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347 lines
11 KiB
347 lines
11 KiB
9 months ago
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import os
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import argparse
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import imageio
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import time
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import mcubes
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import cv2
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import numpy as np
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import torch
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import trimesh
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import rembg
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from PIL import Image
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from torchvision.transforms import v2
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from pytorch_lightning import seed_everything
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from omegaconf import OmegaConf
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from einops import rearrange, repeat
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from tqdm import tqdm
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from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
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from src.utils.train_util import instantiate_from_config
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from src.utils.camera_util import (
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FOV_to_intrinsics,
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get_zero123plus_input_cameras,
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get_circular_camera_poses,
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)
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from src.utils.mesh_util import save_obj
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from src.utils.infer_util import remove_background, resize_foreground, images_to_video
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import tempfile
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from functools import partial
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def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
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"""
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Get the rendering camera parameters.
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"""
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c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
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if is_flexicubes:
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cameras = torch.linalg.inv(c2ws)
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cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
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else:
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extrinsics = c2ws.flatten(-2)
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intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
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cameras = torch.cat([extrinsics, intrinsics], dim=-1)
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cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
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return cameras
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def images_to_video(images, output_path, fps=30):
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# images: (N, C, H, W)
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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frames = []
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for i in range(images.shape[0]):
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frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255)
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assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
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f"Frame shape mismatch: {frame.shape} vs {images.shape}"
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assert frame.min() >= 0 and frame.max() <= 255, \
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f"Frame value out of range: {frame.min()} ~ {frame.max()}"
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frames.append(frame)
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imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264')
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###############################################################################
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# Configuration.
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###############################################################################
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seed_everything(0)
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config_path = 'configs/instant-mesh-large-eval.yaml'
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config = OmegaConf.load(config_path)
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config_name = os.path.basename(config_path).replace('.yaml', '')
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model_config = config.model_config
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infer_config = config.infer_config
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IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
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device = torch.device('cuda')
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# load diffusion model
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print('Loading diffusion model ...')
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pipeline = DiffusionPipeline.from_pretrained(
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"sudo-ai/zero123plus-v1.2",
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custom_pipeline="zero123plus",
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torch_dtype=torch.float16,
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)
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pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
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pipeline.scheduler.config, timestep_spacing='trailing'
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)
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# load custom white-background UNet
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state_dict = torch.load(infer_config.unet_path, map_location='cpu')
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pipeline.unet.load_state_dict(state_dict, strict=True)
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pipeline = pipeline.to(device)
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# load reconstruction model
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print('Loading reconstruction model ...')
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model = instantiate_from_config(model_config)
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state_dict = torch.load(infer_config.model_path, map_location='cpu')['state_dict']
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state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
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model.load_state_dict(state_dict, strict=True)
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model = model.to(device)
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if IS_FLEXICUBES:
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model.init_flexicubes_geometry(device)
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model = model.eval()
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print('Loading Finished!')
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def check_input_image(input_image):
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if input_image is None:
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raise gr.Error("No image uploaded!")
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def preprocess(input_image, do_remove_background):
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rembg_session = rembg.new_session() if do_remove_background else None
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#input_image = Image.open(image_file)
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if do_remove_background:
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input_image = remove_background(input_image, rembg_session)
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return input_image
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def generate_mvs(input_image, sample_steps, sample_seed):
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seed_everything(sample_seed)
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# sampling
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generator = torch.Generator(device=device)
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z123_image = pipeline(
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input_image,
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num_inference_steps=sample_steps,
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generator=generator,
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).images[0]
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show_image = np.asarray(z123_image, dtype=np.uint8)
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show_image = torch.from_numpy(show_image) # (960, 640, 3)
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show_image = rearrange(show_image, '(n h) (m w) c -> (m h) (n w) c', n=3, m=2)
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show_image = Image.fromarray(show_image.numpy())
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return z123_image, show_image
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def make_mesh(mesh_fpath, planes):
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mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
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mesh_dirname = os.path.dirname(mesh_fpath)
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mesh_vis_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
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with torch.no_grad():
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# get mesh
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mesh_out = model.extract_mesh(
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planes,
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use_texture_map=False,
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**infer_config,
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)
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vertices, faces, vertex_colors = mesh_out
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vertices = vertices[:, [0, 2, 1]]
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vertices[:, -1] *= -1
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save_obj(vertices, faces, vertex_colors, mesh_fpath)
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print(f"Mesh saved to {mesh_fpath}")
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return mesh_fpath
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def make3d(images):
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images = np.asarray(images, dtype=np.float32) / 255.0
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images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float() # (3, 960, 640)
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images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2) # (6, 3, 320, 320)
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input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=2.5).to(device)
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render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
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images = images.unsqueeze(0).to(device)
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images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
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mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
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print(mesh_fpath)
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mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
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mesh_dirname = os.path.dirname(mesh_fpath)
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video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4")
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with torch.no_grad():
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# get triplane
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planes = model.forward_planes(images, input_cameras)
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# get video
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chunk_size = 20 if IS_FLEXICUBES else 1
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render_size = 384
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frames = []
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for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
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if IS_FLEXICUBES:
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frame = model.forward_geometry(
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planes,
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render_cameras[:, i:i+chunk_size],
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render_size=render_size,
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)['img']
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else:
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frame = model.synthesizer(
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planes,
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cameras=render_cameras[:, i:i+chunk_size],
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render_size=render_size,
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)['images_rgb']
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frames.append(frame)
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frames = torch.cat(frames, dim=1)
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images_to_video(
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frames[0],
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video_fpath,
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fps=30,
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)
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print(f"Video saved to {video_fpath}")
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mesh_fpath = make_mesh(mesh_fpath, planes)
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return video_fpath, mesh_fpath
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def run_example(image_file):
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preprocessed = preprocess(image_file, False, 0.85)
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mv_images, _ = generate_mvs(preprocessed, 20, 0)
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video_name, mesh_fpath, planes = make3d(mv_images)
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mesh_name = make_mesh(mesh_fpath, planes)
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return preprocessed, mesh_name, video_name
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import gradio as gr
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HEADER = '''
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<h3>
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<b>Official 🤗 Gradio demo</b> for
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<a href='https://github.com/TencentARC/InstantMesh' target='_blank'>
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<b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b>
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</a>.
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</h3>
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<br>
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* If the output is unsatisfying, try to use a different seed.
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'''
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with gr.Blocks() as demo:
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gr.Markdown(HEADER)
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with gr.Row(variant="panel"):
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with gr.Column():
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with gr.Row():
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input_image = gr.Image(
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label="Input Image",
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image_mode="RGBA",
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sources="upload",
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width=256,
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height=256,
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type="pil",
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elem_id="content_image",
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)
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processed_image = gr.Image(
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label="Processed Image",
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image_mode="RGBA",
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width=256,
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height=256,
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type="pil",
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interactive=False
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)
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with gr.Row():
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with gr.Group():
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do_remove_background = gr.Checkbox(
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label="Remove Background", value=True
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)
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sample_seed = gr.Number(value=42, label="Seed", precision=0)
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sample_steps = gr.Slider(
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label="Sample Steps",
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minimum=30,
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maximum=75,
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value=75,
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step=5
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)
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with gr.Row():
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submit = gr.Button("Generate", elem_id="generate", variant="primary")
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with gr.Row(variant="panel"):
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gr.Examples(
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examples=[
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os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
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],
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inputs=[input_image],
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# outputs=[processed_image, output_model_obj, output_video],
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# fn=partial(run_example),
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# cache_examples=True,
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label="Examples",
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examples_per_page=20
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)
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with gr.Column():
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with gr.Row():
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with gr.Column():
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mv_show_images = gr.Image(
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label="Generated Multi-views",
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type="pil",
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width=379,
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interactive=False
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)
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with gr.Column():
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output_video = gr.Video(
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label="video", format="mp4",
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width=379,
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autoplay=True,
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interactive=False
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)
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with gr.Row():
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output_model_obj = gr.Model3D(
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label="Output Model (OBJ Format)",
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width=768,
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interactive=False,
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)
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mv_images = gr.State()
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submit.click(fn=check_input_image, inputs=[input_image]).success(
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fn=preprocess,
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inputs=[input_image, do_remove_background],
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outputs=[processed_image],
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).success(
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fn=generate_mvs,
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inputs=[processed_image, sample_steps, sample_seed],
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outputs=[mv_images, mv_show_images],
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).success(
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fn=make3d,
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inputs=[mv_images],
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outputs=[output_video, output_model_obj]
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)
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demo.queue(max_size=10)
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demo.launch(server_name="0.0.0.0", server_port=43839)
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