from pydoc import describe from huggingface_hub import hf_hub_download import gradio as gr import subprocess import os import datetime from PIL import Image from remove_bg import RemoveBackground net_C = hf_hub_download("radames/PIFu-upright-standing", filename="net_C") net_G = hf_hub_download("radames/PIFu-upright-standing", filename="net_G") remove_bg = RemoveBackground() env = { **os.environ, "CHECKPOINTS_NETG_PATH": net_G, "CHECKPOINTS_NETC_PATH": net_C, "RESULTS_PATH": './results', } def process(img_path): base = os.path.basename(img_path) img_name = os.path.splitext(base)[0] print("image name", img_name) img = Image.open(img_path) # remove background print("remove background") foreground = Image.fromarray(remove_bg.inference(img), 'RGBA') foreground.save("./PIFu/inputs/" + img_name + ".png") print("align mask with input training image") subprocess.Popen(["python", "./apps/crop_img.py", "--input_image", f'./inputs/{img_name}.png', "--out_path", "./inputs"], cwd="PIFu").communicate() print("generate 3D model") subprocess.Popen("./scripts/test.sh", env={ **env, "INPUT_IMAGE_PATH": f'./inputs/{img_name}.png', "VOL_RES": "256"}, cwd="PIFu").communicate() print("inference") return f'./PIFu/results/spaces_demo/result_{img_name}.glb' examples = [["./examples/" + img] for img in os.listdir("./examples/")] description = ''' # PIFu Clothed Human Digitization # PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization This is a demo for PIFu model . The pre-trained model has the following warning: > 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.
More #### Image Credits * Julien and Clem * [StyleGAN Humans](https://huggingface.co/spaces/hysts/StyleGAN-Human) * [Renderpeople: Dennis](https://renderpeople.com) #### More * https://phorhum.github.io/ * https://github.com/yuliangxiu/icon * https://shunsukesaito.github.io/PIFuHD/
''' iface = gr.Interface( fn=process, description=description, inputs=gr.Image(type="filepath", label="Input"), outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0]), examples=examples, allow_flagging="never", cache_examples=True ) if __name__ == "__main__": iface.launch(debug=True, enable_queue=False)