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import os |
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os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" |
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os.environ["CUDA_VISIBLE_DEVICES"]="0" |
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try: |
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os.system("pip install --upgrade torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html") |
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except Exception as e: |
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print(e) |
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|
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from pydoc import describe |
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from huggingface_hub import hf_hub_download |
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import gradio as gr |
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import os |
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from datetime import datetime |
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from PIL import Image |
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import torch |
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import torchvision |
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import skimage |
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import paddlehub |
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import numpy as np |
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from lib.options import BaseOptions |
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from apps.crop_img import process_img |
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from apps.eval import Evaluator |
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from types import SimpleNamespace |
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import trimesh |
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import glob |
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|
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print( |
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"torch: ", torch.__version__, |
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"\ntorchvision: ", torchvision.__version__, |
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"\nskimage:", skimage.__version__ |
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) |
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|
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print("EnV", os.environ) |
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net_C = hf_hub_download("radames/PIFu-upright-standing", filename="net_C") |
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net_G = hf_hub_download("radames/PIFu-upright-standing", filename="net_G") |
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opt = BaseOptions() |
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opts = opt.parse_to_dict() |
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opts['batch_size'] = 1 |
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opts['mlp_dim'] = [257, 1024, 512, 256, 128, 1] |
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opts['mlp_dim_color'] = [513, 1024, 512, 256, 128, 3] |
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opts['num_stack'] = 4 |
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opts['num_hourglass'] = 2 |
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opts['resolution'] = 128 |
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opts['hg_down'] = 'ave_pool' |
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opts['norm'] = 'group' |
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opts['norm_color'] = 'group' |
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opts['load_netG_checkpoint_path'] = net_G |
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opts['load_netC_checkpoint_path'] = net_C |
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opts['results_path'] = "./results" |
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opts['name'] = "spaces_demo" |
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opts = SimpleNamespace(**opts) |
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print("Params", opts) |
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evaluator = Evaluator(opts) |
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bg_remover_model = paddlehub.Module(name="U2Net") |
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|
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def process(img_path): |
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base = os.path.basename(img_path) |
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img_name = os.path.splitext(base)[0] |
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print("\n\n\nStarting Process", datetime.now()) |
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print("image name", img_name) |
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img_raw = Image.open(img_path).convert('RGB') |
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|
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img = img_raw.resize( |
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(512, int(512 * img_raw.size[1] / img_raw.size[0])), |
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Image.Resampling.LANCZOS) |
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|
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try: |
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|
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print("Removing Background") |
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masks = bg_remover_model.Segmentation( |
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images=[np.array(img)], |
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paths=None, |
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batch_size=1, |
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input_size=320, |
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output_dir='./PIFu/inputs', |
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visualization=False) |
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mask = masks[0]["mask"] |
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front = masks[0]["front"] |
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except Exception as e: |
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print(e) |
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|
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print("Aliging mask with input training image") |
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print("Not aligned", front.shape, mask.shape) |
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img_new, msk_new = process_img(front, mask) |
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print("Aligned", img_new.shape, msk_new.shape) |
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try: |
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time = datetime.now() |
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data = evaluator.load_image_from_memory(img_new, msk_new, img_name) |
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print("Evaluating via PIFu", time) |
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evaluator.eval(data, True) |
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print("Success Evaluating via PIFu", datetime.now() - time) |
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result_path = f'./{opts.results_path}/{opts.name}/result_{img_name}' |
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except Exception as e: |
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print("Error evaluating via PIFu", e) |
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try: |
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mesh = trimesh.load(result_path + '.obj') |
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|
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mesh.apply_transform([[-1, 0, 0, 0], |
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[0, 1, 0, 0], |
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[0, 0, -1, 0], |
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[0, 0, 0, 1]]) |
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mesh.export(file_obj=result_path + '.glb') |
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result_gltf = result_path + '.glb' |
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return [result_gltf, result_gltf] |
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|
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except Exception as e: |
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print("error generating MESH", e) |
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|
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examples = sorted(glob.glob('examples/*.png')) |
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description = ''' |
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# PIFu Clothed Human Digitization |
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### PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization |
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<base target="_blank"> |
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|
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This is a demo for <a href="https://github.com/shunsukesaito/PIFu" target="_blank"> PIFu model </a>. |
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The pre-trained model has the following warning: |
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> Warning: The released model is trained with mostly upright standing scans with weak perspectie projection and the pitch angle of 0 degree. Reconstruction quality may degrade for images highly deviated from trainining data. |
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|
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**The inference takes about 180seconds for a new image.** |
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|
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<details> |
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<summary>More</summary> |
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|
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#### Image Credits |
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|
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* Julien and Clem |
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* [StyleGAN Humans](https://huggingface.co/spaces/hysts/StyleGAN-Human) |
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* [Renderpeople: Dennis](https://renderpeople.com) |
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|
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|
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#### More |
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* https://phorhum.github.io/ |
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* https://github.com/yuliangxiu/icon |
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* https://shunsukesaito.github.io/PIFuHD/ |
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|
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</details> |
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''' |
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|
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iface = gr.Interface( |
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fn=process, |
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description=description, |
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inputs=gr.Image(type="filepath", label="Input Image"), |
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outputs=[ |
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gr.Model3D( |
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clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"), |
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gr.File(label="Download 3D Model") |
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], |
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examples=examples, |
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allow_flagging="never", |
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cache_examples=True |
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) |
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|
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if __name__ == "__main__": |
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iface.launch(debug=True, enable_queue=False) |
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