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import argparse
import os
from glob import glob
from typing import Any, List, Union
import gradio as gr
import numpy as np
import torch
import trimesh
from huggingface_hub import snapshot_download
from PIL import Image, ImageOps
from skimage import measure
from midi.pipelines.pipeline_midi import MIDIPipeline
from midi.utils.smoothing import smooth_gpu
def preprocess_image(rgb_image, seg_image):
if isinstance(rgb_image, str):
rgb_image = Image.open(rgb_image)
if isinstance(seg_image, str):
seg_image = Image.open(seg_image)
rgb_image = rgb_image.convert("RGB")
seg_image = seg_image.convert("L")
width, height = rgb_image.size
seg_np = np.array(seg_image)
rows, cols = np.where(seg_np > 0)
if rows.size == 0 or cols.size == 0:
return rgb_image, seg_image
# compute the bounding box of combined instances
min_row, max_row = min(rows), max(rows)
min_col, max_col = min(cols), max(cols)
L = max(
max(abs(max_row - width // 2), abs(min_row - width // 2)) * 2,
max(abs(max_col - height // 2), abs(min_col - height // 2)) * 2,
)
# pad the image
if L > width * 0.8:
width = int(L / 4 * 5)
if L > height * 0.8:
height = int(L / 4 * 5)
rgb_new = Image.new("RGB", (width, height), (255, 255, 255))
seg_new = Image.new("L", (width, height), 0)
x_offset = (width - rgb_image.size[0]) // 2
y_offset = (height - rgb_image.size[1]) // 2
rgb_new.paste(rgb_image, (x_offset, y_offset))
seg_new.paste(seg_image, (x_offset, y_offset))
# pad to the square
max_dim = max(width, height)
rgb_new = ImageOps.expand(
rgb_new, border=(0, 0, max_dim - width, max_dim - height), fill="white"
)
seg_new = ImageOps.expand(
seg_new, border=(0, 0, max_dim - width, max_dim - height), fill=0
)
return rgb_new, seg_new
def split_rgb_mask(rgb_image, seg_image):
if isinstance(rgb_image, str):
rgb_image = Image.open(rgb_image)
if isinstance(seg_image, str):
seg_image = Image.open(seg_image)
rgb_image = rgb_image.convert("RGB")
seg_image = seg_image.convert("L")
rgb_array = np.array(rgb_image)
seg_array = np.array(seg_image)
label_ids = np.unique(seg_array)
label_ids = label_ids[label_ids > 0]
instance_rgbs, instance_masks, scene_rgbs = [], [], []
for segment_id in sorted(label_ids):
# Here we set the background to white
white_background = np.ones_like(rgb_array) * 255
mask = np.zeros_like(seg_array, dtype=np.uint8)
mask[seg_array == segment_id] = 255
segment_rgb = white_background.copy()
segment_rgb[mask == 255] = rgb_array[mask == 255]
segment_rgb_image = Image.fromarray(segment_rgb)
segment_mask_image = Image.fromarray(mask)
instance_rgbs.append(segment_rgb_image)
instance_masks.append(segment_mask_image)
scene_rgbs.append(rgb_image)
return instance_rgbs, instance_masks, scene_rgbs
@torch.no_grad()
def run_midi(
pipe: Any,
rgb_image: Union[str, Image.Image],
seg_image: Union[str, Image.Image],
seed: int,
num_inference_steps: int = 50,
guidance_scale: float = 7.0,
do_image_padding: bool = False,
) -> trimesh.Scene:
if do_image_padding:
rgb_image, seg_image = preprocess_image(rgb_image, seg_image)
instance_rgbs, instance_masks, scene_rgbs = split_rgb_mask(rgb_image, seg_image)
num_instances = len(instance_rgbs)
outputs = pipe(
image=instance_rgbs,
mask=instance_masks,
image_scene=scene_rgbs,
attention_kwargs={"num_instances": num_instances},
generator=torch.Generator(device=pipe.device).manual_seed(seed),
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
decode_progressive=True,
return_dict=False,
)
# marching cubes
trimeshes = []
for _, (logits_, grid_size, bbox_size, bbox_min, bbox_max) in enumerate(
zip(*outputs)
):
grid_logits = logits_.view(grid_size)
grid_logits = smooth_gpu(grid_logits, method="gaussian", sigma=1)
torch.cuda.empty_cache()
vertices, faces, normals, _ = measure.marching_cubes(
grid_logits.float().cpu().numpy(), 0, method="lewiner"
)
vertices = vertices / grid_size * bbox_size + bbox_min
# Trimesh
mesh = trimesh.Trimesh(vertices.astype(np.float32), np.ascontiguousarray(faces))
trimeshes.append(mesh)
# compose the output meshes
scene = trimesh.Scene(trimeshes)
return scene
if __name__ == "__main__":
device = "cuda"
dtype = torch.bfloat16
parser = argparse.ArgumentParser()
parser.add_argument("--rgb", type=str, required=True)
parser.add_argument("--seg", type=str, required=True)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--num-inference-steps", type=int, default=50)
parser.add_argument("--guidance-scale", type=float, default=7.0)
parser.add_argument("--do-image-padding", action="store_true")
parser.add_argument("--output-dir", type=str, default="./")
args = parser.parse_args()
local_dir = "pretrained_weights/MIDI-3D"
snapshot_download(repo_id="VAST-AI/MIDI-3D", local_dir=local_dir)
pipe: MIDIPipeline = MIDIPipeline.from_pretrained(local_dir).to(device, dtype)
pipe.init_custom_adapter(
set_self_attn_module_names=[
"blocks.8",
"blocks.9",
"blocks.10",
"blocks.11",
"blocks.12",
]
)
run_midi(
pipe,
rgb_image=args.rgb,
seg_image=args.seg,
seed=args.seed,
num_inference_steps=args.num_inference_steps,
guidance_scale=args.guidance_scale,
do_image_padding=args.do_image_padding,
).export(os.path.join(args.output_dir, "output.glb"))
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