Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from typing import List, Union | |
| from pathlib import Path | |
| from lib.platform import PM | |
| from lib.info.log import get_logger | |
| from .backend import HSMRBackend | |
| class HSMRService: | |
| # ===== Initialization Part ===== | |
| def __init__(self, backend:HSMRBackend) -> None: | |
| self.example_imgs_root = PM.inputs / 'example_imgs' | |
| self.description = self._load_description() | |
| self.backend:HSMRBackend = backend | |
| def _load_description(self) -> str: | |
| description_fn = PM.inputs / 'description.md' | |
| with open(description_fn, 'r') as f: | |
| description = f.read() | |
| return description | |
| # ===== Funcitonal Private Part ===== | |
| def _inference_img( | |
| self, | |
| raw_img_path:Union[str, Path], | |
| max_instances:int = 5, | |
| ) -> List: | |
| get_logger(brief=True).info(f'Image Path: {raw_img_path}') | |
| get_logger(brief=True).info(f'max_instances: {max_instances}') | |
| if raw_img_path is None: | |
| gr.Warning('No image uploaded yet. Please upload an image first.') | |
| return '<NONE>' | |
| if isinstance(raw_img_path, str): | |
| raw_img_path = Path(raw_img_path) | |
| args = { | |
| 'max_instances': max_instances, | |
| 'rec_bs': 1, | |
| } | |
| output_paths = self.backend(raw_img_path, args) | |
| # bbx_img_path = output_paths['bbx_img_path'] | |
| # mesh_img_path = output_paths['mesh_img_path'] | |
| # skel_img_path = output_paths['skel_img_path'] | |
| blend_img_path = output_paths['front_blend'] | |
| return blend_img_path | |
| # ===== Service Part ===== | |
| def serve(self) -> None: | |
| ''' Build UI and set up the service. ''' | |
| with gr.Blocks() as demo: | |
| # 1a. Setup UI. | |
| gr.Markdown(self.description) | |
| with gr.Tab(label='HSMR-IMG'): | |
| gr.Markdown('> Demo for recoverying human mesh and skeleton from a single image. (For **Pure CPU** demo, inference may take **about 1~3 minutes**.)') | |
| with gr.Row(equal_height=False): | |
| with gr.Column(): | |
| input_image = gr.Image( | |
| label = 'Input', | |
| type = 'filepath', | |
| ) | |
| with gr.Row(equal_height=True): | |
| run_button_image = gr.Button( | |
| value = 'Inference', | |
| variant = 'primary', | |
| ) | |
| with gr.Column(): | |
| output_blend = gr.Image( | |
| label = 'Output', | |
| type = 'filepath', | |
| interactive = False, | |
| ) | |
| # 1b. Add examples sections after setting I/O policy. | |
| example_fns = sorted(self.example_imgs_root.glob('*')) | |
| gr.Examples( | |
| examples = example_fns, | |
| fn = self._inference_img, | |
| inputs = input_image, | |
| outputs = output_blend, | |
| ) | |
| # 2b. Continue binding I/O logic. | |
| run_button_image.click( | |
| fn = self._inference_img, | |
| inputs = input_image, | |
| outputs = output_blend, | |
| ) | |
| # 3. Launch the service. | |
| demo.queue(max_size=20).launch(server_name='0.0.0.0', server_port=7860) |