), Image.Resampling.LANCZOS) | |
| def preprocess(image_path, save_path): | |
| # if not preprocess: | |
| # print("No preprocess") | |
| # # return image_path | |
| # input_raw = Image.open(image_path) | |
| # input_raw.thumbnail([512, 512], Image.Resampling.LANCZOS) | |
| # input_raw = resize_image(input_raw, 512) | |
| # print("image size:", input_raw.size) | |
| # image_sam = sam_out_nosave( | |
| # sam_predictor, input_raw.convert("RGB"), pred_bbox(input_raw) | |
| # ) | |
| save_path = os.path.join(save_path, "input_rgba.png") | |
| # if save_path is None: | |
| # save_path, ext = os.path.splitext(image_path) | |
| # save_path = save_path + "_rgba.png" | |
| # image_preprocess(image_sam, save_path, lower_contrast=False, rescale=True) | |
| subprocess.run([f"python run_sam.py --image_path {image_path} --save_path {save_path}"], shell=True) | |
| # print("image save path = ", save_path) | |
| return save_path | |
| def init_trial_dir(): | |
| trial_dir = tempfile.TemporaryDirectory(dir=EXP_ROOT_DIR).name | |
| os.makedirs(trial_dir, exist_ok=True) | |
| return trial_dir | |
| def infer(image_path: str, | |
| cam_dist: float, | |
| save_path: str, | |
| only_3dgs: bool = False): | |
| data_cfg = deepcopy(base_cfg.data) | |
| data_cfg.only_3dgs = only_3dgs | |
| data_cfg.cond_camera_distance = cam_dist | |
| data_cfg.eval_camera_distance = cam_dist | |
| data_cfg.image_list = [image_path] | |
| dm = tgs.find(base_cfg.data_cls)(data_cfg) | |
| dm.setup() | |
| for batch_idx, batch in enumerate(dm.test_dataloader()): | |
| batch = todevice(batch, device) | |
| system.test_step(save_path, batch, batch_idx, save_3dgs=only_3dgs) | |
| if not only_3dgs: | |
| system.on_test_epoch_end(save_path) | |
| def run(image_path: str, | |
| cam_dist: float, | |
| save_path: str): | |
| infer(image_path, cam_dist, save_path, only_3dgs=True) | |
| gs = glob.glob(os.path.join(save_path, "*.ply"))[0] | |
| # print("save gs", gs) | |
| return gs | |
| def run_video(image_path: str, | |
| cam_dist: float, | |
| save_path: str): | |
| infer(image_path, cam_dist, save_path) | |
| video = glob.glob(os.path.join(save_path, "*.mp4"))[0] | |
| # print("save video", video) | |
| return video | |
| def launch(port): | |
| with gr.Blocks( | |
| title="TGS - Demo" | |
| ) as demo: | |
| with gr.Row(variant='panel'): | |
| gr.Markdown(HEADER) | |
| with gr.Row(variant='panel'): | |
| with gr.Column(scale=1): | |
| input_image = gr.Image(value=None, width=512, height=512, type="filepath", sources="upload", label="Input Image") | |
| gr.Markdown( | |
| """ | |
| **Camera distance** denotes the distance between camera center and scene center. | |
| If you find the 3D model appears flattened, you can increase it. Conversely, if the 3D model appears thick, you can decrease it. | |
| """ | |
| ) | |
| camera_dist_slider = gr.Slider(1.0, 4.0, value=1.9, step=0.1, label="Camera Distance") | |
| # preprocess_ckb = gr.Checkbox(value=True, label="Remove background") | |
| img_run_btn = gr.Button("Reconstruction", variant="primary") | |
| gr.Examples( | |
| examples=[ | |
| "example_images/green_parrot.webp", | |
| "example_images/rusty_gameboy.webp", | |
| "example_images/a_pikachu_with_smily_face.webp", | |
| "example_images/an_otter_wearing_sunglasses.webp", | |
| "example_images/lumberjack_axe.webp", | |
| "example_images/medieval_shield.webp", | |
| "example_images/a_cat_dressed_as_the_pope.webp", | |
| "example_images/a_cute_little_frog_comicbook_style.webp", | |
| "example_images/a_purple_winter_jacket.webp", | |
| "example_images/MP5,_high_quality,_ultra_realistic.webp", | |
| "example_images/retro_pc_photorealistic_high_detailed.webp", | |
| "example_images/stratocaster_guitar_pixar_style.webp" | |
| ], | |
| inputs=[input_image], | |
| cache_examples=False, | |
| label="Examples", | |
| examples_per_page=40 | |
| ) | |
| with gr.Column(scale=1): | |
| with gr.Row(variant='panel'): | |
| seg_image = gr.Image(value=None, width="auto", type="filepath", image_mode="RGBA", label="Segmented Image", interactive=False) | |
| output_video = gr.Video(value=None, width="auto", label="Rendered Video", autoplay=True) | |
| output_3dgs = Model3DGS(value=None, label="3D Model") | |
| trial_dir = gr.State() | |
| img_run_btn.click( | |
| fn=assert_input_image, | |
| inputs=[input_image], | |
| queue=False | |
| ).success( | |
| fn=init_trial_dir, | |
| outputs=[trial_dir], | |
| queue=False | |
| ).success( | |
| fn=preprocess, | |
| inputs=[input_image, trial_dir], | |
| outputs=[seg_image], | |
| ).success(fn=run, | |
| inputs=[seg_image, camera_dist_slider, trial_dir], | |
| outputs=[output_3dgs], | |
| ).success(fn=run_video, | |
| inputs=[seg_image, camera_dist_slider, trial_dir], | |
| outputs=[output_video]) | |
| launch_args = {"server_port": port} | |
| demo.queue(max_size=20) | |
| demo.launch(auth=AUTH, **launch_args) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| args, extra = parser.parse_known_args() | |
| parser.add_argument("--port", type=int, default=7860) | |
| args = parser.parse_args() | |
| launch(args.port) |