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import base64
import logging
import math
import os
import subprocess
import sys
from glob import glob
from io import BytesIO
from typing import Union
import cv2
import imageio
import numpy as np
import PIL.Image as Image
import spaces
import torch
from moviepy.editor import VideoFileClip, clips_array
from tqdm import tqdm
current_file_path = os.path.abspath(__file__)
current_dir = os.path.dirname(current_file_path)
sys.path.append(os.path.join(current_dir, "../.."))
from thirdparty.TRELLIS.trellis.pipelines import TrellisImageTo3DPipeline
from thirdparty.TRELLIS.trellis.renderers.mesh_renderer import MeshRenderer
from thirdparty.TRELLIS.trellis.representations import (
Gaussian,
MeshExtractResult,
)
from thirdparty.TRELLIS.trellis.utils.render_utils import (
render_frames,
yaw_pitch_r_fov_to_extrinsics_intrinsics,
)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
__all__ = [
"render_asset3d",
"merge_images_video",
"filter_small_connected_components",
"filter_image_small_connected_components",
"combine_images_to_base64",
"render_mesh",
"render_video",
]
@spaces.GPU
def render_asset3d(
mesh_path: str,
output_root: str,
distance: float = 5.0,
num_images: int = 1,
elevation: list[float] = (0.0,),
pbr_light_factor: float = 1.5,
return_key: str = "image_color/*",
output_subdir: str = "renders",
gen_color_mp4: bool = False,
gen_viewnormal_mp4: bool = False,
gen_glonormal_mp4: bool = False,
) -> list[str]:
command = [
"python3",
"asset3d_gen/data/differentiable_render.py",
"--mesh_path",
mesh_path,
"--output_root",
output_root,
"--uuid",
output_subdir,
"--distance",
str(distance),
"--num_images",
str(num_images),
"--elevation",
*map(str, elevation),
"--pbr_light_factor",
str(pbr_light_factor),
"--with_mtl",
]
if gen_color_mp4:
command.append("--gen_color_mp4")
if gen_viewnormal_mp4:
command.append("--gen_viewnormal_mp4")
if gen_glonormal_mp4:
command.append("--gen_glonormal_mp4")
try:
subprocess.run(command, check=True)
except subprocess.CalledProcessError as e:
logger.error(f"Error occurred during rendering: {e}.")
dst_paths = glob(os.path.join(output_root, output_subdir, return_key))
return dst_paths
def merge_images_video(color_images, normal_images, output_path) -> None:
width = color_images[0].shape[1]
combined_video = [
np.hstack([rgb_img[:, : width // 2], normal_img[:, width // 2 :]])
for rgb_img, normal_img in zip(color_images, normal_images)
]
imageio.mimsave(output_path, combined_video, fps=50)
return
def merge_video_video(
video_path1: str, video_path2: str, output_path: str
) -> None:
"""Merge two videos by the left half and the right half of the videos."""
clip1 = VideoFileClip(video_path1)
clip2 = VideoFileClip(video_path2)
if clip1.size != clip2.size:
raise ValueError("The resolutions of the two videos do not match.")
width, height = clip1.size
clip1_half = clip1.crop(x1=0, y1=0, x2=width // 2, y2=height)
clip2_half = clip2.crop(x1=width // 2, y1=0, x2=width, y2=height)
final_clip = clips_array([[clip1_half, clip2_half]])
final_clip.write_videofile(output_path, codec="libx264")
def filter_small_connected_components(
mask: Union[Image.Image, np.ndarray],
area_ratio: float,
connectivity: int = 8,
) -> np.ndarray:
if isinstance(mask, Image.Image):
mask = np.array(mask)
num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(
mask,
connectivity=connectivity,
)
small_components = np.zeros_like(mask, dtype=np.uint8)
mask_area = (mask != 0).sum()
min_area = mask_area // area_ratio
for label in range(1, num_labels):
area = stats[label, cv2.CC_STAT_AREA]
if area < min_area:
small_components[labels == label] = 255
mask = cv2.bitwise_and(mask, cv2.bitwise_not(small_components))
return mask
def filter_image_small_connected_components(
image: Union[Image.Image, np.ndarray],
area_ratio: float = 10,
connectivity: int = 8,
) -> np.ndarray:
if isinstance(image, Image.Image):
image = image.convert("RGBA")
image = np.array(image)
mask = image[..., 3]
mask = filter_small_connected_components(mask, area_ratio, connectivity)
image[..., 3] = mask
return image
def combine_images_to_base64(
images: list[str | Image.Image],
cat_row_col: tuple[int, int] = None,
target_wh: tuple[int, int] = (512, 512),
) -> str:
n_images = len(images)
if cat_row_col is None:
n_col = math.ceil(math.sqrt(n_images))
n_row = math.ceil(n_images / n_col)
else:
n_row, n_col = cat_row_col
images = [
Image.open(p).convert("RGB") if isinstance(p, str) else p
for p in images[: n_row * n_col]
]
images = [img.resize(target_wh) for img in images]
grid_w, grid_h = n_col * target_wh[0], n_row * target_wh[1]
grid = Image.new("RGB", (grid_w, grid_h), (255, 255, 255))
for idx, img in enumerate(images):
row, col = divmod(idx, n_col)
grid.paste(img, (col * target_wh[0], row * target_wh[1]))
buffer = BytesIO()
grid.save(buffer, format="PNG")
return base64.b64encode(buffer.getvalue()).decode("utf-8")
@spaces.GPU
def render_mesh(sample, extrinsics, intrinsics, options={}, **kwargs):
renderer = MeshRenderer()
renderer.rendering_options.resolution = options.get("resolution", 512)
renderer.rendering_options.near = options.get("near", 1)
renderer.rendering_options.far = options.get("far", 100)
renderer.rendering_options.ssaa = options.get("ssaa", 4)
rets = {}
for extr, intr in tqdm(zip(extrinsics, intrinsics), desc="Rendering"):
res = renderer.render(sample, extr, intr)
if "normal" not in rets:
rets["normal"] = []
normal = torch.lerp(
torch.zeros_like(res["normal"]), res["normal"], res["mask"]
)
normal = np.clip(
normal.detach().cpu().numpy().transpose(1, 2, 0) * 255, 0, 255
).astype(np.uint8)
rets["normal"].append(normal)
return rets
@spaces.GPU
def render_video(
sample,
resolution=512,
bg_color=(0, 0, 0),
num_frames=300,
r=2,
fov=40,
**kwargs,
):
yaws = torch.linspace(0, 2 * 3.1415, num_frames)
yaws = yaws.tolist()
pitch = [0.5] * num_frames
extrinsics, intrinsics = yaw_pitch_r_fov_to_extrinsics_intrinsics(
yaws, pitch, r, fov
)
render_fn = (
render_mesh if isinstance(sample, MeshExtractResult) else render_frames
)
result = render_fn(
sample,
extrinsics,
intrinsics,
{"resolution": resolution, "bg_color": bg_color},
**kwargs,
)
return result
if __name__ == "__main__":
# Example usage:
merge_video_video(
"outputs/imageto3d/room_bottle7/room_bottle_007/URDF_room_bottle_007/mesh_glo_normal.mp4", # noqa
"outputs/imageto3d/room_bottle7/room_bottle_007/URDF_room_bottle_007/mesh.mp4", # noqa
"merge.mp4",
)
image_base64 = combine_images_to_base64(
[
"outputs/text2image/demo_objects/bed/sample_0.jpg",
"outputs/imageto3d/v2/cups/sample_69/URDF_sample_69/qa_renders/image_color/003.png", # noqa
"outputs/text2image/demo_objects/cardboard/sample_1.jpg",
]
)
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