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import os
import torch
import argparse
from PIL import Image
from utils.zero123_utils import init_model, predict_stage1_gradio, zero123_infer
from utils.sam_utils import sam_init, sam_out_nosave
from utils.utils import pred_bbox, image_preprocess_nosave, gen_poses, convert_mesh_format
from elevation_estimate.estimate_wild_imgs import estimate_elev
def preprocess(predictor, raw_im, lower_contrast=False):
raw_im.thumbnail([512, 512], Image.Resampling.LANCZOS)
image_sam = sam_out_nosave(predictor, raw_im.convert("RGB"), pred_bbox(raw_im))
input_256 = image_preprocess_nosave(image_sam, lower_contrast=lower_contrast, rescale=True)
torch.cuda.empty_cache()
return input_256
def stage1_run(model, device, exp_dir,
input_im, scale, ddim_steps):
# folder to save the stage 1 images
stage1_dir = os.path.join(exp_dir, "stage1_8")
os.makedirs(stage1_dir, exist_ok=True)
# stage 1: generate 4 views at the same elevation as the input
output_ims = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(4)), device=device, ddim_steps=ddim_steps, scale=scale)
# stage 2 for the first image
# infer 4 nearby views for an image to estimate the polar angle of the input
stage2_steps = 50 # ddim_steps
zero123_infer(model, exp_dir, indices=[0], device=device, ddim_steps=stage2_steps, scale=scale)
# estimate the camera pose (elevation) of the input image.
try:
polar_angle = estimate_elev(exp_dir)
except:
print("Failed to estimate polar angle")
polar_angle = 90
print("Estimated polar angle:", polar_angle)
gen_poses(exp_dir, polar_angle)
# stage 1: generate another 4 views at a different elevation
if polar_angle <= 75:
output_ims_2 = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(4,8)), device=device, ddim_steps=ddim_steps, scale=scale)
else:
output_ims_2 = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(8,12)), device=device, ddim_steps=ddim_steps, scale=scale)
torch.cuda.empty_cache()
return 90-polar_angle, output_ims+output_ims_2
def stage2_run(model, device, exp_dir,
elev, scale, stage2_steps=50):
# stage 2 for the remaining 7 images, generate 7*4=28 views
if 90-elev <= 75:
zero123_infer(model, exp_dir, indices=list(range(1,8)), device=device, ddim_steps=stage2_steps, scale=scale)
else:
zero123_infer(model, exp_dir, indices=list(range(1,4))+list(range(8,12)), device=device, ddim_steps=stage2_steps, scale=scale)
def reconstruct(exp_dir, output_format=".ply", device_idx=0, resolution=256):
exp_dir = os.path.abspath(exp_dir)
main_dir_path = os.path.abspath(os.path.dirname("./"))
os.chdir('reconstruction/')
bash_script = f'CUDA_VISIBLE_DEVICES={device_idx} python exp_runner_generic_blender_val.py \
--specific_dataset_name {exp_dir} \
--mode export_mesh \
--conf confs/one2345_lod0_val_demo.conf \
--resolution {resolution}'
print(bash_script)
os.system(bash_script)
os.chdir(main_dir_path)
ply_path = os.path.join(exp_dir, f"mesh.ply")
if output_format == ".ply":
return ply_path
if output_format not in [".obj", ".glb"]:
print("Invalid output format, must be one of .ply, .obj, .glb")
return ply_path
return convert_mesh_format(exp_dir, output_format=output_format)
def predict_multiview(shape_dir, args):
device = f"cuda:{args.gpu_idx}"
# initialize the zero123 model
models = init_model(device, 'zero123-xl.ckpt', half_precision=args.half_precision)
model_zero123 = models["turncam"]
# initialize the Segment Anything model
predictor = sam_init(args.gpu_idx)
input_raw = Image.open(args.img_path)
# preprocess the input image
input_256 = preprocess(predictor, input_raw)
# generate multi-view images in two stages with Zero123.
# first stage: generate N=8 views cover 360 degree of the input shape.
elev, stage1_imgs = stage1_run(model_zero123, device, shape_dir, input_256, scale=3, ddim_steps=75)
# second stage: 4 local views for each of the first-stage view, resulting in N*4=32 source view images.
stage2_run(model_zero123, device, shape_dir, elev, scale=3, stage2_steps=50)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--img_path', type=str, default="./demo/demo_examples/01_wild_hydrant.png", help='Path to the input image')
parser.add_argument('--gpu_idx', type=int, default=0, help='GPU index')
parser.add_argument('--half_precision', action='store_true', help='Use half precision')
parser.add_argument('--mesh_resolution', type=int, default=256, help='Mesh resolution')
parser.add_argument('--output_format', type=str, default=".ply", help='Output format: .ply, .obj, .glb')
args = parser.parse_args()
assert(torch.cuda.is_available())
shape_id = args.img_path.split('/')[-1].split('.')[0]
shape_dir = f"./exp/{shape_id}"
os.makedirs(shape_dir, exist_ok=True)
predict_multiview(shape_dir, args)
# utilize cost volume-based 3D reconstruction to generate textured 3D mesh
mesh_path = reconstruct(shape_dir, output_format=args.output_format, device_idx=args.gpu_idx, resolution=args.mesh_resolution)
print("Mesh saved to:", mesh_path)
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