Datasets:
jblitzar
/
github-python-corpus

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return padded_image
else:
return original_image
def add_background(image, bg_color=(255, 255, 255)):
# given an RGBA image, alpha channel is used as mask to add background color
background = Image.new("RGBA", image.size, bg_color)
return Image.alpha_composite(background, image)
def preprocess_image(image, background_choice, foreground_ratio, backgroud_color):
"""
input image is a pil image in RGBA, return RGB image
"""
print(background_choice)
if background_choice == "Alpha as mask":
background = Image.new("RGBA", image.size, (0, 0, 0, 0))
image = Image.alpha_composite(background, image)
else:
image = remove_background(image, rembg_session, force_remove=True)
image = do_resize_content(image, foreground_ratio)
image = expand_to_square(image)
image = add_background(image, backgroud_color)
return image.convert("RGB")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--inputdir",
type=str,
default="examples/kunkun.webp",
help="dir for input image",
)
parser.add_argument(
"--scale",
type=float,
default=5.0,
)
parser.add_argument(
"--step",
type=int,
default=50,
)
parser.add_argument(
"--bg_choice",
type=str,
default="Auto Remove background",
help="[Auto Remove background] or [Alpha as mask]",
)
parser.add_argument(
"--outdir",
type=str,
default="out/",
)
args = parser.parse_args()
img = Image.open(args.inputdir)
img = preprocess_image(img, args.bg_choice, 1.0, (127, 127, 127))
os.makedirs(args.outdir, exist_ok=True)
img.save(args.outdir+"preprocessed_image.png")
crm_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="CRM.pth")
specs = json.load(open("configs/specs_objaverse_total.json"))
model = CRM(specs).to("cuda")
model.load_state_dict(torch.load(crm_path, map_location = "cuda"), strict=False)
stage1_config = OmegaConf.load("configs/nf7_v3_SNR_rd_size_stroke.yaml").config
stage2_config = OmegaConf.load("configs/stage2-v2-snr.yaml").config
stage2_sampler_config = stage2_config.sampler
stage1_sampler_config = stage1_config.sampler
stage1_model_config = stage1_config.models
stage2_model_config = stage2_config.models
xyz_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="ccm-diffusion.pth")
pixel_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth")
stage1_model_config.resume = pixel_path
stage2_model_config.resume = xyz_path
pipeline = TwoStagePipeline(
stage1_model_config,
stage2_model_config,
stage1_sampler_config,
stage2_sampler_config,
)
rt_dict = pipeline(img, scale=args.scale, step=args.step)
stage1_images = rt_dict["stage1_images"]
stage2_images = rt_dict["stage2_images"]
np_imgs = np.concatenate(stage1_images, 1)
np_xyzs = np.concatenate(stage2_images, 1)
Image.fromarray(np_imgs).save(args.outdir+"pixel_images.png")
Image.fromarray(np_xyzs).save(args.outdir+"xyz_images.png")
glb_path, obj_path = generate3d(model, np_imgs, np_xyzs, "cuda")
shutil.copy(obj_path, args.outdir+"output3d.zip")
# <FILESEP>
##################################################################